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Copy-move forgery detection: Survey, challenges and future directions

Journal paper
Nor Bakiah Abd Warif, Ainuddin Wahid Abdul Wahab, Mohd Yamani Idna Idris, Roziana Ramli, Rosli Salleh, Shahaboddin Shamshirband, Kim-Kwang Raymond Choo
Journal of Network and Computer Applications, Volume 75, Pages 259-278

Abstract

The authenticity and reliability of digital images are increasingly important due to the ease in modifying such images. Thus, the capability to identify image manipulation is a current research focus, and a key domain in digital image authentication is Copy-move forgery detection (CMFD). Copy-move forgery is the process of copying and pasting from one region to another location within the same image. In this paper, we survey the recent developments in CMFD, and describe the entire CMFD process involved. Specifically, we characterize the common CMFD workflow of feature extraction and matching process using block or keypoint-based approaches. Instead of listing the datasets and validations used in the literature, we also categorize the types of copied regions. Finally, we also outline a number of future research directions.

A systematic extreme learning machine approach to analyze visitors׳ thermal comfort at a public urban space

Journal paper
Shahab Kariminia, Shahaboddin Shamshirband, Shervin Motamedi, Roslan Hashim, Chandrabhushan Ro
Renewable and Sustainable Energy Reviews, Volume 58, Pages 751-760

Abstract

Thermal quality of open public spaces in every city influences its residents’ outdoor life. Higher level of thermal comfort attracts more visitors to such places; hence, brings benefits to the community. Previous research works have used the body energy balance or adaptation model for predicting the thermal comfort in outdoor spaces. However, limited research works have applied computational methods in this field. For the first of its’ type, this study applied a systematic approach using a class of soft-computing methodology known as the extreme learning machine (ELM) to forecast the thermal comfort of the subject visitors at an open area in Iran. For data collection, this study used common thermal indices for assessing the thermal perceptions of the subjects. The fieldworks comprised of measuring the microclimatic conditions and interviewing the visitors. This study compared the results of ELM with other conventional soft-computing methods (i.e., artificial neural network (ANN) and genetic programming (GP)). The findings indicate that the ELM results match with the field data. This implies that a model constructed by ELM can accurately predict visitors’ thermal sensations. We conclude that the proposed model’s predictability performance is reliable and superior compared to other approaches (i.e., GP and ANN). Besides, the ELM methodology significantly reduces training time for a Neural Network as compared to the conventional methods.

An adaptive trajectory tracking control of four rotor hover vehicle using extended normalized radial basis function network

Journal paper
Rooh ul Amin, Li Aijun, Muhammad Umer Khan, Shahaboddin Shamshirband, Amirrudin Kamsin
Journal Mechanical Systems and Signal Processing, Volume 83, Pages 53-74

Abstract:

In this paper, an adaptive trajectory tracking controller based on extended normalized radial basis function network (ENRBFN) is proposed for 3-degree-of-freedom four rotor hover vehicle subjected to external disturbance i.e. wind turbulence. Mathematical model of four rotor hover system is developed using equations of motions and a new computational intelligence based technique ENRBFN is introduced to approximate the unmodeled dynamics of the hover vehicle. The adaptive controller based on the Lyapunov stability approach is designed to achieve tracking of the desired attitude angles of four rotor hover vehicle in the presence of wind turbulence. The adaptive weight update based on the Levenberg-Marquardt algorithm is used to avoid weight drift in case the system is exposed to external disturbances. The closed-loop system stability is also analyzed using Lyapunov stability theory. Simulations and experimental results are included to validate the effectiveness of the proposed control scheme.

A novel evolutionary-negative correlated mixture of experts model in tourism demand estimation

Journal paper
SMR Kazemi, Esmaeil Hadavandi, Shahaboddin Shamshirband, Shahrokh Asadi
Computers in Human Behavior Volume, 64 Pages, 641-655

Abstract

Mixtures of experts (ME) model are widely used in many different areas as a recognized ensemble learning approach to account for nonlinearities and other complexities in the data, such as time series estimation. With the aim of developing an accurate tourism demand time series estimation model, a mixture of experts model called LSPME (Lag Space Projected ME) is presented by combining ideas from subspace projection methods and negative correlation learning (NCL). The LSPME uses a new cluster-based lag space projection (CLSP) method to automatically obtain input space to train each expert focused on the difficult instances at each step of the boosting approach. For training experts of the LSPME, a new NCL algorithm called Sequential Evolutionary NCL algorithm (SENCL) is proposed that uses a moving average for the correlation penalty term in the error function of each expert to measure the error correlation between it and its previous experts. The LSPME model was compared with other ensemble models using monthly tourist arrivals to Japan from four markets: The United States, United Kingdom, Hong Kong and Taiwan. The experimental results show that the estimation accuracy of the proposed LSPME model is significantly better than the other ensemble models and can be considered to be a promising alternative for time series estimation problems.

Support vector regression for modified oblique side weirs discharge coefficient prediction

Journal paper
Amir Hossein Zaji, Hossein Bonakdari, Shahaboddin Shamshirband
Flow Measurement and Instrumentation Volume, 51 Pages, 1-7

Abstract

Accurate determination of discharge coefficient is one of the major concerns in the process of the designing of side weirs. Relation between the modified side weirs discharge coefficient to various geometric and hydraulic situations leads to a high flow complexity around the weirs. In this study, two types of support vector regression (SVR) methods were employed to model the discharge coefficient of a modified triangular side weir. Two types of SVR are obtained by using the radial basis and polynomial as the kernel functions. Six different non-dimensional input combinations with different input variables were used to find the most appropriate one. The results show that both SVR-rbf and SVR-poly methods perform better when the number of input variables is higher, and there is no compaction in the non-dimensional input variables. Comparison between the investigated models shows that the SVR-rbf by RMSE of 0.063 performs much better that SVR-poly by RMSE of 0.084.

Shahab Shamshirband Comparative analysis of reference evapotranspiration equations modelling by extreme learning machine

Journal paper
Milan Gocic, Dalibor Petković, Shahaboddin Shamshirband, Amirrudin Kamsin
Computers and Electronics in Agriculture Volume, 127 Pages, 56-63

Abstract

This study presents an extreme learning machine (ELM) approach, for estimating monthly reference evapotranspiration (ET0) in two weather stations in Serbia (Nis and Belgrade stations), for a 31-year period (1980–2010). The data set including minimum and maximum air temperatures, actual vapour pressure, wind speed and sunshine hours was employed for modelling ET0 using the adjusted Hargreaves (ET0,AHARG), Priestley–Taylor (ET0,PT) and Turc (ET0,T) equations. The reliability of the computational model was accessed based on simulation results and using five statistical tests including mean absolute percentage error (MAPE), mean absolute deviation (MAD), root-mean-square error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2). The validity of ELM modelled ET0 are compared with the FAO-56 Penman–Monteith equation (ET0,PM) which is used as the reference model. For the Belgrade and Nis stations, the ET0,AHARG ELM model with MAPE = 9.353 and 10.299%, MAD = 0.142 and 0.151 mm/day, RMSE = 0.180 and 0.192 mm/day, r = 0.994 and 0.992, R2 = 0.988 and 0.984 in testing period, was found to be superior in modelling monthly ET0 than the other models, respectively.

Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure

Journal paper
Kasra Mohammadi, Shahaboddin Shamshirband, Amirrudin Kamsin, PC Lai, Zulkefli Mansor
Renewable and Sustainable Energy Reviews Volume, 63 Pages, 423-434

Abstract

There are several variables that influence the global solar radiation (GSR) prediction; thus, determining the most significant parameters is an important task to achieve accurate predictions. In this paper, adaptive neuro-fuzzy inference system (ANFIS) is employed to identify the most relevant parameters for prediction of daily GSR. Three cities of Isfahan, Kerman and Tabass distributed in central and south central parts of Iran are considered as case studies. The ANFIS process for variable selection includes evaluating several combinations of input parameters for three cases with 1, 2 and 3 inputs to recognize the most relevant sets. To achieve this, nine parameters of sunshine duration (n), maximum possible sunshine duration (N), minimum, maximum and average air temperatures (Tmin, Tmax and Tavg), relative humidity (Rh), water vapor pressure (VP), sea level pressure (P) and extraterrestrial radiation (Ho) are considered. The results reveal that an optimum sets of inputs are not identical for all cities due to difference in climate conditions and solar radiation characteristics. According to the results, considering the most relevant combinations of 2 input parameters is the more appropriate option for all cities to achieve more accuracy and less complexity in predictions. The survey results emphasize the importance of appropriate selection of input parameters to predict daily GSR. Such suitable, simple and accurate prediction is profitable to properly design and evaluate the performance of solar energy systems, which subsequently leads to technical and economic benefits.

Sustainable Cloud Data Centers: A survey of enabling techniques and technologies

Journal paper
Junaid Shuja, Abdullah Gani, Shahaboddin Shamshirband, Raja Wasim Ahmad, Kashif Bilal
Renewable and Sustainable Energy Reviews Volume, 62 Pages, 195-214

Abstract

Cloud computing services have gained tremendous popularity and widespread adoption due to their flexible and on-demand nature. Cloud computing services are hosted in Cloud Data Centers (CDC) that deploy thousands of computation, storage, and communication devices leading to high energy utilization and carbon emissions. Renewable energy resources replace fossil fuels based grid energy to effectively reduce carbon emissions of CDCs. Moreover, waste heat generated from electronic components can be utilized in absorption based cooling systems to offset cooling costs of data centers. However, data centers need to be located at ideal geographical locations to reap benefits of renewable energy and waste heat recovery options. Modular Data Centers (MDC) can enable energy as a location paradigm due to their shippable nature. Moreover, workload can be transferred between intelligently placed geographically dispersed data centers to utilize renewable energy available elsewhere with virtual machine migration techniques. However, adoption of aforementioned sustainability techniques and technologies opens new challenges, such as, intermittency of power supply from renewable resources and higher capital costs. In this paper, we examine sustainable CDCs from various aspects to survey the enabling techniques and technologies. We present case studies from both academia and industry that demonstrate favorable results for sustainability measures in CDCs. Moreover, we discuss state-of-the-art research in sustainable CDCs. Furthermore, we debate the integration challenges and open research issues to sustainable CDCs.

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware

Journal paper
Firdaus Afifi, Nor Badrul Anuar, Shahaboddin Shamshirband, Kim-Kwang Raymond Choo
PloS one Volume, 11 Issue, 9 Pages, e0162627

Abstract

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO)

The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures

Journal paper
Taher Baghaee Moghaddam, Mehrtash Soltani, Hamed Shahrokhi Shahraki, Shahaboddin Shamshirband, Noorzaily Bin Mohamed Noor, Mohamed Rehan Karim
Measurement, Volume, 90 Pages, 526-533

Abstract

To predict fatigue life of Polyethylene Terephthalate (PET) modified asphalt mixture, various soft computing methods such as Genetic Programming (GP), Artificial Neural Network (ANN), and Fuzzy Logic-based methods have been employed. In this study, an application of Support Vector Machine Firefly Algorithm (SVM-FFA) is implemented to predict fatigue life of PET modified asphalt mixture. The inputs are PET percentages, stress levels and environmental temperatures. The performance of proposed method is validated against observed experiment data. The results of the prediction using SVM-FFA are then compared to those of applying ANN and GP approach and it is concluded that SVM-FFA leads to more accurate results when compared to observed experiment data.

Design and state of art of innovative wind turbine systems

Journal paper
Vlastimir Nikolić, Shahin Sajjadi, Dalibor Petković, Shahaboddin Shamshirband, Žarko Ćojbašić, Lip Yee Por
Renewable and Sustainable Energy Reviews, Volume, 61 Pages, 258-265

Abstract

Wind energy using increased dramatically in the last years. Because of that wind turbine design should be improved to increase efficiency. To make wind turbine with the best characteristics and with the highest efficiency it is suitable to analyze factors that are truly relevant to the converted wind energy. Wind turbine innovative design is investigated in this paper by the theory of inventive problem solution (TRIZ) as a systematic methodology for innovation. TRIZ methodology should provide creative conceptual design ideas of wind turbine. The main aim of this work is to show a systematic methodology for innovation as an effective procedure to enhance the capability of developing innovative products and to overcome the main design problems. The TRIZ method will be used in order to eliminate the technical contradictions which appear in the wind turbine systems.

Sensitivity analysis of catalyzed-transesterification as a renewable and sustainable energy production system by adaptive neuro-fuzzy methodology

Journal paper
Baharak Sajjadi, Abdul Aziz Abdul Raman, Rajarathinam Parthasarathy, Shahaboddin Shamshirband
Journal of the Taiwan Institute of Chemical Engineers, Volume, 64 Pages, 47-58

Abstract

The current study aims at introducing a fast and precise method for analyzing the operation of renewable and sustainable energy systems. Accordingly, ultrasound assisted transesterification as a novel method of biodiesel synthesis and biodiesel synthesis using mechanical stirring were selected as the two main systems for renewable energy production. It is necessary to analyze the parameters which are the most influential on transesterification yield estimation and prediction in order to assess transesterification yield. ANFIS (adaptive neuro-fuzzy inference system) was used in this study for selecting the most influential parameters based on five input parameters (operational variables). The effectiveness of the proposed strategy was verified with the simulation results. Experiments were conducted to extract training data for the ANFIS network. Furthermore, RSM (response surface methodology) was used to design the experiments and analyze the interactive and individual effects of the five independent variables in order to evaluate the results predicted by ANFIS. The obtained results clearly demonstrated the effects of operational variables on the final transesterification yield.

Selection of climatic parameters affecting wave height prediction using an enhanced Takagi-Sugeno-based fuzzy methodology

Journal paper
Roslan Hashim, Chandrabhushan Roy, Shervin Motamedi, Shahaboddin Shamshirband, Dalibor Petković
Renewable and Sustainable Energy Reviews, Volume, 60 Pages, 246-25

Abstract

This study dealt with finding the sequence of the most influential parameters among the factors that affect the offshore wave height. A dataset comprising of four climatic input parameters: sea surface wind speed (U), wind direction (θ), air temperature (Ta), and sea surface temperature (Tw); as well as one output parameter (significant wave heights, Hs) was generated. The offshore field measurements were derived from three buoy stations, deployed in the western part of the North Atlantic Ocean. The primary goal of this study was to identify the predominant input parameters that influence prediction of Hs at each buoy station. In this view, ANFIS (an enhanced type of Takagi-Sugeno-based fuzzy inference system) was implemented on the dataset for variable selection. This process found a subset of the entire set of the observed parameters, which was suitable for prediction purposes. As a result, the following sequence of parameter have the most to least influence on the predictions of Hs, U, Ta, Tw, and θ. In addition, it was found that combination of three variables, namely U, Ta, and θ, forms the most influential set of input parameters with RMSEs of 0.82, 0.44 and 0.62, respectively for the predicted Hs at three stations. Most of the previous studies only employed U and θ to predict Hs using the soft-computing methods. As the first study of its type, the findings from this study suggest that the accuracy of wave height prediction may improve when Ta and Tw are included as inputs along with U and θ. The present study can serve as a gear towards enhancing the accuracy in prediction of wave height at various offshore locations.

Sensitivity analysis of heat transfer rate for smart roof design by adaptive neuro-fuzzy technique

Journal paper
Wen Tong Chong, Abdullah Al-Mamoon, Sin Chew Poh, Lip Huat Saw, Shahaboddin Shamshirband, Juwel Chandra Mojumder
Energy and Buildings, Volume 124, Pages 112-119

Abstract

Thermal comfort in the building is a major concern among the design expertise especially in tropical countries. Therefore, it is important to design a smart roof system to reduce the heat gain of the building and enhance the resident’s thermal comfort. The leading feature of this system is using PVC tubes integrated with a layer of insulation material and fans and located at the underside of the roof to act as a moving-air path (MAP) system. Indoor experiment is carried out on the scale roof model and the heat transfer rate into the attic is determined using experimental data. Next, Adaptive Neuro Fuzzy Inference System (ANFIS) is applied as a soft-computing method determine the predominant variables that affecting the thermal comfort in the building. Training and testing data of the ANFIS model are collected from the experimental measurement. Mass flow rate, solar-air temperature, inlet air temperature, outlet air temperature and ambient temperature are the input parameters used to compute the output parameter which is the attic temperature. The results indicated that the combination of mass flow rate and ambient temperature is the primary factor and the best predictor accuracy for thermal comfort in the building.

Thermal sensation prediction by soft computing methodology

Journal paper
Srđan Jović, Nebojša Arsić, Jovana Vilimonović, Dalibor Petković
Journal of Thermal Biology, Publisher Pergamon

Abstract

Thermal comfort in open urban areas is very factor based on environmental point of view. Therefore it is need to fulfill demands for suitable thermal comfort during urban planning and design. Thermal comfort can be modeled based on climatic parameters and other factors. The factors are variables and they are changed throughout the year and days. Therefore there is need to establish an algorithm for thermal comfort prediction according to the input variables. The prediction results could be used for planning of time of usage of urban areas. Since it is very nonlinear task, in this investigation was applied soft computing methodology in order to predict the thermal comfort. The main goal was to apply extreme leaning machine (ELM) for forecasting of physiological equivalent temperature (PET) values. Temperature, pressure, wind speed and irradiance were used as inputs. The prediction results are compared with some benchmark models. Based on the results ELM can be used effectively in forecasting of PET.

A Novel Method to Water Level Prediction using RBF and FFA

Journal paper
Seyed Ahmad Soleymani, Shidrokh Goudarzi, Mohammad Hossein Anisi, Wan Haslina Hassan, Mohd Yamani Idna Idris, Shahaboddin Shamshirband, Noorzaily Mohamed Noor, Ismail Ahmedy
Water Resources Management, Volume 30 Issue 9, Pages 3265-3283

Abstract

Water level prediction of rivers, especially in flood prone countries, can be helpful to reduce losses from flooding. A precise prediction method can issue a forewarning of the impending flood, to implement early evacuation measures, for residents near the river, when is required. To this end, we design a new method to predict water level of river. This approach relies on a novel method for prediction of water level named as RBF-FFA that is designed by utilizing firefly algorithm (FFA) to train the radial basis function (RBF) and (FFA) is used to interpolation RBF to predict the best solution. The predictions accuracy of the proposed RBF–FFA model is validated compared to those of support vector machine (SVM) and multilayer perceptron (MLP) models. In order to assess the models’ performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results show that the developed RBF–FFA model provides more precise predictions compared to different ANNs, namely support vector machine (SVM) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real time water stage measurements. The results specify that the developed RBF–FFA model can be used as an efficient technique for accurate prediction of water stage of river.

Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature

Journal paper
Behnaz Nahvi, Jafar Habibi, Kasra Mohammadi, Shahaboddin Shamshirband, Othman Saleh Al Razgan
Computers and Electronics in Agriculture, Volume 124, Pages 150-160

Abstract

In this study, the self-adaptive evolutionary (SaE) agent is employed to structure the contributing elements to process the management of extreme learning machine (ELM) architecture based on a logical procedure. In fact, the SaE algorithm is utilized for possibility of enhancing the performance of the ELM to estimate daily soil temperature (ST) at 6 different depths of 5, 10, 20, 30, 50 and 100 cm. In the developed SaE-ELM model, the network hidden node parameters of the ELM are optimized using SaE algorithm. The precision of the SaE-ELM is then compared with the ELM model. Daily weather data sets including minimum, maximum and average air temperatures (Tmin, Tmax and Tavg), atmospheric pressure (P) and global solar radiation (RS) collected for two Iranian stations of Bandar Abbas and Kerman with different climate conditions have been utilized. After primary evaluation, Tmin, Tmax and Tavg are considered as final inputs for the ELM and SaE-ELM models due to their high correlations with ST at all depths. The achieved results for both stations reveal that both ELM and SaE-ELM models offer desirable performance to estimate daily ST at all depths; nevertheless, a slightly more precision can be obtained by the SaE-ELM model. The performance of the ELM and SaE-ELM models are verified against genetic programming (GP) and artificial neural network (ANN) models developed in this study. For Bandar Abbass station, the obtained mean absolute bias error (MABE) and correlation coefficient (R) for the ELM model at different depths are in the range of 0.9116–1.5988 °C and 0.9023–0.9840, respectively while for the SaE-ELM model they are in the range of 0.8660–1.5338 °C and 0.9084–0.9893, respectively. In addition, for Kerman Station the attained MABE and RMSE for the ELM model vary from 1.6567 to 2.4233 °C and 0.8661 to 0.9789, respectively while for the SaE-ELM model they vary from 1.5818 to 2.3422 °C and 0.8736 to 0.9831, respectively.

Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran

Journal paper
Shahaboddin Shamshirband, Afram Keivani, Kasra Mohammadi, Malrey Lee, Siti Hafizah Abd Hamid, Dalibor Petkovic
Renewable and Sustainable Energy Reviews, Volume 59, Pages 429-435

Abstract

The prime aim of this study is appraising the suitability of adaptive neuro-fuzzy inference framework (ANFIS) to compute the monthly wind power density. On this account, the extracted wind power from Weibull functions are utilized for training and testing the developed ANFIS model. The proficiency of the ANFIS model is certified by providing thorough statistical comparisons with artificial neural network (ANN) and genetic programming (GP) techniques. The computed wind power by all models are compared with those obtained using measured data. The study results clearly indicate that the proposed ANFIS model enjoys high capability and reliability to estimate wind power density so that it presents high superiority over the developed ANN and GP models. Based upon relative percentage error (RPE) values, all estimated wind power values via ANFIS model are within the acceptable range of −10% to 10%. Additionally, relative root mean square error (RRMSE) analysis shows that ANFIS model has an excellent performance for estimation of wind power density.

Extreme learning machine for prediction of heat load in district heating systems

Journal paper
Shahin Sajjadi, Shahaboddin Shamshirband, Meysam Alizamir, Lip Yee, Zulkefli Mansor, Azizah Abdul Manaf, Torki A Altameem, Ali Mostafaeipour
Energy and Buildings, Volume 122, Pages 222-227

Abstract

District heating systems are important utility systems. If these systems are properly managed, they can ensure economic and environmental friendly provision of heat to connected customers. Potentials for further improvement of district heating systems’ operation lie in improvement of present control strategies. One of the options is introduction of model predictive control. Multistep ahead predictive models of consumers’ heat load are starting point for creating successful model predictive strategy. In this article, short-term, multistep ahead predictive models of heat load of consumer attached to district heating system were created. Models were developed using the novel method based on Extreme Learning Machine (ELM). Nine different ELM predictive models, for time horizon from 1 to 24 h ahead, were developed. Estimation and prediction results of ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ELM approach in comparison with GP and ANN. Moreover, achieved results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in district heating systems. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms.

Evaluation of wind power generation potential using a three hybrid approach for households in Ardebil Province, Iran

Journal paper
Mojtaba Qolipour, Ali Mostafaeipour, Shahaboddin Shamshirband, Omid Alavi, Hossein Goudarzi, Dalibor Petković
Energy Conversion and Management, Volume 118, Pages 295-305

Abstract

The objective of the present paper is to conduct a thorough technical–economic evaluation for the construction of small wind turbines in six areas within Ardabil province of Iran using the Hybrid Optimization of Multiple Energy Resources software, and also to rank these areas by a hybrid approach composed of Data Envelopment Analysis, Balanced Scorecard, and Game Theory methodologies. Higher accuracy of the proposed hybrid approach and its ability to properly detect the relationships between the decision-making components make it preferable over the simple Data Envelopment Analysis method. Technical–economic feasibility study is conducted by analyzing wind speed data for period from 2008 to 2014 using Hybrid Optimization of Multiple Energy Resources software. In the next step, the type of equipment used in the design, benefit, costs, total net costs, depreciation and amount of generated electricity are obtained separately for each location. The results show that; Airport, Nir, Namin, Bilasavar, Firozabad and Ardabil were rank first to last respectively.

Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks

Journal paper
Ashkan Nabavi-Pelesaraei, Shahin Rafiee, Homa Hosseinzadeh-Bandbafha, Shahaboddin Shamshirband
Journal of Cleaner Production, Publisher Elsevier

Abstract

The purpose of this study was to apply artificial neural networks (ANNs) for forecasting and sensitivity analysis of energy inputs and GHG emissions of three groups of kiwifruit orchards of different sizes in Guilan Province, Iran. The initial data were collected from 80 kiwifruit producers in Langroud City, Guilan Province. The total energy input and output were estimated at 37.32 GJ ha−1 and 43.44 GJ ha−1, respectively. The ANOVA (analysis of variance) results showed significant variance among the different orchard sizes from an energy input point of view. The results revealed that the highest share of energy input was that of nitrogen fertilizer use in kiwifruit production. The main reason for the overuse of nitrogen fertilizer is government subsidies provided for chemical fertilizers, followed by high levels of nitrogen leaching due to high rainfall. The average values of some energy indices, such as energy use efficiency, energy productivity, net energy and energy intensiveness, were calculated as 1.16, 0.61 × 10−3 kg GJ−1, 6.12 GJ ha−1 and 3.27 × 10−3 GJ $−1, respectively. The average total GHG emissions were calculated as 1310 kg CO2eq. ha−1. Nitrogen fertilizer had the highest share in GHG emissions for kiwifruit production, with 26.17% of total emissions. The 12-9-9-2 structure ANN model was the best topology for predicting yield and GHG (greenhouse gas) emissions of kiwifruit production in the studied area. The coefficients of determination (R2) of the best topology calculated were 0.987 and 0.992 for yield and greenhouse gas emissions, respectively, indicating the high correlation in the model. The results of model sensitivity analysis indicated that diesel fuel and nitrogen fertilizer were the most sensitive inputs for kiwifruit yield and greenhouse gas emissions, reflecting the important role of nitrogen fertilizer in the excess energy consumption and greenhouse gas emissions of kiwifruit orchards. According to the current study, it is suggested for new policies to be adopted to reduce nitrogen fertilizer consumption.

Survey of the most influential parameters on the wind farm net present value (NPV) by adaptive neuro-fuzzy approach

Journal paper
Dalibor Petković, Shahaboddin Shamshirband, Amirrudin Kamsin, Malrey Lee, Obrad Anicic, Vlastimir Nikolić
Renewable and Sustainable Energy Reviews, Volume 57, Pages 1270-1278

Abstract

The main objective of wind farm modeling is to maximize wind farm efficiency. The optimal wind turbine placement on a wind farm could be modified by taking economic aspects into account. The net present value (NPV) is the most important criteria for project investment estimating. The general approach in deciding the distinctive choice for a task through NPV is to treat the money streams as known with conviction. Even little deviations from the decided beforehand values might effectively negate the choice. To assess the investment risk of wind power project, this paper constructed a process that selected the most influential wind farm parameters on the NPV with adaptive neuro-fuzzy (ANFIS) method. This procedure is typically called variable selection, which corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. Variable seeking utilizing the ANFIS system was performed to figure out how the seven wind farm parameters affect the NPV of the wind farm.

Neuro-fuzzy estimation of passive robotic joint safe velocity with embedded sensors of conductive silicone rubber

Journal paper
Eiman Tamah Al-Shammari, Dalibor Petković, Amir Seyed Danesh, Shahaboddin Shamshirband, Mirna Issa, Lena Zentner
Mechanical Systems and Signal Processing, Volume 72, Pages 486-498

Robotic operations need to be safe for unpredictable contacts. Joints with passive compliance with springs can be used for soft robotic contacts. However the joints cannot measure external collision forces. In this investigation was developed one passive compliant joint which have soft contacts with external objects and measurement capabilities. To ensure it, conductive silicone rubber was used as material for modeling of the compliant segments of the robotic joint. These compliant segments represent embedded sensors. The conductive silicone rubber is electrically conductive by deformations. The main task was to obtain elastic absorbers for the external collision forces. These absorbers can be used for measurement in the same time. In other words, the joint has an internal measurement system. Adaptive neuro fuzzy inference system (ANFIS) was used to estimate the safety level of the robotic joint by head injury criteria (HIC).

Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder

Journal paper
Shahaboddin Shamshirband, Lejla Banjanovic-Mehmedovic, Ivan Bosankic, Suad Kasapovic, Ainuddin Wahid Bin Abdul Wahab
PloS one, Volume 11, Issue 5, Pages e0155697

Abstract

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Estimation of Wind-Driven Coastal Waves Near a Mangrove Forest Using Adaptive Neuro-Fuzzy Inference System

Journal paper
Roslan Hashim, Chandrabhushan Roy, Shahaboddin Shamshirband, Shervin Motamedi, Arnitza Fitri, Dalibor Petković, KI-IL Song
Water Resources Management, Volume 30, Issue 7, Pages 2391-2404

Abstract

At the coastline of the Carey Island, mangroves provide natural protection against the wind-driven coastal waves. The area is located at the west Malaysia within the waters of the Straits of Malacca. Recently, its coastline has been exposed to increasing rates of coastal erosion due to mangrove deforestation. In order to provide mitigating measures, it is necessary to study wave characteristics in this region. For this purpose, we collected 5 years (2009 to 2013) of hourly measurements for wind direction, wave height, wind speed and wave period. Moreover, we used the adaptive neuro-fuzzy inference system (ANFIS) to estimate the wave period and height. The model was trained using the measured data. The validation of the model gave satisfactory R2 values of 0.8484 and 0.9496 for wave height and wave period, respectively. The findings from this study suggest that fuzzy logic based technique satisfactorily predicts the differences between multiple inputs and single output in terms of non-linear relationship. The developed model can be used to further study the effect of non-linear wind-driven waves on the depleting coastal mangrove forests in similar tropical and sub-tropical areas. We suggest further research to test the model in different geographical locations, such as in deep-ocean, narrow straits and other coastal sites, which were not covered in this study.

Application of adaptive neuro-fuzzy methodology for performance investigation of a power-augmented vertical axis wind turbine

Journal paper
WT Chong, M Gwani, S Shamshirband, WK Muzammil, CJ Tan, A Fazlizan, SC Poh, Dalibor Petković, KH Wong
Energy, Volume 102, Pages 630-636

Abstract

Wind power is generating a lot of interest in many countries as a way to produce sustainable and low-cost electrical power. Since the power in the wind is known to be proportional to the cubic power of the wind velocity approaching the wind turbine, this means that any slight increase in wind speed can lead to a substantial increment in the energy output. Power augmentation device is an interesting option in this respect. The aim of this study is to determine the accuracy of a soft computing technique on the rotational speed estimation of a Sistan rotor vertical axis wind turbine with PAGV (power-augmentation-guide-vane) based upon a series of measurements. An ANFIS (adaptive neuro-fuzzy inference system) was used to predict the wind turbine rotational speed. The ANFIS network was developed with three neurons in the input layer, and one neuron in the output layer. The inputs for the network were time (t), wind velocity (v) and presence of the PAGV (0 with PAGV and 1 without PAGV). The precision of ANFIS technique was assessed against the experimental results using RMSE (root-mean-square error) and coefficient of determination (R2).

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

Journal paper
Roslan Hashim, Chandrabhushan Roy, Shervin Motamedi, Shahaboddin Shamshirband, Dalibor Petković, Milan Gocic, Siew Cheng Lee
Atmospheric Research, Volume 171, Pages 21-30

Abstract

Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (View the MathML source), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

Long-Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods

Journal paper
Milan Gocic, Shahaboddin Shamshirband, Zaidi Razak, Dalibor Petković, Sudheer Ch, Slavisa Trajkovic
Advances in Meteorology, Volume 2016, Publisher Hindawi Publishing Corporation

Abstract

The monthly precipitation data from 29 stations in Serbia during the period of 1946–2012 were considered. Precipitation trends were calculated using linear regression method. Three CLINO periods (1961–1990, 1971–2000, and 1981–2010) in three subregions were analysed. The CLINO 1981–2010 period had a significant increasing trend. Spatial pattern of the precipitation concentration index (PCI) was presented. For the purpose of PCI prediction, three Support Vector Machine (SVM) models, namely, SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA), and using the radial basis function (SVM-RBF), were developed and used. The estimation and prediction results of these models were compared with each other using three statistical indicators, that is, root mean square error, coefficient of determination, and coefficient of efficiency. The experimental results showed that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM-Wavelet approach. Moreover, the results indicated the proposed SVM-Wavelet model can adequately predict the PCI.

Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model

Journal paper
Shahaboddin Shamshirband, Kasra Mohammadi, Hossein Khorasanizadeh, Lip Yee, Malrey Lee, Dalibor Petković, Erfan Zalnezhad
Renewable and Sustainable Energy Reviews, Volume 56, Pages 428-435

Abstract

Diffuse solar radiation is a fundamental parameter highly required in several solar energy applications. Despite its significance, diffuse solar radiation is not measured in many locations around the world due to technical and fiscal limitations. On this account, determining the amount of diffuse radiation alternatively based upon precise and reliable estimating methods is indeed essential. In this paper, a coupled model is developed for estimating daily horizontal diffuse solar radiation by integrating the support vector machine (SVM) with wavelet transform (WT) algorithm. To test the validity of the coupled SVM–WT method, daily measured global and diffuse solar radiation data sets for city of Kerman situated in a sunny part of Iran are utilized. For the developed SVM–WT model, diffuse fraction (cloudiness index) is correlated with clearness index as the only input parameter. The suitability of SVM–WT is evaluated against radial basis function SVM (SVM–RBF), artificial neural network (ANN) and a 3rd degree empirical model established for this study. It is found that the estimated diffuse solar radiation values by the SVM–WT model are in favourable agreements with measured data. According to the conducted statistical analysis, the obtained mean absolute bias error, root mean square error and correlation coefficient are 0.5757 MJ/m2, 0.6940 MJ/m2 and 0.9631, respectively. While for the SVM–RBF ranked next the attained values are 1.0877 MJ/m2, 1.2583 MJ/m2 and 0.8599, respectively. In fact, the study results indicate that SVM–WT is an efficient method which enjoys much higher precision than other models, especially the 3rd degree empirical model.

Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine

Journal paper
Iman Mazinani, Zubaidah Binti Ismail, Shahaboddin Shamshirband, Ahmad Mustafa Hashim, Marjan Mansourvar, Erfan Zalnezhad
Entropy, Volume 18, Issue 5, Pages 167

Abstract

: This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM) and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 m × 1.5 m × 2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental results showed an improvement in predictive accuracy, and capability of generalization could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalization performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that utilization of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge.

Evaluating the wind energy potential for hydrogen production: A case study

Journal paper
Ali Mostafaeipour, Mohammad Khayyami, Ahmad Sedaghat, Kasra Mohammadi, Shahaboddin Shamshirband, Mohammad-Ali Sehati, Ehsan Gorakifard
International Journal of Hydrogen Energy, Volume 41, Issue 15, Pages 6200-6210

Abstract

In this paper, the potential of wind energy development for the purpose of hydrogen production in the Fars province of Iran is investigated. To achieve this, wind speeds at 10 m, 30 m and 40 m heights and wind direction at 30 m and 37.5 m recorded in 10 min intervals for the period of one year for four cities of Abadeh, Juyom, Eqleed, and Marvdasht are utilized. Wind energy characteristics have been statistically analyzed to determine the potential of wind power to generate hydrogen in the examined cities. It is found that city of Abadeh has better potential for harnessing wind energy than other cities. Statistical analysis of Abadeh at 40 m height indicates the nominal wind speed of 7.47 m/s which generates maximum energy with the annual power density of 220 W/m2. The performances of four different large-scale wind turbines for producing wind power in Abadeh are evaluated. It is found that hydrogen from wind energy in Abadeh using a small hydrogen producing unit would fuel approximately 22 cars per week if a EWT Direct wind 52/900 model wind turbine to be used.

A Survey of Big Data Management: Taxonomy and State-of-the-Art

Journal paper
Aisha Siddiqa, Ibrahim Abaker TargioHashem, Ibrar Yaqoob, Mohsen Marjani, Shahabuddin Shamshirband, Abdullah Gani, Fariza Nasaruddin
Journal of Network and Computer Applications, Publisher Academic Press

Abstract

The rapid growth of emerging applications and the evolution of cloud computing technologies have significantly enhanced the capability to generate vast amounts of data. Thus, it has become a great challenge in this big data era to manage such voluminous amount of data. The recent advancements in big data techniques and technologies have enabled many enterprises to handle big data efficiently. However, these advances in techniques and technologies have not yet been studied in detail and a comprehensive survey of this domain is still lacking. With focus on big data management, this survey aims to investigate feasible techniques of managing big data by emphasizing on storage, pre-processing, processing and security. Moreover, the critical aspects of these techniques are analyzed by devising a taxonomy in order to identify the problems and proposals made to alleviate these problems. Furthermore, big data management techniques are also summarized. Finally, several future research directions are presented.

A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies

Journal paper
Shahab Kariminia, Shahaboddin Shamshirband, Roslan Hashim, Ahmadreza Saberi, Dalibor Petković, Chandrabhushan Roy, Shervin Motamedi
Energy, Volume 101, Pages 568-580

Abstract

Sustaining outdoor life in cities is decreasing because of the recent rapid urbanisation without considering climate-responsive urban design concepts. Such inadvertent climatic modifications at the indoor level have imposed considerable demand on the urban energy resources. It is important to provide comfortable ambient climate at open urban squares. Researchers need to predict the comfortable conditions at such outdoor squares. The main objective of this study is predict the visitors’ outdoor comfort indices by using a developed computational model termed as SVM-WAVELET (Support Vector Machines combined with Discrete Wavelet Transform algorithm). For data collection, the field study was conducted in downtown Isfahan, Iran (51°41′ E, 32°37′ N) with hot and arid summers. Based on different environmental elements, four separate locations were monitored across two public squares. Meteorological data were measured simultaneously by surveying the visitors’ thermal sensations. According to the subjects’ thermal feeling and their characteristics, their level of comfort was estimated. Further, the adapted computational model was used to estimate the visitors’ thermal sensations in terms of thermal comfort indices. The SVM-WAVELET results indicate that R2 value for input parameters, including Thermal Sensation, PMW (The predicted mean vote), PET (physiologically equivalent temperature), SET (standard effective temperature) and Tmrtwere estimated at 0.482, 0.943, 0.988, 0.969 and 0.840, respectively.

A comparative study for estimation of wave height using traditional and hybrid soft-computing methods

Journal paper
Chandrabhushan Roy, Shervin Motamedi, Roslan Hashim, Shahaboddin Shamshirband, Dalibor Petković
Environmental Earth Sciences, Volume 75, Issue 7, Pages 1-12

Abstract

The present study developed a wave height prediction model by the recorded climatic data. We used 1-year buoy data for training and testing the developed soft-computing model. Models were developed using a novel method based on the Support Vector Machine (SVM) coupled with the Firefly Algorithm (FFA). This research work used the FFA for estimating the optimum parameters. In addition, this work compared the predicted results of SVM-FFA model to the artificial neural networks (ANNs) and genetic programming (GP). The results indicate that the SVM-FFA approach attains an improvement in capability of generalization and predictive accuracy in comparison to the GP and ANN. A thorough statistical analysis was conducted to compare the predictions of three models i.e., among the SVM-FFA, ANN, and GP. A high R2value of 0.979 was obtained for the SVM-FFA predictions. Further, the ANN and GP results showed R2 values of 0.524 and 0.525, respectively. Moreover, achieved results indicate that the developed SVM-FFA model can be used with confidence for future research works on formulating novel models for predictive strategy on wave height. The results also show that the new algorithm can learn thousands of times faster than the former popular learning algorithms. This study finds that the application of SVM-FFA is the likely alternative method for estimating the wave height.

Adaptation of ANFIS model to assess thermal comfort of an urban square in moderate and dry climate

Journal paper
Shahab Kariminia, Shervin Motamedi, Shahaboddin Shamshirband, Dalibor Petković, Chandrabhushan Roy, Roslan Hashim
Stochastic Environmental Research and Risk Assessment, Volume 30, Issue 4, Pages 1189-1203

Abstract

Attractiveness of the open urban spaces, such as plazas or squares, depends on the visitor’s thermal comfort. In this respect, it is important to assess the environment of such open space along with the demographic factors of the visitors. This study used the soft-computing method of adaptive neuro fuzzy inference system (ANFIS) to investigate the thermal comfort of visitors at a public square in Iran during hot and cold weather conditions. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the individual’s comfortable feeling. Model’s training and testing data were collected through the field measurement and survey during hot and cold times of the year. We used 18 input parameters, representative of demographic and environmental factors, to compute visitor’s thermal sensation, comfort feeling, and 4 common indices, namely the mean radiant temperature (Tmrt), mean physiological equivalent temperature (PET), standard effective temperature (SET) and predicted mean vote (PMV). The results indicated that among the examined factors, the air temperature (Ta) is the most influential parameter and best predictor of accuracy for the individual’s comfort feeling at the studied urban square. The results show that Ta can best predict the common indices of outdoor comfort, namely the PMV, PET, SET, thermal sensation, Tmrt, and comfortable felling compared to other parameters with the least error of 1.94, 18.87, 13.67, 0.91, 7.80, and 0.34 %, respectively. Some of the main advantages of the ANFIS scheme are that it is adaptable to the optimization and adaptive methods, and is computationally efficient.

A lightweight radio propagation model for vehicular communication in road tunnels

Journal paper
Muhammad Ahsan Qureshi, Rafidah Md Noor, Azra Shamim, Shahaboddin Shamshirband, Kim-Kwang Raymond Choo
PloS one, Volume 11, Issue 3, Pages e0152727

Abstract

Radio propagation models (RPMs) are generally employed in Vehicular Ad Hoc Networks (VANETs) to predict path loss in multiple operating environments (e.g. modern road infrastructure such as flyovers, underpasses and road tunnels). For example, different RPMs have been developed to predict propagation behaviour in road tunnels. However, most existing RPMs for road tunnels are computationally complex and are based on field measurements in frequency band not suitable for VANET deployment. Furthermore, in tunnel applications, consequences of moving radio obstacles, such as large buses and delivery trucks, are generally not considered in existing RPMs. This paper proposes a computationally inexpensive RPM with minimal set of parameters to predict path loss in an acceptable range for road tunnels. The proposed RPM utilizes geometric properties of the tunnel, such as height and width along with the distance between sender and receiver, to predict the path loss. The proposed RPM also considers the additional attenuation caused by the moving radio obstacles in road tunnels, while requiring a negligible overhead in terms of computational complexity. To demonstrate the utility of our proposed RPM, we conduct a comparative summary and evaluate its performance. Specifically, an extensive data gathering campaign is carried out in order to evaluate the proposed RPM. The field measurements use the 5 GHz frequency band, which is suitable for vehicular communication. The results demonstrate that a close match exists between the predicted values and measured values of path loss. In particular, an average accuracy of 94% is found with R2 = 0.86.

A combined support vector machine-wavelet transform model for prediction of sediment transport in sewer

Journal paper
Isa Ebtehaj, Hossein Bonakdari, Shahaboddin Shamshirband, Kasra Mohammadi
Flow Measurement and Instrumentation, Volume 47, Pages 19-27

Abstract

Technical design of sewer systems requires highly accurate prediction of sediment transport. In this study, the capability of the combined support vector machine-wavelet transform (SVM-Wavelet) model for the prediction of the densimetric Froude number (Fr) was compared to the single SVM and different existing sediment transport equations at the limit of deposition. The performance evaluation was performed using the R-square (R2), three relative indexes (MRE, MARE, MSRE) and three absolute indexes (ME, MAE, RMSE). The factors affecting the Fr were initially determined. After categorizing them into different dimensionless groups, six different models were found to predict the Fr. Comparisons between the obtained results showed that both the SVM and SVM-Wavelet can predict the Fr with high accuracy. However, it was found that the SVM-Wavelet (R2=0.995, MRE=0.002, MARE=0.021, MSRE=0.001, ME=0.007, MAE=0.086 and RMSE=0.114) offers higher performance than the SVM and the existing equations.

Perspectives of support vector regression for static posturographic assessment of patients with cognitive impairment

Journal paper
Ellie Abdi, Redha Taiar, Shahaboddin Shamshirband, Philippe Renault, Dimitra Sifaki-Pistolla, André Chays, François-Constant Boyer, Edwin Regrain
International Journal Series in Multidisciplinary Research (IJSMR)(ISSN: 2455-2461), Volume 2, Issue 2, Pages 1-13

Abstract

The study aims to assess and quantify the discriminate parameters of balance
among patients affected by vestibular dysfunction. Several data were obtained using the
Satel force plate. A total of 14 patients participated in the study. The postural strategies were
studied from the trajectory of the Center Of Pressure (COP), in standing position and under
the cognitive “open eyes (Cognitive-Task-Free-CTF)”and “open eyes with a cognitive task
(Cognitive-Task-Active-CTA)”. Experimental data were used for training of the intelligent

Using ANFIS for selection of more relevant parameters to predict dew point temperature

Journal paper
Kasra Mohammadi, Shahaboddin Shamshirband, Dalibor Petković, Lip Yee, Zulkefli Mansor
Applied Thermal Engineering, Volume 96, Pages 311-319

Abstract

In this research work, for the first time, the adaptive neuro fuzzy inference system (ANFIS) is employed to propose an approach for identifying the most significant parameters for prediction of daily dew point temperature (Tdew). The ANFIS process for variable selection is implemented, which includes a number of ways to recognize the parameters offering favorable predictions. According to the physical factors influencing the dew formation, 8 variables of daily minimum, maximum and average air temperatures (Tmin, Tmax and Tavg), relative humidity (Rh), atmospheric pressure (P), water vapor pressure (VP), sunshine hour (n) and horizontal global solar radiation (H) are considered to investigate their effects on Tdew. The used data include 7 years daily measured data of two Iranian cities located in the central and south central parts of the country. The results indicate that despite climate difference between the considered case studies, for both stations, VP is the most influential variable while Rh is the least relevant element. Furthermore, the combination of Tmin and VP is recognized as the most influential set to predict Tdew. The conducted examinations show that there is a remarkable difference between the errors achieved for most and less relevant input parameters, which highlights the importance of appropriate selection of input parameters. The use of more than two inputs may not be advisable and appropriate; thus, considering the most relevant combination of 2 parameters would be more suitable to achieve higher accuracy and lower complexity in predictions. In the final step, comparisons between the predictions of the ANFIS model using the selected inputs and other soft computing techniques demonstrate that ANFIS has a higher accuracy to predict daily dew point temperature.

Erratum: Erratum to: Application of extreme learning machine for estimation of wind speed distribution

Journal paper
Shahaboddin Shamshirband, Kasra Mohammadi, Chong Wen Tong, Dalibor Petkovic, Emilio Porcu, Ali Mostafaeipour, Sudheer Ch, Ahmad Sedaghat
Climate Dynamics, Volume 46, Pages 2025-2025

A hybrid computational intelligence method for predicting dew point temperature

Journal paper
Mohsen Amirmojahedi, Kasra Mohammadi, Shahaboddin Shamshirband, Amir Seyed Danesh, Ali Mostafaeipour, Amirrudin Kamsin
Environmental Earth Sciences, Volume 75, Issue 5, Pages 1-12

Abstract

Recently, utilization of hybrid models has gained remarkable attention as they take the advantage of specific nature of each technique to enhance the precision and reliability of the predictions. In this research work, a new hybrid approach combined the extreme learning machine (ELM) with wavelet transform (WT) algorithm is proposed to predict daily dew point temperature. To test the validity of the proposed approach, the daily weather data sets for port of Bandar Abass situated in the south costal part of Iran are used. The merit of the proposed ELM-WT method is verified against the ELM, support vector machines and artificial neural network techniques based upon several well-known statistical indicators. The achieved results demonstrate that the hybrid ELM-WT method presents absolute superiority over other powerful techniques applied. It is found that among four considered sets of parameters with 1, 2 and 3 inputs, further accuracy can be achieved using combination of average ambient temperature (Tavg) and relative humidity (Rh). For the best ELM-WT model using Tavg and Rh as inputs, the statistical indicators of mean absolute percentage error, mean absolute bias error, root mean square error and coefficient of determination are 6.1664 %, 0.5495, 0.7621 and 0.9953 °C, respectively. Based on relative percentage error (RPE), for the best ELM-WT model 91 % of the predictions fall within the RPE acceptable range of −10 and +10 %. In a nutshell, the results of this study convincingly advocate that coupling the ELM with WT would be particularly appealing to offer accurate predictions and favorable enhancement in the precision of ELM.

Prediction of Daily Dewpoint Temperature Using a Model Combining the Support Vector Machine with Firefly Algorithm

Journal paper
Eiman Tamah Al-Shammari, Kasra Mohammadi, Afram Keivani, Siti Hafizah Ab Hamid, Shatirah Akib, Shahaboddin Shamshirband, Dalibor Petković
Journal of Irrigation and Drainage Engineering, Volume 142, Issue 5, Pages 04016013

Abstract

In this research work, a hybrid approach of integrating a support vector machine
(SVM) with firefly algorithm (FFA) is proposed to predict daily dewpoint temperature (T dew).
The main aim of employing FFA is to identify the optimal SVM parameters and provide the
possibility of enhancing the SVM’s capability. The weather data sets including 10 years of
measured-daily average air temperature (T avg), relative humidity (R h), atmospheric
pressure (P), and T dew for an Iranian city have been utilized. Seven different sets of

Estimating building energy consumption using extreme learning machine method

Journal paper
Sareh Naji, Afram Keivani, Shahaboddin Shamshirband, U Johnson Alengaram, Mohd Zamin Jumaat, Zulkefli Mansor, Malrey Lee
Energy, Volume 97, Pages 506-516

Abstract

The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth’s sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN.

Support vector machine-based exergetic modelling of a DI diesel engine running on biodiesel–diesel blends containing expanded polystyrene

Journal paper
Shahaboddin Shamshirband, Meisam Tabatabaei, Mortaza Aghbashlo, Lip Yee, Dalibor Petković
Applied Thermal Engineering, Volume 94, Pages 727-747

Abstract

In the present study, four Support Vector Machine-based (SVM-based) approaches and the standard artificial neural network (ANN) model were designed and compared in modelling the exergetic parameters of a DI diesel engine running on diesel/biodiesel blends containing expanded polystyrene (EPS) wastes. For this aim, the SVM was coupled with discrete wavelet transform (SVM-WT), firefly algorithm (SVM-FFA), radial basis function (SVM-RBF) and quantum particle swarm optimization (SVM-QPSO). The exergetic data were computed using mass, energy, and exergy balance equations for the engine at different speeds and loads as well as various biodiesel and EPS wastes quantities. Three statistical indicators namely root means square error, coefficient of determination and Pearson coefficient were used to access the capability of the developed approaches for exergetic performance modelling of the DI diesel engine. The modelling results indicated that the SVM-WT approach was more efficient in exergetic modelling of the engine than the other three approaches. Moreover, the results obtained confirmed the effectiveness of the SVM-WT model in identifying the most exergy-efficient combustion conditions and the best fuel composition for achieving the most cost-effective and eco-friendly combustion process.

Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation

Journal paper
Jamshid Piri, Kasra Mohammadi, Shahaboddin Shamshirband, Shatirah Akib
Environmental Earth Sciences, Volume 75, Issue 3, Pages 1-13

Abstract

Estimation of evaporation is of indispensable significance for management and development of water resources. This study aims to identify the suitability of hybridizing the Cuckoo optimization algorithm (COA) with two well-known approaches of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for prediction of daily pan evaporation. For this aim, two hybrid models of ANN–COA and ANFIS–COA are developed and their performances are compared with single ANN and ANFIS. As case study, the daily climate parameters including the average air temperature (Tavg), sunshine hours (S), relative humidity (Rh), wind speed (W) and pan evaporation (E) measured and collected for three Iranian stations of Zabol, Iranshahr and Shiraz have been utilized. The used data sets are divided into three parts so that 60, 20 and 20 % of the data are applied for training, testing and prediction phases, respectively. The achieved results prove that the models’ performances are variable among cities. It is found that combining the COA with ANN and ANFIS techniques does not enhance the precision of the developed ANN and ANFIS models noticeably in all considered stations. In fact, the results demonstrate that hybridizing the COA with ANN and ANFIS cannot be a viable option for estimation of daily evaporation. Overall, the study results indicate that further accuracy can generally be achieved by the ANN model; consequently, the ANN model can be sufficiently used in the prediction of daily evaporation.

Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide

Journal paper
Erfan Mohammadian, Shervin Motamedi, Shahaboddin Shamshirband, Roslan Hashim, Radzuan Junin, Chandrabhushan Roy, Amin Azdarpour
Environmental Earth Sciences, Volume 75, Issue 3, Pages 1-11

Abstract

Solubility of CO2 in brine is one of the contributing trapping mechanisms by which the injected CO2 is sequestrated in aquifers. In the literature, the solubility data on low salinity range are scarce. Thus, in the current study, the CO2 solubility was experimentally obtained in the NaCl brines of low salinity (0–1.5 wt%) at temperature of 333–373 K and pressures up to 280 MPa through the potentiometric titration methods. The short-term, multistep ahead predictive models of aqueous solubility of carbon dioxide were created. The models were developed using a novel method based on the extreme learning machine (ELM). Estimation and prediction results of the ELM model were compared with the genetic programming (GP) and artificial neural networks (ANNs) models. The results revealed enhancement of the predictive accuracy and generalization capability through the ELM method in comparison with the GP and ANN. Moreover, the results indicate that the developed ELM models can be used with confidence for further work on formulating a novel model predictive strategy for the aqueous solubility of carbon dioxide. The experimental results hinted that the current algorithm can present good generalization performance in the majority of cases. Moreover, in comparison with the conventional well-known learning algorithms, it can learn thousands of times faster. In conclusion, it is conclusively found that application of the ELM is particularly promising as an alternative method to estimate the aqueous solubility of carbon dioxide.

Influence of introducing various meteorological parameters to the Angström–Prescott model for estimation of global solar radiation

Journal paper
Kasra Mohammadi, Hossein Khorasanizadeh, Shahaboddin Shamshirband, Chong Wen Tong
Environmental Earth Sciences, Volume 75, Issue 3, Pages 1-12

Abstract

This study aims to recognize that whether introducing various meteorological parameters to the Angström–Prescott (A–P) model eventuates in enhancing the precision of monthly mean global solar radiation estimation in cities of Bandar Abbas and Jask, situated in the south coast of Iran. To identify the significance of the average, maximum and minimum ambient temperatures, average and maximum relative humidity as well as water vapor and sea level pressures, seven models have been chosen from the literature. Using the long-term measured data and via statistical regression technique, the new regression coefficients have been developed for the original A–P model and the other seven nominated models. The models’ performances have been appraised via commonly utilized statistical indicators. The results indicated that the new models provided only minor improvements over the traditional A–P model; therefore, as more complexity is associated with introducing different meteorological parameters, their applications are not appealing practically. Making comparisons with the existing models developed using PSO (particle swarm optimization) technique demonstrated the superiority of the new established A–P models of this study; consequently, even without any improvement, the simple A–P models are indeed qualified for accurate estimation of global solar radiation in cities of Bandar Abbas and Jask and their neighboring.

Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir's discharge coefficient

Journal paper
Amir Hossein Zaji, Hossein Bonakdari, Saeed Reza Khodashenas, Shahaboddin Shamshirband
Applied Mathematics and Computation, Volume 274, Pages 14-19

Abstract

A principal step in designing dividing hydraulic structures entails determining the side weir discharge coefficient. In this study, Firefly optimization-based Support Vector Regression (SVR-FF) is introduced and examined in terms of predicting the discharge coefficient of a modified labyrinth side weir. Ten non-dimensional parameters of various geometrical and hydraulic conditions are defined as the input parameters for the SVR-FF and the side weir discharge coefficient is defined as the output. Improvements in SVR prediction accuracy are determined by comparing SVR-FF with the traditional SVR model. The results indicate that the SVR-FF model with RMSE of 0.035 is about 10% more accurate than SVR with RMSE of 0.039. Thus, combining the Firefly optimization algorithm with SVR increases the prediction model performance.

Application of adaptive neuro-fuzzy methodology for estimating building energy consumption

Journal paper
Sareh Naji, Shahaboddin Shamshirband, Hossein Basser, Afram Keivani, U Johnson Alengaram, Mohd Zamin Jumaat, Dalibor Petković
Renewable and Sustainable Energy Reviews, Volume 53, Pages 1520-1528

Abstract

The huge demand for energy and construction materials has become an issue of great concern recently. The energy usage of buildings accounts for a large percentage of the total primary energy consumption. The total energy requirement of buildings is influenced by various factors, including environmental and climatic conditions, building envelope materials, insulation, etc. In this respect, estimating the operational energy of buildings is potentially helpful for architects and engineers in the early design and construction stages. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate the energy consumption of buildings according to the main building envelope parameters, namely material thickness and insulation K-value. Up to 180 simulations using different material thickness values and insulation properties are carried out in EnergyPlus software in order to use for estimation. This soft computing methodology is implemented with Matlab/Simulink and the performance is investigated.

Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; case study: City of Kerman, Iran

Journal paper
Kasra Mohammadi, Shahaboddin Shamshirband, Dalibor Petković, Hossein Khorasanizadeh
Renewable and Sustainable Energy Reviews, Volume 53, Pages 1570-1579

Abstract

Identifying the most relevant variables for diffuse solar radiation prediction is of indispensable importance. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is applied to select the most influential parameters for prediction of daily horizontal diffuse solar radiation (Hd). Ten important variables are nominated to analyze their effects on prediction of Hd in the city of Kerman, situated in the south central part of Iran. To achieve this, a thorough variable selection is conducted for three cases with 1, 2 and 3 inputs to introduce the best and worst inputs combinations. For the cases with 2 and 3 inputs, 45 and 120 possible combinations of inputs are considered, respectively. Providing comparisons between the most and least relevant sets of inputs reveals that appropriate selection of input parameters is an important task in prediction of Hd. For the cases with one input, it is found that sunshine duration (n) is the most influential variable. Moreover, combination of horizontal global solar radiation (H) and extraterrestrial solar radiation (Ho) as well as combination of H, Ho and n are the best sets among the cases with 2 and 3 inputs, respectively. The achieved results specify that combinations of either 2 or 3 most relevant inputs would be appropriate to provide a balance between the simplicity and high precision. Predictions using the most influential sets of 2 and 3 inputs indicate that for the ANFIS model with two inputs, the mean absolute percentage error, mean absolute bias error, root mean square error and correlation coefficient are 23.0579%, 1.0176 MJ/m2, 1.3052 MJ/m2 and 0.8247, respectively, and for the ANFIS model with three inputs they are 18.3143%, 0.8134 MJ/m2, 1.1036 MJ/m2 and 0.8783, respectively.

Comparative study of clustering methods for wake effect analysis in wind farm

Journal paper
Eiman Tamah Al-Shammari, Shahaboddin Shamshirband, Dalibor Petković, Erfan Zalnezhad, Lip Yee, Ros Suraya Taher, Žarko Ćojbašić
Energy, Volume 95, Pages 573-579

Abstract

Wind energy poses challenges such as the reduction of the wind speed due to wake effect by other turbines. To increase wind farm efficiency, analyzing the parameters which have influence on the wake effect is very important. In this study clustering methods were applied on the wake effects in wind warm to separate district levels of the wake effects. To capture the patterns of the wake effects the PCA (principal component analysis) was applied. Afterwards, cluster analysis was used to analyze the clusters. FCM (Fuzzy c-means), K-mean, and K-medoids were used as the clustering algorithms. The main goal was to segment the wake effect levels in the wind farms. Ten different wake effect clusters were observed according to results. In other words the wake effect has 10 levels of influence on the wind farm energy production. Results show that the K-medoids method was more accurate than FCM and K-mean approach. K-medoid RMSE (root means square error) was 0.240 while the FCM and K-mean RMSEs were 0.320 and 1.509 respectively. The results can be used for wake effect levels segmentation in wind farms.

Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm

Journal paper
Eiman Tamah Al-Shammari, Afram Keivani, Shahaboddin Shamshirband, Ali Mostafaeipour, Lip Yee, Dalibor Petković, Sudheer Ch
Energy, Volume 95, Pages 266-273

Abstract

District heating systems operation can be improved by control strategies. One of the options is the introduction of predictive control model. Predictive models of heat load can be applied to improve district heating system performances. In this article, short-term multistep-ahead predictive models of heat load for consumers connected to district heating system were developed using SVMs (Support Vector Machines) with FFA (Firefly Algorithm). Firefly algorithm was used to optimize SVM parameters. Seven SVM-FFA predictive models for different time horizons were developed. Obtained results of the SVM-FFA models were compared with GP (genetic programming), ANNs (artificial neural networks), and SVMs models with grid search algorithm. The experimental results show that the developed SVM-FFA models can be used with certainty for further work on formulating novel model predictive strategies in district heating systems.

An optimized magnetostatic field solver on GPU using open computing language

Journal paper
Fiaz Gul Khan, Bartolomeo Montrucchio, Bilal Jan, Abdul Nasir Khan, Waqas Jadoon, Shahaboddin Shamshirband, Anthony Theodore Chronopoulos, Iftikhar Ahmed Khan
Concurrency and Computation: Practice and Experience, Publisher John Wiley & Sons, Ltd

Summary

Recent graphic processing units (GPUs) have remarkable raw computing power, which can be used for very computationally challenging problems. Like in micromagnetic simulations, where the magnetostatic field computation to analyze the magnetic behavior at very small time and space scale demands a huge computation time. This paper presents a multidimensional FFT-based parallel implementation of a magnetostatic field computation on GPUs. We have developed a specialized 3D FFT library for magnetostatic field calculation on GPUs. This made it possible to fully exploit the symmetries inherent in the field calculation and other optimizations specific to the GPUs architecture. We have compared our results with the widely used CPU-based parallel OOMMF program and with an equivalent serial implementation on CPU. The results have shown a speedup of up to 95x and 8.7x for single and 66x and 4.6x for double precision floating point accuracy against equivalent serial implementation and OOMMF, respectively.

Predicting the reference evapotranspiration based on tensor decomposition

Journal paper
Negin Misaghian, Shahaboddin Shamshirband, Dalibor Petković, Milan Gocic, Kasra Mohammadi
Theoretical and Applied Climatology, Pages 1-11

Abstract

Most of the available models for reference evapotranspiration (ET0) estimation are based upon only an empirical equation for ET0. Thus, one of the main issues in ET0 estimation is the appropriate integration of time information and different empirical ET0 equations to determine ET0 and boost the precision. The FAO-56 Penman–Monteith, adjusted Hargreaves, Blaney–Criddle, Priestley–Taylor, and Jensen–Haise equations were utilized in this study for estimating ET0 for two stations of Belgrade and Nis in Serbia using collected data for the period of 1980 to 2010. Three-order tensor is used to capture three-way correlations among months, years, and ET0 information. Afterward, the latent correlations among ET0 parameters were found by the multiway analysis to enhance the quality of the prediction. The suggested method is valuable as it takes into account simultaneous relations between elements, boosts the prediction precision, and determines latent associations. Models are compared with respect to coefficient of determination (R2), mean absolute error (MAE), and root-mean-square error (RMSE). The proposed tensor approach has a R2 value of greater than 0.9 for all selected ET0 methods at both selected stations, which is acceptable for the ET0 prediction. RMSE is ranged between 0.247 and 0.485 mm day−1 at Nis station and between 0.277 and 0.451 mm day−1 at Belgrade station, while MAE is between 0.140 and 0.337 mm day−1 at Nis and between 0.208 and 0.360 mm day−1 at Belgrade station. The best performances are achieved by Priestley–Taylor model at Nis station (R2 = 0.985, MAE = 0.140 mm day−1, RMSE = 0.247 mm day−1) and FAO-56 Penman–Monteith model at Belgrade station (MAE = 0.208 mm day−1, RMSE = 0.277 mm day−1, R2 = 0.975).

Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach

Journal paper
Jalal Shiri, Shahaboddin Shamshirband, Ozgur Kisi, Sepideh Karimi, Seyyed M Bateni, Seyed Hossein Hosseini Nezhad, Arsalan Hashemi
Water Resources Management, Pages 1-13

Abstract

Predicting the dynamics of water-level in lakes plays a vital role in navigation, water resources planning and catchment management. In this paper, the Extreme Learning Machine (ELM) approach was used to predict the daily water-level in the Urmia Lake. Daily water-level data from the Urmia Lake in northwest of Iran were used to train, test and validate the employed models. Results showed that the ELM approach can accurately forecast the water-level in the Urmia Lake. Outcomes from the ELM model were also compared with those of genetic programming (GP) and artificial neural networks (ANNs). It was found that the ELM technique outperforms GP and ANN in predicting water-level in the Urmia Lake. It also can learn the relation between the water-level and its influential variables much faster than the GP and ANN. Overall, the results show that the ELM approach can be used to predict dynamics of water-level in lakes.

Predicting turbulent flow friction coefficient using ANFIS technique

Journal paper
Sara Bardestani, Mohammad Givehchi, Emran Younesi, Shahin Sajjadi, Shahaboddin Shamshirband, Dalibor Petkovic
Signal, Image and Video Processing, Pages 1-7

Abstract

The friction coefficient is widely used for technical and economical design of pipes in irrigation, land drainage, urban sewage systems and intake structures. In the present study, the friction factor in pipes is estimated by using adaptive neuro-fuzzy inference system (ANFIS) and grid partition method. The data derived from the Colebrook’s equation were considered for ascertaining the neuro-fuzzy model. Present approach developed an ANFIS technique to predict the friction coefficient as output variable based on pipe relative roughness and Reynold’s number as input variables. The performance of the ANFIS model was evaluated against conventional procedures. Correlation coefficient (R2), root mean squared error and mean absolute error were used as comparing statistical indicators for the assessment of the proposed approach’s performance. It was found that the adaptive neuro-fuzzy inference system model is more accurate than other empirical equations in modeling friction factor.

Community detection in social networks using user frequent pattern mining

Journal paper
Seyed Ahmad Moosavi, Mehrdad Jalali, Negin Misaghian, Shahaboddin Shamshirband, Mohammad Hossein Anisi
Knowledge and Information Systems, Pages 1-28

Abstract

Recently, social networking sites are offering a rich resource of heterogeneous data. The analysis of such data can lead to the discovery of unknown information and relations in these networks. The detection of communities including ‘similar’ nodes is a challenging topic in the analysis of social network data, and it has been widely studied in the social networking community in the context of underlying graph structure. Online social networks, in addition to having graph structures, include effective user information within networks. Using this information leads to enhance quality of community discovery. In this study, a method of community discovery is provided. Besides communication among nodes to improve the quality of the discovered communities, content information is used as well. This is a new approach based on frequent patterns and the actions of users on networks, particularly social networking sites where users carry out their preferred activities. The main contributions of proposed method are twofold: First, based on the interests and activities of users on networks, some small communities of similar users are discovered, and then by using social relations, the discovered communities are extended. The F-measure is used to evaluate the results of two real-world datasets (Blogcatalog and Flickr), demonstrating that the proposed method principals to improve the community detection quality.

Impact of multi-task on symptomatic patient affected by chronical vestibular disorders

Journal paper
Edwin Regrain, Philippe Regnault, Christopher Kirtley, Shahaboddin Shamshirband, André Chays, François-Constant Boyer, Redha Taiar
Acta Bioeng. Biomech

Abstract

Purpose: After a vestibular deficit some patients may be affected by chronical
postural instability. The aim of this study was to identify the emotional and cognitive factors of
these symptomatic patients. In particular, the double cognitive task and the anxiety disorder
were identified by our patients. Through a retrospective study, 14 patients (65.4±18 years)
participated in the experiment.

Hydrological Hazards in a Changing Environment: Early Warning, Forecasting, and Impact Assessment

Journal paper
Slavisa Trajkovic, Ozgur Kisi, Momcilo Markus, Hossein Tabari, Milan Gocic, Shahaboddin Shamshirband
Advances in Meteorology, Volume 2016

Hydrological hazards of various types present a myriad of technical and public policy issues
worldwide. Defined as extreme events associated with water occurrence, movement, and
distribution, hydrological hazards include droughts and flooding and related events (eg,
landslides and river scour and deposition). Hydrological hazards and their impacts are
associated with climate variability, demographic trends, land cover change, and other
causative factors and could be exasperated by global climate change. The increase in

(In Press) Sensitivity analysis of catalyzed-transesterification as a renewable and sustainable energy production system by adaptive neuro-fuzzy methodology

Journal paper
B Sajjadi, A Bin Abdul Raman, R Parthasarathy, S Shamshirband
Journal of the Taiwan Institute of Chemical Engineers, Pages 1-12

Abstract

The current study aims at introducing a fast and precise method for analyzing the operation of renewable and sustainable energy systems. Accordingly, ultrasound assisted transesterification as a novel method of biodiesel synthesis and biodiesel synthesis using mechanical stirring were selected as the two main systems for renewable energy production. It is necessary to analyze the parameters which are the most influential on transesterification yield estimation and prediction in order to assess transesterification yield. ANFIS (adaptive neuro-fuzzy inference system) was used in this study for selecting the most influential parameters based on five input parameters (operational variables). The effectiveness of the proposed strategy was verified with the simulation results. Experiments were conducted to extract training data for the ANFIS network. Furthermore, RSM (response surface methodology) was used to design the experiments and analyze the interactive and individual effects of the five independent variables in order to evaluate the results predicted by ANFIS. The obtained results clearly demonstrated the effects of operational variables on the final transesterification yield.

Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption

Journal paper
Nima Izadyar, Hossein Ghadamian, Hwai Chyuan Ong, Chong Wen Tong, Shahaboddin Shamshirband
Energy, Volume 93, Pages 1558-1567

Abstract

DHS (District Heating System) is one of the most efficient technologies which has been used to meet residential thermal demand. In this study, the most accurate forecasting of the residential heating demand is investigated via soft computing method. The objective of this study is to obtain the most accurate prediction of the residential heating consumption to employ forecasting result for designing optimum DHS system as a possible substitute of a pipeline natural gas in BAHARESTAN Town. For this purpose, three Support Vector Machine (SVM) models namely SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA) and using the radial basis function (SVM-RBF) were analyzed. The estimation and prediction results of these models were compared with two other soft computing methods (ANN (Artificial Neural Network) and GP (Genetic programming)) by using three statistical indicators i.e. RMSE (root means square error), coefficient of determination (R2) and Pearson coefficient (r). Based on the experimental outputs, the SVM-Wavelet method can lead to slightly accurate forecasting of the monthly overall natural gas demand.

A review of quadrotor UAV: control methodologies and performance evaluation

Journal paper
Roohul Amin, Li Aijun, Shahaboddin Shamshirband
International Journal of Automation and Control, Volume 10, Issue 2, Pages 87-103

Quadrotor a kind of multirotor unmanned air vehicle has gained popularity within research community due to its high manoeuvrability, vertical take-off, landing and hovering capability, and ease of operation in areas where traditional unmanned air vehicle have proved ineffective. This article provides a comprehensive survey of research methodologies proposed for quadrotor control. The methodologies are categorised into three different control design domains and each methodology is discussed in detail. Finally, we discuss the evaluation criteria used for the aforementioned methodologies and highlight the potential challenges that still need to be addressed in quadrotor control domain.

Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

Journal paper
Ali Toghroli, Meldi Suhatril, Zainah Ibrahim, Maryam Safa, Mahdi Shariati, Shahaboddin Shamshirband
Journal of Intelligent Manufacturing, Pages 1-9

Abstract

Evaluation of the parameters affecting the shear strength and ductility of steel–concrete composite beam is the goal of this study. This study focuses on predicting the future output of beam’s strength and ductility based on relative inputs using a soft computing scheme, extreme learning machine (ELM). Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. Referring to the experimental results, as opposed to the GP and ANN methods, the ELM approach enhanced generalization ability and predictive accuracy. Moreover, achieved results indicated that the developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in shear strength and ductility of steel concrete composite. Furthermore, the experimental results indicate that on the whole, the newflanged algorithm creates good generalization presentation. In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability.

Extreme learning machine assessment for estimating sediment transport in open channels

Journal paper
Isa Ebtehaj, Hossein Bonakdari, Shahaboddin Shamshirband
Engineering with Computers, Pages 1-14

Abstract

The minimum velocity required to prevent sediment deposition in open channels is examined in this study. The parameters affecting transport are first determined and then categorized into different dimensionless groups, including “movement,” “transport,” “sediment,” “transport mode,” and “flow resistance.” Six different models are presented to identify the effect of each of these parameters. The feed-forward neural network (FFNN) is used to predict the densimetric Froude number (Fr) and the extreme learning machine (ELM) algorithm is utilized to train it. The results of this algorithm are compared with back propagation (BP), genetic programming (GP) and existing sediment transport equations. The results indicate that FFNN-ELM produced better results than FNN-BP, GP and existing sediment transport methods in both training (RMSE = 0.26 and MARE = 0.052) and testing (RMSE = 0.121 and MARE = 0.023). Moreover, the performance of FFNN-ELM is examined for different pipe diameters.

Historical path of traditional and modern idea of ‘conscious universe’

Journal paper
Mehrnaz Monzavi, Mohd Hazim Shah Abdul Murad, Matin Rahnama, Shahaboddin Shamshirband
Quality & Quantity, Pages 1-13

Abstract

What this study shows is that although the ‘idea of conscious universe’ is given further advance by some discoveries of modern sciences, it was articulated by many ancient and previous thinkers. Philosophically, the idea of ‘Consciousness’ can be traced back to ancient times and can be proven through historical documents in various traditions, cultures and religions. In this study, the ideas are provided from perspectives of West, East and Islam. Simply, the term ‘awareness’ and the concept of ‘ability to be aware of the environment’ imply the notion of ‘Consciousness’ in linguistic and psychology. In a comparative study, we show that philosophical and scientific views in the past and contemporary period highly recommended the existence of ‘Consciousness’ attribute. In fact, all of the mentioned studies are talking about the same ideology based on the qualitative perceptions. However, there are some differences in parts, but the major theme is same.

PBRP: Pattern-based approach for software release planning

Journal paper
Amir Seyed Danesh, Rodina Ahmad, Shahaboddin Shamshirband, Seyed Mahdi Zargarnataj
ASIA LIFE SCIENCES, Volume 25, Issue 1, Pages 479-506

Improved side weir discharge coefficient modeling by adaptive neuro-fuzzy methodology

Journal paper
Shahaboddin Shamshirband, Hossein Bonakdari, Amir Hossein Zaji, Dalibor Petkovic, Shervin Motamedi
KSCE Journal of Civil Engineering, Pages 1-7

Abstract

In this article, the accuracy of a soft computing technique is evaluated in terms of discharge coefficient prediction of an improved triangular side weir. The process includes simulating the discharge coefficient with the Adaptive Neuro-Fuzzy Inference System (ANFIS). Matlab software is used for ANFIS modeling. To identify the most appropriate input variables, eight different input combinations with various numbers of inputs are examined. The performance of the proposed system is confirmed by comparing the ANFIS and experimental results for the testing dataset. The performance evaluation demonstrates that the ANFIS model with five inputs (Root Mean Square Error (RMSE) of 0.014) is more accurate than the ANFIS model with one input (RMSE = 0.088). The ANFIS model results are also compared with the results obtained from previous regression and soft computing studies.

Hybrid auto-regressive neural network model for estimating global solar radiation in Bandar Abbas, Iran

Journal paper
Shahaboddin Shamshirband, Kasra Mohammadi, Jamshid Piri, Dalibor Petković, Ahmad Karim
Environmental Earth Sciences, Volume 75, Issue 2, Pages 1-12

Abstract

In this paper, a neural network auto-regressive model with exogenous inputs (NN-ARX) is utilized for predicting daily horizontal global solar radiation (DHGSR). For this aim, two sets of parameters: (1) sunshine hours (n) and maximum possible sunshine hours (N), and (2) maximum ambient temperature (Tmax) and minimum ambient temperature (Tmin) collected for Bandar Abbas city of Iran are used as inputs. The efficiency of NN-ARX is compared with that of the adaptive neuro-fuzzy inference system (ANFIS), which is a robust methodology. The attained results reveal the superiority of sunshine hours as input over air temperatures so that the NN-ARX (1) and ANFIS (1) models using n and N as inputs offer higher precision than the NN-ARX (2) and ANFIS (2) models using Tmax and Tmin as inputs. Statistical results demonstrate that NN-ARX provides favorable precision and outperforms ANFIS. The relative percentage error analysis shows that the capability of the ANN-ARX (1) model in different days of the year is indeed attractive since 89.25 % of the predictions fall within the acceptable range of −10 to +10 %. The influence of introducing extraterrestrial solar radiation (Ho) as third input on the performance of the NN-ARX models is assessed. It is found that using Ho provides only slight improvements on accuracy for both sunshine duration and temperature-based predictions; thus, considering Ho as the third input may not be really suitable since it also brings further complexity in terms of the required inputs. The survey results prove that NN-ARX would be an efficient alternative approach to predict DHGSR.

TETS: A Genetic-Based Scheduler in Cloud Computing to Decrease Energy and Makespan

Journal paper
Mohammad Shojafar, Maryam Kardgar, Ali Asghar Rahmani Hosseinabadi, Shahab Shamshirband, Ajith Abraham
Hybrid Intelligent Systems, Pages 103-115

Abstract

In Cloud computing environments, computing resources are available for users, and they only pay for used resources The most important issues in cloud computing are scheduling and energy consumption which many researchers worked on them. In these systems a scheduling mechanism has two phases: task prioritization and processor selection. Different priorities may cause to different makespan and for each processor which assigned to the task, the energy consumption is different. So a good scheduling algorithm must assign priority to each task and select the best processor for them, in such a way that makespan and energy consumption be minimized. In this paper, we proposed a two phase’s algorithm for scheduling, named TETS, the first phase is task prioritization and the second phase is processor assignment. We use three prioritization methods for prioritize the tasks and produce optimized initial chromosomes and assign the tasks to processors which is an energy-aware model. Simulation results indicate that our algorithm is better than previous algorithms in terms of energy consumption and makespan. It can improve the energy consumption by 20 % and makespan by 4 %.

Gravitational Search Algorithm to Solve Open Vehicle Routing Problem

Journal paper
Ali Asghar Rahmani Hosseinabadi, Maryam Kardgar, Mohammad Shojafar, Shahab Shamshirband, Ajith Abraham
Innovations in Bio-Inspired Computing and Applications, Pages 93-103

Abstract

Traditional Open Vehicle Routing Problem (OVRP) methods take account to definite responding to the all requests of customers whiles the main goal of proposed approach in OVRP is decreasing the vehicle numbers time and path traveled by vehicles. Therefore, in the present paper, a new optimization algorithm based on Gravity law and mass interactions is introduced to solve the problem. This algorithm being proposed based on random search concepts utilizes two of the four major parameters in physics including speed and Gravity and its researcher agents are a set of masses which are in connection with each other based on Newton’s Gravity and motion laws. The proposed approach is compared with various algorithms and results approve its high effectiveness in solving the above problem.

The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a DI diesel engine running on diesel/biodiesel blends containing polymer waste

Journal paper
Mortaza Aghbashlo, Shahaboddin Shamshirband, Meisam Tabatabaei, Lip Yee, Yaser Nabavi Larimi
Energy, Volume 94, Pages 443-456

Abstract

In this study, a novel method based on Extreme Learning Machine with wavelet transform algorithm (ELM-WT) was designed and adapted to estimate the exergetic performance of a DI diesel engine. The exergetic information was obtained by calculating mass, energy, and exergy balance equations for the experimental trials conducted at various engine speeds and loads as well as different biodiesel and expanded polystyrene contents. Furthermore, estimation capability of the ELM-WT model was compared with that of the ELM, GP (genetic programming) and ANN (artificial neural network) models. The experimental results showed that an improvement in the exergetic performance modelling of the DI diesel engine could be achieved by the ELM-WT approach in comparison with the ELM, GP, and ANN methods. Furthermore, the results showed that the applied algorithm could learn thousands of times faster than the conventional popular learning algorithms. Obviously, the developed ELM-WT model could be used with a high degree of confidence for further work on formulating novel model predictive strategy for investigating exergetic performance of DI diesel engines running on various renewable and non-renewable fuels.

Application of support vector machine for prediction of electrical and thermal performance in PV/T system

Journal paper
Juwel Chandra Mojumder, Hwai Chyuan Ong, Wen Tong Chong, Shahaboddin Shamshirband
Energy and Buildings, Volume 111, Pages 267-277

Abstract

In photovoltaic–thermal (PV/T) system analysis, solar collectors with numerous design concepts have been used to purvey the thermal and electrical energy effectively. In this study, two types of solar thermal collectors in PV/T system are proposed and fabricated called design A and design B respectively. In order to investigate the effects of collector type on the system performance a thin flat metallic sheet (TFMS) and fins were introduced as an effective heat absorber and heat sink in the collectors. Extensive experiments were carried out for different conditions under indoor solar simulator. Then PV/T thermal and electrical efficiency were calculated by using data obtained from experiments. Here, support vector machine (SVM) model is designed to estimate the thermal and electrical output which predicts the values for some input variables. For this purpose, three SVM models namely SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA) and with using the radial basis function (SVM-RBF) were analyzed. The estimation and prediction results of these models were compared with each other using statistical indicators i.e. root means square error, coefficient of determination and Pearson coefficient. The experimental results show that a significant improvement in predictive accuracy and capability of generalization can be achieved by the SVM-Wavelet approach. Moreover, the results indicate that proposed SVM-Wavelet model can adequately predict the electrical and thermal efficiencies of PV/T system. In the final analysis, a proper sensitivity analysis is performed to identify the influence of considered input elements on performance prediction of PV/T system.

Precipitation estimation using support vector machine with discrete wavelet transform

Journal paper
Mohamed Shenify, Amir Seyed Danesh, Milan Gocić, Ros Surya Taher, Ainuddin Wahid Abdul Wahab, Abdullah Gani, Shahaboddin Shamshirband, Dalibor Petković
Water Resources Management, Volume 30, Issue 2, Pages 641-652

Abstract

Precipitation prediction is of dispensable importance in many hydrological applications. In this study, monthly precipitation data sets from Serbia for the period 1946–2012 were used to estimate precipitation. To fulfil this objective, three mathematical techniques named artificial neural network (ANN), genetic programming (GP) and support vector machine with wavelet transform algorithm (WT-SVM) were applied. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2) were used to evaluate the performance of the WT-SVM, GP and ANN models. The achieved results demonstrate that the WT-SVM outperforms the GP and ANN models for estimating monthly precipitation.

Resource management in cropping systems using artificial intelligence techniques: a case study of orange orchards in north of Iran

Journal paper
Ashkan Nabavi-Pelesaraei, Reza Abdi, Shahin Rafiee, Shahaboddin Shamshirband, Majid Yousefinejad-Ostadkelayeh
Stochastic Environmental Research and Risk Assessment, Volume 30, Issue 1, Pages 413-427

Abstract

Management of energy use and reduction of greenhouse gas emissions (GHG) in agricultural system is the important topic. For this purpose, many methods have been proposed in different researches for solution of these items in recent years. Obviously, the selection of appropriate method was a new concern for researchers. Accordingly, the energy inputs and GHG emissions of orange production in north of Iran were modeled and optimized by artificial neural networks (ANN) and multi-objective genetic algorithm (MOGA) in this study and the results obtained were compared with the results of data envelopment analysis (DEA) approach. Results showed that, on average, an amount of 25,582.50 MJ ha−1 was consumed in orange orchards in the region and the nitrogen fertilizer was accounted for 36.84 % of the total input energy. The outcomes of this study demonstrated that on average 803 kg carbon dioxide (kgCO2eq.) is emitted per ha and diesel fuel is responsible for 35.7 % of all emissions. The results of ANN signified that they were capable of modeling crop output and total GHG emissions where the model with a 13-4-2 topology had the highest accuracy in both training and testing steps. The optimization of energy consumption using MOGA revealed that the total energy consumption and GHG emissions of orange production can be reduced to the values of 13,519 MJ ha−1 and 261 kgCO2eq. ha−1, respectively. A comparison between MOGA and DEA clearly showed the better performance of MOGA due to simultaneous application of different objectives and the global optimum solutions produced by the last generation.

A Cloud-Manager-Based Re-Encryption Scheme for Mobile Users in Cloud Environment: a Hybrid Approach

Journal paper
Abdul Nasir Khan, ML Mat Kiah, Mazhar Ali, Shahaboddin Shamshirband
Journal of Grid Computing, Volume 13, Issue 4, Pages 651-675

Abstract

Cloud computing is an emerging computing paradigm that offers on-demand, flexible, and elastic computational and storage services for the end-users. The small and medium-sized business organization having limited budget can enjoy the scalable services of the cloud. However, the migration of the organizational data on the cloud raises security and privacy issues. To keep the data confidential, the data should be encrypted using such cryptography method that provides fine-grained and efficient access for uploaded data without affecting the scalability of the system. In mobile cloud computing environment, the selected scheme should be computationally secure and must have capability for offloading computational intensive security operations on the cloud in a trusted mode due to the resource constraint mobile devices. The existing manager-based re-encryption and cloud-based re-encryption schemes are computationally secured and capable to offload the computationally intensive data access operations on the trusted entity/cloud. Despite the offloading of the data access operations in manager-based re-encryption and cloud-based re-encryption schemes, the mobile user still performs computationally intensive paring-based encryption and decryption operations using limited capabilities of mobile device. In this paper, we proposed Cloud-Manager-based Re-encryption Scheme (CMReS) that combines the characteristics of manager-based re-encryption and cloud-based re-encryption for providing the better security services with minimum processing burden on the mobile device. The experimental results indicate that the proposed cloud-manager-based re-encryption scheme shows significant improvement in turnaround time, energy consumption, and resources utilization on the mobile device as compared to existing re-encryption schemes.

Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran

Journal paper
Shahaboddin Shamshirband, Kasra Mohammadi, Hui-Ling Chen, Ganthan Narayana Samy, Dalibor Petković, Chao Ma
Journal of Atmospheric and Solar-Terrestrial Physics, Volume 134, Pages 109-117

Abstract

Lately, the kernel extreme learning machine (KELM) has gained considerable importance in the scientific area due to its great efficiency, easy implementation and fast training speed. In this paper, for the first time the potential of KELM to predict the daily horizontal global solar radiation from the maximum and minimum air temperatures (Tmaxand Tmin) is appraised. The effectiveness of the proposed KELM method is evaluated against the grid search based support vector regression (SVR), as a robust methodology. Three KELM and SVR models are developed using different input attributes including: (1) Tmin and Tmax, (2) Tmin and Tmax−Tmin, and (3) Tmax and Tmax−Tmin. The achieved results reveal that the best predictions precision is achieved by models (3). The achieved results demonstrate that KELM offers favorable predictions and outperforms the SVR. For the KELM (3) model, the obtained statistical parameters of mean absolute bias error, root mean square error, relative root mean square error and correlation coefficient are 1.3445 MJ/m2, 2.0164 MJ/m2, 11.2464% and 0.9057%, respectively for the testing data. As further examination, a month-by-month evaluation is conducted and found that in six months from May to October the KELM (3) model provides further accuracy than overall accuracy. Based upon the relative root mean square error, the KELM (3) model shows excellent capability in the period of April to October while in the remaining months represents good performance.

A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm

Journal paper
Ozgur Kisi, Jalal Shiri, Sepideh Karimi, Shahaboddin Shamshirband, Shervin Motamedi, Dalibor Petković, Roslan Hashim
Applied Mathematics and Computation ( Tier 1), Volume 270, Pages 731-743

Abstract

Forecasting lake level at various horizons is a critical issue in navigation, water resource planning and catchment management. In this article, multistep ahead predictive models of predicting daily lake levels for three prediction horizons were created. The models were developed using a novel method based on support vector machine (SVM) coupled with firefly algorithm (FA). The FA was applied to estimate the optimal SVM parameters. Daily water-level data from Urmia Lake in northwestern Iran were used to train, test and validate the used technique. The prediction results of the SVM–FA models were compared to the genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results showed that an improvement in the predictive accuracy and capability of generalization can be achieved by the SVM–FA approach in comparison to the GP and ANN in 1 day ahead lake level forecast. Moreover, the findings indicated that the developed SVM–FA models can be used with confidence for further work on formulating a novel model of predictive strategy for lake level prediction.

Estimation of wind turbine wake effect by adaptive neuro-fuzzy approach

Journal paper
Eiman Tamah Al-Shammari, Mohsen Amirmojahedi, Shahaboddin Shamshirband, Dalibor Petković, Nenad T Pavlović, Hossein Bonakdari
Flow Measurement and Instrumentation, Volume 45, Pages 1-6

Abstract

The grouping of turbines in large farms introduces that a wind turbine operating in the wake of another turbine and has a reduced power production because of a lower wind speed after rotor. The flow field in the wake behind the first row turbines is characterized by a significant deficit in wind velocity and increased levels of turbulence intensity. Consequently, the downstream turbines in a wind farm cannot extract as much power from the wind as the first row turbines. Therefore modeling wake effect is necessary because it has a great influence on the actual energy output of a wind farm. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate wake effect in a wind farm according to wind turbine positions in wind farm, distances between turbines in the wind farm and rotor radius as well.

Estimation of the rutting performance of Polyethylene Terephthalate modified asphalt mixtures by adaptive neuro-fuzzy methodology

Journal paper
Taher Baghaee Moghaddam, Mehrtash Soltani, Mohamed Rehan Karim, Shahaboddin Shamshirband, Dalibor Petković, Hassan Baaj
Construction and Building Materials, Volume 96, Pages 550-555

Abstract

In this paper, the accuracy of soft computing techniques was employed for prediction of the rutting performance of Polyethylene Terephthalate (PET) modified asphalt mixture. The process, which simulates the mixture’s deformation, was constructed with adaptive neuro-fuzzy inference system (ANFIS). The inputs were PET percentages, stress levels and temperatures. The performance of proposed system was confirmed by simulation results. The ANFIS results and the results achieved by experiments were compared using root-mean-square error (RMSE) and coefficient of determination. The experimental outcomes suggested that ANFIS approach can be used to improve predictive accuracy and capability of generalization.

An Introduction to Remote Installation Vulnerability in Content Management Systems

Journal paper
Mehdi Dadkhah, Shahaboddin Shamshirband
International Journal of Secure Software Engineering (IJSSE), Volume 6, Issue 4, Pages 52-63

Abstract

Web-based applications are being increasingly used to share data and remote access. These applications form an integral part of the financial, education, and government sectors in most countries. The most important issue in web-based applications is maintaining basic principles of security: integrity, confidentiality and availability. Many web vulnerabilities have been identified and should be always considered by web-based applications developers and security professionals. Lack of vigilance in attending to these vulnerabilities may result in the software system being attacked by hackers and the most important asset of an organization, i.e., protected data, being compromised. This paper identifies a new type of web susceptibility termed remote installation vulnerability (RIV) which renders content management systems (CMS) and websites exploitable to cyber-attacks. A simple strategy is recommended to address this vulnerability.

A combined method to estimate wind speed distribution based on integrating the support vector machine with firefly algorithm

Journal paper
Abdullah Gani, Kasra Mohammadi, Shahaboddin Shamshirband, Torki A Altameem, Dalibor Petković, Sudheer Ch
Environmental Progress & Sustainable Energy

Abstract

A new hybrid approach by integrating the support vector machine (SVM) with firefly algorithm (FFA) is proposed to estimate shape (k) and scale (c) parameters of the Weibull distribution function according to previously established analytical methods. The extracted data of two widely successful methods utilized to compute parameters k and c were used as learning and testing information for the SVM-FFA method. The simulations were performed on both daily and monthly scales to draw further conclusions. The performance of SVM-FFA method was compared against other existing techniques to demonstrate its efficiency and viability. The results conclusively indicate that SVM-FFA method provides further precision in the predictions. Nevertheless, for daily estimations, the applicability of proposed method could not be feasible owing to high day-by-day fluctuations of parameters k, whereas the results of monthly estimation are completely appealing and precise. In summary, the SVM-FFA is a highly viable and efficient technique to estimate wind speed distribution on monthly scale. It is expected that the proposed method would be profitable for wind researchers and experts to be used in many practical applications, such as evaluating the wind energy potential and making a proper decision to nominate the optimal wind turbines. © 2015 American Institute of Chemical Engineers Environ Prog, 35: 867–875, 2016

An automated system for skeletal maturity assessment by extreme learning machines

Journal paper
Marjan Mansourvar, Shahaboddin Shamshirband, Ram Gopal Raj, Roshan Gunalan, Iman Mazinani
PloS one, Volume 10, Issue 9, Pages e0138493

Abstract

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

Estimation of reference evapotranspiration using neural networks and cuckoo search algorithm

Journal paper
Shahaboddin Shamshirband, Mohsen Amirmojahedi, Milan Gocić, Shatirah Akib, Dalibor Petković, Jamshid Piri, Slavisa Trajkovic
Journal of Irrigation and Drainage Engineering, Volume 142, Issue 2, Pages 04015044

Abstract

The ability to optimize an artificial neural network (ANN) and adaptive neuro-fuzzy
inference system (ANFIS) in reference evapotranspiration (ET 0) estimation using the
cuckoo search algorithm (CSA) is studied in this paper. The monthly series of climatic data
(minimum and maximum air temperatures, actual vapor pressure, sunshine hours, and wind
speed at height of 2.0 m) from twelve meteorological stations in Serbia during the period
1983–2010 were used as inputs to the soft computing models. As the reference ET 0

Affiliation Oriented Journals: Don't Worry About Peer Review If You Have Good Affiliation

Journal paper
Mehdi Dadkhah, Adel M Alharbi, Mohammad Hamad Al-Khresheh, Tole Sutikno, Tomasz Maliszewski, Mohammad Davarpanah Jazi, Shahaboddin Shamshirband
International Journal of Electrical and Computer Engineering, Volume 5, Issue 4, Pages 621

Abstract

There has been a growing concern about fraud peer review articles that have
been published in some journals in favor of their authors’ affiliation, which
have been discussed extensively by some researchers. This research paper
introduces a new another challenge in academic world concerning journals’
editors who look at authors’ affiliations rather than papers’ contents. In this
short paper, we will introduce this alarming problem and do an experimental
test by submitting computer generated papers to some journals and finally
present the results of our experiment. The paper is an expression of our
concern about providing for maximum high ethics in and quality of
publication policy of modern scientific journals.

Robust image Watermarking based on Riesz Transformation and IT2FLS

Journal paper
Almas Abbasi, Chaw Seng Woo, Shahaboddin Shamshirband
Measurement ( Tier 1), Publisher Elsevier

Abstract

Conventional digital image watermarking techniques are often vulnerable to image processing and geometric distortions such as Rotation, Scaling, and Translation (RST). These distortions desynchronize the watermark information embedded in an image and thus disable watermark detection. To address this problem, we propose robust watermarking technique using the Riesz Transformation (RT). In the proposed method, watermark is embedded using simple addition and quantization based watermarking technique. In addition, Interval Type-2 Fuzzy Logic System (IT2FLS) is utilized for data fusion and building a model for spatial masking in the Riesz wavelet domain. Cross correlation method based on Neyman–Pearson and normalized correlation (NC) method are deployed for watermark detection and extraction respectively. Experimental results confirmed that the RT based technique is robust against image processing and geometric attack compared to previous techniques and has a good balance between robustness and imperceptibility under the checkmark tool.

Adaptive neuro-fuzzy estimation of diffuser effects on wind turbine performance

Journal paper
Vlastimir Nikolić, Dalibor Petković, Shahaboddin Shamshirband, Žarko Ćojbašić
Energy ( Tier 1), Publisher Pergamon

Abstract

Wind power is generating interest amongst many countries to produce sustainable electrical power. It is well known that the main drawback of wind power is the inherent variable behavior of wind speed. Significant research has been carried out to improve the performance of the wind turbines and establish the power system stability. As power output is proportional to the cubic power of the incident airspeed, any small increase in the incident wind yields a large increase in the energy output. One of the more promising advanced concepts for overcoming the inherent variable behavior of wind speed is the DAWT (diffuser-augmented wind turbine). The diffuser or flanged diffuser generates separation regions behind it, where low-pressure regions appear to draw more wind through the rotors compared to a bare wind turbine. Thus, the output power of the DAWT is much larger than for a unshrouded turbine. To estimate rotor performance of the diffuser-augmented wind turbine, this paper constructed a process which simulates the power output, torque output and rotational speed of the rotor in regard to diffuser effect and wind input speed with ANFIS (adaptive neuro-fuzzy) method. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.

Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology

Journal paper
Dalibor Petković, Milan Gocic, Slavisa Trajkovic, Shahaboddin Shamshirband, Shervin Motamedi, Roslan Hashim, Hossein Bonakdari
Computers and Electronics in Agriculture, Volume 114, Pages 277-284

Abstract

The adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential reference evapotranspiration (ET0) parameters. This procedure is typically called variable selection. It is identical to finding a subset of the full set of recorded variables that illustrates good predictive abilities. The full weather datasets for seven meteorological parameters were obtained from twelve weather stations in Serbia during the period 1980–2010. The monthly ET0 data are obtained by the Penman–Monteith method, which is proposed by Food and Agriculture Organization of the United Nations as the standard method for the estimation of ET0. As the performance evaluation criteria of the ANFIS models the following statistical indicators were used: the root mean squared error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2). Sunshine hours are the most influential single parameter for ET0 estimation (RMSE = 0.4398 mm/day). The obtained results indicate that among the input variables sunshine hours, actual vapor pressure and minimum air temperature, are the most influential for ET0 estimation. The maximum relative humidity and maximum air temperature are the most influential optimal combination of two parameters (RMSE = 0.2583 mm/day).

OVRP_ICA: An Imperialist-Based Optimization Algorithm for the Open Vehicle Routing Problem

Journal paper
Shahab Shamshirband, Mohammad Shojafar, Ali Asghar Rahmani Hosseinabadi, Ajith Abraham
International Conference on Hybrid Artificial Intelligence Systems, Pages 221-233

Abstract

Open vehicle routing problem (OVRP) is one of the most important problems in vehicle routing, which has attracted great interest in several recent applications in industries. The purpose in solving the OVRP is to decrease the number of vehicles and to reduce travel distance and time of the vehicles. In this article, a new meta-heuristic algorithm called OVRP_ICA is presented for the above-mentioned problem. This is a kind of combinatorial optimization problem that can use a homogeneous fleet of vehicles that do not necessarily return to the initial depot to solve the problem of offering services to a set of customers exploiting the imperialist competitive algorithm. OVRP_ICA is compared with some well-known state-of-the-art algorithms and the results confirmed that it has high efficiency in solving the above-mentioned problem.

Potential of particle swarm optimization based radial basis function network to predict the discharge coefficient of a modified triangular side weir

Journal paper
Amir Hossein Zaji, Hossein Bonakdari, Shahaboddin Shamshirband, Sultan Noman Qasem
Flow Measurement and Instrumentation ( Tier 2), Publisher Elsevier

Abstract

Estimating the discharge coefficient is one of the most important steps in the process of side weir design. In this paper, the particle swarm optimization algorithm and radial basis neural network are combined (RBFN-PSO) and employed to model the discharge coefficient of a modified triangular side weir. The developed RBF network has five neurons in the input layer and one neuron in the output layer. The inputs include a wide range of non-dimensional geometrical and hydraulic parameters of a modified triangular side weir, and the output is the discharge coefficient. The RBFN-PSO performance is evaluated using published experimental results and compared with the backpropagation radial basis function network (RBFN-BP) by using the statistical indexes Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and average absolute deviation (%δ). According to the results, the PSO algorithm successfully improved the RBFN while the RBFN-PSO model’s generalization capacity enhanced, with RMSE of 0.071 compared to the RBFN-BP model with RMSE of 0.114 in the testing dataset.

Key management paradigm for mobile secure group communications: Issues, solutions, and challenges

Journal paper
Babak Daghighi, Miss Laiha Mat Kiah, Shahaboddin Shamshirband, Salman Iqbal, Parvaneh Asghari
Journal Computer Communications ( Tier 1), Publisher Elsevier

Abstract

Group communication has been increasingly used as an efficient communication mechanism for facilitating emerging applications that require packet delivery from one or more sources to multiple recipients. Due to insecure communication channels, group key management which is a fundamental building block for securing group communication, has received increasing attention recently. Developing group key management in highly dynamic environments faces additional challenges particularly in wireless mobile networks due to their inherent complexities. On one hand, the constraints of wireless devices in terms of resources scarcity, and on the other hand the mobility of group members increase the complexity of designing a group key management scheme. This article illustrates a survey of existing group key management schemes that specifically consider the host mobility issue in secure group communications in wireless mobile environments. The primary constraints and challenges introduced by wireless mobile environments are identified in order to show their critical influence in designing a secure group communication. The explored schemes are scrutinized and then compared against some pertinent criteria. Finally, the remaining challenges that should be tackled are outlined, and future research directions are also discussed.

Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm

Journal paper
Milan Protić, Shahaboddin Shamshirband, Dalibor Petković, Almas Abbasi, Miss Laiha Mat Kiah, Jawed Akhtar Unar, Ljiljana Živković, Miomir Raos
Energy ( Tier 1), Publisher Pergamon

Abstract

District heating systems are important utility systems. If these systems are properly managed, they can ensure economic and environmentally friendly provision of heat to connected customers. Potentials for further improvement of district heating systems’ operation lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multi-step ahead predictive models of consumers’ heat load are a starting point for creating a successful model predictive strategy. For the purpose of this article, short-term multi-step ahead predictive models of heat load of consumers connected to a district heating system were created. The models were developed using the novel method based on SVM (Support Vector Machines) coupled with a discrete wavelet transform. Nine different SVM-WAVELET predictive models for a time horizon from 1 to 24 h ahead were developed. Estimation and prediction results of the SVM-WAVELET models were compared with GP (genetic programming) and ANN (artificial neural network) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM-WAVELET approach in comparison with GP and ANN.

Potential of neuro-fuzzy methodology to estimate noise level of wind turbines

Journal paper
Vlastimir Nikolić, Dalibor Petković, Lip Yee Por, Shahaboddin Shamshirband, Mazdak Zamani, Žarko Ćojbašić, Shervin Motamedi
Mechanical Systems and Signal Processing ( Tier 1), Publisher Academic Press

Abstract

Wind turbines noise effect became large problem because of increasing of wind farms numbers since renewable energy becomes the most influential energy sources. However, wind turbine noise generation and propagation is not understandable in all aspects. Mechanical noise of wind turbines can be ignored since aerodynamic noise of wind turbine blades is the main source of the noise generation. Numerical simulations of the noise effects of the wind turbine can be very challenging task. Therefore in this article soft computing method is used to evaluate noise level of wind turbines. The main goal of the study is to estimate wind turbine noise in regard of wind speed at different heights and for different sound frequency. Adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the wind turbine noise levels.

Sensitivity analysis of the discharge coefficient of a modified triangular side weir by adaptive neuro-fuzzy methodology

Journal paper
Hossein Bonakdari, Amir Hossein Zaji, Shahaboddin Shamshirband, Roslan Hashim, Dalibor Petkovic
Measurement ( Tier 1), Publisher Elsevier

Abstract

The discharge coefficient of a modified triangular side weir is analyzed regarding various non-dimensional input sets. It is desirable to select and analyze factors or parameters that are truly relevant or the most influential to triangular side weir discharge coefficient estimation and prediction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied for the selection of the most prominent triangular side weir discharge coefficient parameters based on ten input parameters. The input variables were searched using the ANFIS network to specify the input parameters’ effects on the discharge coefficients. According to the obtained results, the side weir included angle has the most effect on modeling the discharge coefficient. Then by using the selected input variables, the discharge coefficient was modeled with ANFIS, artificial neural network, support vector machine and multi non linear regression methods. The results show that ANFIS could predict the discharge coefficient significantly better than the other investigated models.

Extreme learning machine approach for sensorless wind speed estimation

Journal paper
Vlastimir Nikolić, Shervin Motamedi, Shahaboddin Shamshirband, Dalibor Petković, Sudheer Ch, Mohammad Arif
Mechatronics ( Tier 1), Publisher Elsevier

Abstract

Precise predictions of wind speed play important role in determining the feasibility of harnessing wind energy. In fact, reliable wind predictions offer secure and minimal economic risk situation to operators and investors. This paper presents a new model based upon extreme learning machine (ELM) for sensor-less estimation of wind speed based on wind turbine parameters. The inputs for estimating the wind speed are wind turbine power coefficient, blade pitch angle, and rotational speed. In order to validate authors compared prediction of ELM model with the predictions with genetic programming (GP), artificial neural network (ANN) and support vector machine with radial basis kernel function (SVM-RBF). This investigation analyzed the reliability of these computational models using the simulation results and three statistical tests. The three statistical tests includes the Pearson correlation coefficient, coefficient of determination and root-mean-square error. Finally, this study compared predicted wind speeds from each method against actual measurement data. Simulation results, clearly demonstrate that ELM can be utilized effectively in applications of sensor-less wind speed predictions. Concisely, the survey results show that the proposed ELM model is suitable and precise for sensor-less wind speed predictions and has much higher performance than the other approaches examined in this study.

Influence of clay particles on Al2O3 and TiO2 nanoparticles transport and retention through limestone porous media: measurements and mechanisms

Journal paper
Ali Esfandyari Bayat, Radzuan Junin, Rahmat Mohsin, Mehrdad Hokmabadi, Shahaboddin Shamshirband
Journal of Nanoparticle Research, Volume 17, Issue 5, Pages 1-14

Abstract

Utilization of nanoparticles (NPs) for a broad range of applications has caused considerable quantities of these materials to be released into the environment. Issues of how and where the NPs are distributed into the subsurface aquatic environments are questions for those in environmental engineering. This study investigated the influence of three abundant clay minerals namely kaolinite, montmorillonite, and illite in the subsurface natural aquatic systems on the transport and retention of aluminum oxide (Al2O3, 40 nm) and titanium dioxide (TiO2, 10–30 nm) NPs through saturated limestone porous media. The clay concentrations in porous media were set at 2 and 4 vol% of the holder capacity. Breakthrough curves in the columns outlets were measured using a UV–Vis spectrophotometer. It was found that the maximum NPs recoveries were obtained when there was no clay particle in the porous medium. On the other hand, increase in concentration of clay particles has resulted in the NPs recoveries being significantly declined. Due to fibrous structure of illite, it was found to be more effective for NPs retention in comparison to montmorillonite and kaolinite. Overall, the position of clay particles in the porous media pores and their morphologies were found to be two main reasons for increase of NPs retention in porous media.

A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

Journal paper
Shahaboddin Shamshirband, Kasra Mohammadi, Chong Wen Tong, Mazdak Zamani, Shervin Motamedi, Sudheer Ch
Theoretical and Applied Climatology, Pages 1-13

Abstract

In this study, a hybrid support vector machine–firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination (R2) are 3.3252 %, 0.1859 kWh/m2, 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

Trend detection of wind speed probability distribution by adaptive neuro-fuzzy methodology

Journal paper
Shahaboddin Shamshirband, Dalibor Petković, Chong Wen Tong, Eiman Tamah Al-Shammari
Flow Measurement and Instrumentation ( Tier 2 ), Publisher Elsevier

Abstract

The probabilistic distribution of wind speed is one of the critical wind parameters for the evaluation of wind energy potential. The wind energy distribution can be acquired when wind speed probability function is known. The two-parameter Weibull distribution has been regularly utilized and prescribed in expositive expression to express the wind speed distribution function for most wind areas. Consequently, the shape and scale parameters of the distribution are used to plan and portray wind turbines. Therefore modeling the probabilistic distribution of wind speed is necessary because it has a great influence on the actual energy output of a wind farm. Since it is a nonlinear problem in the present study an exertion has been made to figure out the best fitting function of wind speed information by a soft computing methodology. In this paper, we analyze three wind speed models generally utilized for assessing these parameters as a part of request to focus on the model that is best suited. We utilized adaptive neuro-fuzzy inference system (ANFIS) in this paper, which is a particular sort of the neural systems family, to anticipate the wind speed probability density distribution. The main goal is to detect the trend of the wind speed probability density distribution by the ANFIS approach.

Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology

Journal paper
Shervin Motamedi, Chandrabhushan Roy, Shahaboddin Shamshirband, Roslan Hashim, Dalibor Petković, Ki-Il Song
Ultrasonics ( Tier 2 ), Publisher Elsevier

Abstract

Ultrasonic pulse velocity is affected by defects in material structure. This study applied soft computing techniques to predict the ultrasonic pulse velocity for various peats and cement content mixtures for several curing periods. First, this investigation constructed a process to simulate the ultrasonic pulse velocity with adaptive neuro-fuzzy inference system. Then, an ANFIS network with neurons was developed. The input and output layers consisted of four and one neurons, respectively. The four inputs were cement, peat, sand content (%) and curing period (days). The simulation results showed efficient performance of the proposed system. The ANFIS and experimental results were compared through the coefficient of determination and root-mean-square error. In conclusion, use of ANFIS network enhances prediction and generation of strength. The simulation results confirmed the effectiveness of the suggested strategies.

A systematic review of approaches to assessing cybersecurity awareness

Journal paper
Noor Hayani Abd Rahim, Suraya Hamid, Miss Laiha Mat Kiah, Shahaboddin Shamshirband, Steven Furnell
Kybernetes, Volume 44, Issue 4, Pages 606-622

Abstract:

 The purpose of this paper is to survey, explore and inform researchers about the previous methodologies applied, target audience and coverage of previous assessment of cybersecurity awareness by capturing, summarizing, synthesizing and critically comment on it. It is also conducted to identify the gaps in the cybersecurity awareness assessment research which warrants the future work.

 

Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system

Journal paper
Mahmoud Reza Saybani, Shahaboddin Shamshirband, Shahram Golzari Hormozi, Teh Ying Wah, Saeed Aghabozorgi, Mohamad Amin Pourhoseingholi, Teodora Olariu
Iranian Red Crescent medical journal, Volume 17, Issue 4

Abstract

Background:

Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy.

Social Network Applications and Free Online Mobile Numbers: Real Risk

Journal paper
Mehdi Dadkhah, Tole Sutikno, Shahaboddin Shamshirband
International Journal of Electrical and Computer Engineering, Volume 5, Issue 2, Pages 175

Abstract:

Social network applications are being more widely used among users and new types of such
applications are created by developers. Almost all users who use smart phones are users of such application
[1]. Major concern in these applications is privacy and security [2]. We can name WhatsApp, Viber,
Facebook, Telegram, Line, WeChat and Beetalk as the most popular applications. There are also websites
which provide online numbers in order to receive SMS. The goal of this website is providing anonymous
virtual phone number to protect users from spam. Also these sites provide different number from different
countries and let people to can use them for different application. The services of these websites are divided
into two groups: subscription services in which a unique number is assigned to the user by charging him/her
and free services in which user can see the received messages of some online numbers without any
registration. Table 1 shows examples of these websites.

Support vector regression based prediction of global solar radiation on a horizontal surface

Journal paper
Kasra Mohammadi, Shahaboddin Shamshirband, Mohammad Hossein Anisi, Khubaib Amjad Alam, Dalibor Petković
Energy Conversion and Management, Volume 91, Pages 433-441

Abstract

In this paper, the support vector regression (SVR) methodology was adopted to estimate the horizontal global solar radiation (HGSR) based upon sunshine hours (n) and maximum possible sunshine hours (N) as input parameters. The capability of two SVRs of radial basis function (rbf) and polynomial basis function (poly) was investigated and compared with the conventional sunshine duration-based empirical models. For this purpose, long-term measured data for a city situated in sunny part of Iran was utilized. Exploration was performed on both daily and monthly mean scales to accomplish a more complete analysis. Through a statistical comparative study, using 6 well-known statistical parameters, the results proved the superiority of developed SVR models over the empirical models. Also, SVR-rbf outperformed the SVR-poly in terms of accuracy. For SVR-rbf model on daily estimation, the mean absolute percentage error, mean absolute bias error, root mean square error, relative root mean square error and coefficient of determination were 10.4466%, 1.2524 MJ/m2, 2.0046 MJ/m2, 9.0343% and 0.9133, respectively. Also, on monthly mean estimation the values were 1.4078%, 0.2845 MJ/m2, 0.45044 MJ/m2, 2.2576% and 0.9949, respectively. The achieved results conclusively demonstrated that the SVR-rbf is highly qualified for HGSR estimation using n and N.

Optimization of solvent composition and injection rate in vapour extraction process

Journal paper
Ali Esfandyari Bayat, Radzuan Junin, Riyaz Kharrat, Shahaboddin Shamshirband, Shatirah Akib, Zolkepli Buang
Journal of Petroleum Science and Engineering (Tier 1), Publisher Elsevier

Abstract

Vapour extraction (VAPEX) is a technique used to enhance heavy oil recovery. Many studies have been carried out on VAPEX on a macro scale since 1989. However, the number of studies conducted on a mega scale is limited. Solvent design and determination of injection rate are two important parameters that have not been clearly defined on a mega scale. In this study, the VAPEX process was simulated for a heavy oil reservoir to determine the optimum solvent injection rate and composition on a mega scale. Propane (C3) ranging from 5 to 50 mol% and butane (C4) from 5 to 40 mol% as solvents were mixed with methane (C1). A method of estimating injection rate on a mega scale was introduced. Furthermore, it was found that porosity, vertical permeability, and bulk volume size did not have a noticeable effect on injection rate, while injection height from reservoir top, injector length, and amount of solvent in the mixture significantly influenced injection rate. It was also observed that higher amounts of solvents in the mixture caused greater reduction in heavy oil viscosity. Conversely, oil recovery values were higher for the mixtures with lower amounts of C3 and C4. Moreover, C4 seemed more effective for heavy oil viscosity reduction than C3, while the oil recovery value by C3was higher than C4.

To appear in: Flow Measurement and Instrumentation

Journal paper
Isa Ebtehaj, Hossein Bonakdari, Shahaboddin Shamshirband, Kasra Mohammadi

Abstract
Technical design of sewer systems requires highly accurate prediction of sediment transport. In this study, the
capability of the combined support vector machine-wavelet transform (SVM-Wavelet) model for the prediction
of the densimetric Froude number (Fr) was compared to the single SVM and different existing sediment
transport equations at the limit of deposition. The performance evaluation was performed using the R-square
(R2), three relative indexes (MRE, MARE, MSRE) and three absolute indexes (ME, MAE, RMSE). The factors
affecting the Fr were initially determined. After categorizing them into different dimensionless groups, six
different models were found to predict the Fr. Comparisons between the obtained results showed that both the
SVM and SVM-Wavelet can predict the Fr with high accuracy. However, it was found that the SVM-Wavelet
(R2=0.995, MRE=0.002, MARE=0.021, MSRE=0.001, ME=0.007, MAE=0.086 and RMSE=0.114) offers higher
performance than the SVM and the existing equations.

Decreasing environmental impacts of cropping systems using life cycle assessment (LCA) and multi-objective genetic algorithm

Journal paper
Benyamin Khoshnevisan, Elham Bolandnazar, Shahaboddin Shamshirband, Hanifreza Motamed Shariati, Nor Badrul Anuar, Miss Laiha Mat Kiah
Journal of Cleaner Production, Volume 86, Pages 67-77

Abstract

The environmental awareness of people has increased in recent decades, and the demand for environmentally friendly products has caused agro-scientists to give more attention to cleaner production. Life cycle assessment (LCA) has been identified as a suitable tool for assessing environmental impacts associated with a product over its life cycle. The implementation of LCA with other management tools can help LCA practitioners to evaluate agri-food systems from different viewpoints. In this study, LCA, multi-objective genetic algorithm (MOGA), and data envelopment analysis (DEA) were combined, and the pros and cons of their application were investigated. Three impact categories – global warming (GW), respiratory inorganics (RI) and non-renewable energy use (NRE) – were selected to be evaluated. The results revealed mean RI, GW and NRE in a case study of watermelon production of 10.3 kg PM2.5 eq ha−1, 9485.5 kg CO2 eq ha−1 and 186,432 MJ primary energy ha−1 respectively. The results of LCA + MOGA showed that a reduction of 27% in RI and 35% in GW and NRE can occur if an appropriate combination of resources is used in watermelon production. The use of LCA + DEA revealed that if all farmers operate on the efficient frontier (suggested values) impacts in all three categories can be reduced by 8%.

Corrigendum to “TiO2 nanotube coating on stainless steel 304 for biomedical applications”[Ceram. Int. 41 (2015) 2785–2793]

Journal paper
E Zalnezhad, AMS Hamouda, G Faraji, S Shamshirband
Ceramics International, Volume 10, Issue 41, Pages 15297

Transport and retention of engineered Al2O3, TiO2, and SiO2 nanoparticles through various sedimentary rocks

Journal paper
Ali Esfandyari Bayat, Radzuan Junin, Shahaboddin Shamshirband, Wen Tong Chong
Scientific reports, Volume 5, Publisher Nature Publishing Group

Abstract

Engineered aluminum oxide (Al2O3), titanium dioxide (TiO2), and silicon dioxide (SiO2) nanoparticles (NPs) are utilized in a broad range of applications; causing noticeable quantities of these materials to be released into the environment. Issues of how and where these particles are distributed into the subsurface aquatic environment remain as major challenges for those in environmental engineering. In this study, transport and retention of Al2O3, TiO2, and SiO2 NPs through various saturated porous media were investigated. Vertical columns were packed with quartz-sand, limestone, and dolomite grains. The NPs were introduced as a pulse suspended in aqueous solutions and breakthrough curves in the column outlet were generated using an ultraviolet-visible spectrophotometer. It was found that Al2O3 and TiO2 NPs are easily transported through limestone and dolomite porous media whereas NPs recoveries were achieved two times higher than those found in the quartz-sand. The highest and lowest SiO2-NPs recoveries were also achieved from the quartz-sand and limestone columns, respectively. The experimental results closely replicated the general trends predicted by the filtration and DLVO calculations. Overall, NPs mobility through a porous medium was found to be strongly dependent on NP surface charge, NP suspension stability against deposition, and porous medium surface charge and roughness.

Evaluating the legibility of decorative arabic scripts for Sultan Alauddin mosque using an enhanced soft-computing hybrid algorithm

Journal paper
Ahamadreza Saberi, Shervin Motamedi, Shahab Shamshirband, C Lewis Kausel, Dalibor Petković, Esmawee Endut, Sabarinah Sh Ahmad, Roslan Hashim, Chandrabhushan Roy
Computers in Human Behavior ( Tier 1), Publisher Elsevier

Abstract

Ornamental calligraphy features sacred inscriptions in mosques as an integral part of its interior design. This study analyzes the legibility of these Arabic scripts for Malaysian users of mosques, implementing for the first time a quantitative tool for this effort, the ANFIS method. Our purpose is to identify the most influential parameters affecting the readability and understanding of various decorative Arabic scripts. Mosques have important roles in social life and in teaching the Islamic faith to Muslims. We conducted a questionnaire survey handed to the public attending the Sultan Alauddin mosque in Selangor, Malaysia. We subjected the data resulting from this survey to the ANFIS method (the adaptive neuro fuzzy inference system), to identify measurable parameters that play a role in the ability to read decorative Arabic scripts. The ANFIS process for variable selection was implemented in order to detect the predominant variables among the parameters identified. We analyzed how demographic aspects and cognitive skills relate to the ability to correctly interpret these scripts. The results indicated that of the parameters examined, the ability to read Arabic is the one that influences the most, the correct interpretation of ornamental inscriptions of mosques, and the best predictor of accuracy.

Application and Economic Viability of Wind Turbine Installation in Lutak, Iran

Journal paper
Kasra Mohammadi, Ali Mostafaeipour, Ahmad Sedaghat, Shahaboddin Shamshirband, Dalibor Petković
Environmental Earth Sciences (Tier 2), Publisher Springer

Abstract

There is a lack of studies on evaluating the economic feasibility of large-scale wind turbine development in Iran. Thus, this study aims at analyzing the feasibility of large wind turbines installation in Lutak region, situated in the southeast of Iran. For this aim, the 10-min average recorded wind speed data at 40 m height collected from January 2008 to December 2009 in Lutak are analyzed. Based on three reliable statistical indicators, it is found that the Weibull function enjoys excellent capability to analyze the wind data in Lutak. The wind data analysis reveals that the period of May to September is the windiest time of the year for which the wind power classification falls into class 6 or 7. Nevertheless, due to the lower wind potential in the early and late days of year, very high differences are observed between daily mean wind speed as well as wind power values throughout the year. The highest and lowest mean wind speed and wind power occur in July and January. The wind speed lies between 3.85 and 11.26 m/s while the wind power ranges from 78.79 to 1210.32 W/m2. It is also observed that wind blows predominantly from northwest and north directions in Lutak. The performance and economic feasibility of installing four different types of wind turbines with the rated power of 600–900 kW are examined for installation at 40 m height. The attained results indicate that the EWT 52/900 kW wind turbine is a more appropriate economical option.

Adaptive control algorithm of flexible robotic gripper by Extreme Learning Machine

Journal paper
Dalibor Petković, Amir Seyed Danesh, Mehdi Dadkhah, Negin Misaghian, Shahaboddin Shamshirband, Erfan Zalnezhad, Nenad D. Pavlović
Robotics and Computer-Integrated Manufacturing ( Tier 1), Publisher Elsevier

Abstract

Adaptive grippers should be able to detect and recognize grasping objects. To be able to do it control algorithm need to be established to control gripper tasks. Since the gripper movements are highly nonlinear systems it is desirable to avoid using of conventional control strategies for robotic manipulators. Instead of the conventional control strategies more advances algorithms can be used. In this study several soft computing methods are analyzed for robotic gripper applications. The gripper structure is fully compliant with embedded sensors. The sensors could be used for grasping shape detection. As soft computing methods, extreme learning machine (ELM) and support vector regression (SVR) were established. Also other soft computing methods are analyzed like fuzzy, neuro-fuzzy and artificial neural network approach. The results show the highest accuracy with ELM approach than other soft computing methods.

Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation

Journal paper
Shahab Shamshirband, Kasra Mohammadi, Jamshid Piri, Shatirah Akib
Environmental Earth Sciences( Tier 2), Publisher Springer

Extreme learning machine based prediction of daily dew point temperature

Journal paper
Kasra Mohammadi, Shahaboddin Shamshirband, Shervin Motamedi, Dalibor Petković, Roslan Hashim, Milan Gocic
Computers and Electronics in Agriculture, Publisher Elsevier

Abstract

The dew point temperature is a significant element particularly required in various hydrological, climatological and agronomical related researches. This study proposes an extreme learning machine (ELM)-based model for prediction of daily dew point temperature. As case studies, daily averaged measured weather data collected for two Iranian stations of Bandar Abass and Tabass, which enjoy different climate conditions, were used. The merit of the ELM model is evaluated against support vector machine (SVM) and artificial neural network (ANN) techniques. The findings from this research work demonstrate that the proposed ELM model enjoys much greater prediction capability than the SVM and ANN models so that it is capable of predicting daily dew point temperature with very favorable accuracy. For Tabass station, the mean absolute bias error (MABE), root mean square error (RMSE) and correlation coefficient (R) achieved for the ELM model are 0.3240 °C, 0.5662 °C and 0.9933, respectively, while for the SVM model the values are 0.7561 °C, 1.0086 °C and 0.9784, respectively and for the ANN model are 1.0324 °C, 1.2589 °C and 0.9663, respectively. For Bandar Abass station, the MABE, RMSE and R for the ELM model are 0.5203 °C, 0.6709 °C and 0.9877, respectively whereas for the SVM model the values are 1.0413 °C, 1.2105 °C and 0.9733, and for the ANN model are 1.3205 °C, 1.5530 °C and 0.9617, respectively. The study results convincingly advocate that ELM can be employed as an efficient method to predict daily dew point temperature with much higher precision than the SVM and ANN techniques.

Estimation of the rutting performance of Polyethylene Terephthalate modified asphalt mixtures by adaptive neuro-fuzzy methodology

Journal paper
Taher Baghaee Moghaddam, Mehrtash Soltani, Mohamed Rehan Karim, Shahab Shamshirband, Dalibor Petkovic, Hassan Baaj
Construction and Building Materials ( Tier 1), Publisher Elsevier

A comparative evaluation for identifying the suitability of Extreme Learning Machine to predict horizontal global solar radiation

Journal paper
Shahaboddin Shamshirband, Kasra Mohammadi, Lip Yee Por, Dalibor Petkovic, Ali Mostafaeipour
Renewable and Sustainable Energy Reviews Publisher Elsevier

Abstract

In this paper, the extreme learning machine (ELM) is employed to predict horizontal global solar radiation (HGSR). For this purpose, the capability of developed ELM method is appraised statistically for prediction of monthly mean daily HGSR using three different types of input parameters: (1) sunshine duration-based (SDB), (2) difference temperature-based (TB) and (3) multiple parameters-based (MPB). The long-term measured data sets collected for city of Shiraz situated in the Fars province of Iran have been utilized as a case study. The predicted HGSR via ELM is compared with those of support vector machine (SVM), genetic programming (GP) and artificial neural network (ANN) to ensure the precision of ELM. It is found that higher accuracy can be obtained by multiple parameters-based estimation of HGSR using all techniques. The computational results prove that ELM is highly accurate and reliable and shows higher performance than SVM, GP and ANN. For multiple parameters-based ELM model, the mean absolute percentage error, mean absolute bias error, root mean square error, relative root mean square error and coefficient of determination are obtained as 2.2518%, 0.4343 MJ/m2, 0.5882 MJ/m2, 2.9757% and 0.9865, respectively. By conducting a further verification, it is found that the ELM method also offers high superiority over four empirical models established for this study and an intelligent model from the literature. In the final analysis, a proper sensitivity analysis is performed to identify the influence of considered input elements on HGSR prediction in which the results reveal the significance of appropriate selection of input parameters to boost the accuracy of HGSR prediction by the ELM algorithm. In a nutshell, the comparative results clearly specify that ELM technique can provide reliable predictions with further precision compared to the existing techniques.

Sensitivity Analysis of the Photoactivity of Cu-TiO2/ZnO during Advanced Oxidation Reaction by Adaptive Neuro-Fuzzy Selection Technique

Journal paper
Mohammad Reza Delsouz Khakia, Baharak Sajjadi, Abdul Aziz Abdul Raman, Wan Mohd Ashri Wan Daud, Shahaboddin Shmshirband
Measurement ( Tier 1)

Hybrid intelligent model for approximating unconfined compressive strength of cement-based bricks with odd-valued array of peat content (0-29%)

Journal paper
Shahaboddin Shamshirband, Amirmohammad Tavakkoli, Chandra Bhushan Roy, Shervin Motamedi, Song KI-IL, Roslan Hashim, Syed Mofachirul Islam
Powder Technology ( Tier 2), Publisher Elsevier

Abstract

This article presents an innovative approach to estimate the unconfined compressive strength (UCS) of peat-enhanced bricks using a hybrid intelligent system (HIS) resulting from integration of support vector regression (SVR) and Bat meta-heuristic algorithm (hereafter, Bat–SVR). First, peat-enhanced brick specimens were prepared for various compositions of cement, sand, and peat (odd-valued array of peat inclusion in the range of 0–29% from the total specimens’ weight). Further, the experimental works were carried out to obtain the UCS of specimens in different curing period. Finally, HIS model was used to predict the UCS of cement–peat–soil mixture. Basically, we used a newly-developed Bat algorithm for tuning the SVR parameters, because the accuracy of SVR estimation highly relies on these parameters. Results from the experimental study were used to train and estimate the UCS of peat-enhanced bricks. In addition, we compared the accuracy of the developed HIS model to other conventional soft computing techniques (i.e., ANFIS and neural network). It was found that the proposed approach outperforms the other conventional prediction models and better estimates the UCS of peat-enhanced bricks.

Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Journal paper
Abdullah Gani, Kasra Mohammadi, Shahaboddin Shamshirband, Hossein Khorasanizadeh, Amir Seyed Danesh, Jamshid Piri, Zuraini Ismail, Mazdak Zamani
Theoretical and Applied Climatology ( Tier 2), Pages 1-11, Publisher Springer Vienna

Abstract

The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44–0.61 kWh/m2, 0.50–0.71 kWh/m2, and 0.78–0.91, respectively.

Intelligent forecasting of residential heating demand for the District Heating System based on the monthly overall natural gas consumption

Journal paper
Nima Izadyar, Hwai Chyuan Ong, Shahab Shamshirband, Hossein Ghadamian, Chong Wen Tong
Energy and Buildings ( Tier 1)

Abstract

In this study, the residential heating demand of a case study (Baharestan town, Karaj) in Iran was forecasted based on the monthly natural gas consumption data and monthly average of the ambient temperature. Three various methods containing Extreme Learning Machine (ELM), artificial neural networks (ANNs) and genetic programming (GP) were employed to forecast residential heating demand of the case study and the results of these methods were compared after validating via real data. Actually, the main goal of the current study is to obtain the most accurate technique among these 3 common methods in this context. Validation of the forecasting results reveals that the important progress can be achieved in terms of accuracy by the ELM method in comparison with ANN and GP. Moreover, obtained results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy for residential heating demand for the DHS. The outputs reveal that the new procedure can have a suitable performance in major cases and can be learned more rapid compare with other common learning algorithms.

Surface roughness prediction by extreme learning machine constructed with abrasive water jet

Journal paper
Žarko Ćojbašić, Dalibor Petković, Shahab Shamshirband, Chong Wen Tong, Sudheer Ch, Predrag Janković, Nedeljko Dučić, Jelena Baralić
Precision Engineering ( Tier 1)

Abstract

In this study, the novel method based on extreme learning machine (ELM) is adapted to estimate roughness of surface machined with abrasive water jet. Roughness of surface is one of the main attributes of quality of products derived from water jet processing, and directly depends on the cutting parameters, such as thickness of the workpiece, abrasive flow rate, cutting speed and others. In this study, in order to provide data on influence of parameters on surface roughness, extensive experiments were carried out for different cutting regimes. Measured data were used to model the process by using ELM model. Estimation and prediction results of ELM model were compared with genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ELM approach in comparison with GP and ANN. Moreover, achieved results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy for roughness of the surface machined with abrasive water jet. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate the roughness of the surface machined with abrasive water jet.

Evaluation of the most influential parameters of heat load in district heating systems

Journal paper
Dalibor Petković, Milan Protić, Shahab Shamshirband, Shatirah Akib, Miomir Raos, Dušan Marković
Energy and Buildings ( Tier 1)

Abstract

The aim of this study is to investigate the potential of soft computing methods for selecting the most relevant variables for predictive models of consumers’ heat load in district heating systems (DHS). Data gathered from one of the heat substations were used for the simulation process. The ANFIS (adaptive neuro-fuzzy inference system) method was applied to the data obtained from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the short-term multistep prediction of consumers’ heat load in district heating systems. It was also used to select the minimal input subset of variables from the initial set of input variables – current and lagged variables (up to 10 steps) of heat load, outdoor temperature, and primary return temperature. The obtained results could be used for simplification of predictive methods so as to avoid multiple input variables. While the obtained results are promising, further work is required in order to get results that could be directly applied in practice.

Performance investigation of micro and nano-sized particle erosion in a 90° elbow using an ANFIS model

Journal paper
Shahab Shamshirband, Amir Malvandi, Arash Karimipour, Marjan Goodarzi, Meysam Amini, Dalibor Petković, Mahidzal Dahari, Naghmeh Mahmoodian
Powder Technology

Abstract

The accuracy of soft computing technique was employed to predict the performance of micro- and nano-sized particle erosion in a 3-D 90° elbow. The process, capable of simulating the total and maximum erosion rate with adaptive neuro-fuzzy inference system (ANFIS), was constructed. The developed ANFIS network was with three neurons in the input layer, and one neuron in the output layer. The inputs included particle velocity, particle diameter, and volume fraction of the copper particles. The size of these particles was selected in the range of 10 nm to 100 μm. Numerical simulations have been performed with velocities ranging from 5 to 20 m/s and for volume fractions of up to 4%. The governing differential equations have been discretized by the finite volume method for ANFIS training data extraction. The ANFIS results were compared with the CFD results using root-mean-square error (RMSE) and coefficient of determination (R2). The CFD results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following characteristics were obtained: ANFIS model can be used to forecast the maximum and total erosion rate with high reliability and therefore can be applied for practical purposes.

Application of Multiple Linear Regression, Central Composite Design, and ANFIS models in Dye concentration Measurement and Prediction Using Plastic Optical Fiber Sensor

Journal paper
Su Sin Chong, Abdul Aziz Abdul Raman, Sulaiman W. Harun, Hamzah Arof, Shahab Shamshirband
Measurement ( Tier 1)

Abstract

The measurement and prediction of dye concentration is important in the design, planning and management of wastewater treatment. Soft computing techniques can be used as a support tool for analyzing data and making prediction. In this study, Central Composite Design (CCD) and adaptive neuro-fuzzy inference system (ANFIS) are employed to identify and predict the output intensity ratio of light that passes through a plastic optical fiber (POF) sensor in Remazol Black B (RBB) dye solution of different concentrations. The predictive performances of these models are compared to that of the traditional Multiple Linear Regression (MLR). The accuracies of MLR, CCD and ANFIS models are evaluated in terms of square correlation coefficient (R2), root mean square error (RMSE), value accounted for (VAF), and mean absolute percentage error (MAPE) against the empirical data. It is found that the ANFIS model exhibits higher prediction accuracy than the MLR and CCD models.

Soft Computing Methodologies for Estimation of Energy Consumption in Buildings with Different Envelope Parameters

Journal paper
Sareh Naji, Shahab Shamshirband, Hamed Basser, U Johnson Alengaram, Mohd Zamin Jumaat, Mohsen Amirmojahedi
Energy Efficiency

Abstract

In this study, soft computing methods are designed and adapted to estimate energy consumption of the building according to main building envelope parameters such as material thicknesses and insulation K-value. In order to predict the building energy consumption, novel intelligent soft computing schemes, support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) are used. The polynomial, linear, and radial basis function (RBF) is applied as the kernel function of the SVR to estimate the optimal energy consumption of buildings. The performance of proposed optimizers is confirmed by simulation results. The SVR results are compared with the ANFIS, artificial neural network (ANN), and genetic programming (GP) results. The computational results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach in comparison to the SVR estimation. Based on the simulation results, the effectiveness of the proposed optimization strategies is verified. The data used in soft computing were obtained from 180 simulations in EnergyPlus for variations of building envelope parameters.

A Novel Boosted-Neural Network Ensemble for Modeling Multi-Target Regression Problems

Journal paper
Esmaeil Hadavandi, Jamal Shahrabi, Shahaboddin Shamshirband
Engineering Applications of Artificial Intelligence ( Tier 1), Publisher Elsevier

Abstract

In this paper, the concept of ensemble learning is adopted and applied to modeling multi-target regression problems with high-dimensional feature spaces and a small number of instances. A novel neural network ensemble (NNE) model is introduced, called Boosted-NNE based on notions from boosting, subspace projection methods and the negative correlation learning algorithm (NCL). Rather than using an entire feature space for training each component in the Boosted-NNE, a new cluster-based subspace projection method (CLSP) is proposed to automatically construct a low-dimensional input space with focus on the difficult instances in each step of the boosting approach. To enhance diversity in the Boosted-NNE, a new, sequential negative correlation learning algorithm (SNCL) is proposed to train negatively correlated components. Furthermore, the constrained least mean square error (CLMS) algorithm is employed to obtain the optimal weights of components in the combination module. The proposed Boosted-NNE model is compared with other ensemble and single models using four real cases of multi-target regression problems. The experimental results indicate that using the SNCL in combination with the CLSP method offers the capability to improve the diversity and accuracy of the Boosted-NNE. Thus, this model seems a promising alternative for modeling high-dimensional multi-target regression problems.

Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

Journal paper
Dalibor Petković, Milan Gocic, Shahaboddin Shamshirband, Sultan Noman Qasem, Slavisa Trajkovic
Theoretical and Applied Climatology ( Tier 3), Publisher springer

Abstract

Accurate estimation of the reference evapotranspiration (ET0) is important for the water resource planning and scheduling of irrigation systems. For this purpose, the radial basis function network with particle swarm optimization (RBFN-PSO) and radial basis function network with back propagation (RBFN-BP) were used in this investigation. The FAO-56 Penman–Monteith equation was used as reference equation to estimate ET0 for Serbia during the period of 1980–2010. The obtained simulation results confirmed the proposed models and were analyzed using the root mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2). The analysis showed that the RBFN-PSO had better statistical characteristics than RBFN-BP and can be helpful for the ET0 estimation.

Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria

Journal paper
Lanre Olatomiwa, Saad Mekhilef, Shahaboddin Shamshirband, Dalibor Petković
Renewable and Sustainable Energy Reviews, Publisher Elsevier

Abstract

In this paper, the accuracy of a soft computing technique is investigated for predicting solar radiation based on a series of measured meteorological data: monthly mean minimum temperature and, maximum temperature, and sunshine duration obtained from a meteorological station located in Iseyin, Nigeria. The process was developed with an adaptive neuro-fuzzy inference system (ANFIS) to simulate solar radiation. The ANFIS network has three neurons in the input layer, and one neuron in the output layer. The inputs are monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin), and monthly mean sunshine duration (View the MathML source). The performance of the proposed system is obtained through the simulation results. The ANFIS results are compared with experimental results using root-mean-square error (RMSE) and coefficient of determination (R2). The results signify an improvement in predictive accuracy and ANFIS capability to estimate solar radiation. The statistical characteristics of RMSE=1.0854 and R2=0.8544 were obtained in the training phase and RMSE=1.7585 and R2=0.6567 in the testing phase. As a result, the proposed model deemed an efficient techniques to predict global solar radiation for practical purposes.

RAIRS2: A New Expert System for Diagnosing Tuberculosis with Real-World Tournament Selection Mechanism inside Artificial Immune Recognition System

Journal paper
Mahmoud Reza Saybani, Shahaboddin Shamshirband, Shahram Golzari, Teh Ying Wah, Saeed Reza Aghabozorgi, Miss Laiha Mat Kiah, Valentina Emilia Balas
Medical & Biological Engineering & Computing ( Tier 2), Publisher Springer

Abstract

Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.

Diagnosing Breast Cancer with an Improved Artificial Immune Recognition System

Journal paper
Mahmoud Reza Saybani, Teh Ying Wah, Saeed Reza Aghabozorgi, Shahaboddin Shamshirband, Miss Laiha Mat Kiah, Valentina Emilia Balas
Soft Computing ( Tier 2), Publisher Springer

Abstract

Breast cancer is the top cancer in women worldwide. Scientists are looking for early detection strategies which remain the cornerstone of breast cancer control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose this disease. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. To increase the classification accuracy, this study introduces a new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS. The Wisconsin Breast Cancer data set was used as the benchmark data set; it is available through the machine learning repository of the University of California at Irvine. The classification performance was measured through tenfold cross-validation, student’s t test, sensitivity and specificity. With an accuracy of 100 %, the proposed method was able to classify breast cancer dataset successfully.

Application of extreme learning machine for estimation of wind speed distribution

Journal paper
Shahaboddin Shamshirband, Kasra Mohammadi, Chong Wen Tong, Dalibor Petković, Emilio Porcu, Ali Mostafaeipour, CH Sudheer, Ahmad Sedaghat
Climate Dynamics, Publisher Elsevier

Abstract

The knowledge of the probabilistic wind speed distribution is of particular significance in reliable evaluation of the wind energy potential and effective adoption of site specific wind turbines. Among all proposed probability density functions, the two-parameter Weibull function has been extensively endorsed and utilized to model wind speeds and express wind speed distribution in various locations. In this research work, extreme learning machine (ELM) is employed to compute the shape (k) and scale (c) factors of Weibull distribution function. The developed ELM model is trained and tested based upon two widely successful methods used to estimate k and c parameters. The efficiency and accuracy of ELM is compared against support vector machine, artificial neural network and genetic programming for estimating the same Weibull parameters. The survey results reveal that applying ELM approach is eventuated in attaining further precision for estimation of both Weibull parameters compared to other methods evaluated. Mean absolute percentage error, mean absolute bias error and root mean square error for k are 8.4600 %, 0.1783 and 0.2371, while for c are 0.2143 %, 0.0118 and 0.0192 m/s, respectively. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate Weibull k and c factors.

Temperature-based estimation of global solar radiation using soft computing methodologies

Journal paper
Kasra Mohammadi, Shahaboddin Shamshirband, Amir Seyed Danesh, Mohd Shahidan Abdullah, Mazdak Zamani
Theoretical and Applied Climatology ( Tier 2), Pages 1-12, Publisher Springer Vienna

Abstract

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures (Tmax, Tmin, and Tavg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of Tmax, Tmin, and Tavg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using TmaxTmin and Tmax as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.

A systematic review of approaches to assessing cybersecurity awareness

Journal paper
Noor Hayani Abd Rahim, Suraya Hamid, Miss Laiha Mat Kiah, Shahaboddin Shamshirband, Steven Furnell
Kybernetes ( Tier 4), Volume 44, Issue 4, Pages 606 - 622

Abstract:

– The purpose of this paper is to survey, explore and inform researchers about the previous methodologies applied, target audience and coverage of previous assessment of cybersecurity awareness by capturing, summarizing, synthesizing and critically comment on it. It is also conducted to identify the gaps in the cybersecurity awareness assessment research which warrants the future work.

 

Extreme learning machine approach for sensorless wind speed estimation

Journal paper
Nikolić Vlastimir, Motamedi Shervin, Shamshirband Shahaboddin, Petković Dalibor, Ch Sudheer, Arif Mohammad
Mechatronics ( Tier 1 ), Publisher Elsevier

Heat load prediction in district heating systems with adaptive neuro-fuzzy method

Journal paper
Shahaboddin Shamshirband, Dalibor Petković, Rasul Enayatifar, Abdul Abdullah Hanan, Rodina Ahmad, Malrey Lee, Marković Dušan
Renewable and Sustainable Energy Reviews (Tier 1), Publisher Elsevier

Abstract

District heating systems can play significant role in achieving stringent targets for CO2emissions with concurrent increase in fuel efficiency. However, there are a lot of the potentials for future improvement of their operation. One of the potential domains is control and prediction. Control of the most district heating systems is feed forward without any feedback from consumers. With reliable predictions of consumers heat need, production could be altered to match the real consumers’ needs. This will have effect on lowering the distribution cost, heat losses and especially on lowered return secondary and primary temperature which will result in increase of overall efficiency of combined heat and power plants. In this paper, to predict the heat load for individual consumers in district heating systems, an adaptive neuro-fuzzy inferences system (ANFIS) was constructed. Simulation results indicate that further improvements on model are needed especially for prediction horizons greater than 1 h.

Identification and Prioritization of Critical Issues for the Promotion of E-learning in Pakistan

Journal paper
Shahid Farid, Rodina Ahmad, Iftikhar Azim Niaz, Muhammad Arif, Shahaboddin Shamshirband, Muhammad Daud Khattak
Computers in Human Behavior, Publisher Elsevier

Abstract

Integration of information and communication technology in education is emerging as the new paradigm of learning and training. Higher education institutions are struggling to shift to this new paradigm to facilitate more and more learners with the flexibility of any time-anywhere learning. E-learning is not gaining as much popularity in the developing countries as it was expected in the last decade. Little work has been done in this area of research in the developing countries. This study contributes to identify and analyze the impact of critical issues which are creating barriers in the promotion of e-learning in the developing countries like Pakistan. Furthermore, this study contributes in devising taxonomy and proposing new category software for the identified critical issues. A mix mode research model has been applied to collect data from the e-learning experts of different public sector universities of Pakistan to get a deeper understanding of the issues and their impact on the promotion of e-learning in Pakistan. The findings of this study reveal sixteen (16) critical issues which are classified in five (5) dimensions, to be addressed on priority basis to promote e-learning in Pakistan. The identified dimensions and issues have been prioritized according to their importance using the Analytical Hierarchy Process method.

Software SMEs' Unofficial Readiness for CMMI®-based Software Process Improvement

Journal paper
Javed Iqabal, Rodina Ahmad, Mohd Hairul Nizam Md Nasir, Mahmood Niazi, Shahaboddin Shamshirband, Asim Noor
Software Quality Control ( Tier 3 ), Publisher Springer

Abstract

The goal of software process improvement (SPI) is to improve software processes and produce high-quality software, but the results of SPI efforts in small- and medium-sized enterprises (SMEs) that develop software have been unsatisfactory. The objective of this study is to support the prolific and successful CMMI-based implementation of SPI in SMEs by presenting the facts related to the unofficial adoption of CMMI level 2 process area-specific practices by software SMEs. Two questionnaire surveys were performed, and 42 questionnaires were selected for data analysis. The questionnaires were filled out by experts from 42 non-CMMI-certified software SMEs based in Malaysia and Pakistan. In the case of each process area of CMMI level 2, the respondents were asked to choose from three categories, namely ‘below 50 %,’ ‘50–75 %,’ and ‘above 75 %’. The percentages indicated the extent to which process area-specific practices are routinely followed in the respondents’ respective organizations. To deal with differing standards for defining SMEs, the notion of the common range standard has been introduced. The results of the study show that a large segment of software development SMEs informally follows the specific practices of CMMI level 2 process areas and thus has true potential for rapid and effective CMMI-based SPI. The results further indicate that, in the case of four process areas of CMMI level 2, there are statistically significant differences between the readiness of small and medium software enterprises to adopt the specific practices of those process areas, and between trends on their part to do so unofficially. The findings, manifesting various degrees of unofficial readiness for CMMI-based SPI among SMEs, can be used to define criteria for the selection of SMEs that would be included in SPI initiatives funded by relevant authorities. In the interests of developing fruitful CMMI-based SPI and to enhance the success rate of CMMI-based SPI initiatives, the study suggests that ‘ready’ or ‘potential’ SMEs should be given priority for SPI initiatives.

Influence of Clay Particles on Al2O3 and TiO2 Nanoparticles Transport and Retention through Limestone Porous Media: Measurements and Mechanisms

Journal paper
Ali Esfandyari Bayat, Radzuan Juni, Rahmat Mohsin, Mehrdad Hokmabadi, Shahaboddin Shamshirband
Journal of Nanoparticle Research, Publisher Elsevier

Abstract

Utilization of nanoparticles (NPs) for a broad range of applications has caused considerable quantities of these materials to be released into the environment. Issues of how and where the NPs are distributed into the subsurface aquatic environments are questions for those in environmental engineering. This study investigated the influence of three abundant clay minerals namely kaolinite, montmorillonite, and illite in the subsurface natural aquatic systems on the transport and retention of aluminum oxide (Al2O3, 40 nm) and titanium dioxide (TiO2, 10–30 nm) NPs through saturated limestone porous media. The clay concentrations in porous media were set at 2 and 4 vol% of the holder capacity. Breakthrough curves in the columns outlets were measured using a UV–Vis spectrophotometer. It was found that the maximum NPs recoveries were obtained when there was no clay particle in the porous medium. On the other hand, increase in concentration of clay particles has resulted in the NPs recoveries being significantly declined. Due to fibrous structure of illite, it was found to be more effective for NPs retention in comparison to montmorillonite and kaolinite. Overall, the position of clay particles in the porous media pores and their morphologies were found to be two main reasons for increase of NPs retention in porous media.

Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

Journal paper
Sara Rajabi Hamedani, Misbah Liaqat, Shahaboddin Shamshirband, Othman Saleh Al-Razgan, Eiman Tamah Al-Shammari, Dalibor Petković
American Journal of Potato Research ( Tier 2 ), Pages 1-9, Publisher Springer US

Abstract

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to predict potato production in Iran. Data related to potato yield from 2010 to 2011 was collected from 50 potato producers in Hamedan, Iran. The resulting ANFIS network has an input layer with eight neurons and an output layer with a single neuron (potato yield). The energy inputs were manual labor, diesel, chemical fertilizers, and manure from farm animals, chemicals, machinery, water, and seed. The most significant and influential inputs were selected from the eight initial inputs and the ANFIS network was used to choose the parameters that have the most influence on potato yield. A new ANFIS model was created after the three most influential parameters were selected. The new ANFIS model was then utilized to estimate yield using the three energy inputs. Next, the ANFIS model results were compared with the results from the support vector regression (SVR) technique. The end results revealed that ANFIS provided more accurate predictions and had the capacity to generalize. The Pearson correlation coefficient (r) for ANFIS potato yield prediction was 0.9999 in the training and testing phases, while the SVR model had a correlation coefficient of 0.8484 in training and 0.9984 in testing.