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Prof. Dr. Shahab Shamshirband
National Yunlin University of Science and Technology, Taiwan

Basic Info


Research Keywords & Expertise

0 Algorithm Analysis
0 Big Data
0 Biodiesel
0 Bioinformatics
0 Computational Intelligence

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Prediction
Energy & the Environment
Machine Learning
Optimization
Support Vector Machine (SVM)
Forecasting
artificial intelligence
Wireless Sensor Networks (WSN)
Deep Learning
Computational Intelligence
Internet of Things - IoT
meteorological parameters
Biodiesel
hybrid models
heuristic
Big Data
Intrusion Detection
Optimization Algorithms
sensor network
Reinforcement Learning
IDS
Data Science

Honors and Awards

Top 1% scientists in the world

Top 1% scientists in the world in 2017-present based on Essential Science Indicators (Thomson Reuters- ESI- ISI).

Thomson Reuters- ESI- ISI


UM President's Excellence Awards

PhD Candidate with Highest Impact Publications

University of Malaya


World’s Top 2 percent highly cited scientists’ list by Stanford University

list by Stanford University. (https://data.mendeley.com/datasets/btchxktzyw/2). The following results are rank based on composite score c. Duy Tan University. Rank=63348, University of Malaya Rank= 104319, Ton-Duc-Thang University Rank= 139109.

Web of Science




Career Timeline

National Yunlin University of Science and Technology, Yunlin, Taiwan

Institute, Department or Faculty Head

01 March 2021 - 01 March 2025


Biological & Medical Machine Learning Lab (BML), Graduate School of Biomedical Engineering, UNSW, Sydney, Australia.

Senior Scientist or Principal Investigator

01 October 2020 - 01 October 2023


Machine Learning, Bioinformatics, and Computational Biology Lab ( MLBC Lab), Rutgers University Camden, New Jersey, USA.

Institute, Department or Faculty Head

01 October 2020 - 01 October 2025


Transport and Telecommunication Institute (TSI), Riga, Latvia.

Institute, Department or Faculty Head

01 November 2019 - 01 November 2025


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Journal article
Published: 06 August 2021 in Scientific Reports
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In the present study, the simultaneous removal of malachite green (MG) and auramine-O (AO) dyes from the aqueous solution by NaX nanozeolites in a batch system is investigated. Taguchi method and response surface methodology (RSM) were used to optimize and model dye removal conditions. In order to do so, the effect of various factors (dyes concentration, sonication time, ionic strength, adsorbent dosage, temperature, and pH of the solution) on the amount of dye removal was evaluated by the Taguchi method. Then, the most important factors were chosen and modeled by the RSM method so as to reach the highest percentage of dye removal. The proposed quadratic models to remove both dyes were in good accordance with the actual experimental data. The maximum removal efficiencies of MG and AO dyes in optimal operating conditions were 99.07% and 99.61%, respectively. Also, the coefficients of determination (R2) for test data were 0.9983 and 0.9988 for MG and AO dyes, respectively. The reusability of NaX nanozeolites was evaluated during the adsorption process of MG and AO. The results showed that the adsorption efficiency decreases very little up to five cycles. Moreover, NaX nanozeolites were also applied as adsorbents to remove MG and AO from environmental water samples, and more than 98.1% of both dyes were removed from the solution in optimal conditions.

ACS Style

Siroos Shojaei; Saeed Shojaei; Shahab S. Band; Amir Abbas Kazemzadeh Farizhandi; Milad Ghoroqi; Amir Mosavi. Application of Taguchi method and response surface methodology into the removal of malachite green and auramine-O by NaX nanozeolites. Scientific Reports 2021, 11, 1 .

AMA Style

Siroos Shojaei, Saeed Shojaei, Shahab S. Band, Amir Abbas Kazemzadeh Farizhandi, Milad Ghoroqi, Amir Mosavi. Application of Taguchi method and response surface methodology into the removal of malachite green and auramine-O by NaX nanozeolites. Scientific Reports. 2021; 11 ():1.

Chicago/Turabian Style

Siroos Shojaei; Saeed Shojaei; Shahab S. Band; Amir Abbas Kazemzadeh Farizhandi; Milad Ghoroqi; Amir Mosavi. 2021. "Application of Taguchi method and response surface methodology into the removal of malachite green and auramine-O by NaX nanozeolites." Scientific Reports 11, no. : 1.

Article
Published: 29 July 2021 in Cluster Computing
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The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed “EdgeSDN-I4COVID” architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.

ACS Style

Anichur Rahman; Chinmay Chakraborty; Adnan Anwar; Razaul Karim; Jahidul Islam; Dipanjali Kundu; Ziaur Rahman; Shahab S. Band. SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic. Cluster Computing 2021, 1 -18.

AMA Style

Anichur Rahman, Chinmay Chakraborty, Adnan Anwar, Razaul Karim, Jahidul Islam, Dipanjali Kundu, Ziaur Rahman, Shahab S. Band. SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic. Cluster Computing. 2021; ():1-18.

Chicago/Turabian Style

Anichur Rahman; Chinmay Chakraborty; Adnan Anwar; Razaul Karim; Jahidul Islam; Dipanjali Kundu; Ziaur Rahman; Shahab S. Band. 2021. "SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic." Cluster Computing , no. : 1-18.

Journal article
Published: 28 June 2021 in Journal of Cleaner Production
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Solid Waste (SW) is one of the critical challenges of urban life. These SWs are considered environmental pollutants that are a threat to ecology and human health. Predicting SW generation is an essential topic for scholars to better manage SWs. This study investigates the application of optimised ANN models for predicting monthly SW generation in Iran using datasets about seven Iranian megacities. The Archimedes Optimisation Algorithm (AOA), Sine Cosine Algorithm (SCA), Particle Swarm Optimisation (PSO) technique, and Genetic Algorithms (GA) were used for training the ANN model. The enhanced gamma test was used to determine the best input combination. AOA and the gamma test were used concurrently to reduce the time needed for choosing the best input combination. Gross domestic product (GDP), population, household size, and numbers of months were the best input combination set. This best input combination was then inputted into the hybrid and standalone ANN models for predicting monthly SW generation. During the final phase, the outputs of ANN-AOA, ANN-SCA, ANN-PSO, ANN-GA, and ANN models were used as inputs for an inclusive multiple model (IMM) in order to enhance model accuracy. The IMM model reduced the training phase root mean square error (RMSE) of ANN-AOA, ANN-SCA, ANN-PSO, ANN-GA, and ANN models by 55%, 59%, 68%, 72%, and 73%, respectively. Although ANN-AOA provided higher R2 and lower RMSE values than ANN-PSO, ANN-SCA, ANN-GA and ANN models, the IMM model outperformed ANN-AOA, considering that it integrates the advantages of all models used in the current study. The current study also used the fuzzy reasoning concept for modifying ANN model structures. The results indicated that such ANN models' time requirement was lower than those without fuzzy reasoning concept. The general results of the current study indicate that the ANN-AOA and the fuzzy-reasoning based Inclusive Multiple Model have a high ability for predicting different target variables.

ACS Style

Guoxi Liang; Fatemeh Panahi; Ali Najah Ahmed; Mohammad Ehteram; Shahab S. Band; Ahmed Elshafie. Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components. Journal of Cleaner Production 2021, 315, 128039 .

AMA Style

Guoxi Liang, Fatemeh Panahi, Ali Najah Ahmed, Mohammad Ehteram, Shahab S. Band, Ahmed Elshafie. Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components. Journal of Cleaner Production. 2021; 315 ():128039.

Chicago/Turabian Style

Guoxi Liang; Fatemeh Panahi; Ali Najah Ahmed; Mohammad Ehteram; Shahab S. Band; Ahmed Elshafie. 2021. "Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components." Journal of Cleaner Production 315, no. : 128039.

Journal article
Published: 06 June 2021 in Alexandria Engineering Journal
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Gully erosion is one of the advanced forms of water erosion. Identifying the effective factors and gully erosion predicting is one of the important tools to control and manage such phenomenon. The main purpose of this study is to evaluate the effect of four different resampling algorithms including cross-validation (5-fold and 10-fold) and bootstrapping (Bootstrap and Optimism bootstrap) on boosted regression tree (BRT), support vector machine (SVM), and random forest (RF) models in spatial modeling and evaluation of head-cut gully erosion in Konduran watershed. For this purpose, based on an extensive field survey, the points of the head-cut of the gully erosion were identified first, and a map of the distribution of head-cut gully erosion in the study area was prepared. Then 18 variable identify and prepare as factors affecting the occurrence of head-cut gully erosion. To assess the efficiency of the models, receiver operating characteristics (ROC) and area under the curve (AUC) were used. The results of the assessment indicated that the use of resampling algorithms increases the efficiency of the models. The integrated optimism-bootstrap-BRT, optimism-bootstrap-SVM, and Optimism-Bootstrap-RF models with AUC 0.85, 0.823 and 0.89 respectively, outperformed the cross-validation 5fold (BRT, SVM, RF), Cross-validation 10fold (BRT, SVM, RF) and Bootstrap (BRT, SVM, RF) integrated algorithms.

ACS Style

Fengjie Wang; Mehebub Sahana; Bahareh Pahlevanzadeh; Subodh Chandra Pal; Pravat Kumar Shit; Jalil Piran; Saeid Janizadeh; Shahab S. Band; Amir Mosavi. Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility. Alexandria Engineering Journal 2021, 60, 5813 -5829.

AMA Style

Fengjie Wang, Mehebub Sahana, Bahareh Pahlevanzadeh, Subodh Chandra Pal, Pravat Kumar Shit, Jalil Piran, Saeid Janizadeh, Shahab S. Band, Amir Mosavi. Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility. Alexandria Engineering Journal. 2021; 60 (6):5813-5829.

Chicago/Turabian Style

Fengjie Wang; Mehebub Sahana; Bahareh Pahlevanzadeh; Subodh Chandra Pal; Pravat Kumar Shit; Jalil Piran; Saeid Janizadeh; Shahab S. Band; Amir Mosavi. 2021. "Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility." Alexandria Engineering Journal 60, no. 6: 5813-5829.

Article
Published: 30 April 2021 in International Journal of Fuzzy Systems
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In this paper, a novel control approach is proposed for a class of uncertain nonlinear system with unmodeled dynamics. Each output of the system is modeled by several first-order dynamic fractional-order fuzzy systems. The best dynamic model is selected at a period of time and the control signal is designed based on this model. The dynamic fractional-order models are based on the special case of general type-2 fuzzy systems which are called interval type-3 fuzzy logic systems (IT3FLSs). The adaptation laws for the consequent parameters of IT3FLSs are derived through stability analysis of the fractional-order systems based on the linear matrix inequality approach. The effectiveness of the proposed scheme is verified by normal simulation on the hyperchaotic Lorenz system with unmodeled dynamics, real-time simulation on the chaotic model of the brushless DC motors using Arduino boards and experimental examination on a heat transfer system with fully unknown dynamics.

ACS Style

Ardashir Mohammadzadeh; Oscar Castillo; Shahab S. Band; Amirhosein Mosavi. A Novel Fractional-Order Multiple-Model Type-3 Fuzzy Control for Nonlinear Systems with Unmodeled Dynamics. International Journal of Fuzzy Systems 2021, 1 -19.

AMA Style

Ardashir Mohammadzadeh, Oscar Castillo, Shahab S. Band, Amirhosein Mosavi. A Novel Fractional-Order Multiple-Model Type-3 Fuzzy Control for Nonlinear Systems with Unmodeled Dynamics. International Journal of Fuzzy Systems. 2021; ():1-19.

Chicago/Turabian Style

Ardashir Mohammadzadeh; Oscar Castillo; Shahab S. Band; Amirhosein Mosavi. 2021. "A Novel Fractional-Order Multiple-Model Type-3 Fuzzy Control for Nonlinear Systems with Unmodeled Dynamics." International Journal of Fuzzy Systems , no. : 1-19.

Journal article
Published: 23 March 2021 in Computers and Electronics in Agriculture
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Classifying satellite images with medium spatial resolution such as Landsat, it is usually difficult to distinguish between plant species, and it is impossible to determine the area covered with weeds. In this study, a Landsat 8 image along with UAV images utilized to separate pistachio cultivars and separate weed from trees. To use the high spatial resolution of UAV images, image fusion was carried out through the high-pass filter, wavelet, principal component transformation, BROVEY, IHS, and Gram Schmidt methods. ERGAS, RMSE, and correlation criteria were applied to assess their accuracy. The results represented that the wavelet method with R2, RMSE, and ERGAS 0.91, 12.22 cm, and 2.05 respectively had the highest accuracy in combining these images. Then, images obtained by this method were chosen with a spatial resolution of 20 cm for classification. Different classification methods including unsupervised method, maximum likelihood, minimum distance, fuzzy artmap, perceptron, and tree methods were evaluated. Moreover, six soil classes, Ahmad Aghaei, Akbari, Kalleh Ghoochi, Fandoghi, and a mixing class of Kalleh Ghoochi and Fandoghi were applied, and also three classes of soil, pistachio tree and weeds were extracted from the trees. The results demonstrated that the fuzzy artmap method had the highest accuracy in separating weeds from trees, differentiating various pistachio cultivars with Landsat image and also classification with combined image and had 0.87, 0.79, and 0.87 kappa coefficients respectively. The comparison between pistachio cultivars through Landsat image and the combined image showed that the validation accuracy obtained from harvest has raised by 17% because of the combination of images. The results of this study indicated that the combination of UAV and Landsat 8 images affects well to separate pistachio cultivars and determine the area covered with weeds.

ACS Style

Hamid Reza Ghafarian Malamiri; Fahime Arabi Aliabad; Saeed Shojaei; Mortaz Morad; Shahab S. Band. A study on the use of UAV images to improve the separation accuracy of agricultural land areas. Computers and Electronics in Agriculture 2021, 184, 106079 .

AMA Style

Hamid Reza Ghafarian Malamiri, Fahime Arabi Aliabad, Saeed Shojaei, Mortaz Morad, Shahab S. Band. A study on the use of UAV images to improve the separation accuracy of agricultural land areas. Computers and Electronics in Agriculture. 2021; 184 ():106079.

Chicago/Turabian Style

Hamid Reza Ghafarian Malamiri; Fahime Arabi Aliabad; Saeed Shojaei; Mortaz Morad; Shahab S. Band. 2021. "A study on the use of UAV images to improve the separation accuracy of agricultural land areas." Computers and Electronics in Agriculture 184, no. : 106079.

Journal article
Published: 22 March 2021 in Results in Physics
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The aim of this research is to investigate the relationships between the counts of cases with Covid-19 and the deaths due to it in seven countries that are severely affected from this pandemic disease. First, the Pearson’s correlation is used to determine the relationships among these countries. Then, the factor analysis is applied to categorize these countries based on their relationships.

ACS Style

Mohammad Reza Mahmoudi; Dumitru Baleanu; Shahab S. Band; Amir Mosavi. Factor Analysis Approach to Classify COVID-19 Datasets in Several Regions. Results in Physics 2021, 104071 .

AMA Style

Mohammad Reza Mahmoudi, Dumitru Baleanu, Shahab S. Band, Amir Mosavi. Factor Analysis Approach to Classify COVID-19 Datasets in Several Regions. Results in Physics. 2021; ():104071.

Chicago/Turabian Style

Mohammad Reza Mahmoudi; Dumitru Baleanu; Shahab S. Band; Amir Mosavi. 2021. "Factor Analysis Approach to Classify COVID-19 Datasets in Several Regions." Results in Physics , no. : 104071.

Technical note
Published: 27 January 2021 in Genes
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Bioinformatics and computational biology have significantly contributed to the generation of vast and important knowledge that can lead to great improvements and advancements in biology and its related fields. Over the past three decades, a wide range of tools and methods have been developed and proposed to enhance performance, diagnosis, and throughput while maintaining feasibility and convenience for users. Here, we propose a new user-friendly comprehensive tool called VIRMOTIF to analyze DNA sequences. VIRMOTIF brings different tools together as one package so that users can perform their analysis as a whole and in one place. VIRMOTIF is able to complete different tasks, including computing the number or probability of motifs appearing in DNA sequences, visualizing data using the matplotlib and heatmap libraries, and clustering data using four different methods, namely K-means, PCA, Mean Shift, and ClusterMap. VIRMOTIF is the only tool with the ability to analyze genomic motifs based on their frequency and representation (D-ratio) in a virus genome.

ACS Style

Pedram Rajaei; Khadijeh Jahanian; Amin Beheshti; Shahab Band; Abdollah Dehzangi; Hamid Alinejad-Rokny. VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis. Genes 2021, 12, 186 .

AMA Style

Pedram Rajaei, Khadijeh Jahanian, Amin Beheshti, Shahab Band, Abdollah Dehzangi, Hamid Alinejad-Rokny. VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis. Genes. 2021; 12 (2):186.

Chicago/Turabian Style

Pedram Rajaei; Khadijeh Jahanian; Amin Beheshti; Shahab Band; Abdollah Dehzangi; Hamid Alinejad-Rokny. 2021. "VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis." Genes 12, no. 2: 186.

Research article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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In the present study, water electrolysis was employed for Hydroxy gas (HHO) production as a gaseous additive. The engine test was performed using the Diesel, B5, and B20 as pilot fuels. HHO was imported into the engine's combustion chamber at three volumetric flow rates of 3, 4, and 5 cc/s through the inlet manifold as the low-level HHO rate.The engine test setup was a single-cylinder dual-fueled diesel engine at a constant speed (1500 rpm) and full load condition. According to the results, HHO by 3 and 4 cc/s did not have a significant effect on BP, BTE, and BSFC. Using HHO gas by 5 cc/s significantly increased BP by about 2.5, 3.1, and 0.5% compared with Diesel, B5 and B20, respectively, and decreased BSFC by about 11, 7.8, and 13.5% compared with Diesel, B5, and B20, respectively.HHO gas by 5 cc/s significantly decreased CO2 by about 7, 6.3, and 10.6% compared with Diesel, B5, and B20, respectively, and decreased CO emissions by about 6, 14.3, and 21.2% compared with Diesel, B5 and B20, respectively. However, the use of HHO gas and biodiesel increased NOx emission by about 16, 13.7, and 10.5% compared with Diesel, B5, and B20, respectively.

ACS Style

Bahman Najafi; Farid Haghighatshoar; Sina Ardabili; Shahab S. Band; Kwok Wing Chau; Amir Mosavi. Effects of low-level hydroxy as a gaseous additive on performance and emission characteristics of a dual fuel diesel engine fueled by diesel/biodiesel blends. Engineering Applications of Computational Fluid Mechanics 2021, 15, 236 -250.

AMA Style

Bahman Najafi, Farid Haghighatshoar, Sina Ardabili, Shahab S. Band, Kwok Wing Chau, Amir Mosavi. Effects of low-level hydroxy as a gaseous additive on performance and emission characteristics of a dual fuel diesel engine fueled by diesel/biodiesel blends. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):236-250.

Chicago/Turabian Style

Bahman Najafi; Farid Haghighatshoar; Sina Ardabili; Shahab S. Band; Kwok Wing Chau; Amir Mosavi. 2021. "Effects of low-level hydroxy as a gaseous additive on performance and emission characteristics of a dual fuel diesel engine fueled by diesel/biodiesel blends." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 236-250.

Research article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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Monitoring the water contaminants is of utmost importance in water resource management. Prediction of the total dissolved solid (TDS) is particularly essential for water quality management and planning in the areas exposed to a mixture of pollutants. TDS primarily includes inorganic minerals and organic matters, and various salts and increasing the concentration of TDS causes the esthetic problems. The reflection of the pollutant burden of the aquatic system can remarkably determined by TDS magnitudes. This study focuses on the prediction of TDS and several biochemical parameters such as Na, Ca, HCO3, and Mg in a river system. To overcome nonstationarity, randomness, and nonlinearity of the TDS data, a multi-step supervised machine learning evolutionary algorithm (MSMLEA) is proposed to improve the model's performance at two gaging stations, namely Rig-Cheshmeh and Soleyman-Tangeh, in the Tajan River, Iran. In addition, a hybrid model that recruits intrinsic time-scale decomposition (ITD) for frequency resolution of the input data as well as a multivariate adaptive regression spline (MARS) were adopted. A novel metaheuristic optimization algorithm, crow search algorithm (CSA), was also implemented to compute the optimal parameter values for the MARS model. To validate the proposed hybrid model, standalone MARS, empirical mode decomposition (EMD)-based models, and hybrid ITD-MARS as well as a MARS-CSA were considered as the benchmark models. Results suggest the ITD-MARS-CSA outperforms other models.

ACS Style

Kangjie Sun; Mohammad Rajabtabar; Seyedehzahra Samadi; Mohammad Rezaie-Balf; Alireza Ghaemi; Shahab S. Band; Amir Mosavi. An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction. Engineering Applications of Computational Fluid Mechanics 2021, 15, 251 -271.

AMA Style

Kangjie Sun, Mohammad Rajabtabar, Seyedehzahra Samadi, Mohammad Rezaie-Balf, Alireza Ghaemi, Shahab S. Band, Amir Mosavi. An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):251-271.

Chicago/Turabian Style

Kangjie Sun; Mohammad Rajabtabar; Seyedehzahra Samadi; Mohammad Rezaie-Balf; Alireza Ghaemi; Shahab S. Band; Amir Mosavi. 2021. "An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 251-271.

Journal article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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Owing to the importance of municipal waste as a determining factor in waste management, developing data-driven models in waste generation data is essential. In the current study, solid waste generation is taken as the function of several parameters, namely month, rainfall, maximum temperature, average temperature, population, household size, educated man, educated women, and income. Two different stand-alone computational models, namely, gene expression programming and optimally pruned extreme machine learning techniques, are used in this study to establish their reliability in municipal solid waste generation forecasting, followed by Mallow’s coefficient feature selection method. The lowest Mallow’s coefficient defines the optimal parameters in solid waste generation forecasting. The novel hybrid models of intrinsic time-scale decomposition-gene expression programming and intrinsic time-scale decomposition- optimally pruned extreme machine learning methods based on Monte-Carlo resampling are employed, and an empirical equation is presented for solid waste generation prediction. For examining the reliability of these models, five statistical criteria, namely coefficient of determination, root mean square error, percent mean absolute relative error, uncertainty at 95% and Willmott’s index of agreement, are implemented. Considering Willmott’s index, the Monte Carlo-intrinsic time-scale decomposition-gene expression programming model attains the closest value (0.957) to the ideal value in the training stage and 0.877 in the testing stage. The hybrid ensemble model of intrinsic time-Scale decomposition-gene expression programming presented lower values of root mean square error (12.279) and percent mean absolute relative error (4.310) in the training phase and in the testing, phase compared to gene expression programming with (12.194) and (5.195), respectively. Overall, the prediction results of the hybrid model of intrinsic time-scale decomposition-gene expression programming using Monte-Carlo resampling technique agrees well with the observed solid waste generation data.

ACS Style

Linyuan Fan; Maryam Abbasi; Kazhal Salehi; Shahab S. Band; Kwok-Wing Chau; Amir Mosavi. Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm. Engineering Applications of Computational Fluid Mechanics 2021, 15, 1159 -1175.

AMA Style

Linyuan Fan, Maryam Abbasi, Kazhal Salehi, Shahab S. Band, Kwok-Wing Chau, Amir Mosavi. Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):1159-1175.

Chicago/Turabian Style

Linyuan Fan; Maryam Abbasi; Kazhal Salehi; Shahab S. Band; Kwok-Wing Chau; Amir Mosavi. 2021. "Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 1159-1175.

Journal article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named wavelet-support vector regression (W-SVR) and wavelet-Gaussian process regression (W-GPR)) are used to forecast groundwater level in Semnan plain (arid area) for the next month. Three different wavelet transformations, namely Haar, db4, and Symlet, are tested. Four statistical metrics, namely root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nah-Sutcliffe efficiency (NS), are used to evaluate performance of different methods. The results reveal that SVR with RMSE of 0.04790 (m), MAPE of 0.00199%, R2 of 0.99995, and NS of 0.99988 significantly outperforms GPR with RMSE of 0.55439 (m), MAPE of 0.04363%, R2 of 0.99264, and NS of 0.98413. Besides, the hybrid W-GPR-1 model (i.e. GPR with Harr wavelet) remarkably improves the accuracy of GWL prediction compared to GPR. Finally, the hybrid W-SVR-3 model (i.e. SVR with Symlet) provides the best GWL prediction with RMSE, MAPE, R2, and NS of 0.01290 (m), 0.00079%, 0.99999, and 0.99999, respectively. Overall, the findings indicate that hybrid models can accurately predict GWL in arid regions.

ACS Style

Shahab S. Band; Essam Heggy; Sayed M. Bateni; Hojat Karami; Mobina Rabiee; Saeed Samadianfard; Kwok-Wing Chau; Amir Mosavi. Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics 2021, 15, 1147 -1158.

AMA Style

Shahab S. Band, Essam Heggy, Sayed M. Bateni, Hojat Karami, Mobina Rabiee, Saeed Samadianfard, Kwok-Wing Chau, Amir Mosavi. Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):1147-1158.

Chicago/Turabian Style

Shahab S. Band; Essam Heggy; Sayed M. Bateni; Hojat Karami; Mobina Rabiee; Saeed Samadianfard; Kwok-Wing Chau; Amir Mosavi. 2021. "Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 1147-1158.

Review
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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In biodiesel production by trans-esterification, one of the essential compound is glycerin. Global glycerin production is increasing significantly, projecting a global value reduction for glycerol. Consequently the scientific community had been encouraged to investigate converting glycerol into more valuable products. In this research, the primary sources and processes of biodiesel production are surveyed. Where the processes that involve glycerin are reviewed and the diesel engine performance and emissions under variant states are discussed. According to the results of this study, it is reported that the choice of an optimal diesel/biodiesel significantly depends on the materials, additives and the engine condition. Glycerol etherification, carboxylation, and glycerol carbonate, however, had been identified as the widely manufactured and used additives. It is further observed that the use of these such additives has reduced several emissions, which is an important factor. In addition, it is suggested that using glycerin additives improves the properties of biodiesel. Acetone, on the other hand is introduced as one of the most important additives in the combination of diesel and biodiesel fuel due to the reduction of maximum emission. The presence of hydroxyl groups can reduce NOx. Finally, the diethyl ether additive can be mentioned which increases the thermal efficiency and increases the brake-specific fuel consumption (BSFC).

ACS Style

Farid Haghighat Shoar; Bahman Najafi; Shahab S. Band; Kwok-Wing Chau; Amir Mosavi. Different scenarios of glycerin conversion to combustible products and their effects on compression ignition engine as fuel additive: a review. Engineering Applications of Computational Fluid Mechanics 2021, 15, 1191 -1228.

AMA Style

Farid Haghighat Shoar, Bahman Najafi, Shahab S. Band, Kwok-Wing Chau, Amir Mosavi. Different scenarios of glycerin conversion to combustible products and their effects on compression ignition engine as fuel additive: a review. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):1191-1228.

Chicago/Turabian Style

Farid Haghighat Shoar; Bahman Najafi; Shahab S. Band; Kwok-Wing Chau; Amir Mosavi. 2021. "Different scenarios of glycerin conversion to combustible products and their effects on compression ignition engine as fuel additive: a review." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 1191-1228.

Research article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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Accurate prediction of the scour hole depth and dimensions downstream of ski-jump spillways has been an important issue among hydraulic researchers for decades. In recent years, computing methods such as Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs) and Support Vector Regression (SVR) have shown a powerful performance in the prediction of scour characteristics owing to their flexibility and learning nature. In the present paper, a new hybrid approach has been proposed for the first time in order to improve the estimation power of the SVR tool for scour hole geometry prediction below ski-jump spillways. The principal characteristics of the scour hole pattern in the equilibrium phase have been predicted using SVR optimized with Fruitfly Optimization Algorithms (FOAs). The hybrid model is compared with the corresponding simple SVR model. To evaluate the proposed hybrid model further, it is also compared with other machine learning and empirical methods, such as ANNs, ANFISs and regression equations. The results show that the proposed SVR-FOA method performs well, improves remarkably on Support Vector Machines (SVMs) results, estimates scour hole geometrical parameters more accurately than the simple SVR model, and can be applied as an alternative reliable scheme for estimations on which simple SVR and other methods demonstrate shortcomings. The proposed hybrid method improves the precision level for scour depth prediction by about 8% compared with simple SVM in terms of the correlation coefficient.

ACS Style

Xinpo Sun; Yuzhang Bi; Hojat Karami; Shayan Naini; Shahab S. Band; Amir Mosavi. Hybrid model of support vector regression and fruitfly optimization algorithm for predicting ski-jump spillway scour geometry. Engineering Applications of Computational Fluid Mechanics 2021, 15, 272 -291.

AMA Style

Xinpo Sun, Yuzhang Bi, Hojat Karami, Shayan Naini, Shahab S. Band, Amir Mosavi. Hybrid model of support vector regression and fruitfly optimization algorithm for predicting ski-jump spillway scour geometry. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):272-291.

Chicago/Turabian Style

Xinpo Sun; Yuzhang Bi; Hojat Karami; Shayan Naini; Shahab S. Band; Amir Mosavi. 2021. "Hybrid model of support vector regression and fruitfly optimization algorithm for predicting ski-jump spillway scour geometry." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 272-291.

Research article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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The present study proposes the hybrid machine learning algorithm of artificial neural network-genetic algorithm-response surface methodology (ANN-GA-RSM) to modelthe performance and the emissionsof a single cylinder diesel engine fueled by diesel and propylene glycol additive. The evaluations areperformed using the correlation coefficient (CC), and the root mean square error (RMSE) values. The best model for prediction of the dependent variables is reported ANN-GA with the RMSE values of 0.0398, 0.0368, 0.0529, 0.0354, 0.0509 and 0.0409 and CC 0.988, 0.987, 0.977, 0.994, 0.984, 0.990, respectively for brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), CO, CO2, NOx and SO2. The proposed hybrid model reduces BSFC, NOx, and CO by −30.82%, 21.32%, and 11.32%, respectively. The model also increases the engine efficiency and CO2 emission by 17.29% and 31.05%, respectively, compared to a single RSM in the optimized level of independent variables (69% of biodiesel's oxygen content and 32% of the oxygen content of propylene glycol).

ACS Style

Haleh Karimmaslak; Bahman Najafi; Shahab S. Band; Sina Ardabili; Farid Haghighat-Shoar; Amir Mosavi. Optimization of performance and emission of compression ignition engine fueled with propylene glycol and biodiesel–diesel blends using artificial intelligence method of ANN-GA-RSM. Engineering Applications of Computational Fluid Mechanics 2021, 15, 413 -425.

AMA Style

Haleh Karimmaslak, Bahman Najafi, Shahab S. Band, Sina Ardabili, Farid Haghighat-Shoar, Amir Mosavi. Optimization of performance and emission of compression ignition engine fueled with propylene glycol and biodiesel–diesel blends using artificial intelligence method of ANN-GA-RSM. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):413-425.

Chicago/Turabian Style

Haleh Karimmaslak; Bahman Najafi; Shahab S. Band; Sina Ardabili; Farid Haghighat-Shoar; Amir Mosavi. 2021. "Optimization of performance and emission of compression ignition engine fueled with propylene glycol and biodiesel–diesel blends using artificial intelligence method of ANN-GA-RSM." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 413-425.

Research article
Published: 29 December 2020 in Engineering Applications of Computational Fluid Mechanics
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Due to industrial development, the volume of carbon dioxide (CO2) is rapidly increasing.. Several techniques have been used to eliminate CO2 from the output gas mixtures. One of these methods is CO2 capturing by ionic liquids (ILs). Computational models for estimating the CO2 solubility in ILS is of utmost importance. In this research, a white box model in the form of a mathematical correlation using the largest data bank in literature is presented by the group method of data handling (GMDH). This research investigates the application of GMDH intelligent method as a powerful computational approach for predicting CO2 solubility in different ionic liquids with temperature lower and upper than 324 K. In this regard, 4726 data points including the solubility of CO2 in 60 ILs were used for model development Moreover, seven different ionic liquids were selected to perform the external test. To evaluate the validity and efficiency of the suggested model, regression analysis was implemented on the actual and estimated target values. As a result, a proper fit between the experimental and predicted data was obtained and presented by various figures and statistical parameters. It is also worth noting that the predicted negative values in the proposed models are considered zero. Also, the results of the established correlation were compared to other proposed models exist in the literature of ionic liquids. The terminal form of the models suggested by GMDH approach and obtained based on temperature are two simple mathematical correlations by exerting input parameters of temperature (T), pressure (P), critical temperature (Tc ), critical pressure (Pc ) and, acentric factor (ω) which does not suffer from the black box property of other neural network algorithms. The model suggested in this work, would be a promising one which can act as an efficient predictor for CO2 solubility estimation in ILs and is capable of being used in different industries.

ACS Style

Hamed Moosanezhad-Kermani; Farzaneh Rezaei; Abdolhossein Hemmati-Sarapardeh; Shahab S. Band; Amir Mosavi. Modeling of carbon dioxide solubility in ionic liquids based on group method of data handling. Engineering Applications of Computational Fluid Mechanics 2020, 15, 23 -42.

AMA Style

Hamed Moosanezhad-Kermani, Farzaneh Rezaei, Abdolhossein Hemmati-Sarapardeh, Shahab S. Band, Amir Mosavi. Modeling of carbon dioxide solubility in ionic liquids based on group method of data handling. Engineering Applications of Computational Fluid Mechanics. 2020; 15 (1):23-42.

Chicago/Turabian Style

Hamed Moosanezhad-Kermani; Farzaneh Rezaei; Abdolhossein Hemmati-Sarapardeh; Shahab S. Band; Amir Mosavi. 2020. "Modeling of carbon dioxide solubility in ionic liquids based on group method of data handling." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 23-42.

Review
Published: 28 November 2020 in Journal of Biomedical Informatics
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In the last few years, the application of Machine Learning approaches like Deep Neural Network (DNN) models have become more attractive in the healthcare system given the rising complexity of the healthcare data. Machine Learning (ML) algorithms provide efficient and effective data analysis models to uncover hidden patterns and other meaningful information from the considerable amount of health data that conventional analytics are not able to discover in a reasonable time. In particular, Deep Learning (DL) techniques have been shown as promising methods in pattern recognition in the healthcare systems. Motivated by this consideration, the contribution of this paper is to investigate the deep learning approaches applied to healthcare systems by reviewing the cutting-edge network architectures, applications, and industrial trends. The goal is first to provide extensive insight into the application of deep learning models in healthcare solutions to bridge deep learning techniques and human healthcare interpretability. And then, to present the existing open challenges and future directions.

ACS Style

Shahab Shamshirband; Mahdis Fathi; Abdollah Dehzangi; Anthony Theodore Chronopoulos; Hamid Alinejad-Rokny. A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues. Journal of Biomedical Informatics 2020, 113, 103627 .

AMA Style

Shahab Shamshirband, Mahdis Fathi, Abdollah Dehzangi, Anthony Theodore Chronopoulos, Hamid Alinejad-Rokny. A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues. Journal of Biomedical Informatics. 2020; 113 ():103627.

Chicago/Turabian Style

Shahab Shamshirband; Mahdis Fathi; Abdollah Dehzangi; Anthony Theodore Chronopoulos; Hamid Alinejad-Rokny. 2020. "A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues." Journal of Biomedical Informatics 113, no. : 103627.

Journal article
Published: 28 November 2020 in Alexandria Engineering Journal
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This work is devoted to apply the parametric and nonparametric techniques to construct test of hypothesis about the equality of the probabilistic behaviors of several time series models with fractional Brownian motion errors fitted on several independent datasets. The accuracy and power of the introduced method are studied using the simulated and real datasets. The results indicate that the introduced approach is more powerful than other alternative approaches, in non-stationary cases.

ACS Style

Mohammad Reza Mahmoudi; Dumitru Baleanu; Sultan Noman Qasem; Amirhosein Mosavi; Shahab S. Band. Testing the equality of several independent stationary and non-stationary time series models with fractional Brownian motion errors. Alexandria Engineering Journal 2020, 60, 1767 -1775.

AMA Style

Mohammad Reza Mahmoudi, Dumitru Baleanu, Sultan Noman Qasem, Amirhosein Mosavi, Shahab S. Band. Testing the equality of several independent stationary and non-stationary time series models with fractional Brownian motion errors. Alexandria Engineering Journal. 2020; 60 (1):1767-1775.

Chicago/Turabian Style

Mohammad Reza Mahmoudi; Dumitru Baleanu; Sultan Noman Qasem; Amirhosein Mosavi; Shahab S. Band. 2020. "Testing the equality of several independent stationary and non-stationary time series models with fractional Brownian motion errors." Alexandria Engineering Journal 60, no. 1: 1767-1775.

Preprint
Published: 21 November 2020
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Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber-attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, and etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing & collecting of IoT data, and defend against cyber-threats. To address these issues, the author presents distributed and decentralized Blockchain-Software Defined Networking (SDN) based energy-optimized architecture for IoT in smart cities. Thus, SDN continuous observing, controlling, managing IoT devices activities and detect possible attacks in the network; Blockchain provides adequate security & privacy against cyber-attacks, reduces the central point of failure issues; Network Function Virtualization (NFV) are used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a Cluster Head Selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g. throughput, response time, gas consumption, communication overhead) and demonstrating the result that provides higher throughput, lower response time, lower gas consumption than existing works for smart cities.

ACS Style

Jahidul Islam; Anichur Rahman; Sumaiya Kabir; Razaul Karim; Uzzal Kumar Acharjee; Mostofa Kamal Nasir; Shahab S. Band; Amirhosein Mosavi. Blockchain-SDN based Energy Optimized and Distributed Secure Architecture for IoTs in Smart Cities. 2020, 1 .

AMA Style

Jahidul Islam, Anichur Rahman, Sumaiya Kabir, Razaul Karim, Uzzal Kumar Acharjee, Mostofa Kamal Nasir, Shahab S. Band, Amirhosein Mosavi. Blockchain-SDN based Energy Optimized and Distributed Secure Architecture for IoTs in Smart Cities. . 2020; ():1.

Chicago/Turabian Style

Jahidul Islam; Anichur Rahman; Sumaiya Kabir; Razaul Karim; Uzzal Kumar Acharjee; Mostofa Kamal Nasir; Shahab S. Band; Amirhosein Mosavi. 2020. "Blockchain-SDN based Energy Optimized and Distributed Secure Architecture for IoTs in Smart Cities." , no. : 1.

Preprint content
Published: 20 November 2020
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Susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic.

ACS Style

Ahmad Sedaghat; Seyed Amir Abbas Oloomi; Mahdi Ashtian Malayer; Shahab S. Band; Amir Mosavi; Laszlo Nadai. Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction. 2020, 1 .

AMA Style

Ahmad Sedaghat, Seyed Amir Abbas Oloomi, Mahdi Ashtian Malayer, Shahab S. Band, Amir Mosavi, Laszlo Nadai. Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction. . 2020; ():1.

Chicago/Turabian Style

Ahmad Sedaghat; Seyed Amir Abbas Oloomi; Mahdi Ashtian Malayer; Shahab S. Band; Amir Mosavi; Laszlo Nadai. 2020. "Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction." , no. : 1.