S Band
S Band
Associate Professor of CS (SMIEEE)
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Cited by
Cited by
Sustainable business models: A review
S Nosratabadi, A Mosavi, S S Band, EK Zavadskas, A Rakotonirainy, ...
Sustainability 11 (6), 1663, 2019
A deep learning ensemble approach for diabetic retinopathy detection
S Qummar, FG Khan, S Shah, A Khan, S S Band, ZU Rehman, IA Khan, ...
Ieee Access 7, 150530-150539, 2019
State of the art of machine learning models in energy systems, a systematic review
A Mosavi, M Salimi, S Faizollahzadeh Ardabili, T Rabczuk, ...
Energies 12 (7), 1301, 2019
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: application of the simulated annealing feature selection method
FS Hosseini, B Choubin, A Mosavi, N Nabipour, S S Band, H Darabi, ...
Science of the total environment 711, 135161, 2020
A survey of deep learning techniques: application in wind and solar energy resources
S Band, T Rabczuk, KW Chau
IEEE Access 7, 164650-164666, 2019
Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions
SN Fallah, RC Deo, M Shojafar, M Conti, S S Band
Energies 11 (3), 596, 2018
A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues
S S Band, M Fathi, A Dehzangi, AT Chronopoulos, H Alinejad-Rokny
Journal of Biomedical Informatics 113, 103627, 2021
Comprehensive review of deep reinforcement learning methods and applications in economics
A Mosavi, Y Faghan, P Ghamisi, P Duan, SF Ardabili, E Salwana, ...
Mathematics 8 (10), 1640, 2020
AI-empowered, blockchain and SDN integrated security architecture for IoT network of cyber physical systems
SA Latif, FBX Wen, C Iwendi, FW Li-Li, SM Mohsin, Z Han, SS Band
Computer Communications 181, 274-283, 2022
Modeling pan evaporation using Gaussian process regression K-nearest neighbors random forest and support vector machines; comparative analysis
S Shabani, S Samadianfard, MT Sattari, A Mosavi, S Band, T Kmet, ...
Atmosphere 11 (1), 66, 2020
Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm
S Samadianfard, S Hashemi, K Kargar, M Izadyar, A Mostafaeipour, ...
Energy Reports 6, 1147-1159, 2020
Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms
SS Band, S Janizadeh, S Chandra Pal, A Saha, R Chakrabortty, ...
Remote Sensing 12 (21), 3568, 2020
Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues
S Shamshirband, M Fathi, AT Chronopoulos, A Montieri, F Palumbo, ...
Journal of Information Security and Applications 55, 102582, 2020
Evaluation of electrical efficiency of photovoltaic thermal solar collector
MH Ahmadi, A Baghban, M Sadeghzadeh, M Zamen, A Mosavi, ...
Engineering Applications of Computational Fluid Mechanics 14 (1), 545-565, 2020
Data science in economics: comprehensive review of advanced machine learning and deep learning methods
S Nosratabadi, A Mosavi, P Duan, P Ghamisi, F Filip, SS Band, U Reuter, ...
Mathematics 8 (10), 1799, 2020
Coronary artery disease diagnosis; ranking the significant features using a random trees model
JH Joloudari, E Hassannataj Joloudari, H Saadatfar, M Ghasemigol, ...
International journal of environmental research and public health 17 (3), 731, 2020
Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support …
S Shamshirband, A Mosavi, T Rabczuk, N Nabipour, K Chau
Engineering Applications of Computational Fluid Mechanics 14 (1), 805-817, 2020
Snow avalanche hazard prediction using machine learning methods
B Choubin, M Borji, A Mosavi, F Sajedi-Hosseini, VP Singh, ...
Journal of Hydrology 577, 123929, 2019
A Hybrid clustering and classification technique for forecasting short‐term energy consumption
M Torabi, S Hashemi, MR Saybani, S Band, A Mosavi
Environmental progress & sustainable energy 38 (1), 66-76, 2019
Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning
A Mosavi, S Band, E Salwana, K Chau, JHM Tah
Engineering Applications of Computational Fluid Mechanics 13 (1), 482-492, 2019
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