Mohammad Teshnehlab
Mohammad Teshnehlab
Professor, Dept. of Control Engineering, K.N.Toosi University of Technology
Verified email at - Homepage
Cited by
Cited by
A novel binary particle swarm optimization
MA Khanesar, M Teshnehlab, MA Shoorehdeli
2007 Mediterranean conference on control & automation, 1-6, 2007
Using adaptive neuro-fuzzy inference system for hydrological time series prediction
M Zounemat-Kermani, M Teshnehlab
Applied soft computing 8 (2), 928-936, 2008
Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks
R Rasti, M Teshnehlab, SL Phung
Pattern Recognition 72, 381-390, 2017
Brain tumor detection using deep neural network and machine learning algorithm
M Siar, M Teshnehlab
2019 9th international conference on computer and knowledge engineering …, 2019
Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods
MA Shoorehdeli, M Teshnehlab, AK Sedigh, MA Khanesar
Applied Soft Computing 9 (2), 833-850, 2009
Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation
M Forouzanfar, N Forghani, M Teshnehlab
Engineering Applications of Artificial Intelligence 23 (2), 160-168, 2010
Extended Kalman filter based learning algorithm for type-2 fuzzy logic systems and its experimental evaluation
MA Khanesar, E Kayacan, M Teshnehlab, O Kaynak
IEEE Transactions on Industrial Electronics 59 (11), 4443-4455, 2011
Training ANFIS structure with modified PSO algorithm
VS Ghomsheh, MA Shoorehdeli, M Teshnehlab
2007 Mediterranean Conference on Control & Automation, 1-6, 2007
An anomaly detection method to detect web attacks using stacked auto-encoder
AM Vartouni, SS Kashi, M Teshnehlab
2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), 131-134, 2018
Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter
MA Shoorehdeli, M Teshnehlab, AK Sedigh
Fuzzy Sets and Systems 160 (7), 922-948, 2009
Best practices in E government: A review of some Innovative models proposed in different countries
SM Alhomod, MM Shafi, MN Kousarrizi, F Seiti, M Teshnehlab, H Susanto, ...
International Journal of Electrical & Computer Sciences 12 (1), 1-6, 2012
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
D Sadeghi, A Shoeibi, N Ghassemi, P Moridian, A Khadem, ...
Computers in Biology and Medicine 146, 105554, 2022
Face recognition using convolutional neural network and simple logistic classifier
H Khalajzadeh, M Mansouri, M Teshnehlab
Soft Computing in Industrial Applications: Proceedings of the 17th Online …, 2014
Discrete binary cat swarm optimization algorithm
Y Sharafi, MA Khanesar, M Teshnehlab
2013 3rd IEEE international conference on computer, control and …, 2013
Analysis of the noise reduction property of type-2 fuzzy logic systems using a novel type-2 membership function
MA Khanesar, E Kayacan, M Teshnehlab, O Kaynak
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41 …, 2011
Feature extraction and classification of EEG signals using wavelet transform, SVM and artificial neural networks for brain computer interfaces
MRN Kousarrizi, ARA Ghanbari, M Teshnehlab, MA Shorehdeli, ...
2009 international joint conference on bioinformatics, systems biology and …, 2009
Modified Multi-objective Particle Swarm Optimization for electromagnetic absorber design
S Chamaani, SA Mirtaheri, M Teshnehlab, MA Shoorehdeli, V Seydi
Progress In Electromagnetics Research (PIER) 79, 353–366, 2008
negFIN: An efficient algorithm for fast mining frequent itemsets
N Aryabarzan, B Minaei-Bidgoli, M Teshnehlab
Expert Systems with Applications 105, 129-143, 2018
Multi-view deep network: a deep model based on learning features from heterogeneous neural networks for sentiment analysis
H Sadr, MM Pedram, M Teshnehlab
IEEE access 8, 86984-86997, 2020
A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks
H Sadr, MM Pedram, M Teshnehlab
Neural processing letters 50, 2745-2761, 2019
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