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
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
Using adaptive neuro-fuzzy inference system for hydrological time series prediction
M Zounemat-Kermani, M Teshnehlab
Applied soft computing 8 (2), 928-936, 2008
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
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
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
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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