A hybrid of neuro-fuzzy inference system and hidden Markov Model for activity-based mobility modeling of cellphone users

“A hybrid of neuro-fuzzy inference system and hidden Markov Model for activity-based mobility modeling of cellphone users” has been published By: Shiva Rahimipour, Mehdi Ghatee, S.M. Hashemi, Ahmad Nickabadi, in Computer Communications Volume 173, 1 May 2021, Pages 79-94. Abstract: The aim of this paper is to develop an activity-based travel demand model by receiving… Continue reading A hybrid of neuro-fuzzy inference system and hidden Markov Model for activity-based mobility modeling of cellphone users

A Systematic Review on Overfitting Control in Shallow and Deep Neural Networks

“A Systematic Review on Overfitting Control in Shallow and Deep Neural Networks” By: Mohammad Mahdi Bejani, & Mehdi Ghatee has been published in “Artificial Intelligence Review” Abstract: Shallow neural networks process the features directly, while deep networks extract features automatically along with the training. Both models suffer from overfitting or poor generalization in many cases.… Continue reading A Systematic Review on Overfitting Control in Shallow and Deep Neural Networks

Least auxiliary loss-functions with impact growth adaptation (Laliga) for convolutional neural networks

By Mohammad Mahdi Bejani & Mehdi Ghatee Published: Neurocomputing, 2021. Abstract: Model selection is a challenge, and a popular Convolutional Neural Networks (CNN) usually takes extra-need parameters. It causes overfitting in real applications. Besides, the extracted hidden features would be lost when the number of convolution layers increases. We use the least auxiliary loss-functions to… Continue reading Least auxiliary loss-functions with impact growth adaptation (Laliga) for convolutional neural networks

Decision Support and Expert Systems in Intelligent Transportation

Decision Support and Expert Systems in Intelligent Transportation is written by Dr. Mehdi Ghatee, and has been published by Amirkabir University of Technology Press, (in Persian) 2021. Abstract: Intelligent transportation systems are essential parts of our life and provide various services such as data collection and processing, traffic management, travel information, public transit, freight transportation,… Continue reading Decision Support and Expert Systems in Intelligent Transportation

Optimization Techniques in Intelligent Transportation Systems

By: Mehdi Ghatee Published: Metaheuristics and Optimization in Computer and Electrical Engineering, Springer, Switzerland, (2021). Abstract: Intelligent Transportation Systems (ITS) refer to a range of transportation applications based on communication and information technology. These systems by the aid of modern ideas, provide comfortable, efficient and safe services for transportation users.They are located in the linkage… Continue reading Optimization Techniques in Intelligent Transportation Systems

Theory of adaptive SVD regularization for deep neural networks

By: Mohammad Mahdi Bejani & Mehdi Ghatee Published: Neural Networks, Volume 128, August 2020, Pages 33-46. Abstract: Deep networks can learn complex problems, however, they suffer from overfitting. To solve this problem, regularization methods have been proposed that are not adaptable to the dynamic changes in the training process. With a different approach, this paper… Continue reading Theory of adaptive SVD regularization for deep neural networks

Adaptive neural tree exploiting expert nodes to classify high-dimensional data

By: Shadi Abpeikar, Mehdi Ghatee, Gian Luca Foresti & Christian Micheloni Published: Neural Networks, Volume 124, April 2020, Pages 20-38. Abstract: Classification of high dimensional data suffers from curse of dimensionality and over-fitting. Neural tree is a powerful method which combines a local feature selection and recursive partitioning to solve these problems, but it leads… Continue reading Adaptive neural tree exploiting expert nodes to classify high-dimensional data

Convolutional Neural Network With Adaptive Regularization to Classify Driving Styles on Smartphones

By: Mohammad Mahdi Bejani, Mehdi Ghatee Published: IEEE Transactions on Intelligent Transportation Systems,┬áVolume: 21, Issue: 2, Feb. 2020. Abstract: Driving style evaluation by smartphones depends on the quality of the features extracted from sensors data. Typically, these features are extracted based on experiments, expertness, or heuristics. In more modern approaches, some automatic methods such as… Continue reading Convolutional Neural Network With Adaptive Regularization to Classify Driving Styles on Smartphones