Farajollah Tahernezhad-Javazm

Farajollah Tahernezhad-Javazm
Ulster University · School of Computing and Intelligent Systems

M.Sc. in Mechatronics Engineering

About

6
Publications
492
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24
Citations
Introduction
Farajollah finished his BSc in Electronic Engineering from Yazd University, Iran. Subsequently, He received his MSc as the First-Ranked GPA student (4.0/4.0) in Mechatronics Engineering from University of Tabriz, Iran. Currently, he is working as a PhD researcher under the supervision of Prof. Damien Coyle and Dr. Debbie Rankin in Intelligent Systems Research Centre at Ulster University, NI, UK. He is working on Neuro-evolution algorithms that are enhanced with Reinforcement Learning methods.

Publications

Publications (6)
Preprint
Multi-objective evolutionary algorithms can be categorized into three basic groups: domination-based, decomposition-based, and indicator-based algorithms. Hybrid multi-objective evolutionary algorithms, which combine algorithms from these groups, are gaining increased popularity in recent years. This is because hybrid algorithms can compensate for...
Conference Paper
Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms' operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) an...
Preprint
Full-text available
An ontology makes a special vocabulary which describes the domain of interest and the meaning of the term on that vocabulary. Based on the precision of the specification, the concept of the ontology contains several data and conceptual models. The notion of ontology has emerged into wide ranges of applications including database integration, peer-t...
Article
Objective: Considering the importance and the near future development of noninvasive Brain-Machine Interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach: The paper is divided into two main parts. In...
Conference Paper
Full-text available
The current paper presents Particle Swarm Optimized Wavelet Neural Network (PSOWNN) as a classification method for surface electromyogram (sEMG) pattern classification. According to the literature, a change in the spectrum of surface electromyogram has largely been attributed to the change in muscle conduction velocity. Therefore, such signals are...
Conference Paper
The present paper focuses on a multimodal system based on electrooculography, image processing, and speech recognition for simple and real-time controlling of robotic systems such as manipulators. Electrooculogram was based on voluntary eye movements and processed by thresholds; the average accuracy obtained was about 75%. Two real-time and simple...

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