Faris B. Ali Jasim

Work place: University Of Technology/ Electrical Engineering Department, Baghdad, Iraq

E-mail: faris2005@gmail.com


Research Interests: Computer systems and computational processes, Artificial Intelligence, Evolutionary Computation, Computer Architecture and Organization, Data Structures and Algorithms


Faris Ali Jasim received his Bachelor’s Degree from the Electrical and Electronics Engineering Department at the University of Technology in 1982 Iraq, Baghdad. He received his Master’s degree from the same university, at the Electrical and Electronics Engineering Department in 2004.  His major field of study was in the electronic circuits, artificial intelligence, evolutionary algorithms based on FPGA, and biomedical engineering.

Author Articles
Intelligent Training Algorithm for Artificial Neural Network EEG Classifications

By Hanan A. R. Akkar Faris B. Ali Jasim

DOI: https://doi.org/10.5815/ijisa.2018.05.04, Pub. Date: 8 May 2018

Artificial neural networks (ANN) have been widely used in classification. They are complicated networks due to the training algorithm used to fix their weights. To achieve better neural network performance, many evolutionary and meta-heuristic algorithms are used to optimize the network weights. The aim of this paper is to implement recently evolutionary algorithms for optimizing neural weights such as Grass Root Optimization (GRO), Artificial Bee Colony (ABC), Cuckoo Search Optimization (CSA) and Practical Swarm Optimization (PSO). This ANN was examined to classify three classes of EEG signals healthy subjects, subjects with interictal epilepsy seizure, and subjects with ictal epilepsy seizures. The above training algorithms are compared according to classification rate, training and testing mean square error, average time, and maximum iteration.

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