Emmanuel Gbenga Dada

Work place: Department of Mathematical Sciences, Faculty of Science, University of Maiduguri, Maiduguri, Nigeria

E-mail: gbengadada@unimaid.edu.ng


Research Interests: Machine Learning


Emmanuel G. Dada is an Associate Professor of Computer Science at the at Department of Mathematical Sciences, University of Maiduguri, Nigeria. He received his PhD in Computer Science from Universiti Malaya, Malaysia (UM), MSc in Computer Science from University of Ibadan, Ibadan (UI), Nigeria and a Bachelor of Science in Computer Science from the University of Ilorin, Ilorin, Nigeria. His current research interests are in Softcomputing Techniques and Machine Learning Algorithms with their application to Medical Imaging, Cyber Security, COVID-19 detection, diagnosis, and Surveillance. He has published several over 70 academic papers in reputable International and local journals, conference proceedings and book chapters. He has been appointed as a reviewer of several ISI and Scopus indexed international journals such as ACM Survey, IEEE Access, Lecture Notes in Computational Vision and Biomechanics. He is a member of IEEE, International Society for Knowledge Organization (ISOK-WA), International Association of Engineers (IAENG) and Computer Professionals Registration Council of Nigeria (CPN). He is presently a lecturer at Department of Mathematical Sciences (Computer Science Unit), University of Maiduguri, Nigeria.

Author Articles
Moth Flame Optimization Algorithm for Optimal FIR Filter Design

By Zainab Muhammad Adamu Emmanuel Gbenga Dada Stephen Bassi Joseph

DOI: https://doi.org/10.5815/ijisa.2021.05.03, Pub. Date: 8 Oct. 2021

This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical sub-models. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.

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