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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Rozaida Ghazali
Universiti Tun Hussein Onn Malaysia (UTHM)
Electrical & Electronics Engineering

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Levenberg-marquardt backpropagation neural network with techebycheve moments for face detection Ali Nadhim Razzaq; Rozaida Ghazali; Nidhal Khdhair El Abbadi; Hussein Ali Hussein Al Naffakh
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2364

Abstract

Face detection is an intelligent approach used in a variety of applications that identifies human faces in digital images. This work presents a new method which composes of a neural network and Techebycheve transforms for face detection. For feature extraction, Tchebychev transform was applied, in which a discrete Tchebychev transform is given for different sampling patterns and several samples here were performed on color images. A Levenberg-Marquardt backpropagation neural network was applied to the transformed image to find faces in the face detection dataset and FDDB benchmarked database. Model performance was measured based on its accuracy and the best result from the newly proposed method was 98.9%. Simulation results showed that the proposed method handles face detection efficiently.
Improved grey wolf algorithm for optimization problems Hafiz Maaz Asgher; Yana Mazwin Mohmad Hassim; Rozaida Ghazali; Muhammad Aamir
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1573-1579

Abstract

The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.
A comparative review on deep learning models for text classification Muhammad Zulqarnain; Rozaida Ghazali; Yana Mazwin Mohmad Hassim; Muhammad Rehan
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp325-335

Abstract

Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.
Co-Authors Ali Nadhim Razzaq Hafiz Maaz Asgher Hussein Ali Hussein Al Naffakh Muhammad Aamir Muhammad Rehan Muhammad Zulqarnain Nidhal Khdhair El Abbadi Yana Mazwin Mohmad Hassim