Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Science in Information Technology Letters
Abdulrazak Yahya Saleh
Universiti Malaysia Sarawak
Computer Science & IT Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Hybrid of convolutional neural network algorithm and autoregressive integrated moving average model for skin cancer classification among Malaysian Chee Ka Chin; Dayang Azra binti Awang Mat; Abdulrazak Yahya Saleh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp707-716

Abstract

Skin cancer is a widely spreading cause of mortality among the people specifically living on or near the equatorial belt. Early detection of skin cancer significantly improves the recovery prevalence and the chance of surviving. Without the assist of computer-aided decision (CAD) system, skin cancer classification is the challenging task for the dermatologist to differentiate the type of skin cancer and provide the suitable treatment. Recently, the development of machine learning and pretrained deep neural network (DNN) shows the tremendous performance in image classification task which also provide the promising performance in medical field. However, these machine learning methods cannot get the deep features from network flow which resulting in low accuracy and the pretrained DNN has the complex network with a huge number of parameters causes the limited classification accuracy. This paper focuses on the classification of skin cancer to identify whether it is basal cell carcinoma, melanoma or squamous cell carcinoma by using the development of hybrid convolutional neural network algorithm and autoregressive integrated moving average model (CNN-ARIMA). The CNNARIMA model was trained and found to produce the best accuracy of 92.25%.
Palm oil classification using deep learning Abdulrazak Yahya Saleh; Ermawatih Liansitim
Science in Information Technology Letters Vol 1, No 1: May 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i1.1

Abstract

Deep Convolutional Neural Networks (CNNs) have been established as a dominant class of models for image classification problems. This study aims to apply and analyses the accuracy of deep learning for classifying ripes on palm oil fruit.  The CNN used to classify 628 images into 2 different classes. Furthermore, the experiment of CNN with 5 epochs gives promising classification results with an accuracy of 98%, which is better than previous methods.  To sum up, this study was successfully solving an image classification by detected and differentiated the ripeness of oil palm fruit.
Co-Authors Chee Ka Chin Dayang Azra binti Awang Mat Ermawatih Liansitim