Ilorin Journal of Computer Science and Information Technology https://iljcsit.com.ng/index.php/ILJCSIT <p style="text-align: justify;"><strong>ILJCSIT</strong> is a high quality, bi-annual and double-blind peer reviewed research journal published by the Department of Computer Science, Faculty of Communication and Information Sciences of the University of Ilorin. It provides a platform for researchers, academics and professionals to publish both research and editorial articles contributing to the body of knowledge in the field of computing. The journal is currently inviting submissions for its forthcoming volume 3 issue 1(June) and Issue 2 (December), 2020.&nbsp; The journal receives submission bordering on but not limited to the following areas:</p> <ul> <li class="show"><strong>Computing and Communication Technologies</strong></li> <li class="show"><strong>Computer forensics and Cyber security</strong></li> <li class="show"><strong>Big data analytics, data mining, machine learning and deep learning</strong></li> <li class="show"><strong>Internet of Things, Digital and wireless communication systems</strong></li> <li class="show"><strong>Information Science, Information System and Knowledge Management</strong></li> <li class="show"><strong>ICT and disaster risk management</strong></li> <li class="show"><strong>Software Engineering and its Applications</strong></li> <li class="show"><strong>Algorithms </strong></li> <li class="show"><strong>Bioinformatics </strong></li> <li class="show"><strong>Theory of Computing</strong></li> <li class="show"><strong>Artificial Intelligence and Robotics</strong></li> <li class="show"><strong>Cloud and Fog Computing </strong></li> <li class="show"><strong>Cryptography</strong></li> <li class="show"><strong>Biometrics</strong></li> </ul> en-US info@iljcsit.com.ng (Amos O. Bajeh) mojeed.ha@unilorin.edu.ng (H. A. Mojeed) Thu, 30 Jun 2022 16:41:11 +0100 OJS 3.1.2.4 http://blogs.law.harvard.edu/tech/rss 60 A Review of Dominant Information Technology Adoption Frameworks and their Relevance to Digital Library Adoption and Usage Studies https://iljcsit.com.ng/index.php/ILJCSIT/article/view/57 <p><em>Information technologies adoption and usage have been studied in great detail by scholars from various fields, especially from information systems. This has led to various theories and models aimed at explaining factors that influence the adoption and usage of technologies. This study which is a</em>&nbsp;<em>part of large research on Digital Libraries (DL),</em>&nbsp;<em>systematically reviewed major theories and models of adoption and usage of technologies and their application in the study of DL. An extensive review of five of the major theories and models (Theory of Reasoned Action, Theory of Planned Behaviour, Technology Acceptance Model, Motivational Model, Diffusion of Innovation) was carried out. The strengths and weaknesses of each of the reviewed theories and models were highlighted. Of all the theories and models that attempt to explain factors responsible for individuals’ decision to either adopt or reject technology, only DOI provided a comprehensive theory that captured DL adoption and usage scenario through its innovation-decision process model. Based on the outcome of the review, the paper recommended an innovation-decision process model for an in-depth understanding of factors responsible for the use and non-use of DL services.</em></p> Abdulmumin Isah, Muhammed Lawal Akanbi, Athulang Mutshewa Copyright (c) 2022 Ilorin Journal of Computer Science and Information Technology https://iljcsit.com.ng/index.php/ILJCSIT/article/view/57 Thu, 30 Jun 2022 16:26:43 +0100 Offline Yoruba Word Recognition System based on Capsule Neural Network https://iljcsit.com.ng/index.php/ILJCSIT/article/view/61 <p>The need for Digital Document archiving has resulted in several efforts in Image Document Analysis, where scanned <em>historical/ indigenous</em> documents can be stored in digitized form and made readily accessible and available for future use.&nbsp; Visual system in human uses an highly space variant in sampling, coding, processing and understanding of variations in human handwritings, which possess lots of challenges or difficulty for machine to understand. Consequently, it is important to preserve our indigenous heritage values by way of digitization.&nbsp; In this regard, the research work models a Yoruba Script Recognition System using Capsnet, based on the peculiar features of Yoruba alphabet. Yoruba bird names were gathered from the Yoruba dictionary and were written by 20 indigenous literate writers in 50 different writing styles. This was used to model HYSR system. The training time and testing after training different epoch produces 89.2222 and the testing time generated is 0.8667. The model accuracy gives 85.49%, which shows a promising result.</p> Jumoke Ajao, Modinat Abolore Mabayoje, Emmanuel Olorunmaiye , Shakirat Ronke Yussuf, Amos Bajeh Copyright (c) 2022 Ilorin Journal of Computer Science and Information Technology https://iljcsit.com.ng/index.php/ILJCSIT/article/view/61 Thu, 30 Jun 2022 00:00:00 +0100 A Metaheuristic Approach to Network Intrusion Detection https://iljcsit.com.ng/index.php/ILJCSIT/article/view/60 <p>The prevention of intrusion in networks is vital; hence, an intrusion detection system is extremely desirable through potent intrusion detection mechanism. Several studies have been conducted in the domain of intrusion detections. However, some of them suffer from high false alarms, in terms of the usage of a raw dataset that contains redundancy. This paper, therefore, proposes a multi-level dimensionality reduction framework that is based on meta-heuristic optimization and principal component analysis (PCA). To achieve the aim of this research, PCA was applied for feature extraction. Genetic Algorithm and Particle Swarm Optimization, that is GA-PSO, algorithms were employed for feature selection to extract the most discriminative features to develop intrusion detection model. In the classification stage, Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms were used to develop intrusion detection, using kddcup.data_10_percent dataset. Experimental results show that the proposed framework achieved an accuracy of 99.7% and ROC of 99.9%, while the time taken to build model is 0.23 seconds. To a very high extent, incidences of high false alarm are allayed through the GA-PSO induced feature selection method.</p> Modinat Abolore Mabayoje, K. S. Adewole, Omonigho E. Ekeruvwe, Jumoke F. Ajao, Akinyemi O. Akinrotimi, Abdullateef O. Balogun Copyright (c) 2022 Ilorin Journal of Computer Science and Information Technology https://iljcsit.com.ng/index.php/ILJCSIT/article/view/60 Thu, 30 Jun 2022 16:33:49 +0100