Ilorin Journal of Computer Science and Information Technology <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 (Amos O. Bajeh) (H. A. Mojeed) Fri, 23 Feb 2024 19:42:26 +0100 OJS 60 A comparison of Boosting techniques for Classification of Microarray data <p>The advancements in technology, particularly microarrays, have played a pivotal role in enhancing crucial aspects within the domains of genomics and bioinformatics. These advancements have significantly contributed to the enhancement of illness diagnosis, evaluation of therapy response in patients, and advancements in cancer research. A single tissue sample obtained from a microarray has the potential to encompass a substantial number of distinct gene expressions, reaching into the tens of thousands. Consequently, the process of scrutinizing the data to identify significant patterns can be quite daunting when employing conventional statistical approaches. Microarray data often exhibits a substantial likelihood of encompassing extraneous and duplicative factors, hence introducing noise into the dataset. Numerous studies are currently being conducted to enhance the analysis of microarray data, with the aim of enhancing performance and prediction accuracy at an accelerated pace.&nbsp; Boosting algorithms were utilized in this study to classify microarray data. The microarray data underwent pre-processing, during which significant features carrying essential information were retrieved. These features were then inputted into the AdaBoost, Gradient Boost, and XGBoost algorithms. The experimental findings indicate that XGBoost demonstrates superior performance compared to other boosting approaches, with an accuracy rate of 98.18%.</p> Ronke Seyi Babatunde, Akinbowale Nathaniel Babatunde, Bukola Fatimah Balogun, Ibrahim Aliyu Yakubu, Roseline Oluwaseun Ogundokun, Kolawole Yusuf OBIWUSI, Emmanuel Umar Copyright (c) 2023 Ilorin Journal of Computer Science and Information Technology Fri, 23 Feb 2024 19:39:46 +0100