Racheal Akinbo

Federal University of Technology Akure, Computer Science, Graduate Student

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Racheal Shade Akinbo is an active teacher, writer, author, and researcher in Computer Science. She has lectured in the Department of Computer Science at McPherson UNIVERSITY, Ogun, Nigeria, where she teaches courses in Computer Science. She is currently at the Federal University of Technology Akure, Nigeria. She was the Head of Operations at Citylink Couriers Nigeria Limited between 2007 till 2014. She was awarded a Valued Judge of the Royal Commonwealth Society in The Queen’s Commonwealth Essay Competition 2022. Racheal is a recipient of the Super Computing Conference Scholarship award 2021 and the Grace Hopper Student Scholarship Award 2018. She is the founder of a non-profit organization, Ladies in Science, Technology, Engineering and Mathematics (LiSTEM Africa), promoting the representation of women in STEM. She obtained her first degree in Computer Science from Adekunle Ajasin University, Akungba Akoko, Nigeria, and a Master of Technology in Computer Science from the Federal University of Technology, Akure, Nigeria. She is pursuing her Ph.D. in Computer Science from the Federal University of Technology, Akure, Nigeria. Her research interests include Machine Learning, Artificial Intelligence, and Information Technology.
Supervisors: Professor O. Olabode and Dr. Mrs. O.A Daramola

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Machine Learning - Algorithms, Models and Applications [Working Title]

by Bongs Lainjo and Racheal Akinbo

Artificial Intelligence, 2021

Machine learning (ML) is the ability of a system to automatically acquire, integrate, and then de... more Machine learning (ML) is the ability of a system to automatically acquire, integrate, and then develop knowledge from large-scale data, and then expand the acquired knowledge autonomously by discovering new information, without being specifically programmed to do so. In short, the ML algorithms can find application in the following: (1) a deeper understanding of the cyber event that generated the data under study, (2) capturing the understating of the event in the form of a model, (3) predicting the future values that will be generated by the event based on the constructed model, and (4) proactively detect any anomalous behavior of the phenomenon so that appropriate corrective actions can be taken beforehand. ML is an evolutionary area, and with recent innovations in technology, especially with the development of smarter algorithms and advances in hardware and storage systems, it has become possible to perform a large number of tasks more efficiently and precisely, which were not even imaginable a couple of decades before. Over the past few years, deep learning (DL), a specialized subset of machine learning that involves more complex architectures, algorithms, and models for solving complex problems and predicting future outcomes of complex events, has also evolved. Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. There are numerous applications of machine learning that have emerged or are evolving at present in several business domains, such as medicines and healthcare, finance and investment, sales and marketing, operations and supply chain, human resources, media and entertainment, and so on. Of late, the applied ML systems in the industry are exhibiting some prominent trends. These trends will utilize the power of ML and artificial intelligence (AI) systems even further to reap benefits in business and society, in general. Some of these trends are as follows: (1) less volume of code and faster implementation of ML systems; (2) increasing use of light-weight systems suitable for working on the resource-constrained internet of things (IoT) devices; (3) automatic generation of codes for building ML models; (4) designing novel processes for robust management of the development of ML systems for increased reliability and efficiency; (5) more wide-spread adoption of deep-learning solutions into products of all domains and applications; (6) increased use of generative adversarial networks (GAN)-based models for various image processing applications including image searching, image enhancement, etc.; (7) more prominence of unsupervised learning-based systems that require less or no human intervention for their operations; (8) use of reinforcement learning-based systems; and finally, (9) evolution of few-shots, if not zero-shot learning-based systems. In tune with the increasing importance and relevance of ML models, algorithms, and their applications and with the emergence of more innovative uses-cased of DL-and AI-based systems, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate

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