Olalekan J. Awujoola

Olalekan Awujoola is a seasoned Chief Systems Analyst Programmer for the Nigerian Defence Academy, boasting over two decades of experience. His expertise spans a broad spectrum of skills acquired through dedicated work in the field. His passion for academia knows no bounds, as evidenced by his acquisition of the following degrees: a National Certificate in Education (NCE) in Mathematics/Physics from the Institute of Education at ABU in Zaria, a Bachelor of Technology (B.Tech) in Mathematics with Computer Science from the Federal University of Technology Minna, a Master of Science (M.Sc) in Computer Science from Ahmadu Bello University in Zaria, a Master of Science (M.Sc) in Nuclear and Radiation Physics from the Nigerian Defence Academy, and a Master of Science (M.Sc) in Information Technology from the National Open University of Nigeria. He is currently on the cusp of completing his PhD in Computer Science, eagerly anticipating the final stages of external assessment. He also lectures Cadets of computer science in the department of computer science. Mr. Awujoola is deeply entrenched in the pragmatic application of machine learning, deep learning, artificial intelligence, the Internet of Things, and computer vision—an enthusiasm that is reflected in his specialization in Python-based machine learning. This dedication is further substantiated by his contributions to an array of research papers and book chapters. In terms of technical proficiency, he excels in developing and implementing machine learning models to tackle real-world challenges. His formidable toolkit includes expertise in Python, R, and Java, complemented by hands-on experience with frameworks such as TensorFlow, PyTorch, and scikit-learn.

Publications

Improved Breast Cancer Detection in Mammography Images: Integration of Convolutional Neural Network and Local Binary Pattern Approach
Improved Breast Cancer Detection in Mammography Images: Integration of Convolutional Neural Network and Local Binary Pattern Approach
Olalekan Joel Awujoola, Theophilus Enem Aniemeka, Francisca N. Ogwueleka, Oluwasegun Abiodun Abioye, Abidemi Elizabeth Awujoola, Celestine Ozoemenam Uwa. © 2024. 28 pages.
Cancer, characterized by uncontrolled cell division, is an incurable ailment, with breast cancer being the most prevalent form globally. Early detection remains critical in...
Improving Leukemia Detection Accuracy: Contrast Limited Adaptive Histogram Equalization-Enhanced Image Preprocessing Combined ResNet101 and Haralick Feature Extraction
Improving Leukemia Detection Accuracy: Contrast Limited Adaptive Histogram Equalization-Enhanced Image Preprocessing Combined ResNet101 and Haralick Feature Extraction
Olalekan Joel Awujoola, Theophilus Enem Aniemeka, Oluwasegun Abiodun Abioye, Abidemi Elizabeth Awujoola, Fiyinfoluwa Ajakaiye, Olayinka Racheal Adelegan. © 2024. 34 pages.
The study explores ResNet-101 CNNs and Haralick texture analysis for leukemia cell detection. Leveraging CLAHE preprocessing and hybrid feature extraction, it enhances model...
Enhancing Credit Card Fraud Detection and Prevention: A Privacy-Preserving Federated Machine Learning Approach With Auto-Encoder and Attention Mechanism
Enhancing Credit Card Fraud Detection and Prevention: A Privacy-Preserving Federated Machine Learning Approach With Auto-Encoder and Attention Mechanism
Olalekan J. Awujoola, Theophilus Aniemeka Enem, Ogwueleka Nonyelum Francisca, Olayinka Racheal Adelegan, Abioye Oluwasegun, Celesine Ozoemenam Uwa, Victor Uneojo Akuboh, Oluwaseyi Ezekiel Olorunshola, Hadiza Hassan. © 2023. 25 pages.
This chapter explores using auto-encoders and attention mechanisms to enhance privacy-preserving capabilities of federated machine learning for credit card fraud detection. The...