Olalekan Joel Awujoola

Olalekan Joel Awujoola is a seasoned Chief System Analyst Programmer for Nigerian Defence academy with over two decades of experience. His expertise encompasses a broad spectrum of skills acquired through dedicated work in the field. His passion for academics has no limit, has he acquired the following degrees. National certificate in Education (NCE) Mathematics/Physics from Institute of Education Ahmadu Bello University. Zaria, Bachelor of Technology (B.Tech) Mathematics with Computer science from Federal University of Technology, Masters of Science (M.sc) Computer Science from Ahmadu Bello University Zaria, Master of Science (Msc) Nuclear and Radiation Physics from Nigerian Defence Academy, Master of Science (M.sc) Information Technology from National Open University of Nigeria. He is currently on the verge of completing his PhD in Computer Science, eagerly anticipating the final stages of external assessment. He is deeply rooted in the pragmatic application of machine learning, Deep Learning, Artificial Intelligence, Internet of Things and Computer Vision an enthusiasm reflected in specializing in Python-based machine learning. This commitment is further evidenced by his contributions to an array of research papers and book chapters. In terms of technical proficiency, he excel in developing and implementing machine learning models to tackle real-world challenges and his favourable toolkit includes proficiency in Python, R, and Java, along with hands-on experience with TensorFlow, PyTorch, and scikit-learn frameworks.

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...