Theophilus Enem Aniemeka

Theophilus Aniemeka Enem holds a PhD in Computer Science from Babcock University, Ilisan Remo, Ogun State, Nigeria. He has several years’ experience of teaching computer science and cyber security courses at the university level. Currently, the HOD of Cyber Security Department, Air Force Institute of Technology, Kaduna, Nigeria. Enem is a full member of the Nigeria Computer Society (NCS) and the Computer Professional Registration Council of Nigeria (CPN). His areas of interest are Networking, Telecommunications, and Forensic Science. He has published works in several journals of international repute. His hobbies include reading and playing squash game. He is presently the head of the department of Cyber Security, Airforce Institute of Technology. Nigeria.

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...