ZUBAIR FOLOHUNSHO ADAM

Computer Science

At the Computer Science department office

Appointment on Visitation important

Topic: AI-Driven Diagnostic Modelling In Medical Imaging Using Computer Vision

Description: his research investigates the integration of computer vision and artificial intelligence AI to develop machine learning ML -based diagnostic models for medical imaging. The aim is to enhance the accuracy, efficiency, and accessibility of medical diagnoses through the automated analysis of visual data such as X-rays, CT scans, MRIs, and ultrasound images. With the growing complexity and volume of medical data, traditional diagnostic processes are increasingly challenged, making AI-driven approaches a critical frontier in modern healthcare.The research focuses on the development and validation of deep learning models particularly convolutional neural networks CNNs , transformer-based vision models, and ensemble methods capable of detecting and classifying pathological features with high precision. Key areas of application include the early detection of cancer, cardiovascular abnormalities, neurological disorders, and infectious diseases such as COVID-19.In addition to model development, the study emphasises the importance of data quality, annotation accuracy, and interpretability. Techniques such as data augmentation, transfer learning, and explainable AI XAI are employed to improve generalisability and foster trust in automated diagnostics. Performance is evaluated using metrics such as sensitivity, specificity, accuracy, ROC-AUC, and clinical validation studies.This research contributes to the advancement of AI-assisted healthcare systems by promoting timely, reproducible, and cost-effective diagnostic support tools. It also addresses ethical considerations including patient data privacy, algorithmic bias, and the integration of AI within existing clinical workflows, ultimately supporting a future of precision medicine and improved patient outcomes.

#	Certificate	School	Year
1.	Ph.D (Computer Science)	Department of Computer Science, Lagos State Univeristy	2023

Geospatial Inference through Social Media Text Analysis: A Data Science Approach for Identifying Subjects State of Origin

Introduction: In the digital age, the abundance of user-generated content on social media platforms presents a unique opportunity for research. This study focuses on leveraging data science to identify an individual's state of origin through an analysis of their social media chat history. The aim is to uncover subtle linguistic patterns indicative of specific geographical regions.

Aims/Objectives: This research seeks to develop a robust methodology for accurately determining a subject's state of origin based on their social media text data. Key objectives include identifying region-specific linguistic features, establishing a reliable classification algorithm, and testing the model's accuracy across diverse demographic and linguistic contexts.

Methodology: The study will employ a comprehensive methodology, involving the collection of a representative dataset from major social media platforms. Preprocessing steps, including text cleaning and feature extraction, will be followed by the application of advanced natural language processing (NLP) techniques and machine learning algorithms such as deep neural networks.

Expected Results: Anticipated outcomes include the successful development of a predictive model with high accuracy in identifying users' state of origin. The research aims to reveal region-specific linguistic markers and explore their significance in user-generated content. Insights into model limitations will inform future refinements.

Contribution to Knowledge/Society: This research contributes to scientific knowledge and societal understanding by applying data science to social media analysis. The methodology's potential to enhance geographic inference from online communications addresses a current research gap and has implications for sociolinguistics, cybersecurity, and public policy. The findings may prompt discussions on privacy and responsible use of personal data in the digital communication era.

ZUBAIR ADAM is a Lecturer II at the Department of Computer Science

ZUBAIR has a Ph.D in Computer Science from Department of Computer Science, Lagos State Univeristy