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Selorm Adablanu

Lecturer / Department of ICT EDUCATION · University of Education, Winneba

https://researchid.co/sadablanu
@uew.edu.gh
6Scopus Publications
91Google Scholar Citations
4Google Scholar h-index
3Google Scholar i10-index

Research Interests

Deep Learning and Machine Learning, Application Development in Education, Multimedia Authoring in Education, Computer Science Applications, Database Management System

Biography

Selorm Adablanu is a computer scientist at the University of Education, Winneba. He holds a Master of Technology (M.Tech) degree in Computer Science, a Bachelor of Science degree in Computer Science, and a Master of Business Administration (MBA) with a specialization in Information Technology Management. He is currently pursuing a Ph.D. in Computer Science and Engineering. Selorm has both academic and industry experience, having previously served as a lecturer at the School of Computer Science, Data Link University (Ghana), and as an instructor at All-soft Institute (IBM® software solutions) in India. His research interests include machine learning, deep learning, computer vision, image processing, medical image processing, and artificial intelligence in education. His work is driven by the goal of building AI systems that can learn effectively from limited data and make robust, accurate decisions-particularly in applications related to healthcare and education.

Education

PhD Researcher - Assam down town University - On-going MBA Information Technology Management - Vignan University, India (2024) PgD Teaching and Learning in Higher Education - University of Education, Winneba-Ghana (2024) MTECH Computer Science And Engineering - Rayat Bahra University, India (2019) Bsc Computer Science - Data Link University, Ghana (2014)

Recent Scopus Publications

  1. 15 Years of optimizers in medical deep learning: A systematic review
    Neuroscience Informatics, 2026
  2. Transforming Skin Cancer Detection With AI-Based Convolutional and Transformer Models
    Iradiology, 2026
  3. Feature Extraction and Selection Methods Outperform Machine Learning and Deep Learning Techniques
    Feature Selection and Feature Extraction on Omics Data, 2026
  4. Comparative Efficacy of Focal and Binary Cross-Entropy Loss in Handling Class Imbalance for Stroke CT Classification
    2026 6th International Conference on Advances in Electrical Computing Communications and Sustainable Technologies Icaect 2026, 2026
  5. A novel hybrid adaptive transformer framework with multihead self attention for stroke detection
    Discover Neuroscience, 2025

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