Selorm Adablanu

@uew.edu.gh

Lecturer / Department of ICT EDUCATION
University of Education, Winneba

Selorm Adablanu


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https://researchid.co/sadablanu
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)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Computer Vision and Pattern Recognition
6

Scopus Publications

91

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • 15 Years of optimizers in medical deep learning: A systematic review
    Selorm Adablanu, Utpal Barman, Dulumani Das
    Neuroscience Informatics, 2026
  • Transforming Skin Cancer Detection With AI-Based Convolutional and Transformer Models
    Selorm Adablanu, Utpal Barman, Dulumani Das, Tuward Jade Dweh
    Iradiology, 2026
    Background Skin cancer is a major cause of mortality, and early detection is vital for effective treatment. Diagnosis is challenging because of lesion variability. This study adapts VINCE‐NET, a hybrid deep‐learning model originally designed for stroke detection, to classify melanoma using dermoscopic images. Methods VINCE‐NET combines vision transformer layers for global context, convolutional neural networks for local features, and long short‐term memory for spatial sequence modeling. During preprocessing, Gaussian blur, normalization, and augmentation were applied to reduce noise and handle class imbalance. During training, the public HAM10000 dataset was used in a central processing unit‐only Google Colab environment (12.72 GB random access memory, 107.7 GB disk) with an AdamW optimizer, a batch size of 12, learning‐rate scheduling, and early stopping (patience = 50). VINCE‐NET's performance was compared with those of a convolutional neural networks, long short‐term memory, residual network with 50 layers (ResNet‐50), visual geometry group network with 16 and 19 layers (VGG‐16/19), and densely connected convolutional network with 121 and 201 layers (DenseNet‐121/201) under identical preprocessing conditions. Results VINCE‐NET achieved 94.1% accuracy, 95.5% precision, 90.4% recall, a 92.9% F1‐score, and an area under the receiver operating characteristic curve of 0.98 at a training time of 34,308.42 s. Benchmarks showed that VINCE‐NET outperformed baselines while being computationally efficient. Conclusion VINCE‐NET provides competitive performance for melanoma classification and feasibility in resource‐limited settings. Although promising, VINCE‐NET has not been clinically validated yet. Future work will address resolution, ablation studies, interpretability, and external validation.
  • Feature Extraction and Selection Methods Outperform Machine Learning and Deep Learning Techniques
    Tuward Jade Dweh, Selorm Adablanu
    Feature Selection and Feature Extraction on Omics Data, 2026
  • Comparative Efficacy of Focal and Binary Cross-Entropy Loss in Handling Class Imbalance for Stroke CT Classification
    Selorm Adablanu, Utpal Barman, Dulumani Das
    2026 6th International Conference on Advances in Electrical Computing Communications and Sustainable Technologies Icaect 2026, 2026
  • A novel hybrid adaptive transformer framework with multihead self attention for stroke detection
    Selorm Adablanu, Utpal Barman, Dulumani Das
    Discover Neuroscience, 2025
  • Advancing deep learning for automated stroke detection: a review
    Selorm Adablanu, Utpal Barman, Dulumani Das
    Brain Hemorrhages, 2025

RECENT SCHOLAR PUBLICATIONS

  • From Education to Employment: A Deep Learning Approach to Understanding Job Market Trends in Africa
    DK Dake, E Ofori, S Adablanu
    International Journal of Information and Education Technology 16 (4) , 2026
    2026
  • Comparative Efficacy of Focal and Binary Cross-Entropy Loss in Handling Class Imbalance for Stroke CT Classification
    S Adablanu, U Barman, D Das
    2026 Sixth International Conference on Advances in Electrical, Computing … , 2026
    2026
  • Transforming Skin Cancer Detection With AI‐Based Convolutional and Transformer Models
    S Adablanu, U Barman, D Das, TJ Dweh
    iRADIOLOGY 4 (1), 51-62 , 2026
    2026
    Citations: 1
  • Engaging 21st Century Learners and Differentiating Instruction with Multimedia: An Empirical Case Study of the University of Education, Winneba, Ghana
    CS Achulo, S Adablanu, DA Quaye
    International Journal of Computer Applications 975, 8887 , 2026
    2026
  • Feature Extraction and Selection Methods Outperform Machine Learning and Deep Learning Techniques
    TJ Dweh, S Adablanu
    Feature Selection and Feature Extraction on Omics Data, 194-213 , 2026
    2026
  • 15 Years of Optimizers in Medical Deep Learning: A Systematic Review
    S Adablanu, U Barman, D Das
    Neuroscience Informatics, 100249 , 2025
    2025
    Citations: 5
  • A novel hybrid adaptive transformer framework with multihead self attention for stroke detection
    S Adablanu, U Barman, D Das
    Discover Neuroscience 20 (1), 24 , 2025
    2025
    Citations: 1
  • A Thorough Review of AI Developments in Education: Historical Progress, Current Applications, and Future Directions
    S Adablanu, B Ghansah, BB Benuwa, SO Oppong
    Journal of Artificial Intelligence in Education 1 (2), 52-63 , 2025
    2025
  • The Internet of Things in Education: Adoption Patterns and Learning Outcomes from a Ghanaian Case Study
    PK Matey, OA Rubbin, S Adablanu
    International Journal of Computer Applications 187 (51), 32-41 , 2025
    2025
    Citations: 1
  • Advancing deep learning for automated stroke detection: a review
    S Adablanu, U Barman, D Das
    Brain Hemorrhages , 2025
    2025
    Citations: 13
  • Brain Hemorrhages
    S Adablanu, U Barman, D Das
    PRISMA 15, 16 , 2025
    2025
  • Adopting sustainable mobile learning: Investigating long-term integration at UEW with a focus on infrastructure, resources, and institutional support
    S Adablanu, M Offei, A Boateng
    Advances in Mobile Learning Educational Research 4 (2), 1173-1189 , 2024
    2024
    Citations: 4
  • Adversarial Machine Learning for Robust Intrusion Detection Systems
    F Olaoye, P Broklyn, S Adablanu
    EasyChair , 2024
    2024
    Citations: 2
  • The Intersection of Artificial Intelligence and Cybersecurity
    A Olukemi, P Broklyn, S Adablanu
    EasyChair , 2024
    2024
    Citations: 1
  • Homomorphic Encryption for Secure Cloud Computing
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
    Citations: 2
  • Explainable Neural Networks for Interpretable Cybersecurity Decisions
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
    Citations: 1
  • Reinforcement Learning for Adaptive Cybersecurity Policy Optimization
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
  • EasyChair Preprint Adversarial Machine Learning for Cybersecurity Defense
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
  • Blockchain-based Security Solutions for the Internet of Things (IoT)
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
    Citations: 2
  • AI-Powered Diagnostics and Imaging Analysis: Revolutionizing Medical Decision- Making
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Artificial intelligence in education: Trends, opportunities and pitfalls for institutes of higher education in Ghana
    AMA Nsoh
    International Journal of Computer Science and Mobile Computing (IJCSMC) , 2023
    2023
    Citations: 33
  • Multimodal Deep Learning for Integrated Cybersecurity Analytics (No. 14011)
    K Potter, D Stilinski, S Adablanu
    EasyChair , 2024
    2024
    Citations: 19
  • Advancing deep learning for automated stroke detection: a review
    S Adablanu, U Barman, D Das
    Brain Hemorrhages , 2025
    2025
    Citations: 13
  • 15 Years of Optimizers in Medical Deep Learning: A Systematic Review
    S Adablanu, U Barman, D Das
    Neuroscience Informatics, 100249 , 2025
    2025
    Citations: 5
  • Adopting sustainable mobile learning: Investigating long-term integration at UEW with a focus on infrastructure, resources, and institutional support
    S Adablanu, M Offei, A Boateng
    Advances in Mobile Learning Educational Research 4 (2), 1173-1189 , 2024
    2024
    Citations: 4
  • AI-Powered Diagnostics and Imaging Analysis: Revolutionizing Medical Decision- Making
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
    Citations: 3
  • Adversarial Machine Learning for Robust Intrusion Detection Systems
    F Olaoye, P Broklyn, S Adablanu
    EasyChair , 2024
    2024
    Citations: 2
  • Homomorphic Encryption for Secure Cloud Computing
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
    Citations: 2
  • Blockchain-based Security Solutions for the Internet of Things (IoT)
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
    Citations: 2
  • Fasttracking Healthcare Services for Students through the Design of a Hospital Information System
    H Techie-Menson, D Danso Essel, G Kudjo Bada, S Adablanu
    Journal of Education and Practice 2 , 2022
    2022
    Citations: 2
  • Transforming Skin Cancer Detection With AI‐Based Convolutional and Transformer Models
    S Adablanu, U Barman, D Das, TJ Dweh
    iRADIOLOGY 4 (1), 51-62 , 2026
    2026
    Citations: 1
  • A novel hybrid adaptive transformer framework with multihead self attention for stroke detection
    S Adablanu, U Barman, D Das
    Discover Neuroscience 20 (1), 24 , 2025
    2025
    Citations: 1
  • The Internet of Things in Education: Adoption Patterns and Learning Outcomes from a Ghanaian Case Study
    PK Matey, OA Rubbin, S Adablanu
    International Journal of Computer Applications 187 (51), 32-41 , 2025
    2025
    Citations: 1
  • The Intersection of Artificial Intelligence and Cybersecurity
    A Olukemi, P Broklyn, S Adablanu
    EasyChair , 2024
    2024
    Citations: 1
  • Explainable Neural Networks for Interpretable Cybersecurity Decisions
    K Potter, S Adablanu, D Stilinski
    EasyChair , 2024
    2024
    Citations: 1
  • Review on Automatic Smart Car Parking System
    S Adablanu
    International Journal of Computer Science and Mobile Computing 11 (8), 79-83 , 2022
    2022
    Citations: 1
  • From Education to Employment: A Deep Learning Approach to Understanding Job Market Trends in Africa
    DK Dake, E Ofori, S Adablanu
    International Journal of Information and Education Technology 16 (4) , 2026
    2026
  • Comparative Efficacy of Focal and Binary Cross-Entropy Loss in Handling Class Imbalance for Stroke CT Classification
    S Adablanu, U Barman, D Das
    2026 Sixth International Conference on Advances in Electrical, Computing … , 2026
    2026
  • Engaging 21st Century Learners and Differentiating Instruction with Multimedia: An Empirical Case Study of the University of Education, Winneba, Ghana
    CS Achulo, S Adablanu, DA Quaye
    International Journal of Computer Applications 975, 8887 , 2026
    2026
  • Feature Extraction and Selection Methods Outperform Machine Learning and Deep Learning Techniques
    TJ Dweh, S Adablanu
    Feature Selection and Feature Extraction on Omics Data, 194-213 , 2026
    2026