Dig Connectivity Research Laboratory (DCRLab), 600040, Kampala, Uganda
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- Academic History: Information Technology & Computer Science
- Member of IEEE, Springer, WoS, MDPI, SCI, IGI
- Junior AI Researcher and Data Scientist
- International Speaker in Technology Conferences
- International Reviewer & Evaluator of Journal Papers
- Session Chair Eliveser & Springer Conclaves
- Edited and Authored Books in AI, IoT, Healthcare, Agriculture & Data Science
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He has 5+ years of teaching and researching experiences in Information Technology (Advanced Network Security, Advanced Algorithms and Complexity, Artificial Intelligence, Data Engineering, Data Science, Data Analytics, IoT-based Technologies, Data and Application Migration Strategies, Advanced Distributed Systems, among others.
EDUCATION
PhD in Computer Science at Universiti Brunei Darussalam, Brunei Darussalam
MSc in Information Technology Engineering, (Computer and Communication Networks), Iran.
BSc in Information Technology, Faculty of Science & Computing, Ndejje University, Uganda.
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Agricultural and Biological Sciences, Computer Vision and Pattern Recognition, Information Systems
267
Scopus Publications
5001
Scholar Citations
37
Scholar h-index
142
Scholar i10-index
Scopus Publications
Comparative evaluation of deep learning models for cardiovascular disease diagnosis and classification Iman Bhia, Soroush Soltanizadeh, Wasswa Shafik Scientific Reports, 2026 Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the need for accurate and computationally efficient diagnostic tools. Electrocardiography (ECG) is widely used for the diagnosis of CVD and is a noninvasive, cost-effective method. However, manual interpretation requires substantial time and can lead to human error. In this study, we propose and systematically compare low-complexity Artificial Intelligence (AI)–driven Deep Learning (DL) models for multi-class ECG-based CVD diagnosis and classification. Using the publicly available PTB Diagnostic ECG (PTB-ECG) dataset, which comprises multiple CVD categories, we evaluate CNN, LSTM, MLP, CNN-MLP, and ConvLSTM architectures, focusing on balancing diagnostic performance and computational complexity. Discrete Wavelet Transform (DWT)–based feature extraction and data-level imbalance handling are employed to enhance efficiency and robustness. Experimental results show that the LSTM model achieves the best overall performance, with an accuracy of 99.98%, an F1 score of 98%, and a precision of 100%, while maintaining low computational complexity, measured in Real Multiplications Per Symbol (RMpS). These findings demonstrate that high diagnostic accuracy can be achieved without high computational complexity, supporting the feasibility of real-time and resource-constrained clinical deployment.
Lightweight vision transformer and ResNet-9 models for real-time plant disease detection and pest classification with SHAP explainability Wasswa Shafik, Ali Tufail, Liyanage Chandratilak De Silva, Rosyzie Anna Awg Haji Mohd Apong BMC Plant Biology, 2026 The rapid advancement of digital technologies alongside increased global commitment to sustainability has intensified the need for efficient crop management solutions. Across agricultural systems, delayed detection and misclassification of plant diseases remain major contributors to reduced yields, threatening food security and undermining progress toward Sustainable Development Goals such as Zero Hunger, No Poverty, Good Health and Well-being, Climate Action, and Life on Land. Plant pests, diseases, and excessive chemical use further exacerbate these challenges. Early, automated visual detection offers a pathway to environmentally responsible and economically viable agricultural practices. This study aims to develop and evaluate an automated, real-time plant disease classification framework using Vision Transformers (ViT) and hybrid ViT–CNN architectures, with the goal of supporting farmers and agronomists in early decision-making and sustainable crop protection. The research employs deep learning techniques, including ViT and a combined ViT–CNN model built on ResNet-9, trained and evaluated using four publicly available datasets: the Turkey Plant Pests and Diseases (TPPD) dataset (15 classes), the Namibia Maize Image Dataset (3 classes), the Banana Image Dataset (3 classes), and the Tanzania Maize Dataset (3 classes). SHapley Additive exPlanations (SHAP) were applied to generate saliency maps for interpretability. Comparative analyses assessed performance, accuracy, and classification speed across attention-based and hybrid architectures. The proposed model achieved strong performance with 97.4% accuracy, 96.4% precision, 97.09% recall, a 95.7% F1-score, and high agreement measured by Cohen’s Kappa, outperforming existing benchmark models. SHAP visualizations highlighted that the model leverages high-activation areas, edge features, color patterns, texture, shape, and contextual cues in its predictions. While attention-based models improved accuracy, they also caused reduced classification speed. However, integrating attention blocks with CNN layers effectively compensated for this slowdown, achieving both high accuracy and efficient inference. To evaluate the interpretability and deployment feasibility of the proposed model, we considered several parameters are considered to have an integration of faithfulness, localization quality, sparsity, latency, and energy of the models, including pointing accuracy, localization IoU, Centroid Localization Error (pixels), Attribution Sparsity (%), Insertion AUC, Deletion AUC, Time per Explanation (ms/image), Energy Consumption (J/image), Memory Footprint (MB). This research provides a transparent and high-performing deep learning solution for plant disease classification, promoting sustainable agricultural development. By reducing reliance on excessive pesticide and herbicide use, enhancing early diagnosis, and improving decision-making, the model supports environmentally responsible farming practices and contributes to global efforts toward food security and ecological resilience. The significance of this evaluation is that it comprehensively assesses the model’s interpretability and real-world deployability by measuring explanation reliability (faithfulness), spatial precision (localization quality), efficiency (latency and memory), and sustainability (energy consumption), ensuring the model is not only accurate but also transparent, efficient, and practical for deployment.
Understanding and Managing Anxiety, Schizophrenia, Depression, and Bipolar Disorder in Children: Our Children's Care - Key to Sustainable Inclusion Wasswa Shafik Cases on Psychiatric Medicine, 2026 Understanding and managing anxiety, schizophrenia, depression, and bipolar disorder in children is critical for promoting their mental health and well-being. These conditions can manifest differently in younger populations, requiring tailored approaches to diagnosis and treatment. Anxiety in children often presents as excessive worry or fear, while schizophrenia may involve delusions, hallucinations, and impaired social functioning. Depression in children can lead to mood disturbances, irritability, and withdrawal from activities, while bipolar disorder is characterized by extreme mood swings, ranging from manic episodes to deep depression. Early identification and intervention, including therapy, medication, and support from caregivers, are essential to managing these disorders effectively. A comprehensive approach that includes medical, psychological, and educational strategies can help children cope with and manage these conditions, improving their overall quality of life.
Personalization, Equity, and Ethics: AI Virtual Tutors in K12 Education Wasswa Shafik Virtual Tutors and AI Powered Instructional Tools in K 12 Settings, 2026 As the digital age transforms how knowledge is created and shared, artificial intelligence (AI) emerges as a powerful ally in reimagining K-12 education. This chapter explores the integration of AI-driven instructional tools that support personalized learning pathways, foster adaptive assessment practices, and enhance teacher effectiveness. It examines how intelligent tutoring systems, learning analytics, and classroom automation are reshaping traditional pedagogies while offering new opportunities for student engagement and curriculum innovation. Grounded in real-world examples and educational theory, the chapter also addresses critical considerations such as equity, data privacy, and ethical use of AI in the classroom. By highlighting both the potential and the limitations of these technologies, the chapter sets the stage for thoughtful adoption and sustainable implementation of AI in primary and secondary education.
Bee Vectoring Technology (BVT) as a nature-based strategy for sustainable pest control management Wasswa Shafik Green Technologies and Sustainability, 2026 Global agriculture continues to face increasing abiotic and biotic stresses, with pest and disease outbreaks representing major constraints to crop productivity and sustainability. Conventional chemical-based control measures, while effective, pose environmental risks and compromise pollinator health. Bee Vectoring Technology (BVT) offers an innovative, nature-based strategy that leverages the natural foraging behavior of managed pollinators, principally Apis mellifera and Bombus spp. , to deliver beneficial microbial control agents directly to floral targets. This targeted delivery system facilitates the deposition of antagonistic microorganisms, including Clonostachys rosea CR-7 , Aureobasidium pullulans, Bacillus thuringiensis, Pseudomonas fluorescens, and Trichoderma harzianum, precisely at infection courts where pathogen ingress typically occurs. By reducing reliance on synthetic pesticides, BVT enhances ecological resilience, minimizes non-target contamination, and supports pollinator safety. This paper evaluates BVT mechanisms of action, hive-mounted dispenser deployment, and integration with digital agriculture platforms such as sensor-enabled smart hives and the Internet of Things (IoT)-based field monitoring for real-time decision support. Evidence from diverse cropping systems (for instance, berries, tomatoes, and sunflowers) demonstrates significant reductions in disease incidence, improved yield quality, and economic viability under large-scale implementation. The regulatory landscape, biosafety considerations, and compatibility with integrated pest management (IPM) frameworks are critically assessed. Therefore, BVT emerges as a scalable, climate-smart innovation that aligns with Sustainable Development Goals (SDGs) by advancing environmentally responsible, economically feasible, and pollinator-friendly pest management practices. • Nature suffers from plant abiotic and biotic stress, including pests. • Disruptive agricultural technologies effectively control pests and plant diseases. • Bee Vectoring Technology (BVT) contributes to global food security and health. • BVT offers a targeted, eco-friendly alternative to pesticide overuse. • The study suggests mitigation strategies for potential BVT concerns.
Food and Agriculture: Concept and Implications Wasswa Shafik, Ali Tufail, Liyanage Chandratilak De Silva, Rosyzie Anna Awg Haji Mohd Apong Food and Water Security Sustainable Solutions, 2026
Leveraging Remote Sensing for Agriculture Mapping: Techniques, Applications, and Future Directions Artificial Intelligence and Computer Vision for Ecological Informatics, 2025
Unlocking the Democratized Generative Artificial Intelligence in Small and Medium-Sized Enterprises (SMEs) Democratized Generative AI Principles Challenges and Applications, 2025
Security, Privacy, and Trust in Fintech Wasswa Shafik Fintech and Financial Inclusion Leveraging Digital Finance for Economic Empowerment and Sustainable Growth, 2025
Computer Vision Applications for Sustainable Agriculture Wasswa Shafik, Ali Tufail, Liyanage Chandratilak De Silva, Rosyzie Anna Awg Haji Mohd Apong Computational Intelligence and Image Processing in Agriculture Applications and Innovations, 2025
Artificial intelligence and blockchain technology enabling cybersecurity in telehealth systems Artificial Intelligence and Blockchain Technology in Modern Telehealth Systems, 2024
Health Equity, Global Access, and the Digital Divide in Oncology W Shafik, K Kalinaki Precision Digital Oncology, 144-156 , 2027 2027
Ethics, Privacy, and Regulatory Challenges in Digital Oncology W Shafik, H Tumwiine Precision Digital Oncology, 133-143 , 2027 2027
Precision Digital Oncology: Disruptive Science in the Fight Against Cancer W Shafik, JK Pandey, H Liu CRC Press , 2026 2026
Textbook of Green Growth and Technology: Balancing Ecology, Economy, and Equity W Shafik Springer Nature , 2026 2026
Sustainable Cities and Smart Infrastructure for Green Growth W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Digital Twins, IoT, and Big Data for Environmental Monitoring and Optimization W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Sustainable Food Systems: Tech-Driven Innovations for Agriculture and Supply Chains W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Ethical AI and Responsible Innovation in Green Growth W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Industrial Decarbonization: Pathways to Net-Zero Manufacturing W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Green Growth in the Digital Era: The Convergence of Technology, Sustainability, and Economy W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Artificial Intelligence and Machine Learning for Sustainable Development W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
The Role of Smart Technologies in Resource Efficiency and Environmental Conservation W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Education, Capacity Building, and Digital Skills for a Sustainable Society W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Circular Economy and Closed-Loop Systems: Rethinking Production and Consumption W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Renewable Energy Transformation: Innovations and Integration into the Grid W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Decoupling Economic Growth from Environmental Degradation: A Technological Perspective W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Resilience, Adaptation, and Climate Justice in a Technology-Driven World W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Financing Green Growth: Sustainable Investments and Climate Finance W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
Biodiversity, Ecosystem Services, and the Role of Technology in Conservation W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
The Road Ahead: A Blueprint for Global Collaboration in Green Growth W Shafik Textbook of Green Growth and Technology: Balancing Ecology, Economy, and … , 2026 2026
MOST CITED SCHOLAR PUBLICATIONS
A systematic literature review on plant disease detection: motivations, classification techniques, datasets, challenges, and future trends W Shafik, A Tufail, A Namoun, LC De Silva, RAAHM Apong Ieee Access 11, 59174-59203 , 2023 2023 Citations: 220
Using transfer learning-based plant disease classification and detection for sustainable agriculture W Shafik, A Tufail, C De Silva Liyanage, RAAHM Apong BMC Plant Biology 24 (1), 136 , 2024 2024 Citations: 191
Artificial intelligence application in cybersecurity and cyberdefense Y Jun, A Craig, W Shafik, L Sharif Wireless communications and mobile computing 2021 (1), 3329581 , 2021 2021 Citations: 139
Cybersecurity in unmanned aerial vehicles: A review W Shafik, SM Matinkhah, F Shokoor International Journal on Smart Sensing and Intelligent Systems 16 (1) , 2023 2023 Citations: 95
Introduction to chatgpt W Shafik Advanced applications of generative AI and natural language processing … , 2024 2024 Citations: 91
Generative AI for Social Good and Sustainable Development W Shafik Generative AI: Current Trends and Applications, 185-217 , 2024 2024 Citations: 85
Artificial intelligence analysis in cyber domain: A review L Zhao, D Zhu, W Shafik, SM Matinkhah, Z Ahmad, L Sharif, A Craig International Journal of Distributed Sensor Networks 18 (4), 15501329221084882 , 2022 2022 Citations: 84
A Comprehensive Cybersecurity Framework for Present and Future Global Information Technology Organizations W Shafik Effective Cybersecurity Operations for Enterprise-Wide Systems, 56-79 , 2023 2023 Citations: 77
Wearable medical electronics in artificial intelligence of medical things W Shafik Handbook of security and privacy of ai-enabled healthcare systems and … , 2023 2023 Citations: 71
Toward a More Ethical Future of Artificial-Intelligence and Data Science S Wasswa The Ethical Frontier of AI and Data Analysis 1, 362-388 , 2024 2024 Citations: 70
Cyber Security Perspectives in Public Spaces: Drone Case Study W Shafik Handbook of Research on Cybersecurity Risk in Contemporary Business Systems … , 2023 2023 Citations: 68
Deep Learning Impacts in the Field of Artificial Intelligence W Shafik Deep Learning Concepts in Operations Research, 9-26 , 2024 2024 Citations: 63
A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection W Shafik, A Tufail, C Liyanage De Silva, RA Awg Haji Mohd Apong Scientific Reports 15 (1), 3936 , 2025 2025 Citations: 60
Internet of things-based energy management, challenges, and solutions in smart cities W Shafik, SM Matinkhah, M Ghasemzadeh Journal of communications technology, electronics and computer science 27, 1-11 , 2020 2020 Citations: 58
Evaluating the potential of artificial intelligence in islamic religious education: A SWOT analysis overview MS Abubakari, W Shafik, AF Hidayatullah AI-enhanced teaching methods, 216-239 , 2024 2024 Citations: 56
Impact of facebook and newspaper advertising on sales: a comparative study of online and print media Y Lin, Z Ahmad, W Shafik, SK Khosa, Z Almaspoor, H Alsuhabi, F Abbas Computational intelligence and neuroscience 2021 (1), 5995008 , 2021 2021 Citations: 56
Blockchain-Based Internet of Things (B-IoT) Challenges, Solutions, Opportunities, Open Research Questions, and Future Trends W Shafik Blockchain-based Internet of Things: Opportunities, Challenges and Solutions … , 2024 2024 Citations: 55
Using a novel convolutional neural network for plant pests detection and disease classification W Shafik, A Tufail, CDS Liyanage, RAAHM Apong Journal of the Science of Food and Agriculture 103 (12), 5849-5861 , 2023 2023 Citations: 55
Fog computing architectures, privacy and security solutions S Mostafavi, W Shafik Journal of Communications Technology, Electronics and Computer Science 24, 1-14 , 2019 2019 Citations: 55
Making Cities Smarter: IoT and SDN Applications, Challenges, and Future Trends S Wasswa Opportunities and Challenges of Industrial IoT in 5G and 6G Networks 1, 73-94 , 2023 2023 Citations: 52