Wasswa Shafik

@dcrlab.org

Dig Connectivity Research Laboratory (DCRLab)
Dig Connectivity Research Laboratory



                                

https://researchid.co/wasswashafik

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

119

Scopus Publications

1872

Scholar Citations

26

Scholar h-index

55

Scholar i10-index

Scopus Publications

  • A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection
    Wasswa Shafik, Ali Tufail, Chandratilak Liyanage De Silva, and Rosyzie Anna Awg Haji Mohd Apong
    Springer Science and Business Media LLC

  • Ethical and privacy concerns in bioinformatics and cyber-physical systems integration in healthcare
    Wasswa Shafik, Rufai Yusuf Zakari, and Kassim Kalinaki
    IGI Global
    The current advancement, adoption, and access to technology in healthcare has emerged with increased benefits and some technological limitations and ac-acceptance. Introducing and integrating bioinformatics and cyber-physical systems in healthcare demonstrates increased technology-based healthcare. Its rapid evolution has brought forth a spectrum of privacy and ethical challenges to the health sector in general due to increased patient data breaches in digital healthcare systems like electronic health records and health management information systems. The study explicates the core concepts of bioinformatics and cyber-physical systems in healthcare. The study delves into the intricate landscape of privacy concerns, shedding light on managing sensitive healthcare data and the implications of regulatory frameworks. The study further highlights the real-world implications of these ethical and privacy, recommendations to mitigate these issues and heightened transparency, setting the stage for responsible innovation in the healthcare domain and future research directions.

  • Ethical and legal considerations in digital counseling: Navigating counseling in the digital age
    Wasswa Shafik
    IGI Global
    As counselor practices and the counseling profession continue to evolve and change, digital counseling offered on digital platforms makes traditional educational and mental health services more accessible than they have ever been. Therapy sessions increasingly are virtual or conducted through direct messaging platforms and chatbots, rather than a 1-to-1 (face-to-face) model. Virtual therapy has grown significantly over the past several years, with very few of those clients ever seeing an actual person, counselor, or therapist. However, counseling online is not better than or greater than face-to-face counseling and is not for everyone. For many, remote technology adds to the therapeutic conversation, but makes it difficult to provide privacy, confidentiality, emotional tactile presence, and possibly an erosion of relationships between client and therapist. As a continually evolving field, it is essential for current and future counseling and therapist practitioners to stay up to date on current ethical and legal standards pertaining to digital counseling.

  • Sustainable agriculture and diet in the metaverse era: Sustainable farming and nutritious eating in the metaverse
    Wasswa Shafik
    IGI Global
    In the era of the metaverse, sustainable agriculture and diet practices are evolving through innovative technologies and virtual environments. As virtual spaces increasingly influence real-world behaviors, the metaverse offers a unique platform for educating, simulating, and promoting sustainable food production and consumption. Integrating digital tools with eco-friendly farming practices can help reduce environmental impacts, enhance food security, and foster sustainable agricultural models. Through exploring virtual reality-based agricultural education, precision farming, and digital diets, this research examines how the metaverse can reshape our approach to food sustainability. With the potential to bridge geographical barriers and enhance consumer awareness, sustainable agriculture in the metaverse era promises a transformative impact on both the future of farming and dietary choices worldwide.

  • An enhanced deep convolutional neural network for plant disease detection and classification: Elevating sustainable agriculture
    Wasswa Shafik, Ali Tufail, Liyanage Chandratilak De Silva, and Rosyzie Anna Haji Mohd Apong
    IGI Global
    This research introduces a novel enhanced deep convolutional neural network for plant disease detection and classification, a cutting-edge tool that is set to revolutionize the field. The study enhances the ResNet50 network by replacing the fully connected layer with three layers that improve discrimination and feature extraction, namely the convolution, batch normalization, and Leaky rectified linear unit (RELU) activation layers. Experimental performance assessments were conducted to evaluate the performance of the proposed model in comparison to the original ResNet50, EfficientNet, DesNet201, and Inception Version 3 using popular evaluation criteria such as precision, recall, and F1-Score. The proposed model achieved an accuracy of 99.33% and 93.93% on the Namibia University of Science and Technology Maize Dataset and the Nelson Mandela African Institution of Science and Technology Maize dataset, respectively.

  • Revolutionizing agriculture with automated plant disease detection: Techniques, applications, challenges, future directions, and sustainability impacts
    Ahmad Fathan Hidayatullah and Wasswa Shafik
    IGI Global
    Automated plant disease detection using computer vision has transformed agriculture by addressing challenges in plant health management, productivity, and sustainability. This chapter explores advancements from traditional methods to AI-enhanced deep learning and multi-modal imaging, enabling early disease detection, real-time processing, and precise interventions. Applications like precision agriculture, IoT integration, and data-driven decision-making foster eco-friendly practices and resource efficiency. Despite challenges such as data quality, scalability, and accessibility, future innovations in data collection, sustainable hardware, and collaboration promise to shape resilient agricultural systems. By aligning technology with sustainability, automated plant disease detection supports food security, environmental conservation, and the evolution of modern farming practices.

  • Investigating generative artificial intelligence readiness in the internet of medical things: Are we progressing technologically?
    Wasswa Shafik
    IGI Global
    Generative Artificial Intelligence (AI) is reshaping the Internet of Medical Things (IoMT), driving innovations in healthcare diagnostics, personalized treatment, and patient monitoring. This integration promises to enhance medical decision-making, optimize resource allocation, and improve patient outcomes. However, despite technological advancements, challenges persist, including data privacy concerns, regulatory hurdles, and the need for robust ethical frameworks. The progress is evident in AI-driven tools, such as predictive analytics and virtual health assistants, yet their widespread adoption is hindered by interoperability issues and uneven technological infrastructure. As generative AI continues to evolve, addressing these barriers is crucial for realizing its full potential in transforming the IoMT landscape, ensuring that technological advancements translate into sustainable and equitable healthcare progress.








  • The future of healthcare: Data-driven trends and innovation


  • Using transfer learning-based plant disease classification and detection for sustainable agriculture
    Wasswa Shafik, Ali Tufail, Chandratilak De Silva Liyanage, and Rosyzie Anna Awg Haji Mohd Apong
    BMC Plant Biology Springer Science and Business Media LLC
    AbstractSubsistence farmers and global food security depend on sufficient food production, which aligns with the UN's “Zero Hunger,” “Climate Action,” and “Responsible Consumption and Production” sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming. Notably, how plant pests and diseases can be appropriately detected and timely prevented is a hotspot paradigm in smart, sustainable agriculture remains unknown. In recent years, deep transfer learning has demonstrated tremendous advances in the recognition accuracy of object detection and image classification systems since these frameworks utilize previously acquired knowledge to solve similar problems more effectively and quickly. Therefore, in this research, we introduce two plant disease detection (PDDNet) models of early fusion (AE) and the lead voting ensemble (LVE) integrated with nine pre-trained convolutional neural networks (CNNs) and fine-tuned by deep feature extraction for efficient plant disease identification and classification. The experiments were carried out on 15 classes of the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 categories. Hyperparameter fine-tuning was done with popular pre-trained models, including DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, ResNet18, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. We test these CNNs on the stated plant disease detection and classification problem, both independently and as part of an ensemble. In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that PDDNet-AE and PDDNet-LVE achieved 96.74% and 97.79%, respectively, compared to current CNNs when tested on several plant diseases, depicting its exceptional robustness and generalization capabilities and mitigating current concerns in plant disease detection and classification.

  • Efficient white blood cell identification with hybrid inception-xception network
    Radhwan A. A. Saleh, Mustafa Ghaleb, Wasswa Shafik, and H. Metin ERTUNÇ
    Springer Science and Business Media LLC

  • Revolutionizing skin cancer diagnosis with artificial intelligence: Insights into machine learning techniques
    Wasswa Shafik
    IGI Global
    A frequent cancer worldwide is skin cancer. Non-melanoma exists. Melanoma kills more than non-melanoma skin malignancies. Successful treatment and early diagnosis improve skin cancer survival. Cancer burden and prognosis vary depending on the diagnosis type and stage. The biopsy method used to diagnose skin cancer is imprecise. To diagnose and treat skin cancer early, onco-dermatologists must enhance diagnostic accuracy. Doctors use several tools to diagnose skin lesions. Through image processing, AI has enhanced early skin cancer diagnosis. Radiology adopted artificial intelligence (AI) sooner than dermatology. AI is now more accessible because of technology, AI-powered expert systems can detect skin cancer early. This chapter examines early skin cancer diagnosis using machine learning (ML) models and the problem of automating skin cancer diagnosis with AI algorithms. This study sheds light on past and future efforts to diagnose early skin cancer and other concerns.

  • Safeguarding data privacy and security in federated learning systems
    Wasswa Shafik, Kassim Kalinaki, Khairul Eahsun Fahim, and Mumin Adam
    CRC Press

  • Exploring the potential of internet of things (IoT) and challenges in enabling circular economy practices in smart cities
    Wasswa Shafik
    Smart Cities and Circular Economy: The Future of Sustainable Urban Development Emerald Publishing Limited

  • Artificial intelligence-enabled internet of medical things for enhanced healthcare systems
    Wasswa Shafik
    Smart Healthcare Systems: AI and IoT Perspectives CRC Press


  • Social media insights into consumer behavior
    Wasswa Shafik
    IGI Global
    The link between social media and consumer behavior shows how digital landscapes affect consumer choices. From social media's constant change, organizations learn valuable lessons. Critically analyzing user-generated material and applying creative analytics reveal customer preferences and habits. Social media analytics and indicators help organizations understand audience engagement and online interactions. Influencer marketing, transient digital content, and smart technology elevate this exploration. These phases show that evaluation demands flexibility, a customer-centric approach, and the capacity to draw practical conclusions from enormous data sets. Future technologies like natural language processing and expert systems will enable more customization and customer emotion understanding. This analysis goes beyond consumer behavior to highlight agility, resilience, and honesty in devising techniques that appeal to the ever-discerning electronic client. Social media analysis demonstrates the evolving role of customer awareness and strategic company orchestration in the digital age.

  • Leveraging natural language processing for enhanced text analysis in business intelligence
    Ahmad Fathan Hidayatullah, Kassim Kalinaki, Haji Gul, Rufai Zakari Yusuf, and Wasswa Shafik
    IGI Global
    Business intelligence (BI) is crucial for informed decision-making, optimizing operations, and gaining a competitive edge. The rapid growth of unstructured text data has created a need for advanced text analysis techniques in BI. Natural language processing (NLP) is essential for analyzing unstructured textual data. This chapter covers foundational NLP techniques for text analysis, the role of text analysis in BI, and challenges and opportunities in this area. Real-world applications of NLP in BI demonstrate how organizations use NLP-driven text analysis to gain insights, improve customer experience, and anticipate market trends. Future directions and emerging trends, including multimodal learning, contextualized embeddings, conversational AI, explainable AI, federated learning, and knowledge graph integration, were explored. These advancements enhance the scalability, interpretability, and privacy of NLP-driven BI systems, enabling organizations to derive deeper insights and drive innovation in data-driven business landscapes.

  • Incorporating artificial intelligence for urban and smart cities' sustainability
    Wasswa Shafik
    IGI Global
    The study provides an overview of the comprehensive exploration of smart cities and their integration of artificial intelligence (AI) for urban sustainability. It covers the definition of smart cities, the importance of AI, key challenges and opportunities, foundational aspects of AI, sustainable infrastructure development, enhancing public services, data governance, citizen engagement, economic development, policy frameworks, future trends, case studies, and conclusions. The study demonstrated that AI assist in surveillance and mitigating environmental threats, such as deforestation, environment destruction, and contamination. Citizen engagement additionally promotes transparency and responsibility, encouraging residents to hold federal government agencies and other stakeholders accountable for their information techniques. The chapter encapsulates the breadth and depth of the discussion within the provided chapter layout, offering insights into the transformative potential of AI in shaping sustainable and inclusive smart cities.

RECENT SCHOLAR PUBLICATIONS

  • Security, Privacy, and Trust in Fintech
    W Shafik
    FinTech and Financial Inclusion: Leveraging Digital Finance for Economic 2025

  • Integrating emerging technologies for digital asset management
    W Shafik
    Intelligent Data-Driven Techniques for Security of Digital Assets, 1-19 2025

  • Consumer Behaviour Analysis
    W Shafik
    Multiple-Criteria Decision-Making (MCDM) Techniques and Statistics in 2025

  • Human-Artificial Intelligence Collaborations in Polycystic Ovary Syndrome (PCOS) Clinical Trials and Research
    W Shafik
    AI-Based Nutritional Intervention in Polycystic Ovary Syndrome (PCOS), 307-330 2025

  • Ethical and Privacy Concerns in Bioinformatics and Cyber-Physical Systems Integration in Healthcare
    W Shafik, RY Zakari, K Kalinaki
    AI-Driven Personalized Healthcare Solutions 1, 333 2025

  • Ecological Health Impact in the Marine World
    W Shafik
    Radiation Status in the Marine World, 307-335 2025

  • Ethical and Legal Considerations in Digital Counseling: Navigating Counseling in the Digital Age
    W Shafik
    Enhancing School Counseling With Technology and Case Studies 1, 287 2025

  • Pathways to Climate Neutrality: Understanding Ice Cap Reduction, Glacial Melt, and Sea Level Change and Adoption
    W Shafik
    Climate Neutrality Through Smart Eco-Innovation and Environmental 2025

  • Transforming Healthcare Sector Through Artificial Intelligence and Environmental Sustainability
    S Rubee, S Wasswa, C David, V Kumar
    Springer Singapore 2025

  • Generative Adversarial Networks: Security, Privacy, and Ethical Considerations
    W Shafik
    Generative Artificial Intelligence (AI) Approaches for Industrial 2025

  • 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

  • Zero Carbon Industry Challenges and Opportunities in Enhancing Urban Water Sector for Environmental Sustainability and Innovation
    W Shafik
    Zero Carbon Industry, Eco-Innovation and Environmental Sustainability, 269-289 2025

  • Artificial Intelligence Transparency and Explainability in Sustainable Healthcare
    W Shafik, R Singh, V Kumar
    Transforming Healthcare Sector Through Artificial Intelligence and 2025

  • Artificial Intelligence Assisted Internet of Medical Things (AIoMTs) in Sustainable Healthcare Ecosystem
    W Shafik
    Wellness Management Powered by AI Technologies, 75-101 2025

  • The Next Generation of Health Monitoring: Digital Twins and Medical Wearables
    W Shafik
    AI-Powered Digital Twins for Predictive Healthcare: Creating Virtual 2025

  • Emerging Technologies for Small and Medium Enterprises (SMEs) Growth: ChatGPT, Blockchain, Robotics, and Artificial Intelligence
    W Shafik
    Fostering Economic Diversification and Sustainable Business Through Digital 2025

  • Ethical and Legal Considerations in Digital Counseling
    W Shafik
     2025

  • Digital twins tools and technologies
    W Shafik
    Digital Twins for Smart Cities and Villages, 55-80 2025

  • Sustainable Agriculture and Diet in the Metaverse Era: Sustainable Farming and Nutritious Eating in the Metaverse
    W Shafik
    Food in the Metaverse and Web 3.0 Era: Intersecting Food, Technology, and 2025

  • An Enhanced Deep Convolutional Neural Network for Plant Disease Detection and Classification: Elevating Sustainable Agriculture
    W Shafik, A Tufail, LC De Silva, RAHM Apong
    Artificial Intelligence and Data Science for Sustainability: Applications 2025

MOST CITED SCHOLAR PUBLICATIONS

  • Artificial intelligence application in cybersecurity and cyberdefense
    Y Jun, A Craig, W Shafik, L Sharif
    Wireless communications and mobile computing 2021 (1), 3329581 2021
    Citations: 93

  • 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
    Citations: 75

  • Green internet of things and big data application in smart cities development
    Z Yang, L Jianjun, H Faqiri, W Shafik, A Talal Abdulrahman, M Yusuf, ...
    Complexity 2021 (1), 4922697 2021
    Citations: 73

  • 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
    Citations: 58

  • A Comprehensive Cybersecurity Framework for Present and Future Global Information Technology Organizations
    W Shafik
    Effective Cybersecurity Operations for Enterprise-Wide Systems, 56-79 2023
    Citations: 57

  • Cyber Security Perspectives in Public Spaces: Drone Case Study
    W Shafik
    Handbook of Research on Cybersecurity Risk in Contemporary Business Systems 2023
    Citations: 53

  • Fog computing architectures, privacy and security solutions
    S Mostafavi, W Shafik
    Journal of Communications Technology, Electronics and Computer Science 24, 1-14 2019
    Citations: 51

  • 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
    Citations: 48

  • 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
    Citations: 47

  • 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
    Citations: 41

  • Cybersecurity in unmanned aerial vehicles: A review
    W Shafik, SM Matinkhah, F Shokoor
    International Journal on Smart Sensing and Intelligent Systems 16 (1) 2023
    Citations: 40

  • Theoretical understanding of deep learning in uav biomedical engineering technologies analysis
    W Shafik, SM Matinkhah, M Ghasemzadeh
    SN Computer Science 1 (6), 307 2020
    Citations: 37

  • Introduction to ChatGPT
    W Shafik
    Advanced applications of generative AI and natural language processing 2024
    Citations: 36

  • 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
    Citations: 36

  • Network resource management drives machine learning: a survey and future research direction
    W Shafik, M Matinkhah, MN Sanda
    Journal of Communications Technology, Electronics and Computer Science 2020 2020
    Citations: 35

  • Wearable medical electronics in artificial intelligence of medical things
    W Shafik
    Handbook of security and privacy of ai-enabled healthcare systems and 2024
    Citations: 34

  • Smart grid empowered by 5G technology
    SM Matinkhah, W Shafik
    2019 Smart Grid Conference (SGC), 1-6 2019
    Citations: 34

  • Electronic Devices in the Artificial Intelligence of the Internet of Medical Things (AIoMT)
    KE Fahim, K Kalinaki, W Shafik
    Handbook of Security and Privacy of AI-Enabled Healthcare Systems and 2023
    Citations: 33

  • Artificial intelligence of internet of medical things (AIoMT) in smart cities: a review of cybersecurity for smart healthcare
    K Kalinaki, M Fahadi, AA Alli, W Shafik, M Yasin, N Mutwalibi
    Handbook of security and privacy of ai-enabled healthcare systems and 2023
    Citations: 31

  • A 5g beam selection machine learning algorithm for unmanned aerial vehicle applications
    H Meng, W Shafik, SM Matinkhah, Z Ahmad
    Wireless Communications and Mobile Computing 2020 (1), 1428968 2020
    Citations: 31