Wasswa Shafik
@dcrlab.org
Dig Connectivity Research Laboratory (DCRLab)
Dig Connectivity Research Laboratory
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
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
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.
Radhwan A. A. Saleh, Mustafa Ghaleb, Wasswa Shafik, and H. Metin ERTUNÇ
Springer Science and Business Media LLC
Wasswa Shafik, Kassim Kalinaki, Khairul Eahsun Fahim, and Mumin Adam
CRC Press
Wasswa Shafik
Smart Cities and Circular Economy: The Future of Sustainable Urban Development Emerald Publishing Limited
Wasswa Shafik
Smart Healthcare Systems: AI and IoT Perspectives CRC Press
Wasswa Shafik
Auerbach Publications
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.
Wasswa Shafik
IGI Global
Recently, biosensor-based drones (BBD) have emerged and have proven to be highly influential in the convergence of modern technology and agriculture. These drones possess the capacity to bring about significant changes in the realm of sustainable agriculture development. This study presents a comprehensive insight into the crucial element of anomaly detection (AD) in integrating BBD and investigates their diverse uses in promoting sustainability in agriculture. With various advanced sensor technologies, BBD collects and transmits real-time data for accurate monitoring of agricultural crops, soil quality, and environmental parameters. The utilization of a diverse range of sensors, including multispectral, hyperspectral, thermal infrared, global positioning system (GPS), light detection and ranging (LiDAR), environmental, chemical, and crop health sensors, provides farmers with the capability to make informed decisions based on data. The management of extensive datasets produced by these sensors presents a considerable obstacle. The utilization of AD techniques is crucial to exploit the capabilities of drones equipped with biosensors fully. Machine learning (ML) algorithms and artificial intelligence (AI) systems significantly impact the processing and interpretation of sensor data. They are essential in detecting deviations from anticipated trends and notifying farmers of abnormalities that could indicate crop stress, illnesses, or pest presence. Detecting issues early enables prompt action, decreasing crop production losses and reducing reliance on chemical treatments. This, in turn, supports the adoption of sustainable farming methods. The utilization of BBD in advancing sustainable agriculture encompasses a wide range of applications. The practices encompass precision irrigation management, targeted fertilization, disease and insect control, land optimization, and minimization of environmental impact. These applications collectively enhance resource efficiency, augment agricultural yields, mitigate environmental impact, and promote sustainable agriculture. Selected studies were obtained from six top academic research databases. The authors used an exhaustive data extraction technique, focusing on the study objectives and type of AD in a smart operation, such as smart agriculture, transportation and smart things settings. According to this analysis, several studies have shown that deep learning (DL) and ML are more employed in preventing point and collective anomalies. Statistical approaches are more applicable in contextual and collaborative AD. The study presents an AD summary of ML, DL, and statistical-based approaches. Conclusively, the study identifies AD's future research directions from a drone operations perspective.
Wasswa Shafik
Nutrition Controversies and Advances in Autoimmune Disease IGI Global
This chapter discusses how AI can create nutrition regimens and monitor nutrition with food recognition systems and chatbots. Real-world case studies to teach viewers how to employ appropriate AI applications are shown. It concludes with key findings, insights, and how AI is changing nutritional monitoring. This carefully structured examination explains the synergies between AI and nutritional delivery as well as the honest issues, obstacles, and advanced tendencies that constitute this revolutionary confluence of innovation and healthcare.
Wasswa Shafik
Building Community Resiliency and Sustainability With Tourism Development IGI Global
Economic success, social equality, and environmental protection are needed to change global standards for sustainable development. Understanding and executing sustainable entrepreneurship is complicated, as this chapter shows. Sustainable service versions are evaluated globally for environmental and social health. Actual prototypes demonstrate sustainable solutions beyond revenue margins and a big service standard departure. CSR shows sustainable entrepreneurship's complicated campaign network. It examines firm, activities, and society's CSR motivations and effects on online reputation and sustainable development. Phase examined industry-specific issues and potential to improve sustainability across industries. The impact of eco-friendly technologies on IT business is examined. Ecological responsibility trends are examined for organizational change. Knowledge is used to create dependable, flexible, and sustainable enterprise plans and identify improvement opportunities.
Wasswa Shafik
Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems IGI Global
The integration of artificial intelligence (AI), the internet of things (IoT), with medical devices avails the recent development in the medical sector, specifically digital health, referred to as the internet of medical things (IoMT). AIoMT combines technologies like body movement detection, sleep monitoring, and rehab assessment, simplifying healthcare and offering personalized experiences. By leveraging AI, big data, mobile internet, cloud computing, and microelectronics, patient data is efficiently processed, enhancing healthcare's efficiency and personalization. During the pandemic, AI applications saved lives by streamlining data analysis. This chapter explores wearable medical electronics sensor architecture and addresses challenges like data security, aiming to elevate medical standards. It also outlines future research opportunities in AIoMT.
Wasswa Shafik
CRC Press
Wasswa Shafik
Exploring Youth Studies in the Age of AI IGI Global
Recent technological developments influence different daily human activities, including education and lifestyle. This chapter explores the significance of mobile learning and bringing your own device to enhance education in the digital age. It highlights the growing use of mobile devices in educational settings and their advantages and drawbacks. The literature review analyses existing research, frameworks, and best practices for utilizing mobile devices and smartphones in educational settings. The study examines pedagogical approaches, mobile resources, and educational applications that utilize mobile technology for personalized and engaging learning. It also discusses related policies, implementation difficulties, and successful case studies of technology adoption. The chapter offers best practices for maximizing the benefits, such as management strategies, and a safe learning environment. The chapter also speculates on the future developments and effects of mobile learning in the classroom, exploring new technologies and innovations that may influence education.
The bibliometric analysis plays a crucial role in understanding the evolution of research trends and knowledge in various fields. This study applies bibliometric analysis to explore the growth of the research paradigm on agility in fintech literature, using co-citation analysis and bibliographic coupling of selected articles. Based on this bibliometric analysis, the evolution of research on agility in the fintech domain has been prepared, focusing on the literature related to fintech agility between 1984 and 2022. In this study, the authors also address the limitations of individual analyses from Scopus and Web of Science (WOS) and propose a comprehensive approach by merging the two research databases. The results reveal significant disparities between authors, publication influences, and keyword occurrences between the WOS and merged databases.
Wasswa Shafik
CRC Press
Wasswa Shafik
IGI Global
Tourism impacts people and the environment beyond site-seeing. Healthcare difficulties affect end customers; therefore, this study analyzes medical tourism (MT) employing disruptive technology. The healthcare industry has predicted these changes and demands understanding. Healthcare delivery changes with technology. The IoT functions in MT are examined. Discusses AI in medical diagnosis, decision-making, image analysis, and personalized care. MT technologies such as surgery robots and automation are studied for surgery, therapy, and senior care. A study suggests blockchain technology may improve healthcare data security, interoperability, and patient privacy. Pain management, surgical simulators, and other mental health treatments are featured. Telemedicine, wearables, and smartphone apps assess chronic disease treatment. Addressing future healthcare technology regulatory, data privacy, and ethical issues. Research, teamwork, and strict control are needed to exploit these technologies amidst the enjoyment of using sustainable tourism and its associated benefits.
Wasswa Shafik
Practical Approaches to Agile Project Management IGI Global
Artificial intelligence (AI) transforms agile project management (APM) by enhancing efficiency and decision-making but poses challenges like job displacement, bias, and data privacy concerns. This study reconnoiters AI types, including rule-based AI, machine learning (ML), neural networks, and their APM applications: chatbots, image recognition, and predictive analytics. Prospects show AI's potential benefits and hurdles. Upholding ethical development is crucial for AI's secure, beneficial integration in APM, ensuring alignment with societal values while advancing technology responsibly. The study further presents the benefits of AI in the APM, followed by the challenges. It is crucial to consider AI's implications and guarantee that its development and deployment are ethical, secure, and beneficial for everyone. Finally, the future of AI in the APM is categorically presented. As AI continues to evolve, it will undoubtedly bring benefits and challenges, and it is essential to develop AI responsibly in a way that aligns with ethical values and protects individual rights.
Wasswa Shafik
Powering Industry 5.0 and Sustainable Development Through Innovation IGI Global
The UN Sustainable Development Goals (SDGs) enhance health, ecology, vitality, and the global economy. This chapter carefully reviews scholarly literature to identify technological, economic, and sociological hurdles to a net-zero economy. It highlights this transformation's economic, social, and environmental benefits. This study provides empirical evidence and policy recommendations using case studies. The fundamental purpose is to aid governments and others in efficiently solving human problems for sustainable value. The need to address these challenges and seize chances to create a sustainable, net-zero future that ensures global prosperity and environmental health. Support governments and other relevant stakeholders in efficiently addressing human-related concerns to achieve optimal sustainable value. Finally, it accentuates the importance of addressing these issues and leveraging possibilities to achieve a sustainable, net-zero future that ensures global prosperity and ecological well-being.
Wasswa Shafik
IGI Global
The healthcare industry is transforming significantly due to the rapid emergence of the internet of medical things (IoMT). The integration of cutting-edge technologies facilitates this paradigm shift. A new age of healthcare system optimization and patient care is being ushered in. This study provides a comprehensive overview of the future trends and open issues in adopting the IoMTs. It explores the current status of IoMT and forecasts its evolution. The study examines the policy and regulatory ramifications and the essential ethical and data privacy aspects. More still elucidates the urgent security, interoperability, and scalability difficulties while underscoring the imperative for collaborative efforts and standards within the industry. This study affords insights for future research by presenting a set of unanswered inquiries and corresponding possible implications, accompanied by relevant cases. Finally, it emphasizes the significant impact the IoMT can have on the healthcare industry by availing lightweight medical digital trust architectures.
Wasswa Shafik
Enhancing and Predicting Digital Consumer Behavior with AI IGI Global
Emotional intelligence (EI) is a vital aspect of human experience, influencing personal and professional success, empathy, and relationships. It is rooted in psychology and neuroscience and is developed through self-awareness, self-regulation, empathy, social skills, and intrinsic motivation. EI is influenced by cognitive and neurological mechanisms, such as the amygdala's function in processing emotions and the prefrontal cortex's adaptability. EI is assessed using various tools, such as self-report questionnaires and multisource feedback instruments. To develop EI, individuals and organizations can use strategies like mindfulness practices and empathetic listening drills, as presented in this study. EI has transformative potential in personal and professional contexts, affecting leadership, teamwork, and organizational dynamics. However, barriers such as cultural and societal influences and personal resistance to change can hinder the journey to EI mastery.
Wasswa Shafik
AI and IoT for Proactive Disaster Management IGI Global
The main goal is to appropriately utilize advanced algorithms to analyze environmental data, improve early disease detection and intervention tactics, and reduce the harmful effects of forest fires on human beings. Analyze the challenges faced by traditional methods in addressing the constantly evolving nature of wildfires and the need for more adaptable and proactive approaches, and highlight the advantages of AI. Discusses the main constituents incorporated into the AI model, comprising meteorological data, satellite imagery, and historical fire records. It analyzes the selection of AI algorithms specifically tailored for forest fire prevention, considering parameters. Analyze the challenges faced during the creation and implementation of AI models for forest fire prevention and viability of integrating artificial intelligence models into existing fire management infrastructure and emergency response systems. It showcases the current research, progress, and use of AI-driven solutions to address the challenges posed by wildfires and provides a concise overview of the chapter's findings.