Wasswa Shafik
@dcrlab.org
Dig Connectivity Research Laboratory (DCRLab)
DCRLab
Wasswa Shafik (IEEE member, P.Eng ) received a B.Sc. degree with honor rank in Mathematics and Computer Science in 2016 from Ndejje University, and an M.Sc. degree in Information Technology Engineering (MIT) in 2020, from the Computer Engineering Department, Yazd University, Islamic Republic of Iran. Prior to that, he was an associate researcher at the Computer Science Department and Network Interconnectivity Lab at Yazd University, Islamic Republic of Iran. He has authored and co-authored more than 60+ papers, refereed IEEE/ACM/Springer/Elsevier journals, conference papers, books, and book chapters. He is a founder and lead investigator of the Dig Connectivity Research Laboratory.
EDUCATION
2021, MSc. In Information Technology Engineering, Computer and Communication Networks Option, Computer Engineering Department, Yazd University, Iran.
2016, BSc. Information Mathematics and Computer Science, Faculty of Science and Information Technology, Ndejje University, Uganda.
2012, Uganda Advanced Certificate of Education, Mulusa Academy, Luweero, Uganda East Africa.
RESEARCH INTERESTS
Ecological Informatics, Computer Vision, AI-enabled IoT/IoMTs/IIoTs, Cyber Security, and Privacy, Smart Cities.
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.
Wasswa Shafik
CRC Press
Wasswa Shafik
IGI Global
This chapter explores the significance of mobile learning (ML) and bring your own device (BYOD) to enhance education in the digital age (DA). It highlights the growing use of mobile devices in educational settings and their advantages and drawbacks. The literature review analyzes existing research, frameworks, and best practices for utilizing mobile devices and smartphones in educational settings. The study examines pedagogical approaches, mobile resources, and educational apps that utilize mobile technology for personalized and engaging learning. It also discusses BYOD policies, implementation difficulties, and successful case studies of BYOD adoption. The chapter offers best practices for maximizing the benefits of BYOD, including efficient teaching methods, classroom management strategies, and a safe learning environment. The chapter also speculates on future developments and effects of BYOD and mobile learning in the classroom, exploring new technologies and innovations that may influence education.
Wasswa Shafik and Kassim Kalinaki
IGI Global
Smart cities are imperative in terms of smart buildings, transportation, parking, healthcare, agriculture, traffic systems, and public safety aided by the fifth generation (5G) computation standards. They are entirely capable of controlling real-time devices and delivering relevant smart information to the citizens. However, different architectural stages experience privacy and security concerns. Therefore, in this survey, an internet of things (IoT) based architecture is proposed, showing the critical layers that are key to ensure secure smart IoT implementation. The study further covers the recent approaches to security applications for information centric SCs. 5G security solutions have been highlighted in SCs' settings and proposed. Comparably, a comprehensive SC current 5G security and numerous open security concerns are demonstrated. Lastly, offer potential research directions and motivations mainly in academia and industry, outlining these concerns that need to be considered to enhance smart daily operations.
Wasswa Shafik, Kassim Kalinaki, and Rufai Yusuf Zakari
IGI Global
This study examines the fusion of blockchain technology with the internet of things (IoT) in smart cities, exploring how Blockchain's traits like immutability, decentralization, and consensus can address smart cities' concerns. It scrutinizes IoT's applications in smart cities like traffic management and waste control, highlighting data's critical role. It accentuates blockchain's significance in device authentication, securing data integrity, and transactions in a decentralized network. Examining some case studies, it vitrines the benefits of integrating blockchain in smart cities such as optimized operations, enhanced security, and participant trust and confidence. Finally, it demonstrates privacy and security bottlenecks, including energy consumption, scalability, and regulations, emphasizing the need for solutions to overcome these challenges.
Wasswa Shafik
IGI Global
This study examines the complex array of impediments and potential advantages of internet of things (IoT)-enabled secure and intelligent smart healthcare devices (IESISHDs) associated with the shift towards enabling smart cities, motivated by the pressing necessity to address climate change and promote sustaining smart healthcare systems. This study looks at the technological, economic, and social problems that need to be solved in order to make cities smarter with IoT. It does this by reading a lot of scholarly sources. Most stupendously, it emphasizes the environmentally sustainable merits, potential for economic growth, and improvements in societal well-being that can arise from this transition. It further depicts selected case studies to demonstrate sustainable empirical evidence and provide policy recommendations. The paradigm is to assist governments and other stakeholders in effectively managing human-associated challenges to attain maximum sustainable value and an innovative healthcare future that guarantees worldwide prosperity and ecological welfare.
Kassim Kalinaki, Wasswa Shafik, Magezi Masha, and Adam A. Alli
IGI Global
The current surge in interconnected devices, which includes the Internet of Things (IoT) devices and the continually expanding cloud infrastructure, marks a new era of digital transformation and convenience. This transformative wave is reshaping industries, ushering in the age of smart cities, autonomous vehicles, and effortless remote collaboration. Yet, the growing complexity and reach of these technologies bring an accompanying increase in potential vulnerabilities and security risks. Thus, this study delves into the convergence of artificial intelligence (AI), cloud computing, and IoT security. It investigates how these state-of-the-art technologies can be leveraged to protect networks, data, and devices, presenting inventive solutions to address the ever-evolving threat landscape. Additionally, it sheds light on the challenges posed by AI-powered techniques and offers insights into future trends, making it a valuable resource for researchers, students, and cybersecurity professionals.
Wasswa Shafik
Improving Security, Privacy, and Connectivity Among Telemedicine Platforms IGI Global
This study examines the future of telemedicine by investigating the influence of developing technologies on healthcare. It emphasizes the need to comprehend the impact of these advancements on healthcare provision, given their swift progression. The debate encompasses a range of technologies, including internet of things (IoT) devices, artificial intelligence (AI), augmented reality (AR), robotics, blockchain, virtual reality (VR), genomics, and wearable tech within healthcare settings. It highlights the capacity for patient monitoring, diagnosis, tailored therapy, and improved access to healthcare. Furthermore, it tackles the legal, privacy, and ethical issues linked to these breakthroughs and emphasizes the need for ongoing study, collaboration, and strong regulation to exploit their capabilities fully.
Kassim Kalinaki, Wasswa Shafik, Sarah Namuwaya, and Sumaya Namuwaya
IGI Global
The emergence of the internet of things (IoT) has revolutionized many sectors of the economy, including logistics and supply chain management. By seamlessly integrating IoT into logistics operations, real-time tracking and monitoring of shipments becomes a reality, and optimizing routes and equipment performance becomes a breeze. Accordingly, supply chain operations have become streamlined like never before. This study delves into the various perspectives, applications, and challenges of deploying IoT in the logistics industry, offering a comprehensive overview for stakeholders, researchers, and students alike. With the potential for improved efficiency, effectiveness, and sustainability, the benefits of IoT in logistics are undeniable. The authors highlight future directions of this exciting field and learn how IoT shapes how we do business.
Wasswa Shafik
IGI Global
Examining the ethical aspects of artificial intelligence (AI) and data science (DS) recognizes their impressive progress in innovation while emphasizing the pressing necessity to tackle intricate ethical dilemmas. The chapter provides a detailed framework for navigating the changing environment, beginning with an examination of the increasing ethical challenges. The study highlights transparency, fairness, and responsibility as crucial for cultivating confidence in AI systems. The chapter emphasizes the urgent requirement to address problems such as algorithmic bias and privacy breaches with strong mitigation techniques. Furthermore, it promotes flexible policies that strike a balance between innovation and ethical safeguards. The examination of societal effects, particularly on various socioeconomic groups, economies, and cultures, is conducted thoroughly, with a focus on equity and the protection of individual rights. Finally, to proactively tackle future ethical challenges in technology, it is advisable to employ proactive solutions such as implementing AI ethics by design.
Wasswa Shafik
IGI Global
Artificial intelligence (AI) and robotics are becoming more popular globally, which makes Africa a potential hub for innovation and development in these fields. However, for the full benefits of these technologies to be realized, it is vital to understand and deal with the unique challenges and barriers that make it hard for them to be used and integrated in Africa. This chapter presents an overview of the current state of robotics and AI in Africa and explores the challenges associated with their adoption, including infrastructure limitations, inadequate technical expertise, and ethical considerations. It further discusses potential strategies for overcoming these challenges, such as investment in infrastructure and education, international collaboration, and the development of ethical frameworks for AI. Finally, the chapter suggests some future directions for continued attention and investment in the responsible and equitable development of AI and robotics in Africa to benefit stagnant and slow-growing African economies.
Wasswa Shafik
Chapman and Hall/CRC
Wasswa Shafik, Ahmad Fathan Hidayatullah, Kassim Kalinaki, and Muhammad Muzamil Aslam
Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem CRC Press
Yan Wang, Wasswa Shafik, Jin-Taek Seong, Aned Al Mutairi, Manahil SidAhmed Mustafa, and Mourad R. Mouhamed
Elsevier BV
Wasswa Shafik
IGI Global
The more technology advances, the extra benefits to the public and devices that connect to the internet have increased as well, commonly known as internet of things (IoT). The battery lifespan of these devices rises with technical concerns where an alternative to traditional energy attainment is needed. As the way forward, wireless sensor networks (WSNs) and IoT are tested to be used as novel energy alternatives through energy harvesting (EH). This study identifies the availability of energy by location. Similarly, it focuses on the sensor node's architecture with EH capabilities expanding to the classification of five EH techniques. It evaluates the EH developments in search of minimal resource utilization associated with WSNs. Its extensive distribution of interconnected devices is connected via the internet and other related high-tech innovations. Finally, it discusses the feasibility of energy storage and its potential for WSNs, paving the way for future trends and motivations.
Wasswa Shafik and Kassim Kalinaki
IGI Global
This chapter explores the growing use of technology in various aspects of people's lives and focuses on smart cities. First, it provides a comprehensive survey that examines the need for smart cities, their architectural elements, and the characteristics and purposes of different architectural layers. The chapter also offers an overview of notable smart cities such as London, New York, Singapore, Busan, Amsterdam, and Sunshine Coast Regions, highlighting their unique features. Next, privacy and security concerns associated with smart cities are addressed, emphasizing the importance of privacy issues and suggesting potential solutions. The chapter discusses future research directions, including the integration of blockchains, security considerations, collaborative filtering, and infrastructure upgrades in smart city applications. The analysis of privacy and security concerns is organized into three subsections: smart city security traits, leveraging issues, and privacy challenges and solutions. Finally, the chapter concludes by presenting future research trends in this field.
Wasswa Shafik, Ali Tufail, Chandratilak De Silva Liyanage, and Rosyzie Anna Awg Haji Mohd Apong
Wiley
BACKGROUND
Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security.
METHODOLOGY
An enhanced convolutional neural network (CNN) along with long short-term memory (LSTM) using a majority voting (MVE) ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre-trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM-CNN model for detecting plant pests and diseases. Experiments were carried out using Turkey Dataset, with 4,447 apple pests and diseases categorized into 15 different classes.
RESULTS
The study has been evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting (EMV) classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1-score, accuracy, and specificity using LSTM, ELM, and LR classifiers.
CONCLUSION
The presented model attained 99.2% accuracy in comparison to the cutting-edge models on different classifiers like LSTM, LR, and ELM, and performed better in comparison to transfer learning (TL). Pre-trained models, like VGG-19, VGG-18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared to other layers. This article is protected by copyright. All rights reserved.
Wasswa Shafik
IGI Global
As the public use drones (aircraft that can operate semi or autonomous), sometimes referred to as unmanned aerial vehicles or automotive aircrafts, to ease daily people's lifestyles, there are cyber security threats and cyber-attacks that hinder public safety and privacy during the moments when these drones are used. Cyber threats are analyzed based on the commonly known approaches to evaluate the cyber perspective and its effect on the public. Public drones' cyber security hazards are well tested using the STRIDE approach connected with the considered threats. The evaluation is highly dependent on the accuracy of drone mission definition, potential intruders, social, and other human-related cases. This chapter therefore encompasses the most current studies focusing on possible intruders portrayed as critical when carrying out a cyber security assessment. A brief future direction to mitigate cyber-related threats as it entails the public are conclusively depicted.
Sundus Naji Alaziz, Bakr Albayati, Abd al-Aziz H. El-Bagoury, and Wasswa Shafik
IGI Global
The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is cooperating persistently to find some ways to decrease its effect. The time series is one of the basic criteria that play a fundamental part in developing an accurate prediction model for future estimations regarding the expansion of this virus with its infective nature. The authors discuss in this paper the goals of the study, problems, definitions, and previous studies. Also they deal with the theoretical aspect of multi-time series clusters using both the K-means and the time series cluster. In the end, they apply the topics, and ARIMA is used to introduce a prototype to give specific predictions about the impact of the COVID-19 pandemic from 90 to 140 days. The modeling and prediction process is done using the available data set from the Saudi Ministry of Health for Riyadh, Jeddah, Makkah, and Dammam during the previous four months, and the model is evaluated using the Python program. Based on this proposed method, the authors address the conclusions.
Ahmad Fathan Hidayatullah, Kassim Kalinaki, Muhammad Muzamil Aslam, Rufai Yusuf Zakari, and Wasswa Shafik
IEEE
Social media platforms like Twitter have become substantial sources of user-generated content, enabling people to easily express their emotions and opinions. However, this freedom has increased the spread of harmful content, such as abusive language, sexually explicit content, and hate speech. This poses challenges for content moderation and user safety. In order to guarantee a safer, more receptive, and more pleasurable online environment for users of all ages, it is essential to develop a system capable of recognizing abusive and sexually explicit material on Twitter. Despite the growing importance of content moderation, a research gap exists in Indonesian tweets, with limited comprehensive studies on negative content identification. This research addresses this gap by evaluating the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) models in the Indonesian context, which were primarily developed for English and other languages. This research aims to identify abusive, adult, and neutral content in Indonesian tweets by examining and fine-tuning BERT-based models to maintain a healthy online environment for optimal tweet classification. Based on our experiments, the BERT-based models showed promising results in detecting negative tweets. Among the BERT-based models, IndoBERTweet achieved the best precision, recall, and macro F1 scores with 97.03, 96.88, and 96.94, respectively.
Wasswa Shafik, S. Mojtaba Matinkhah, and Fawad Shokoor
Walter de Gruyter GmbH
Abstract Context With the rapid advancement of unmanned aerial vehicle (UAV) technology, ensuring these autonomous systems’ security and integrity is paramount. UAVs are susceptible to cyberattacks, including unauthorized access, control, or manipulation of their systems, leading to potential safety risks or unauthorized data retrieval. Moreover, UAVs encounter limited computing resources, wireless communication and physical vulnerabilities, evolving threats and techniques, necessity for compliance with regulations, and human factors. Methods This review explores the potential cyberthreats faced by UAVs, including hacking, spoofing, and data breaches, and highlights the critical need for robust security measures. It examines various strategies and techniques used to protect UAVs from cyberattacks, e.g., encryption, authentication, and intrusion detection systems using cyberthreat analysis and assessment algorithms. The approach to assess the UAVs’ cybersecurity hazards included STRIDE (a model for identifying computer security-related threats) connected with the threats considered. Findings Emphasis was laid on the evaluation highly depending on the accuracy of UAV mission definition, potential intruders, and social and other human-related situations. The review discovered that most studies focused on possible intruders’ portraits, which can be crucial when conducting a cybersecurity assessment. Based on a review, future research directions to mitigate cybersecurity risks are presented. Significance Protecting UAVs from cyberthreats ensures safe operations and data integrity and preserves public trust in autonomous systems.
Wasswa Shafik
IGI Global
This chapter examines how education, technology, national and international regulations contribute to a comprehensive cybersecurity framework for present and future global IT companies. IT-driven enterprises may utilize the following security recommendations. Businesses who seek to examine their external and internal security with security upload and establish settings for success regardless of location must solve these issues. To produce more effective legislation, education efforts, and technologies that are resistant to cyberattacks, this work explores fundamental research gaps in cybersecurity and demonstrates how cybersecurity may be divided into these three fundamental categories and integrated to tackle problems such as the creation of training environments for authentic cybersecurity situations. It will explain links between technology and certification and discuss legislative standards and instructional frameworks for merging criteria for system accreditation and cybersecurity. The study finishes with wireless network security recommendations.
Wasswa Shafik
IGI Global
The internet of things (IoT) entails all devices that can get onto the internet. This is mainly because of the technological advancement. This exponential growth of IoT increases on the dense nodes with a huge data volume on the network that affect the collision and network congestion probabilities. This chapter presents a comprehensive description of the central and supporting innovations that are used to make cities smarter, focusing on the fifth generation (5G) IoT paradigm from a software-based network viewpoint. Furthermore, the main initiatives of international significance are discussed. Also, the chapter presents software-defined networking (SDN), IoT, and network function virtualization (NFV) challenges as it relates to the user privacy and security, IoT security, energy consumption, integration of IoT with subsystems, and architecture design. A segment of the top five future trends that are made and will make cities smarter is conclusively included.