Research Articles

Permanent URI for this collection

https://dir.muni.ac.ug/handle/20.500.12260/17

Browse

Browsing Research Articles by Author "Adamopoulos, Ioannis"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    A survey on artificial intelligence in cybersecurity for smart agriculture: state-of-the-art, cyber threats, artificial intelligence applications, and ethical concerns.
    (Mesopotamian Academic Press, Imam Ja'afar Al-Sadiq University, 2024-07-20) Ali, Guma; Mijwil, Maad M.; Buruga, Bosco Apparatus; Abotaleb, Mostafa; Adamopoulos, Ioannis
    Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review described advancements from conventional agriculture to modern SA, including architecture and emerging technology. It digs into SA’s numerous uses, emphasizing its potential to transform farming efficiency, production, and sustainability. The growing reliance on SA introduces new cyber threats that endanger its integrity and dependability and provide a complete analysis of their possible consequences. Still, the research examined the essential role of AI in combating these threats, focusing on its applications in threat identification, risk management, and real-time response mechanisms. The survey also discusses ethical concerns such as data privacy, the requirement for high-quality information, and the complexities of AI implementation in SA. This study, therefore, intends to provide researchers and practitioners with insights into AI’s capabilities and future directions in the security of smart agricultural infrastructures. This study hopes to assist researchers, policymakers, and practitioners in harnessing AI for robust cybersecurity in SA, assuring a safe and sustainable agricultural future by comprehensively evaluating the existing environment and future trends.
  • Thumbnail Image
    Item
    Harnessing the potential of artificial intelligence in managing viral hepatitis
    (Mesopotamian journal of Big Data, 2024-08-15) Ali, Guma; Mijwil, Maad M.; Adamopoulos, Ioannis; Buruga, Bosco Apparatus; Gök, Murat; Sallam, Malik
    Viral hepatitis continues to be a serious global health concern, impacting millions of people, putting a strain on healthcare systems across the world, and causing significant morbidity and mortality. Traditional diagnostic, prognostic, and therapeutic procedures to address viral hepatitis are successful but have limits in accuracy, speed, and accessibility. Artificial intelligence (AI) advancement provides substantial opportunities to overcome these challenges. This study investigates the role of AI in revolutionizing viral hepatitis care, from early detection to therapy optimization and epidemiological surveillance. A comprehensive literature review was conducted using predefined keywords in the Nature, PLOS ONE, PubMed, Frontiers, Wiley Online Library, BMC, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar databases. Peer-reviewed publications written in English between January 2019 and August 2024 were examined. The data of the selected research papers were synthesized and analyzed using thematic and narrative analysis techniques. The use of AI-driven algorithms in viral hepatitis control involves many significant aspects. AI improves diagnostic accuracy by integrating machine learning (ML) models with serological, genomic, and imaging data. It enables tailored treatment plans by assessing patient-specific characteristics and predicting therapy responses. AI-powered technologies aid in epidemiological modeling, and AI-powered systems effectively track treatment adherence, identify medication resistance, and control complications associated with chronic hepatitis infections. It is vital in identifying new antiviral medicines and vaccines, speeding the development pipeline through high-throughput screening and predictive modeling. Despite its transformational promise, using AI in viral hepatitis care presents various challenges, including data privacy concerns, the necessity for extensive and varied datasets, and the possibility of algorithmic biases. Ethical considerations, legal frameworks, and multidisciplinary collaboration are required to resolve these issues and ensure AI technology’s safe and successful use in clinical practice. Exploiting the full AI’s potential for viral hepatitis management provides unparalleled prospects to improve patient outcomes, optimize public health policies, and, eventually, and alleviate the disease’s negative impact worldwide. This study seeks to provide academics, medics, and policymakers with the fundamental knowledge they need to harness AI’s potential in the fight against viral hepatitis.
  • Thumbnail Image
    Item
    Securing the internet of wetland things (IoWT) using machine and deep learning methods: a survey
    (Mesopotamian journal of Computer Science, 2025-02-03) Ali, Guma; Wamusi, Robert; Mijwil, Maad M.; Sallam, Malik; Ayad, Jenan; Adamopoulos, Ioannis
    Wetlands are essential ecosystems that provide ecological, hydrological, and economic benefits. However, human activities and climate change are degrading their health and jeopardizing their long-term sustainability. To address these challenges, the Internet of Wetland Things (IoWT) has emerged as an innovative framework integrating advanced sensing, data collection, and communication technologies to monitor and manage wetland ecosystems. Despite its potential, the IoWT faces substantial security and privacy risks, compromising its effectiveness and hindering adoption. This survey explores integrating machine learning (ML) and deep learning (DL) techniques as solutions to address the security threats, vulnerabilities, and challenges inherent in IoWT ecosystems. The survey examines findings from 231 sources, encompassing peer-reviewed journal articles, conference papers, books, book chapters, and websites published between 2020 and 2025. It consolidates insights from prominent platforms such as the Springer Nature, Emerald Insight, ACM Digital Library, Frontiers, Wiley Online Library, SAGE, Taylor & Francis, IGI Global, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar. Machine learning and DL methods have proven highly effective in detecting adversarial attacks, identifying anomalies, recognizing intrusions, and uncovering man-in-the-middle attacks, which are crucial in securing systems. These techniques also focus on detecting phishing, malware, and DoS/DDoS attacks and identifying insider and advanced persistent threats. They help detect botnet attacks and counteract jamming and spoofing efforts, ensuring comprehensive protection against a wide range of cyber threats. The survey examines case studies and the unique requirements and constraints of IoWT systems, such as limited energy resources, diverse sensor networks, and the need for real-time data processing. It also proposes future directions, such as developing lightweight, energy-efficient algorithms that operate effectively within the constrained environments typical of IoWT applications. Integrating ML and DL methods strengthens IoWT security while protecting and preserving wetlands through intelligent and resilient systems. These findings offer researchers and practitioners valuable insights into the current state of IoWT security, helping them drive and shape future advancements in the field.