Muni Repository :: Browsing by Author "Mijwil, Maad M."

Browsing by Author "Mijwil, Maad M."

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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.
Analysing the connection between ai and industry 4.0 from a cybersecurity perspective: defending the smart revolution
(Mesopotamian Journal of Big Data, 2023-05-05) Bala, Indu; Mijwil, Maad M.; Ali, Guma; Sadıkoğlu, Emre
In recent years, the significance and efficiency of business performance have become dependent heavily on digitization, as jobs in companies are seeking to be transformed into digital jobs based on smart systems and applications of the fourth industrial revolution. Cybersecurity systems must interact and continuously cooperate with authorized users through the Internet of Things and benefit from corporate services that allow users to interact in a secure environment free from electronic attacks. Artificial intelligence methods contribute to the design of the Fourth Industrial Revolution principles, including interoperability, information transparency, technical assistance, and decentralized decisions. Through this design, security gaps may be generated that attackers can exploit in order to be able to enter systems, control them, or manipulate them. In this paper, the role of automated systems for digital operations in the fourth industrial revolution era will be examined from the perspective of artificial intelligence and cybersecurity, as well as the most significant practices of artificial intelligence methods. This paper concluded that artificial intelligence methods play a significant role in defending and protecting cybersecurity and the Internet of Things, preventing electronic attacks, and protecting users' privacy.
Artificial intelligence solutions for health 4.0: overcoming challenges and surveying applications
(Mesopotamian Journal of Artificial Intelligence in Healthcare, 2023-03-10) Al-Mistarehi, Abdel-Hameed; Mijwil, Maad M.; Filali, Youssef; Bounabi, Mariem; Ali, Guma; Abotaleb, Mostafa
In recent years, the term Health 4.0 has appeared in health services and is related to the concept of Industry 4.0. The term Health 4.0 focuses on replacing traditional care in hospitals and medical clinics with home health services that are based on artificial intelligence techniques through the use of telemedicine applications that allow the monitoring of patients in a virtual environment. This term is utilized to represent digital change in the healthcare sector. Governments aim to develop the level of medical care in hospitals and clinics to ensure the provision of healthcare benefits at low costs and increase patient satisfaction. It has become vital for hospitals to grow their environment into digital environments in their services through the use of a set of computer programs based on artificial intelligence. Artificial intelligence techniques in Health 4.0 provide a set of procedures that benefit patients and healthcare workers, including early diagnosis, make inquiries into treatment, data analysis, reports on the patient's condition, and others. The primary purpose of this article is to determine the significance of Health 4.0 and AI techniques in healthcare by mentioning the most important benefits and weaknesses of using AI techniques in healthcare.
A Comprehensive review on cybersecurity issues and their mitigation measures in FinTech
(Al-Iraqia Univeristy, 2024-06-10) Ali, Guma; Mijwil, Maad M.; Buruga, Bosco Apparatus; Abotaleb, Mostafa
The fourth industrial revolution has seen the evolution and wide adoption of game-changing and disruptive innovation, "financial technologies (FinTech), around the globe. However, the security of FinTech systems and networks remains critical. This research paper comprehensively reviews cybersecurity issues and their mitigation measures in FinTech. Four independent researchers reviewed relevant literature from IEEE Xplore, ScienceDirect, Taylor & Francis, Emerald Insight, Springer, SAGE, WILEY, Hindawi, MDPI, ACM, and Google Scholar. The key findings of the analysis identified privacy issues, data breaches, malware attacks, hacking, insider threats, identity theft, social engineering attacks, distributed denial-of-service attacks, cryptojacking, supply chain attacks, advanced persistent threats, zero-day attacks, salami attacks, man-in-the-middle attacks, SQL injection, and brute-force attacks as some of the significant cybersecurity issues experienced by the FinTech industry. The review paper also suggested authentication and access control mechanisms, cryptography, regulatory compliance, intrusion detection and prevention systems, regular data backup, basic security training, big data analytics, use of artificial intelligence and machine learning, FinTech regulatory sandboxes, cloud computing technologies, blockchain technologies, and fraud detection and prevention systems as mitigation measures for cybersecurity issues. However, tackling cybersecurity issues will be paramount if FinTech is to realize its full potential. Ultimately, this research will help develop robust security mechanisms for FinTech systems and networks to achieve sustainable financial inclusion.
Cybersecurity for sustainable smart healthcare: State of the Art, taxonomy, mechanisms, and essential roles
(Mesopotamian Journal of CyberSecurity, 2024-05-23) Ali, Guma; Mijwil, Maad M.
Cutting-edge technologies have been widely employed in healthcare delivery, resulting in transformative advances and promising enhanced patient care, operational efficiency, and resource usage. However, the proliferation of networked devices and data-driven systems has created new cybersecurity threats that jeopardize the integrity, confidentiality, and availability of critical healthcare data. This review paper offers a comprehensive evaluation of the current state of cybersecurity in the context of smart healthcare, presenting a structured taxonomy of its existing cyber threats, mechanisms and essential roles. This study explored cybersecurity and smart healthcare systems (SHSs). It identified and discussed the most pressing cyber threats and attacks that SHSs face, including fake base stations, medjacking, and Sybil attacks. This study examined the security measures deployed to combat cyber threats and attacks in SHSs. These measures include cryptographic-based techniques, digital watermarking, digital steganography, and many others. Patient data protection, the prevention of data breaches, and the maintenance of SHS integrity and availability are some of the roles of cybersecurity in ensuring sustainable smart healthcare. The long-term viability of smart healthcare depends on the constant assessment of cyber risks that harm healthcare providers, patients, and professionals. This review aims to inform policymakers, healthcare practitioners, and technology stakeholders about the critical imperatives and best practices for fostering a secure and resilient smart healthcare ecosystem by synthesizing insights from multidisciplinary perspectives, such as cybersecurity, healthcare management, and sustainability research. Understanding the most recent cybersecurity measures is critical for controlling escalating cyber threats and attacks on SHSs and networks and encouraging intelligent healthcare delivery.
The effect of human-computer interaction on new applications by exploring the use case of ChatGPT in healthcare services. In modern technology in healthcare and medical education: Blockchain, IoT, AR, and VR
(IGI Global, 2024) Mijwil, Maad M.; Naji, Aseel Shakir; Doshi, Ruchi; Hiran, Kamal Kant; Bala, Indu; Ali, Guma
Human-Computer interaction (HCI) is a domain that focuses on growing the interaction between humans and computer systems by designing and developing user interfaces that are efficient and delightful to use. In this chapter, the authors focus on the importance of deep human-computer interaction on new applications with an emphasis on using ChatGPT applications in the health services domain. This chapter provides full details on the importance of executing ChatGPT in various health-related scenarios while highlighting the importance of HCI to enhance user interactions in personalized medical advice in a ChatGPT application. This chapter concludes that the capabilities of ChatGPT and artificial intelligence applications can revolutionize the healthcare industry by enhancing the accessibility and effectiveness of new media communications between the user and applications while creating innovative resolutions to improve healthcare services.
The evolving role of artificial intelligence in the future of distance learning: Exploring the next frontier
(Mesopotamian Journal of Computer Science, 2023-05-17) Mijwil, Maad M.; Ali, Guma; Sadıkoğlu, Emre
In recent years, education has become especially related to the applications provided by artificial intelligence technology through a digital environment that includes a set of tools that assist in processing and storing information. Artificial intelligence techniques contribute to the development of students' skills by providing them with advanced scientific content and building their mental capabilities faster. Moreover, these techniques support analysing student data and suggest suitable educational materials and activities for them. Artificial intelligence is a noteworthy tool for the growth of distance education, especially after the development of expert systems that have become a human advisor in many domains, as this leads to the development of education systems that adjust the level of difficulty of materials based on the student’s performance in the electronic classroom, which ensures that the student continues in education and is not frustrated. This article will review the influential role of artificial intelligence applications in growing distance learning, improving the quality of education, and making it an adaptable and practical environment for students.
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.
Innovative Livestock: A Survey of artificial intelligence techniques in livestock farming management
(Wasit Journal of Computer and Mathematics Science, 2023-12-30) Mijwil, Maad M.; Adelaja, Oluwaseun; Badr, Amr; Ali, Guma; Buruga, Bosco Apparatus; Pudasaini, Pramila
Modern technology has recently become a meaningful part of all life sectors, as software, sensors, smart machines, and expert systems are successfully integrated into the physical environment. This technology relies in its work on artificial intelligence techniques to make the right decisions at the right time. These technologies have a significant role in improving productivity, product quality, and industry outputs by significantly reducing human labour and errors that humans may cause. Artificial intelligence techniques are increasingly being integrated into animal husbandry and animal revolution management because they provide advantages and means that serve agriculturalists. These techniques monitor the emotional state of animals, milk production and herd management, feeding habits, the movement of animals, and their health status. AI-powered sensors can monitor the health of livestock and detect early signs of illness or stress to which they are exposed. Also, these techniques contribute to assisting agriculturalists in customising feeding programs, reducing waste, and improving product quality. This article will discuss the role of artificial intelligence techniques in animal control, farm management, disease surveillance, and sustainable resource optimisation practices.
Innovative livestock: A survey of artificial intelligence techniques in livestock farming management
(Wasit Journal of Computer and Mathematics Science, 2023-12-30) Mijwil, Maad M.; Adelaja, Oluwaseun; Badr, Amr; Ali, Guma; Buruga, Bosco Apparatus; Pudasaini, Pramila
Modern technology has recently become a meaningful part of all life sectors, as software, sensors, smart machines, and expert systems are successfully integrated into the physical environment. This technology relies in its work on artificial intelligence techniques to make the right decisions at the right time. These technologies have a significant role in improving productivity, product quality, and industry outputs by significantly reducing human labour and errors that humans may cause. Artificial intelligence techniques are increasingly being integrated into animal husbandry and animal revolution management because they provide advantages and means that serve agriculturalists. These techniques monitor the emotional state of animals, milk production and herd management, feeding habits, the movement of animals, and their health status. AI-powered sensors can monitor the health of livestock and detect early signs of illness or stress to which they are exposed. Also, these techniques contribute to assisting agriculturalists in customising feeding programs, reducing waste, and improving product quality. This article will discuss the role of artificial intelligence techniques in animal control, farm management, disease surveillance, and sustainable resource optimisation practices.
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.
Sensing of type 2 diabetes patients based on Internet of Things solutions: An Extensive survey
(IGI Global, 2024) Mijwil, Maad M.; Bala, Indu; Ali, Guma; Aljanabi, Mohammad; Abotaleb, Mostafa; Doshi, Ruchi; Hiran, Kamal Kant; El-Kenawy, El-Sayed M.
Internet of things solutions have brought about a significant revolution in the development of healthcare by providing remote monitoring capabilities and providing doctors with reports on patients in real-time, which leads to developing the care of patients with type 2 diabetes and enhancing their health condition. Through several sensors, IoT devices can collect patients' health data, such as glucose level, blood pressure, heart rate, and physical activity, so that healthcare workers can assess patients' health status and disease development within the body. These devices contribute to saving patients' lives by providing continuous monitoring of vital signs and disease management by physicians and healthcare workers. In this context, this article contributes to reviewing the development of IoT solutions in providing information and mechanisms adopted in monitoring patients with type 2 diabetes, data security issues, privacy concerns, and interoperability.

Browsing by Author "Mijwil, Maad M."

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