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

Browsing by Author "Mijwil, Maad M."

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A Survey on artificial intelligence and blockchain applications in cybersecurity for smart cities
(2025-01-10) Asiku, Denis; Adebo, Thomas; Wamusi, Robert; Aziku, Samuel; Kabiito, Simon Peter; Zaward, Morish; Sallam, Malik; Ali, Guma; Mijwil, Maad M.
Smart cities rapidly evolve into transformative ecosystems where advanced technologies work together to improve urban living. These interconnected environments use emerging technologies to offer efficient services and sustainable solutions for urban challenges. As these systems become more complex, their vulnerability to cybersecurity threats also increases. Integrating artificial intelligence (AI) and Blockchain technologies to address these challenges presents promising solutions that ensure secure and resilient infrastructures. This study provides a comprehensive survey of integrating AI, Blockchain, cybersecurity, and smart city technologies based on an analysis of peer-reviewed journals, conference proceedings, book chapters, and websites. Seven independent researchers reviewed relevant literature published between January 2021 and December 2024 using ACM Digital Library, Wiley Online Library, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, IGI Global, and Google Scholar. The study explores how AI can enhance threat detection, anomaly detection, and predictive analytics, enabling real-time responses to cyber threats. It examines various AI methodologies, including machine learning and deep learning, to identify vulnerabilities and prevent attacks. It discusses the role of Blockchain in securing data integrity, improving transparency, and providing decentralized control over sensitive information. Blockchain’s tamper-proof ledger and smart contract capabilities offer innovative solutions for identity management, secure transactions, and data sharing among smart city stakeholders. The study also highlights how combining AI and Blockchain can create robust cybersecurity frameworks, enhancing resilience against emerging threats. The survey concludes by outlining future research directions and offering recommendations for policymakers, urban planners, and cybersecurity professionals. This study identifies emerging trends and applications for enhancing the security and resilience of smart cities through innovative technological solutions. The survey provides valuable insights for researchers and practitioners who aim to utilize AI and Blockchain to improve smart city cybersecurity.
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.
An Editorial vision for civil engineering: navigating intelligence and innovation
(Mesopotamian Academic Press, 2025-11-01) Mijwil, Maad M.; Adamopoulos, Ioannis; Ali, Guma
The integration of Artificial Intelligence in civil engineering has seen a major advancement over the applications of primitive data-driven models, reaching advanced hybrid physics-informed models. This evolution represents a significant change of paradigm from the basic predictive analytics to what is now called "structural cognition." But in this cutting-edge paradigm, the evolutionary learning of AI beyond the structural prediction is further developed, with the ability to learn underlying causal relationships in complicated engineering systems. This not only allows them to diagnose potential problems, but also to proactively propose well-targeted remedial measures, giving engineers a greater understanding of the situation as well as better, better-informed decision-making with respect to infrastructure sustainability and safety [1], [2]. A critical element to this evolution is the emergence of "perceptive infrastructure." This idea is further greatly supported by similar inventions like wavelet-diffusion architectures, and it is very useful in the design of the most robust vision-based monitoring systems. The systems are especially well-suited to demanding environments, such as low-light and other bad weather conditions where conventional surveillance techniques tend to break down. By equipping infrastructure with the ability to sense and make real-time sense of their environment with high fidelity, these technologies are ushering in a new era of self-sensing, self-reporting and even self-predicting civil assets, heading toward constant intelligent self-diagnosis [1]. Figure 1 illustrates artificial intelligence tools in civil engineering.
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.
Blockchain and deep Q-learning for trusted cloud-enabled drone network in smart forestry: A Survey
(Imam Ja’afar Al-Sadiq University, 2025-12-14) Ali, Guma; Wamusi, Robert; Mijwil, Maad M.; Al-Hamzawi, Hassan A. Hameed; Al Sailawi, Ali S. Abed; Salau, Ayodeji Olalekan
The convergence of drone technology, cloud computing, and intelligent decision-making is revolutionizing precision forestry. However, deploying large-scale drone networks in smart forestry faces challenges such as trust, security, data integrity, and autonomous coordination. This survey examines how combining Blockchain technology with deep Q-learning (DQL) can address these issues within cloud-enabled drone networks. Drawing on 102 peer-reviewed sources published between 2022 and 2025 from reputable platforms such as ACM Digital Library, Frontiers, Wiley Online Library, PLoS, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, and Google Scholar, this work highlights recent advancements in secure and intelligent drone ecosystems. Blockchain provides a decentralized, tamper-resistant framework for validating transactions and securing data exchange among autonomous drones, ensuring the integrity, confidentiality, and authenticity of environmental data. This is critical in forestry, where data manipulation and unauthorized access pose significant risks. Complementing this, DQL enables drones to make autonomous decisions by interpreting real-time environmental data and learning from past experiences, allowing drones to adjust their flight paths, optimize resource utilization, and enhance data collection in dynamic forest environments, such as wildfires or illegal logging operations. Together, Blockchain and DQL create a resilient, scalable architecture that supports secure, real-time, and intelligent forest monitoring. This framework lays the groundwork for developing autonomous and trustworthy drone networks that promote sustainable and climate-smart forestry management.
Blockchain and federated learning in edge-fog-cloud computing environments for smart logistics
(Mesopotamian Academic Press, 2025-07-22) Ali, Guma; Adebo, Thomas; Mijwil, Maad M.; Al-Mahzoum, Kholoud; Sallam, Malik; Salau, Ayodeji Olalekan; Adamopoulos, Ioannis; Bala, Indu; Al-jubori, Aseed Yaseen Rashid
The rapid growth of smart logistics, driven by IoT devices and data-intensive applications, necessitates secure, scalable, and efficient computing frameworks. As the edge-fog-cloud (EFC) paradigm supports real-time data processing, it faces significant security threats and attacks, including privacy risks, data breaches, and unauthorized access. To address these security threats and attacks, blockchain and federated learning (FL) have gained popularity as potential solutions in EFC computing environments for smart logistics. This survey reviews the current landscape in EFC computing environments for smart logistics, highlighting the existing benefits and challenges identified in 134 research studies published between January 2023 and June 2025. The applications of blockchain and FL demonstrate their ability to enhance data security and privacy, improve real-time tracking and monitoring, and ensure inventory and supply chain optimization. Although these technologies offer promising solutions, challenges such as scalability issues, data quality, interoperability and standardization hinder their effective implementation. The survey suggests future research directions focused on developing advanced blockchain and FL, integrating emerging technologies, developing policies and regulations, fostering collaborative research, and ensuring cross-industry adoption and interoperability. Integrating blockchain and FL within the EFC model offers a transformative path toward building secure, intelligent, and resilient logistics systems.
Blockchain and quantum machine learning approach for securing smart water management systems: A Scoping review.
(Peninsula Publishing Press, 2025-09-01) Ali, Guma; Mijwil, Maad M.; Adamopoulo, Ioannis; Dhoska, Klodian
Smart water management systems (SWMS) increasingly rely on Internet of Things (IoT) devices to enhance water distribution, detect leaks, and support sustainable resource use, but this reliance also heightens exposure to cyberattacks, data manipulation, and privacy risks. Conventional security approaches often fall short due to the decentralized design and real-time demands of these systems. This scoping review analyzes 266 studies published between January 2022 and December 2025 to assess how integrating Blockchain and quantum machine learning (QML) can strengthen the security, privacy, and reliability of SWMS. The review examines Blockchain-enabled water management, quantum computing applications, and QML-based security frameworks, using thematic analysis to categorize emerging architectures and challenges. Findings of the focused studies show growing adoption of Blockchain for secure data logging, access control, and tamper-proof auditing. At the same time, QML demonstrates strong potential in anomaly detection, predictive maintenance, and optimizing distribution networks. Although these technologies offer a promising foundation for resilient water infrastructure, most research remains conceptual, with limited real-world deployment or scalability assessments. Integrating Blockchain with QML could create robust, privacy-preserving SWMS frameworks. However, significant barriers persist, including the computational intensity of quantum models, interoperability issues with existing IoT infrastructures, and the absence of standardized protocols. Addressing these gaps is essential for practical implementation. This review underscores the need for scalable hybrid designs, applied validation, and cross-disciplinary standards to advance secure, efficient, and sustainable smart water management solutions.
Chinese generative AI models challenge western AI in clinical chemistry MCQs: A Benchmarking follow-up study on AI use in health education
(Mesopotamian Press, 2025-02-08) Sallam, Malik; Al-Mahzoum, Kholoud; Eid, Huda; Al-Salahat, Khaled; Sallam, Mohammed; Ali, Guma; Mijwil, Maad M.
Background: The emergence of Chinese generative AI (genAI) models, such as DeepSeek and Qwen, has introduced strong competition to Western genAI models. These advancements hold significant potential in healthcare education. However, benchmarking the performance of genAI models in specialized medical disciplines is crucial to assess their strengths and limitations. This study builds on prior research evaluating ChatGPT (GPT-3.5 and GPT-4), Bing, and Bard against human postgraduate students in Medical Laboratory Sciences, now incorporating DeepSeek and Qwen to assess their effectiveness in Clinical Chemistry Multiple-Choice Questions (MCQs). Methods: This study followed the METRICS framework for genAI-based healthcare evaluations, assessing six models using 60 Clinical Chemistry MCQs previously administered to 20 MSc students. The facility index and Bloom’s taxonomy classification were used to benchmark performance. GenAI models included DeepSeek-V3, Qwen 2.5-Max, ChatGPT-4, ChatGPT-3.5, Microsoft Bing, and Google Bard, evaluated in a controlled, non-interactive environment using standardized prompts. Results: The evaluated genAI models showed varying accuracy across Bloom’s taxonomy levels. DeepSeek-V3 (0.92) and ChatGPT-4 (1.00) outperformed humans (0.74) in the Remember category, while Qwen 2.5-Max (0.94) and ChatGPT-4 (0.94) surpassed human performance (0.61) in the Understand category. ChatGPT-4 (+23.25%, p < 0.001), DeepSeek-V3 (+18.25%, p = 0.001), and Qwen 2.5-Max (+18.25%, p = 0.001) significantly outperformed human students. Decision tree analysis identified cognitive category as the strongest predictor of genAI accuracy (p < 0.001), with Chinese AI models performing comparably to ChatGPT-4 in lower-order tasks but exhibiting lower accuracy in higher-order domains. Conclusions: The findings highlighted the growing capabilities of Chinese genAI models in healthcare education, proving that DeepSeek and Qwen can compete with, and in some areas outperform, Western genAI models. However, their relative weakness in higher-order reasoning raises concerns about their ability to fully replace human cognitive processes in clinical decision-making. As genAI becomes increasingly integrated into health education, concerns regarding academic integrity, genAI dependence, and the validity of MCQ-based assessments must be addressed. The study underscores the need for a re-evaluation of medical assessment strategies, ensuring that students develop critical thinking skills rather than relying on genAI for knowledge retrieval.
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.
Enhancing cybersecurity in smart education with deep learning and computer vision: A Survey.
(Mesopotamian Academic Press, 2025-06-26) Ali, Guma; Aziku, Samuel; Mijwil, Maad M.; Al-Mahzoum, Kholoud; Sallam, Malik; Salau, Ayodeji Olalekan; Bala, Indu; Dhoska, Klodian; Melekoglu, Engin
The rapid digital transformation of education, driven by the widespread adoption of smart devices and online platforms, has ushered in the era of smart education. While this shift enhances learning experiences, it also introduces significant cybersecurity risks that threaten the confidentiality, integrity, and availability of educational resources, student data, and institutional systems. This survey examines how deep learning (DL) and computer vision (CV) techniques can enhance cybersecurity in smart education environments. By reviewing 202 peer-reviewed research papers published between January 2022 and June 2025 across leading publishers such as ACM Digital Library, Frontiers, Wiley Online Library, IGI Global, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, BMC, and Google Scholar, the study explores the integration of these advanced technologies to address emerging threats. It highlights the use of DL in intrusion detection, anomaly detection, and biometric authentication to protect digital learning platforms. It also examines how CV techniques, such as facial recognition, behavioral analysis, and emotion detection, enhance security and foster adaptive learning environments. The survey also addresses key challenges, including data quality, model interpretability, computational costs, and ethical considerations. By identifying research gaps and proposing future directions, this survey offers valuable insights for researchers, educators, and policymakers aiming to develop robust, scalable, and ethical AI-driven cybersecurity solutions for smart education.
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.
Explainable AI for healthcare: training healthcare workers to use artificial intelligence techniques to reduce medical negligence in Ghana’s Public Health Act, 2012 (Act 851)
(Peninsula Publishing Press, 2025-01-10) Mensah, George Benneh; Mijwil, Maad M.; Abotaleb, Mostafa; Ali, Guma; Awwad, Emad Mahrous; Dutta, Pushan Kumar; Mzili, Toufik; Eid, Marwa M.
This analysis examines whether Ghana’s Public Health Act, 2012 (Act 851) imposes adequate legal responsibilities on healthcare facilities concerning personnel training on artificial intelligence (AI) systems and implementation of medical negligence reduction measures. Through an evaluative review of Act 851 provisions on staff qualifications, technology deployment, quality care, safety planning, and risk management benchmarks relative to precedents in Ghana and other countries, critical gaps in binding regulations to incentivize organizational capacity building for mitigating errors, hazards and liabilities from substandard practices were identified. Key recommendations include amending Act 851 to mandate credentialing assurance frameworks, clinical audits, risk assessment models and transparency requirements around reporting quality indicators. Strengthening policy directives will compel internal monitoring, governance, and accountability among healthcare facilities as multilayered negligence prevention strategies. Scientific contributions highlight deficiencies in Ghana’s health legislation regarding contemporary challenges like AI adoption risks and propose legal reforms to modernize regulations to support safer, responsible healthcare delivery nationwide.
Fusion of blockchain, IoT, artificial intelligence, and robotics for efficient waste management in smart cities
(Association of Talent under Liberty in Technology, 2025-08-09) Ali, Guma; Asiku, Denis; Mijwil, Maad M.; Adamopoulos, Ioannis; Dudek, Marek
Rapid urbanization and population growth are accelerating waste generation in cities worldwide, posing serious environmental and socio-economic challenges. Traditional waste management systems, often centralized and infrastructure-deficient, struggle with inefficiencies, unscheduled collection, and a lack of real-time data. These limitations hinder progress toward smart and sustainable urban environments. Blockchain, the Internet of Things (IoT), Artificial Intelligence (AI), and Robotics are reshaping waste collection, sorting, and recycling. This review examines how these technologies integrate to create secure, efficient, and sustainable waste management in smart cities. An analysis of 184 studies published between January 2022 and July 2025 reveals key shortcomings in conventional waste management systems and showcases the benefits of smart waste management solutions. The results showed that cities are already using IoT-enabled smart bins, AI-driven route optimization, Blockchain for waste tracking, and robotic sorting. However, challenges such as data privacy concerns, limited Blockchain scalability, system interoperability gaps, sensor reliability issues, and high computational demands limit broader adoption. The review outlines future research priorities, including AI-powered waste forecasting, swarm robotics, real-time edge computing, and enhanced cybersecurity. By providing a roadmap for technological innovation and integration, this study supports policymakers, urban planners, and industry leaders in developing intelligent, cost-effective, and environmentally resilient waste management systems.
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.
High performance medicine: Involving artificial intelligence models in enhancing medical laws and medical negligence matters A Case study of Act, 2009 (Act 792) in Ghana
(Peninsula Publishing Press, 2025-01-10) Mensah, George Benneh; Mijwil, Maad M.; Abotaleb, Mostafa; Ali, Guma; Dutta, Pushan Kumar
This paper examines Ghana's Interpretation Act, 2009 for applicability in AI medical negligence cases. Doctrinal analysis focuses on causation and liability apportionment provisions. Findings reveal opacity and distributed responsibility issues in attributing algorithm harm via "but-for" and related tests. However, contributory liability and proportionality stipulations provide means for an equitable remedy. Recommendations include codifying AI accountability through updated laws and jurisprudence, plus transparency requirements for medical AI approvals. Ensuring current law dynamically governs emerging technologies remains vital for public welfare. The analysis aims to spur policy adaptations, balancing innovation with adequate causation tests and flexible liability rules for AI medical harms.
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.

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