Browsing by Author "Ali, Guma"

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    A secure and efficient blockchain and distributed ledger technology-based optimal resource management in digital twin beyond 5G networks using hybrid energy valley and levy flight distributer optimization algorithm.
    (IEEE, 2024-08-19) Kumar, K. Suresh; Alzubi, Jafar A.; Sarhan, Nadia M.; Awwad, E. M.; Kandasamy, V.; Ali, Guma
    This paper aims to establish a virtual object management system, as well as optimal task scheduling using the foundation of Digital Twins (DT), to improve the user’s experience with management and to accomplish the task efficiently. On the other hand, offloading tasks using IoT gadgets to edge computing, fails to speed up control by users. The capabilities of the DT are provided by executing processes such as visualization, virtualization, synchronization, and simulation. The optimal selection of the virtual objects for the DT is done by utilizing the implemented Hybrid Energy Valley with Lévy Flight Distribution Optimization (HEV-LFDO) in order to optimally offload the task by the edge devices. The optimal selection of the virtual objects is done with the aid of the HEV-LFDO in the DT by considering the total cost of executing all tasks using the selected virtual objects and the decision variables to determine whether a virtual object is taken for executing a task or not as the constraint. The data for performing resource management is secured using the blockchain or distributed ledger technology. This accounts for the minimization of the local loss function. Finally, the secured data is considered for optimal resource management tasks. The optimal resource management is done using the same HEV-LFDO. This optimal resource management is carried out by considering the constraints like the cost of assigning a virtual object for the task to the edge device, and the cost of assigning the task to the edge device. These two costs are analyzed by taking the network’s bandwidth, energy consumption, and computational resources into consideration. Experimental verifications are conducted on the executed optimal resource management scheme to prove the ability of the implemented model to be integrated with the edge computing network. The overall processing time as well as the latency are also minimized by executing the optimal resource management scheme.
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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.
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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.
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    AI Driven railway crack detection system using Convolutional Neural Network and IoT
    (IEEE, 2025-06-02) Sudha, M.; Saranya, R.; Ali, Guma; Ganesh, C; Umamaheswari, S.
    The Railway Track fractures identification Using AI with IoT project aims to increase railway safety by automating the identification of fractures in railway tracks using artificial intelligence (AI) and Internet of Things (IoT) technology. Even minor cracks in railway tracks, which are crucial components of the transportation system, can cause major accidents if they not immediately identified also repaired. Conventional manual assessing technique are expensive, time-consuming, and error-prone. Convolutional Neural Networks, a kind deep learning model, will used in this study to automatically identify cracks in photos of railroad tracks. The model trained on a dataset of track photographs in order to discern parts are defective (cracked) and non-defective (intact). Once trained, The CNN model is employed to analyze images captured by cameras mounted on trains or inspection vehicles as part of a real-time monitoring system driven by the Internet of Things. Every time a fracture is discovered, the system sends the information to the Blynk IoT platform, notifying and alerting maintenance personnel. Additionally, an LCD display and a buzzer alarm are activated in the field to alert technicians to the detected defect. By combining AI and IoT, the initiative aims to reduce overall maintenance costs, improve safety through early fault detection, and speed up the track inspection process. By providing a more automated, accurate, and efficient method of tracking the present state of railroad lines, the system ultimately increases the harmlessness and reliability of railway transit.
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    AI-driven pathogen detection systems for rapid and accurate diagnosis
    (IEEE, 2025-09-24) Deepika, M.; Murugan, Sundara Bala; Subasini, V.; Rufus, N. Herald Anantha; Atkinswestley, A.; Ali, Guma
    Pathogen detection at speed along with precision serves as a basis for disease diagnosis and overall control effectiveness. Several current detection methods based on culture-based techniques and PCR face drawbacks of extended processing times, high expenses, and the need for expert operators. It adopts deep learning Convolutional Neural Networks with an attention mechanism in a system designed to overcome existing limitations of pathogen detection. The model was trained using a multi-modal dataset that combined spectral and biological signals through feature extraction optimization and privacy-protected training methods based on federated learning. The proposed system reaches a 96.8% detection accuracy together with 15% reduced false alarm rates and 35% faster operation speeds than conventional models. Experimental data measurements showed that this system achieved 92.3% F1-score together with 97.1% AUC for real-time identification of pathogens. AI has proven its effectiveness through these findings in changing the way pathogen diagnostics operate. Further work will concentrate on extending datasets while creating real-time deployment protocols for clinical use.
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    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.
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    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.
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    Artificial intelligence in corneal topography: A short article in enhancing eye care
    (Mesopotamian Journal of Artificial Intelligence in Healthcare, 2023-06-17) Ali, Guma; Eid, Marwa M.; Ahmed, Omar G.; Abotaleb, Mostafa; Alaabdin, Anas M. Zein; Buruga, Bosco Apparatus
    The eye is a critical part of the human being, as it provides complete vision and the ability to receive and process visual details, and any deficiency in it may affect vision and loss of sight. Corneal topography is one of the essential diagnostic tools in the field of ophthalmology, as it can provide important information about the cornea and the problems that appear in it. Artificial intelligence strategies contribute to the development of the healthcare domain through a group of approaches that have a significant and vital impact on improving the field of ophthalmology. The primary purpose of this paper is to highlight the efficiency of artificial intelligence in extracting features from corneal topography and how these techniques contribute to helping ophthalmologists diagnose corneal topography. Furthermore, the focus is on the performance of AI algorithms, their diagnostic capabilities, and their importance in helping physicians and patients. The effects of this paper confirm the effectiveness and efficiency of artificial intelligence algorithms in the clinical diagnosis of various eye concerns.
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    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.
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    Augmented heritage: AR and QR code integration for interactive cultural storytelling in the UAE
    (IEEE, 2025-12-05) Lal, Mily; Dash, Soumyakant; Keerthika, J; Kumar, Bura Vijay; Ali, Guma; Shukla, Vinod Kumar
    This study has outlined a mobile-augmented reality (AR) storytelling framework utilizing Quick Response (QR) code technology to provide cultural engagement at heritage sites in the United Arab Emirates. The project set out to merge traditional heritage with augmented immersive technology and focused on a four-phase methodology: (1) Content curation and narrative design through historians and cultural experts; (2) Planning QR codes in spaces cooperatively designed through spatial design and semiotics of culture; (3) Content development of AR experiences in Unity and Vuforia using finite state machine for stability and usability; and (4) User testing in three major heritage locations. The quantitative and qualitative evaluations measured user engagement, narrative retention, usability of the system, and cultural sensemaking. Relative to baseline digital experiences, the AR-QR platform saw significant gains: 25-30% improvement in engagement; 20-25% improvement in narrative retention; and over 15% improvement in usability and cultural relevance. The study evidence demonstrates the promise of AR-QR storytelling and approach to deliver contextually rich and interactive heritage experiences. Although there are limitations in device affordances/conceivability and longer-term evaluations, this study suggests---immersive storytelling can make heritage more engaging, accessible, and meaningful to a diverse population.
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    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.
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    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.
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    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.
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    Comparative analysis of PWM AC choppers with different loads with and without neural network application.
    (Wasit Journal of Computer and Mathematics Science, 2023-09) Bounab, Mariem; Ali, Guma
    In this paper, we focus on the "Artificial Neural Network (ANN) based PWM-AC chopper". This system is based on the PWM AC chopper-encouraged single-phase induction motor. The main purpose of this paper is to design and implement an ideal technique regarding speed control. Here analyzed PWM-based AC-AC converter with resistive load, R-L load and finally, the PWM AC chopper is fed to single phase induction for speed control. Using other soft computing and optimization techniques such as Artificial Neural Networks, Fuzzy Logic, Convolution algorithm, PSO, and Neuro Fuzzy can control the Speed. We used Artificial Neural Network to control the Speed of the PWM-AC Single phase induction motor drive. The Neural Network toolbox has been further used for getting desired responses. Neural system computer programs are executed in MATLAB. The performance of the proposed method of ANN system of PWM AC Chopper fed single phase induction motor drive is better than other traditional and base methods for controlling the Speed, based on the MOSFET.
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    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.
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    Creating electric vehicle battery management with IoT: Using intelligent algorithms to enhance safety, efficiency, and charging time
    (IEEE, 2025-10-28) Balapriya, S.; Pandi, V. Samuthira; Sabitha, M.; Veena, K.; Balasubramaniyan, R.; Ali, Guma
    The expanding electric vehicles (EV) market has raised the demand for better-optimized intelligent battery management systems (BMS) that can improve safety, performance, and charging time. Older BMS representative solutions employ earlier monitoring and control techniques, often without real-time adaptability and predictive capabilities. This paper investigated Internet of Things (IoT)-specialized and smart algorithms-integrated EV battery management to increase efficiency, safety, and a superior charging process. The proposed architecture uses machine learning model, data analytics, and IoT-enabled sensors to enable real-time monitoring of critical battery parameters such as state of charge (SoC), state of health (SoH), temperature, and voltage variations. Predictive analytics enable the early detection of potential battery degradation, minimize thermal runaway, and enhance energy distribution among individual battery cells. Furthermore, advanced charging algorithms optimize charging rates in response to instantaneous battery states and grid demand, maximizing charging speed while avoiding overcharging and wearout. Cloud-hosted IoT platforms enable remote monitoring and data-based decision-making, improving user experience and prolonging battery life. We develop a prototype implementation to showcase the effectiveness of the system, which results in efficient energy management, early faults detection, and reduced charging cycles. Application of the proposed system was compared against the state-of-the-art BMS and used as a reference standard and the comparison results revealed excellent performance in terms of safety, compactness and flexibility of the system with the existing BMS. The study utilizing IoT as well as artificial intelligence helps to further emerge smart electric vehicle technology that leads human being for sustainable and rich electric mobility solution.
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    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.
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    Deep learning-based neural network modeling for economic performance prediction: An empirical study on Iraq
    (Peninsula Publishing Press, 2025-02-20) Shaker, Atheel Sabih; Ali, Guma; Wamusi, Robert; Habib, Hassan
    This study investigates the application of deep learning-based neural networks for predicting Iraq’s economic performance. Traditional econometric models impose restrictive assumptions that limit their predictive accuracy, especially in volatile economic environments. To overcome these limitations, we propose an artificial neural network (ANN) model trained on six key macroeconomic indicators: Gross Domestic Product (GDP), inflation rate, unemployment rate, exchange rate, trade volume, and government spending. The dataset spans from 2000 to 2023, sourced from authoritative economic institutions. The methodology incorporates feature scaling, hyperparameter tuning, and backpropagation optimization to minimize mean squared error (MSE) and enhance generalization performance. The model is validated through cross-validation and out-of-sample testing. Descriptive statistical analysis highlights the variability of macroeconomic indicators, while the ANN model effectively captures nonlinear dependencies. The results indicate that GDP and government spending are the most influential factors in economic performance prediction, while unemployment rate and exchange rate exhibit lower predictive significance. The model demonstrates superior accuracy compared to traditional regression-based approaches, with minimal error in both training and testing phases. This research contributes to the empirical literature on machine learning in economic forecasting by presenting a robust alternative to conventional predictive models. The findings provide policymakers with valuable insights for designing data-driven economic policies. Future work should explore hybrid models integrating deep learning with traditional econometrics to improve interpretability while maintaining predictive power.
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    Design a hybrid approach for the classification and recognition of traffic signs using machine learning
    (Wasit Journal of Computer and Mathematics Science, 2023-07) Ali, Guma; Sadıkoğlu, Emre; Abdelhak, Hatim
    Advanced Driver Assistance Systems (ADAS) are a fundamental part of various vehicles, and the automatic classification of traffic signs is a crucial component. A traffic image is classified based on its recognizable features. Traffic signs are designed with specific shapes and colours, along with text and symbols that are highly contrasted with their surroundings. This paper proposes a hybrid approach for classifying traffic signs by combining SIFT with SVM for training and classification. There are four phases to the proposed work: pre-processing, feature extraction, training, and classification. A real traffic sign image is used for classification in the proposed framework, and MATLAB is used to implement the framework
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    Development, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning
    (Springer Nature, 2025-12-01) Elwakeel, Abdallah Elshawadfy; Elden, Abdallah Zein; Ahmed, Saad F.; Issa, Sali; Li, Changyou; Ali, Khaled Abdeen Mousa; Hanafy, Waleed M.; Ali, Guma; Alzahrani, Fawaz; Fathy, Atef
    Sugarcane is a vital global crop, serving as a primary source of sugar, biofuel, and renewable energy. Advancements in harvesting are critical to meeting rising demand, enhancing profitability, and supporting eco-friendly agricultural practices in the sugarcane sector. Based on the current challenges of sugarcane harvesting in developed countries, the current study aimed to develop a semiautomatic whole-stalk sugarcane harvester (SWSH) for harvesting two rows of sugarcane stalks at a time and to be front-mounted on a classic four-wheel agricultural tractor. Then performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using Feedforward Neural Network (FNN) and Deep Neural Network (DNN) at different levels of forward speeds (3, 3.5, 4.5, and 5 km/h), row spacing (71, 78.89, and 88.75 cm), cutting heights (0, 2, and 4 cm), and numbers of knives (2 and 4) of the cutting systems. The obtained results showed that the cutting efficiency of the developed SWSH reached 100%. Where the higher cutting efficiency was observed at a cutting height equal to zero (ground level), forward speed of 3 km/hand row spacing of 71 cm using both 2 and 4 knives. The minimum total operating cost of the developed SWSH was about 4.42 USD/ha, and it was detected when using a forward speed of 4.5 km/h, row spacing of 88.75 cm, a cutting height of 4 cm, and two knives only on the cutting disk. Furthermore, at a row spacing of 88.75 cm, the maximum field capacity of the developed SWSH was 0.554 ha/h, observed at a forward speed of 4.5 km/h.