Hybrid Grey Wolf and Al-Biruni Earth Radius Optimization for Sustainable Electric Vehicle Routing

Mostafa Abotaleb

doi:https://doi.org/10.54216/JAIM.110209

International Journal of Artificial Intelligence and Education Technology | 2024

AI-Enabled Strategic Planning for Educational Institutions: An Education Technology Readiness Framework for Transformation

Educational institutions are under growing pressure to integrate artificial intelligence (AI) and education technology (EdTech) in ways that improve teaching, governance, and service delivery rather than merely expand digital procurement. Strategic planning is therefore a core institutional capability: it aligns infrastructure, teacher readiness, student access, digital learning resources, and governance routines into a coherent transformation agenda. This study develops an AI-enabled strategic planning framework using the public 2023 World Bank EdTech Readiness Index (ETRI) pilot evidence. The framework converts traffic-light dashboard indicators into pillar-level maturity scores, strategic gaps, and a multi-criteria readiness benchmark. Empirical analysis of the Ho Chi Minh City and Dominican Republic pilot dashboards shows that school management is the strongest readiness domain in both settings, whereas connectivity and digital education resources remain more constrained. The paper contributes a managerial decision model that translates readiness evidence into institutional priorities, implementation roadmaps, and governance checkpoints. Unlike tool-centred studies, the analysis treats AI as a decision-support capability for educational planning. The framework offers a transparent and reproducible approach for organising EdTech strategy while keeping final decisions anchored in professional judgement and educational value.

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Aaras Kraidi mail

link https://doi.org/10.54216/IJAIET.030202

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Generalized Marcinkiewicz Operators associated to Twisted Surfaces

This paper is concerned with studying the mapping properties of generalized Marcinkiewicz integral operators along twisted surfaces. Under certain conditions on these surfaces we obtain certain Lp estimates for these operators provided that the kernel functions are in Lq Sn−1 (× Sm−1 . By) an extrapolation argument, we prove that these operators are bounded on Lp(Rn × Rm ) for 1 < p < ∞ under very weak conditions on the kernel functions. Our results extend and improve many previously known results.

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Mohammed Ali mail

link https://doi.org/10.54216/IJNS.270243

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

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Metaheuristic Optimization Review | 2026

AI-Driven Smart Cities: A Comprehensive Review of Technologies, Applications, and Future Directions

Artificial intelligence-based monitoring has become an essential technological direction in the development of smart cities, where large-scale sensing, data analytics, and automated decision-support systems are increasingly used to improve urban efficiency, sustainability, safety, and quality of life. As modern cities face growing challenges related to traffic congestion, environmental pollution, energy consumption, public safety, waste management, infrastructure degradation, and rapid population growth, conventional monitoring approaches are no longer sufficient for supporting timely and adaptive urban decision-making. This review examines the role of artificial intelligence in smartcity monitoring by analyzing how machine learning, deep learning, computer vision, Internet of Things sensing, edge computing, and cloud-based analytics contribute to real-time observation, prediction, anomaly detection, and intelligent control across different urban domains. The review highlights major application areas, including traffic-flow monitoring, air-quality prediction, energy management, smart surveillance, waste monitoring, disaster detection, infrastructure inspection, and public-service optimization. It also discusses how artificial intelligence enables cities to move from reactive management toward predictive and preventive governance by identifying hidden patterns in heterogeneous urban data and supporting faster responses to emerging risks. Despite these advantages, the deployment of AI-based monitoring in smart cities remains associated with several challenges, including data privacy, cybersecurity, algorithmic bias, limited interoperability, high infrastructure cost, dependence on reliable sensor networks, and the need for transparent and explainable decision-making. Overall, this review shows that AI-based monitoring can significantly strengthen the operational intelligence of smart cities when it is implemented within ethical, secure, scalable, and citizen-centered governance frameworks.

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Ahmed Zakaria mail

link https://doi.org/10.54216/MOR.060201

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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Metaheuristic Optimization Review | 2026

The Role of GANs, VAEs, and Autoregressive Models in Neurological and Psychological Research: A Comprehensive Review

Generative models, including GANs, VAEs, and autoregressive models, have drastically improved neurological and psychological analyses. They allow for the formation of complex data representations that imitate cognitive processes in a human brain and thus help to understand the workings of the brain and mental diseases. Thus, GANs trained with adversarial mechanisms can synthesize nearly photorealistic fake samples, which can mimic neurological disorders or assess the effectiveness of treatment. VAEs are known to give a formidably well founded method of learning the hidden representations of psychological states, therefore allowing researchers to study the potential causes of mental health problems. Autoregressive models, in contrast, are most applicable in time series data, which is highly important when the neurological signal or behavior under investigation needs to be studied over time. This broad survey discusses the assets and liabilities of these generative approaches, emphasizing their usability in simulating elaborate psychological processes and deploying evidence-based observations to understand the assessment and therapy of mental disorders. Therefore, this work aims to reveal the significant connections between these methodologies and their further potential for investigating human cognition and behavior through the coordinated usage of highly effective computational methods.

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Mona Yassen mail

link https://doi.org/10.54216/MOR.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2026

Generative Models in Early Detection of Neurodevelopmental Disabilities: A Comprehensive Review of Applications, Innovations, and Challenges

Neurodevelopmental disorders are a broad category that estimates fifteen million people and include autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and intellectual disabilities that, if not found at an early age, present substantial lifelong challenges. Modern technologies in artificial intelligence with generative models mean new possibilities in early diagnostics and prevention. This review aims to review the biomarker potentials of generative models, including GANs, VAEs, and diffusion models, in the early diagnosis of neurodevelopmental disabilities. Having synthesized what is currently known about these models, we explore how the models improve diagnostic precision, minimize the use of invasive procedures, and manage data deficiency. The significant applications discussed involve generative models in analyzing neuroimaging data, modeling speech and behavior, and synthesizing new datasets that are valuable in handling privacy issues and biased datasets. In addition, this paper discusses some of the limitations associated with generative model deployment in clinical practice; these include interpretability, model stability, and the fact that the models rely on extensive and diverse datasets. Finally, we bridge the gap by looking into the future and discussing what future research could bring and ethical concerns regarding generative models and their potential to revolutionize handling cases of early neurodevelopmental disorders and enable early, more effective interventional approaches.

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Ika Agustin mail -

Dwi Retnowardani mail

link https://doi.org/10.54216/MOR.060203

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2026

Practical Implementation of Artificial Intelligence in Mental Healthcare: An Integrative Review of Approaches and Case Studies

Artificial Intelligence in mental healthcare is a revolution in the delivery, evaluation and administration of mental health services. This review also maps current literature covering the practical application of AI in mental health practice, focusing on the descriptive use and specific integration and case analyses of those technologies in context. AI solutions are as follows: Solutions that assist with the diagnosis of psychiatric patients, as well as solutions enabled by artificial Intelligence that foretell situations of severe mental health emergencies of individual citizens. Some good examples, Like the REACH VET program, show how AI, using EHRs, can identify suicidal veterans’ risk and prevent potential suicides. Nevertheless, numerous challenges exist when using AI in mental health, such as workers’ resistance, ethical questions about patient data, and clinician engagement. Concerning implementation methodology, this review has incorporated ideas from implementation science, showing the need to employ a guided approach when implementing AI technologies in clinical practice. Based on the study results, there is potential for increasing patient outcomes through artificial Intelligence and group-specific treatment options, better tools for diagnosing diseases and practical cooperation, but constant work of technologists, clinicians, and policymakers will be needed to eliminate existing issues and ensure equal access to innovations in the sphere of mental health.

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Nureize bt Arbaiy mail -

Massila Kamalrudin mail

link https://doi.org/10.54216/MOR.060204

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2025

AHMAD (PBUH) Model: A Lean Transformational Framework for Organizational Change – Insights from the Leadership of Prophet Muhammad (PBUH)

The dynamic business world of today has introduced a necessity of efficient models of organizational change that are adaptive and ethical in nature. Organizations have been challenged by the necessity of innovative models of change management based on the ethical leadership dimension and culture awareness. The aim of this study is to examine the AHMAD Model as a change model for organizational transformation, drawing on the leadership behavior of Prophet Muhammad (PBUH). It would like to explore how applicable the model is in contemporary organizational contexts and if it can bring together ethical leadership and effective change management practices. Comparative analysis of AHMAD Model earlier Islamic scholarship and recent organizational transformation theories by Kotter's 8-Step Change Model, Lewin's Change Theory, and Agile methodologies will be employed. Adaptability, holism, motivation, accountability, and discipline are the five key pillars of the AHMAD Model. The acronym is "AHMAD" as pronounced by the followers of the Holy Prophet Muhammad (PBUH); it encourages ethical leadership and further provides participative decision-making, reactiveness as three important ingredients of successful change projects and effective communication. The AHMAD Model can serve as a template for organizations that strive to embark on changing initiatives founded on high moral and people-centered principles. Driven by such values, these organizations will be capable of triggering a process that humanizes the workplace and creates a teamwork-based work environment and more plural. This paper fills an important gap in literature by connecting religious-influenced leadership frameworks with classical organizational expectations. This paper offers a new paradigm of strategic leadership based on the Prophet's practices where ethics supersede modern management. The model gives an organization a change management process that is methodical in approach but moral in nature. Future studies can be done on how AHMAD Model can be implemented in different cultures and the impact of that on organizational performance. Similarly, research on long-term effects of the implementation of this model on organizational culture and employee morale would be useful.

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Ahmed Fahim Elgendi mail -

Ghada Moukhtar Elgendi mail -

Nael Zabel mail

link https://doi.org/10.54216/IJBES.110104

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2025

A Reconsideration of the Mathematical Frameworks for Fuzzy and Neutrosophic Supply Chain Management (FSCM and NSCM)

Numerous frameworks have been developed to address uncertainty in various domains. Among the most prominent are Fuzzy Sets,26 Rough Sets,15 Intuitionistic Fuzzy Sets,4 Hesitant Fuzzy Sets,23 Neutrosophic Sets,3 as well as other emerging theories that continue to be actively explored. Supply Chain Management (SCM) involves planning, coordinating, and optimizing the flow of goods, information, and finances across the entire supply network.9, 16 In this paper, we introduce rigorous Mathematical Frameworks for Fuzzy Supply Chain Management (FSCM) and Neutrosophic Supply Chain Management (NSCM). We hope that these formulations will foster further advances in both supply chain optimization and the development of Fuzzy Set and Neutrosophic Set-based models.

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Takaaki Fujita mail

link https://doi.org/10.54216/JNFS.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

Potato Disease Detection via Joint Feature Selection and Hyperparameter Optimization of the Feature Tokenizer Transformer with iHOW

Potato leaf diseases represent a persistent threat to global food security, demanding diagnostic systems that are not only accurate but also computationally efficient and deployable in resource-constrained agricultural environments. This study introduces a unified framework that integrates the Feature Tokenizer Transformer (FT-Transformer) with the Improved iHOW Optimization Algorithm, a metaheuristic designed for simultaneous feature selection and hyperparameter tuning. The FT-Transformer alone provides a competitive baseline, achieving 81.61% accuracy and an F1-score of 81.45%. Incorporating iHOW-based feature selection raises accuracy to 90.90% while reducing redundancy and enhancing precision, recall, and generalization. A final stage of iHOW-guided hyperparameter optimization further improves performance to 98.35% accuracy and 98.33% F1-score, outperforming all comparative metaheuristic variants. Beyond predictive gains, the optimized model demonstrates strong efficiency, requiring only 12.45 s of training time and 256.8 MB of memory. Taken together, these results establish iHOW+FT-Transformer as a scalable, interpretable, and resource-conscious solution for real-time plant disease classification. The proposed pipeline advances the state of precision agriculture by enabling robust, low-overhead crop monitoring systems adaptable to diverse and low-resource farming contexts.

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Doaa Sami Khafaga mail

link https://doi.org/10.54216/JAIM.110208

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

Hybrid Grey Wolf and Al-Biruni Earth Radius Optimization for Sustainable Electric Vehicle Routing

In this chapter, a novel hybrid optimization algorithm, which integrates the Grey Wolf Optimizer (GWO) with the Al-Biruni Earth Radius (BER) method to address the problem of electric vehicle (EV) path planning, is introduced. By incorporating the simulation of wolf social hierarchies for efficient exploration alongside the spatial analysis capabilities of BER, the proposed approach effectively determines optimal, energy-efficient routes across diverse terrains. The hybrid approach systematically considers key factors such as distance, terrain characteristics, and energy consumption to guide vehicle movements, thereby minimizing energy usage, enhancing travel efficiency, and ensuring smooth traffic flow. Experimental results demonstrate the effectiveness of the proposed hybrid method compared to conventional metaheuristic approaches, achieving an average error of 0.530218. This chapter presents the technical framework of this hybrid approach, its applications in EV route optimization, and its broader implications for sustainable mobility and real-world transportation challenges.

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Mostafa Abotaleb mail

link https://doi.org/10.54216/JAIM.110209

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new