Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques

Qasim

doi:https://doi.org/10.54216/GJMSA.130105

Metaheuristic Optimization Review | 2026

Energy Optimization Problems: A Comprehensive Review of Metaheuristic Algorithms and Recent Advances

Introducing renewable energy into contemporary power systems is crucial to guaranteeing sustainable solutions and improving energy performance. Optimizing energy generation, demand forecasting, and system stability have become difficult with the increasing popularity of renewable energy sources like wind and solar energy systems. This literature review explores recent advances in addressing these challenges by applying artificial intelligence (AI), machine learning (ML), and metaheuristic optimization algorithms. Some of those papers are reviewed because they show advancements in forecasting renewable energy generation, controlling hybrid microgrids, and managing energy in smart grids. Particular attention is given to innovative models such as adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) for wind speed prediction, the Evolutionary Neural Machine Inference Model (ENMIM) for residential energy consumption, and the Wolf-Inspired Optimized Support Vector Regression (WIOSVR) for building energy forecasts. Further, the review discusses the emergence of hybrid renewable energy systems and evaluates advancements in techno-economic optimization. The works under review explore advancements in prediction performance, system availability, and cost, thus making a real contribution to further developing reliable and effective energy systems. Thus, these findings may be used to change to more sustainable energy systems in urban and off-grid environments. It will also lead to further exploration of new optimization techniques and improved synergistic application of renewable energy into electricity networks worldwide.

groups

Safina Shokeen mail -

Vishal Srivastava mail

link https://doi.org/10.54216/MOR.060107

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

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Metaheuristic Optimization Review | 2026

Deep Learning-Based Classification of Brain Tumors from Magnetic Resonance Imaging Scans Using a Convolutional Neural Network Model

Brain tumors are serious neurological conditions that require accurate and timely classification to support medical evaluation and treatment planning. This project presents a deep learning-based system for classifying brain Magnetic Resonance Imaging (MRI) scans into four categories: glioma, meningioma, pituitary tumor, and no tumor. The proposed system uses a Convolutional Neural Network (CNN) trained on a balanced dataset of 7,200 MRI images collected from publicly available sources. The images were preprocessed through RGB conversion, resizing, tensor transformation, and normalization to ensure consistent input for model training and testing. The trained model achieved an overall classification accuracy of 94.31% on a held-out test set of 1,600 MRI images, demonstrating strong performance in multi-class brain tumor classification. A Streamlit-based web application was also developed to allow users to upload MRI images and view the predicted class, confidence score, and probability distribution across the four categories. The system is intended for educational and research purposes only and should not replace professional medical diagnosis, clinical judgment, or radiological evaluation.

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Karim Eldreny mail

link https://doi.org/10.54216/MOR.060108

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

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Journal of International Economics Research | 2026

The China–Pakistan Economic Corridor and the Dynamics of Regional Connectivity

The China–Pakistan Economic Corridor (CPEC), the flagship project of China’s Belt and Road Initiative (BRI), has emerged as a transformative framework for enhancing regional connectivity and economic integration across South Asia, Central Asia, and the Middle East. This study examines the role of CPEC in strengthening regional connectivity through infrastructure development, energy cooperation, trade facilitation, and strategic partnerships. Drawing on the conceptual relationship between infrastructure and regional integration, the chapter analyzes how investments in transportation networks, energy projects, Gwadar Port, and special economic zones have improved Pakistan’s internal connectivity and created opportunities for broader cross-border linkages. Particular attention is given to the potential of CPEC in fostering connectivity between Pakistan, China, Afghanistan, Central Asia, Iran, and Saudi Arabia. The findings suggest that CPEC has significantly improved Pakistan’s energy and transport infrastructure, reduced logistical constraints, and established the foundations for regional economic cooperation. Furthermore, the corridor provides landlocked Central Asian states and Afghanistan with access to global markets through Gwadar Port while creating new prospects for trade, energy collaboration, and regional integration. The study concludes that although CPEC has laid the groundwork for enhanced regional connectivity, its long-term success depends on political stability, security cooperation, and sustained collaboration among participating countries. Overall, CPEC represents a strategic geo-economic initiative with the potential to reshape regional connectivity and promote shared economic development across a wider Eurasian landscape.

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Maaz Ahmad mail -

Suhrob Gadoev mail -

Arif Khan mail

link https://doi.org/10.54216/JIER.040206

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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American Journal of Business and Operations Research | 2026

Business Data Analytics for GCC Travel and Tourism SMEs Under Geopolitical Disruption

The paper creates the business data analytics vision of how small and medium-sized enterprises (SMEs) of GCC travel and tourism ecosystems can mitigate the commercial disruption as the perceived cost, uncertainty, or inconvenience of air travel increases due to geopolitical friction in the region. Since there is a lack of public GCC micro-level booking and itinerary data, the research paper relies on a similar public dataset: the travel mode-choice dataset published under the name of statsmodels and initially based on the intercity mode-choice literature. The benchmark is operationalized as an analogue of disruption-sensitive travel demand reallocation and poses a managerial question, not a simply transport question: in the event of a shock that increases generalized cost and waiting frictions on the most exposed mode what are the most likely demand reallocations and how should SMEs respond? Empirical design transforms the data in the long-format alternative-choice form into an analytical platform that is business-facing and integrates multinomial logit, random forest, gradient boosting, and scenario stress testing into a single analytical framework. The findings indicate that the random forest model provides the best out of sample predictive performance (accuracy 0.981; macro-F1 0.973), whereas the multinomial logit model is useful in translating scenarios that can be understood. Average predicted air share decreases by 28.0 to 16.1 percent with simulated air-travel disruption, and train-like substitutes acquire most of the share. The results suggest that GCC travel, hospitality, and mobility SMEs cannot afford to trust one open channel when a period of geopolitical escalation occurs, but rather they should develop substitution-ready packages, flexible repricing guidelines, and portfolios of partnering that encompass low-friction options. The article adds a unique business analytics template of demand reallocation sensitive to crisis through the use of repeatable public information and underlines practical resilience solutions as opposed to self-forecasting wars.

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Shummaila Afzal mail -

Sidra Sohail mail -

Sana Ullah mail

link https://doi.org/10.54216/AJBOR.140204

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2026

International Perspectives on Transforming Teaching and Learning in Higher Education

Background: The rapid development of artificial intelligence (AI) is changing the sphere of higher education, creating a pressing need for graduates with competencies related to AI. Conventional degree-based credentialing models are difficult to adapt to the rate, interdisciplinary, and job alignment demanded in AI-driven economies. Because of this, the other forms of AI credentials, including micro-credentials, certificates, and stackable learning pathways, have become of worldwide significance. This review critically evaluates international views of AI credentialing and reviews the effects of these credentials on changing the teaching and learning practices in higher education. Methods: Narrative review methodology was used to summarize peer-reviewed articles, policy reports, and international frameworks published in 2015-2025. The important sources were Scopus, Web of Science, ERIC, UNESCO, OECD, and publications of the World Economic Forum. Thematic analysis was employed to determine the global trends, pedagogical changes, and policy implications. The review shows an increasing convergence through competency oriented, flexible, and industry-oriented AI credentials in the regions. Such qualifications facilitate curriculum modularization, experiential and personalized learning, and graduate employability. Nevertheless, there are issues of quality assurance, standardization, fair use of AI, and fair access. Conclusion: AI credentials are both a great offer to curriculum innovation, personnel growth, and lifelong learning, and a strong governance and collaboration across sectors. AI credentials are a disruptive process of rethinking teaching and learning in higher education. Their implementation should take place in strategic, ethically based, and globally coordinated action so as to achieve their full potential.

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Ashmun Nisha mail

link https://doi.org/10.54216/IJAIET.050202

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2026

Optimizing Translation Accuracy and Contextual Sensitivity in Language: A Comparative Evaluation of AI-Based and Human Translation Approaches

This study examines the effectiveness of artificial intelligence (AI)–based language translation in comparison with human translation, with particular attention to accuracy and contextual sensitivity in educational settings. As higher education institutions increasingly integrate AI tools into language instruction and multilingual course delivery, evaluating their pedagogical reliability and cultural appropriateness has become essential. Using a mixed-methods design, the study analyzes translation outputs produced by leading AI systems and professional human translators across academic texts, lectures, and instructional materials in multiple language pairs. Quantitative measures assess linguistic accuracy, while qualitative analysis focuses on cultural nuance, disciplinary appropriateness, and contextual meaning. Semi-structured interviews with university lecturers and students further explore perceptions of the benefits and limitations of AI-assisted translation in academic contexts. Findings indicate that although AI tools offer substantial advantages in speed and accessibility, they remain less effective in maintaining disciplinary precision and cultural depth. The study recommends a complementary model in which AI technologies support, rather than replace, human expertise in multilingual academic communication.

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Paulina Williams-Onyeji mail

link https://doi.org/10.54216/IJAIET.050203

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2026

OccuTwin: Occupancy-Predictive HVAC Optimisation through Deep Learning and a BIM-Coupled Digital Twin

Buildings account for nearly 40% of global final energy consumption, with heating, ventilation, and air conditioning systems responsible for the largest single share of that load. Conventional schedule-based HVAC controllers operate on fixed occupancy assumptions and are consequently unable to exploit the predictable but irregular occupancy patterns that characterise modern working environments. This paper proposes OccuTwin, an integrated framework that couples multi-step occupancy forecasting with a BIM-based digital twin to enable genuinely predictive HVAC optimisation. Four sequence models—Long Short-Term Memory, Gated Recurrent Unit, XGBoost with lag features, and a Temporal Fusion Transformer—are trained on the publicly available Mind Your Building occupancy dataset and the multi-building Building Data Genome Project benchmark to predict room-level occupancy at five-minute resolution. The best-performing Transformer model achieves 94.8% accuracy and an improved weighted F1- score on the 30% hold-out set, outperforming the LSTM baseline by 1.4 percentage points. An IFC-coupled co-simulation environment links real-time occupancy predictions to a virtual HVAC thermal model implemented in EnergyPlus, enabling zone-level set-point optimisation driven by predicted rather than sensed occupancy. Annual co-simulation across nine building zones documents 21.2% energy savings over a schedule-based rule controller while simultaneously improving the fraction of time within ASHRAE thermal comfort bounds from 92.4% to 97.2%. Ablation experiments identify temporal lag features and SMOTE-based class rebalancing as the two most critical preprocessing choices, and noise-injection tests confirm that the Transformer retains 95.1% accuracy under 15% sensor noise—a critical property for edge-deployed IoT environments.

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Anwar Shanwan mail -

Mariam Altaema mail -

Raphaël Omran mail

link https://doi.org/10.54216/IJBES.120209

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2026

Dynamic OpenBIM-LCA Integration for Embodied Carbon Assessment of Structural Systems

Embodied-carbon assessment has become an essential component of structural design, yet many life-cycle assessment workflows remain separated from the evolving building information model. This paper proposes a dynamic OpenBIM–LCA framework that connects structural geometry, construction-system material records, and environmental factors within a transparent computational loop. The method extracts element quantities from an IFC-oriented structural model, maps them to a material library, and updates embodied-carbon indicators whenever design variables are modified. The framework enables rapid comparison of structural alternatives, element-level hotspot diagnosis, and sensitivity-based interpretation without requiring a separate assessment model to be rebuilt after each design change. The study demonstrates how BIM quantities and material attributes can be translated into a rigorous carbon-calculation procedure for early-stage decision-making. The contribution lies in transforming BIM from a passive source of schedules into an active environmental decision-support environment for low-carbon structural design.

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Yazan Alshawabkeh mail -

Yousef Labib mail

link https://doi.org/10.54216/IJBES.120106

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2026

On Lagrange Equations: Theory, Solution Methods and Mathematical Applications

This is the complete study of Lagrange equations, a basic formulation in mathematical analysis and classical mechanics. In this report, we present derivation and classification as well as analytical solution techniques for Lagrange type differential equations. These include the Lagrange equations of motion from the calculus of variations, Lagrange multipliers for constrained optimization, and Lagrange interpolating polynomials. All types all begin with the mathematical proof and step to solution algorithms. Several relevant examples are provided to demonstrate the application of these equations in pure & applied mathematics, along with their detailed solutions.

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Hind Kolaib mail

link https://doi.org/10.54216/GJMSA.130104

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2026

Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques

Initial value problems (IVPs) of ordinary differential equations (ODEs) are ubiquitous in science and engineering applications, and the classical fourth-order Runge–Kutta (RK4) method is by far the most popular solver due to its good accuracy-to-cost ratio. Among all four-stage fourth-order explicit RK methods there are two free node parameters left after satisfying the eight B-series order conditions, thus allowing further systematic enhancements. Here we employ a hybrid multi-seed Particle Swarm Optimization (PSO)-Nelder–Mead algorithm to search for optimal RK node parameters (c₂, c₃) with respect to a minimax normalized objective over eight commonly used nonlinear benchmark problems. The resulting PSO-RK4 method with (c₂ = 0.323665, c₃ = 0.653527) retains both the exact same order of convergence and absolute stability region as the classical RK method called the 3/8-rule, but exhibits reduced maximum global error on each of the eight benchmarks when N = 300; average improvement of 27.9% with gains up to 46.0% on the Bernoulli equation, 29.7% on logistic growth, and 21.3% on exponential. Robustness of these gains with respect to multi-step-size (N = 100, 200, 300, 500) is demonstrated.

groups

Qasim Tayyeh mail

link https://doi.org/10.54216/GJMSA.130105

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new