Artificial Intelligence and Optimization Techniques in Earthquake Engineering: A Systematic Review

Ahmed Mohamed; Hala B.; Hossam El-Din; El-Sayed M.

doi:https://doi.org/10.54216/MOR.050204

Metaheuristic Optimization Review | 2026

Metaheuristic Algorithms for Accurate Renewable Energy Forecasting: A Literature Review

Groundwater sources can significantly meet the agricultural, industrial, and domestic demands especially in the arid and semi-arid areas. Nonetheless, ground water has depleted and its quality has declined greatly due to over-pumping, climate fluctuation and ever-growing population pressure. High quality modeling and optimiza-tion techniques that are able to address the complexity and uncertainty of the groundwater system are needed to efficiently manage and provide sustainable use of these resources. In many cases whenever handling nonlin-earity, high dimensionality and multiple competing objectives properties of many groundwater problems, the traditional deterministic or gradient based methods are insufficient. In this respect, metaheuristic optimization algorithms have become an effective tool in groundwater management tasks in general. This paper will show a detailed usage of metaheuristic optimization methods to solve some important problems in ground water mod-eling and management such as well location, optimal pumping rate optimization, ground water contamination, and aquifer parameter estimation. Metaheuristics such as Genetic Algorithms (GA), Particle Swarm Opti-mization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO) have demonstrated their effectiveness in exploring large and complex search spaces and avoiding local optima. These algorithms are combined with computer modeling of groundwater flow and transport (e.g., MODFLOW and MT3DMS) so as to simulate the dynamics of the system and test solutions generated by the algorithms iteratively, and within a feedback environment. The hybridization of metaheuristic methods with surrogate modeling approaches, including artificial neural networks (ANNs) and support vector machines (SVMs), is also explored to reduce computational burdens associated with repeated model evaluations. By integrating optimization algorithms together with data-driven models, the framework produces a tradeoff between the accuracy of the solution and efficiency o f c alculation. I n a ddition, multiple o bjective o ptimization is a pplied i n o rder t o h ave trade-offs between competing objectives e.g. minimizing cost and maximizing aquifer sustainability or minimizing the contaminant spreading and maximizing water delivery. To illustrate the generality and validity of the suggested method, a real-word example of an aquifer system is applied. Findings reveal that metaheuristic approaches are better alternatives to conventional methods regarding the quality of solution, the rate of convergence, and the flexibility to uncertain or incomplete d ata. The framework has the potential of providing the optimized man-agement methods that can help the decision-makers come up with such policies that can be acted upon where the use of groundwater will be sustainable. On balance, the current study informs the current knowledge on intelligent water resources management by ensuring that the power/flexibility of metaheuristic optimization in groundwater context goes into record. The results provide a clear rationale in why synergizing computational intelligence with hydrological science to a groundwater sustainability challenge is important.

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Asifa Iqbal mail

link https://doi.org/10.54216/MOR.050102

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Vol. Volume 5 / Iss. Issue 1

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Metaheuristic Optimization Review | 2026

Metaheuristic Optimization in AI-Based Detection of Deepfake: A Comprehensive Literature Review

The emergence of artificial intelligence has transformed the landscape of digital security, communication, and media authenticity. Among its most consequential manifestations are Deepfakes, hyper-realistic synthetic media that undermine trust, destabilize communication ecosystems, and challenge legal and ethical frameworks. This study presents a comprehensive synthesis of methodological contributions across domains such as cybersecurity, communication networks, social media governance, digital forensics, and abuse detection. By organizing the literature into distinct categories, the research highlights how artificial intelligence operates as both the generator of risk and the foundation for its mitigation. Methodological trajectories include conceptual surveys of dual-use AI in cybersecurity, ensemble models for fraud detection, adaptive frameworks for phishing prevention, federated learning for privacy-preserving analytics, and the integration of AI with IoTenabled communication systems. Furthermore, interdisciplinary approaches extend the scope of detection and governance into educational, psychological, and social contexts, demonstrating that the challenge is not solely technical but systemic. The findings underscore recurring themes of hybridization, interpretability, resilience, and ethical responsibility, revealing that the future of AI-based defense mechanisms lies in their capacity to integrate technical rigor with human-centered and institutional perspectives. Ultimately, this review positions Deepfake detection and related AI applications within a wider constellation of methodological innovation, emphasizing that the problem of synthetic deception cannot be resolved through isolated technical solutions but requires coordinated, adaptive, and ethically grounded strategies capable of evolving alongside adversarial threats.

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Al-Seyday.T. Qenawy mail

link https://doi.org/10.54216/MOR.050103

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Metaheuristic Optimization Review | 2026

Metaheuristic Optimization in Cancer Detection: A Comprehensive Literature Review

This paper presents a comprehensive synthesis of recent advancements in the application of metaheuristic optimization algorithms for cancer detection, classification, and prediction. Drawing from a curated collection of studies spanning diverse cancer types including breast, lung, skin, cervical, oral, thyroid, and brain cancers, the work emphasizes how metaheuristics address challenges inherent to biomedical data, such as high dimensionality, noise, and limited sample sizes. A methodology table was developed to categorize each study by cancer domain, optimization method, and specific research task, enabling a comparative analysis of algorithmic patterns and hybridization strategies. The synthesis reveals that no single metaheuristic algorithm consistently outperforms others; instead, success depends on aligning algorithmic strengths with the characteristics of the diagnostic task and data. The discussion highlights the dominance of hybrid approaches, the emerging role of multi-objective optimization, the potential for cross-domain adaptation, and the necessity of addressing ethical, reproducibility, and clinical integration challenges. This work contributes both a structured reference and a roadmap for future research aimed at advancing computational oncology through strategic algorithm selection and design.

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Marwa Metwally mail

link https://doi.org/10.54216/MOR.050104

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Metaheuristic Optimization Review | 2026

Metaheuristic Optimization in Monkeypox Detection: A Comprehensive Literature Review

Monkeypox (mpox) has emerged as a significant re-emerging zoonotic threat, with the 2022–2023 global outbreak underscoring the need for rapid detection, genomic monitoring, and predictive intervention strategies. This work presents a structured synthesis of three major research domains: (1) detection and classification, encompassing convolutional neural networks (CNNs), transformer-based architectures, capsule networks, transfer learning, feature selection, ensemble methods, and explainability tools applied to lesion images for accurate diagnosis; (2) genomics, prediction, and reviews, covering time series modeling of viral genome mutations using long short-term memory (LSTM) networks, phylogenetic analysis, mutation hotspot identification, and critical reviews of AI-based diagnostic methods and metaheuristic optimization strategies; and (3) intervention support, focusing on outbreak forecasting, gradient boosting risk models, and non-stationary LSTM frameworks for scenario planning and resource allocation. Across categories, recurring challenges include limited and imbalanced datasets, inconsistent reporting, and the gap between algorithmic accuracy and clinical or operational integration. This synthesis highlights methodological trends, identifies limitations, and outlines research priorities: developing multicenter datasets, leveraging multimodal integration of phenotype and genotype, adopting federated and semi-supervised learning to address data scarcity, and coupling predictive models with operational feasibility assessments. By linking technical innovation with practical outbreak management needs, this work bridges the gap between computational research and public health application, offering a roadmap for mpox preparedness and control in both endemic and non-endemic regions.

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Mostafa Abotaleb mail

link https://doi.org/10.54216/MOR.050105

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Metaheuristic Optimization Review | 2026

AI-Enabled Strategies for Reducing CO2 Emissions in Cement and Concrete: A Comprehensive Study of Materials, Models, and Industry Practices

Modern infrastructure is supported by concrete, which, however, is one of the most significant sources of anthropogenic CO2 emissions on an industrial scale, mainly due to clinker manufacturing, energy-intensive processing, and the widespread use of virgin aggregates. Following the intensification of climate regulations and net-zero goals, the literature investigating the practical use of low-carbon binders, CO2-sequestering concrete, circular-material solutions, and sophisticated modelling applications has increased exponentially as a plausible approach to decarbonizing the cement and concrete value chain. This paper synthesizes recent developments in three interconnected domains: (i) material innovations, including CO2-carbonated concretes, recycled aggregate and recycled cement systems, LC3 and CSA-based binders, alkali-activated and geopolymer materials, and waste-derived supplementary cementitious components; (ii) data-driven and AI-based frameworks for predicting mechanical performance, durability, and embodied emissions, encompassing supervised learning, hybrid optimization (e.g., ANN–GA, PSO-, and gradient-boosted models), generative mix design, and uncertainty-aware forecasting; and (iii) process- and system-level strategies such as plant-scale operational optimization, carbon capture integration, electricity-based emission accounting, and national or regional emission scenario modelling. Throughout these threads, the review demonstrates that multi-objective optimization and machine learning can reduce embodied CO2 by significant margins while simultaneously achieving or exceeding traditional performance metrics. Alternative binders and circular solutions have the potential to reduce process emissions by 20-80% under the right conditions, and intelligent operational control can provide an immediate and low-capital benefit in additional mitigation. The remaining issues are data standardization, model transferability, interpretability, and the incorporation of technological innovations, along with policy, economic, and implementation limitations. It is based on these insights that the paper proposes a research and implementation agenda: material innovation is coupled with AI-enabled design, monitoring, and decision support to accelerate the shift toward sustainable, intelligent, and climate-resilient concrete infrastructure.

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Omnia M. Osama mail -

Marwa M. Eid mail -

El-Sayed M. El-Rabaie mail

link https://doi.org/10.54216/MOR.050106

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Metaheuristic Optimization Review | 2026

Enhancing EEG-Based Brain–Computer Interface Performance: A Review of Machine Learning Algorithms

Brain-computer interface (BCI) systems based on electroencephalography (EEG) are applications that allow human-to-machine communication with intuitive (near-transparent) control, whose neural commands are decoded based on intentional movement. Recent research on the topic of machine learning (ML) has been able to greatly enhance the classification of the EEG-signals associated with the movement of the hands, head movements, and mobility movements of the eyes. The developments allow various utilization across assistive technologies, prosthetic control, and non-verbal communication. EEG, however, is highly non-stationery and noise-sensitive, so advanced preprocessing and optimization methods have to be applied to optimize performance in classification. This paper outlines an in-depth review of some of the most popular ML algorithms, i.e. support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), and optimization methods, i.e., genetic algorithms (GAs), particle swarm optimization (PSO), and transfer learning. We point out existing problems in the processing of EEG signals and suggest directions in the future that will improve the robustness, generalization, and real-time behavior of BCI.

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Ahmed EL-Emam mail -

Hossam El-Din Moustafa mail -

W. Mustafa mail -

Islam Ismael mail -

EL-Sayed M.El-Kenawy mail

link https://doi.org/10.54216/MOR.050201

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Vol. Volume 5 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2025

A Novel approach for Minimizing the Process Voltage Temperature Variation (PVT) Detector for Digital Converter Design

Time-to-digital converters (TDCs) are vital components in digital circuitry, crucial for synchronization and precise measurement, demanding high resolution and accuracy. This brief introduces a novel TDC designed in order to reduce the impact of fluctuations in process, voltage, and temperature. A process voltage temperature detector using an extra delay line that is optimized for locking situations is incorporated into the suggested TDC to distinguish PVT corners effectively. Implemented in a 90-nm process, on-silicon measurements reveal impressive performance achieving 30-ps resolution.

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R. Shanmuga Sundaram mail -

R. Mohanraj mail -

S. Sasidevi mail

link https://doi.org/10.54216/IJWAC.090205

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Vol. Volume 9 / Iss. Issue 2

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Metaheuristic Optimization Review | 2026

Hybrid Metaheuristic–Deep Learning Frameworks for Intelligent Detection and Mitigation of Power Quality Disturbances in Renewable-Integrated Smart Grids: A Comprehensive Review

Power quality disturbances (PQDs) have become an increasingly critical concern in modern power systems due to the rising integration of renewable energy resources, widespread use of power-electronic interfaced loads, and the growing complexity of cyber-physical grid infrastructures. These evolving conditions have introduced new sources of variability, uncertainty, and vulnerability into power networks, making it significantly more challenging to maintain voltage stability, waveform purity, and overall system reliability. Traditional deterministic methods for disturbance detection and classification are no longer sufficient to address the nonlinear, nonstationary, and high-dimensional nature of contemporary PQD phenomena. As the operational landscape grows more dynamic, there is a pressing need for analytical frameworks that can adaptively learn, generalize, and respond to diverse disturbance scenarios in real time. This study provides a comprehensive examination of recent advancements in PQD research, emphasizing the evolution toward hybrid analytical frameworks that integrate advanced signal processing, machine learning, and metaheuristic optimization. The literature demonstrates that hybrid models—such as continuous wavelet transform (CWT) combined with convolutional neural networks (CNNs), Stockwell transform integrated with kernel-based extreme learning machines (ELMs), and metaheuristic-optimized classifiers—significantly enhance detection accuracy, robustness against noise, and adaptability to varying operating conditions. These hybrid systems leverage the strengths of each component: signal transforms enrich the representational quality of PQD features, deep architectures facilitate automatic feature learning, and optimization algorithms refine model parameters to achieve optimal performance across complex and uncertain environments. Metaheuristic algorithms including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Differential Evolution (DE), and hybrid variants have proven particularly effective for feature selection, classifier optimization, system-level enhancement, and mitigation strategies. Their ability to handle large, multimodal, and nonlinear search spaces makes them especially suitable for modern PQD challenges, where disturbance signatures may overlap, evolve over time, or be obscured by noise introduced by renewable energy fluctuations. Furthermore, recent work demonstrates the potential of ensemble-based and deep learning models optimized with metaheuristics to outperform conventional approaches in both accuracy and computational efficiency, thereby advancing the state-of-the-art in PQD detection technologies. Additionally, the convergence of PQD analysis with cybersecurity highlights an emerging and increasingly urgent research frontier. As smart grids become more interconnected and reliant on information and communication technologies, they face heightened risks from cyber-attacks capable of inducing, masking, or mimicking PQ disturbances. Such adversarial actions pose significant threats to grid stability, integrity, and operational safety. Metaheuristic-enhanced deep learning methods have shown promise for cyber-physical intrusion detection by enabling classification models to identify subtle, intentionally disguised anomalies within PQ data streams. This hybrid approach provides a pathway toward resilient PQ monitoring frameworks that are capable of learning and adapting to evolving attack strategies. Despite notable advancements, several key challenges persist. First, the lack of standardized real-world datasets limits the generalizability and reproducibility of PQD research, particularly within renewable-dominated or cyber-physical grid environments. Second, the high computational demands of hybrid models hinder their deployment in real-time or resource-constrained settings, calling for advancements in lightweight architectures, model compression, and edge-intelligent PQD systems. Third, the field lacks unified frameworks that integrate PQ detection, classification, and mitigation with operational decision-making, economic constraints, and regulatory requirements. Finally, adaptive cyber-physical intrusion detection frameworks remain underdeveloped, especially for zero-day attacks and data-limited conditions. Overall, this review underscores the necessity of holistic, intelligent, and scalable approaches to PQD management. It identifies critical directions for future research, including real-time system integration, computationally efficient hybrid architectures, multiobjective optimization strategies, and robust cybersecurity-aware PQD analytics. These innovations are essential for achieving resilient next-generation smart grids capable of maintaining high power quality under increasingly dynamic, decentralized, and uncertain operating conditions.

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Safa S. Abdul-Jabbar mail -

Faris H. Rizk mail

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

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Vol. Volume 5 / Iss. Issue 2

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Metaheuristic Optimization Review | 2026

Hybrid Metaheuristic-Optimized Deep Learning for Interpretable and Fair Early Detection of Oral Squamous Cell Carcinoma: A Systematic Review and Methodological Framework

Oral cancer remains a significant global health concern, particularly due to the high rates of late-stage detection and the limitations of traditional diagnostic modalities. This study proposes a hybrid diagnostic framework that integrates deep learning with metaheuristic optimization to enhance the accuracy, efficiency, and interpretability of oral cancer classification. The architecture combines convolutional and recurrent neural network components with an adaptive optimization layer designed using swarm intelligence-inspired algorithms. This hybridization enables precise feature selection, architecture tuning, and parameter optimization, resulting in improved generalization and robustness across heterogeneous clinical datasets. The model is further augmented with explainable decision support features, enabling clinicians to visualize lesion relevance and interpret classification outcomes. Empirical evaluations demonstrate superior performance in terms of sensitivity, specificity, and computational efficiency compared to conventional training strategies. Additionally, the proposed framework is designed for portability and scalability, supporting potential deployment in mobile and edge-based diagnostic systems. The integration of interpretability, fairness constraints, and clinical adaptability underscores the model’s readiness for real-world implementation. This work contributes to the growing field of intelligent medical diagnostics and highlights the transformative potential of metaheuristic optimization in addressing complex, high-dimensional clinical classification tasks.

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Khaled Sh. Gaber mail -

Shahid Mahmood mail

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

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Vol. Volume 5 / Iss. Issue 2

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Metaheuristic Optimization Review | 2026

Artificial Intelligence and Optimization Techniques in Earthquake Engineering: A Systematic Review

This comprehensive review examines the current state of artificial intelligence and computational optimization techniques applied to earthquake engineering challenges. The paper systematically analyzes recent advances across three primary domains: machine learning (ML), deep learning (DL), and optimization methods, each contributing distinct capabilities to seismic hazard mitigation. Through an extensive analysis of peer-reviewed studies, this review synthesizes methodologies employed in earthquake prediction, early warning systems, structural damage assessment, emergency response optimization, and seismic hazard analysis. Machine learning approaches have demonstrated significant effectiveness in liquefaction prediction, slope displacement analysis, and seismic event classification, with models such as XG Boost and Random Forest achieving high predictive accuracy. Deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, have revolutionized real-time earthquake detection, P-wave recognition, and landslide susceptibility mapping, with several models achieving accuracy rates exceeding 90%. Optimization techniques, particularly metaheuristic algorithms like Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO), have proven valuable for emergency logistics, shelter allocation, and structural design optimization. The review reveals current trends toward hybrid frameworks integrating multiple computational approaches, enhanced model interpretability, and real-time implementation capabilities. Future research directions emphasize the development of uncertainty-aware models, scalable frameworks for global application, and integration of social and economic factors in disaster preparedness strategies. This review provides researchers and practitioners with a structured understanding of computational methodologies in earthquake engineering and identifies critical gaps requiring further investigation.

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Ahmed Mohamed Zaki mail -

Hala B. Nafea mail -

Hossam El-Din Moustafa mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/MOR.050204

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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