The Applications of Runge-Kutta Numerical Methods to Numerical Solutions of Several Neutrosophic Problems

Belal

doi:https://doi.org/10.54216/IJNS.250346

Metaheuristic Optimization Review | 2024

A Review on the Role of Machine Learning in Predicting the Spread of Infectious Diseases

AI and the development of the ML system are expected to play a crucial role in preventing and controlling infectious diseases as part of global health issues. Typically, conventional epidemic models give a narrow perspective of the distribution of diseases and their causes, which leads to the use of AI/ML solutions. Some of these tools utilize genomic data and environmental and patient information to boost forecasts' accuracy and facilitate real-time disease surveillance. The human-driven models of pandemic identification were replaced by sophisticated artificial intelligence models such as deep learning and advanced neural networks indicating patterns, the possibility of future outbreaks, and driving the concept of public health interventions. Many examples can be provided to support the efficiency of ML's approaches to combating antimicrobial resistance, tuberculosis relapse, and the spatial-temporal modeling of an alternative disease such as measles or COVID-19; nonetheless, data standardization, scaling, ethics, and bias issues are limitations to the application of such solutions. Controlling unfairness consists of the problem of transparency, patient data confidentiality, and disparities in the deployment of AI systems. However, practical and comparable implementations of these systems require cross-sector cooperation and global data sharing for varied conditions in the broader healthcare environment. Future developments point to the opportunity to enrich epidemic prediction models by blending genomic precision systems, explainable artificial intelligence, and interdisciplinary studies. This review provides evidence for how AI/ML has revolutionized infectious disease management, calls for responsible innovation and ethical deployment of AI, and encourages international collaborations to safeguard the global health sector against new and emerging diseases. Subsequently, unexpected events with high fatality rates and global impact, such as disease outbreaks, epidemics and pandemics, are still a threat to life; therefore, the ability of AI and ML to advance epidemic preparedness and response in the future is promising to enhance global health protection to future pandemics.

groups

S. K. Towfek mail -

Mohamed Elkanzi mail

link https://doi.org/10.54216/MOR.020102

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2024

Artificial Intelligence for Skin Cancer Detection: A Review of Current Approaches

AI is emerging as a potential tool for revolutionizing dermatology in the early detection and diagnosis of skin cancers. This Review looks into the most recent innovations in AI technology, such as machine learning, deep learning, and explainable AI (XAI)) Moreover, it presents how one can achieve diagnostic accuracy similar to or exceeding that of well-experienced dermatologists. Access to such diagnostic tools in under resourced areas has been enhanced, inter-observer variability has increased, and workflows in clinical practice have been streamlined. Nevertheless, issues regarding diversity in data, generalization of models, and the inscrutability of many AI systems remain, and the use of these systems in clinical practice needs to be improved. The paper emphasizes the need for interdisciplinary collaboration, diverse dataset collection, and lightweight and interpretable AI models to solve these issues. Lastly, it brings together important findings and identifies research gaps, showing AI's potential to change the dermatology world for all patients.

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Abdelaziz A. Abdelhamid mail

link https://doi.org/10.54216/MOR.020103

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2024

A Review of Metaheuristic Algorithms for Load Forecasting in Smart Grids

Smart electrical grids (SGs) have emerged to advance the management of power systems by solving issues such as voltage instability, reactive loads, power loss, and the integration of renewable energy resources. This review focuses on the applicability of metaheuristic algorithms to energy distribution systems, improve operation, and overcome the repercussions affecting the environment and overall costs. PSO, GA, and GWO have been identified for their effectiveness in dealing with the complexity of PS due to the nonlinear and dynamic nature of today's energy systems. The review also addresses the extension of methods in machine learning for enhancing load forecasting and real-time energy control, which are key factors for shifting to innovative and renewable energy systems. Based on the literature review of the state of the art over the last five years, this research highlights some achievements and limitations. It provides recommendations for further directions in advancing Smart Grid algorithms. These results highlight the use of meta-heuristics in redesigning processes that offer optimal, reliable and sustainable energy facilities.

groups

Mohammed A. Saeed mail -

Amal H. Alharbi mail

link https://doi.org/10.54216/MOR.020104

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2024

Machine Learning in Stock Price Prediction: A Review of Techniques and Challenges

Future stock price prediction is one of the most important and complex tasks in the lecture on finance, mainly due to the characteristics of the financial world. Machine learning techniques have greatly improved this area: problems with frequent data and nonlinear processes, which cannot be solved using conventional models, have been solved. In this paper, the author looks at how the methodology of data preprocessing and two modeling techniques, namely, the high-frequency data model and the sentiment analysis model, have helped improve the efficiency of stock price forecasts. Among the proposed techniques, Temporal Convolutional Networks (TCN), Attention Mechanisms, and Transformer-based architectures are mentioned due to their capability to distill complex market dynamics. However, issues like data quality and fluctuations in the market remain sticky even as we see the speed of innovation picking up, and thus, the importance of model robustness and interpretability. Drawing on recent advances and mapping out the directions for future studies, this paper reveals how machine learning may revolutionize stock market prediction and investment decision-making in a continuously transforming financial environment.

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Doaa Sami Khafaga mail -

Sunil Kumar mail

link https://doi.org/10.54216/MOR.020105

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2024

A Review of Artificial Intelligence for Sentiment Analysis in Social Media Data

Social media sentiment analysis has benefited from the miracle of artificial intelligence (AI), mainly how it can handle large, conflated data sets and distill valuable insights. In this review, the authors consider the positive impact of AI in business, health care, politics, and social justice, including marketing, mental health screening, misinformation, and multilingualism. Using ML and NLP, artificial intelligence technologies empower real-time analysis of the social trends and behaviors that affect decision-making and social interactions. However, many challenges are still reflected in data imbalance, ethical concerns relating to privacy and consent, and difficulties in processing dynamic content and several modalities, languages, and emotional states. Such limitations call for interdisciplinary collaboration and comprehensible ethical guidelines, including the FAIR principles for bettering data stewardship and ensuring no biases in AI systems. When developed as scalable, context-aware, and equitable systems, opinion mining may help solve social dilemmas and build an inclusive digital environment. Based on current trends, challenges, and suggested future directions, this review underlines the need for ethical, interdisciplinary, and culturally sensitive approaches to unlock the proper potential of AI in SA and social media sentiments.

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Manish Kumar Singla mail -

Amel Ali Alhussan mail

link https://doi.org/10.54216/MOR.020201

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2024

Metaheuristic Optimization for Scheduling in Cloud Computing Environments: A Review

This review reviews metaheuristic optimization algorithms for solving various important issues in cloud computing, such as scheduling, resource provisioning and energy consumption. Specifically, PSO, GA, and DRL are application area-specific intelligent scheduling algorithms that offer high scalability, flexibility, and efficiency in solving NP-hard problems, thereby improving system performance and QoS. The following are some of the key strengths in the study: The energy utilization and the cost utilization as key strengths are presented; the weaknesses are programs and things such as scalability and integration issues that arise when using hybrid systems. The focus for the future lies in combining machine-learning techniques, improving the further development of hybrid approaches, and testing them in real cloud systems to cope with the increasing sophistication of distributed systems. This paper provides an outline of metaheuristic optimization with an emphasis on how this area can contribute to enhancements and further developments in the capacity, recyclability, and dependability of cloud computing.

groups

Rokaia M. Zaki mail

link https://doi.org/10.54216/MOR.020202

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2024

A Review of Machine Learning in Predicting Heart Disease Risk Based on Medical Data

Heart diseases go on to be the primary cause of such mortality all over the world and hence call for accurate and efficient diagnostic tools. Traditional diagnostics are not scalable and precise in analyzing large and complex datasets generated in healthcare. Machine learning has come as a revolutionary solution in the form of advanced prediction models in the diagnosis and risk assessment of heart diseases. The authors present all machine-learning techniques like Random Forest, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, and hybrid models containing deep learning versions like CNN and LSTM in the study. These techniques consumed multi-source data found in Cleveland, Statlog, and UCI repositories and combined feature selection methods with different data preprocessing techniques to achieve improved accuracy, reliability, and scalability of outcomes while applying ensemble methods like majority voting and boosting to show enhancements in model working robustness and adopting SMOTE to tackle the imbalanced data scenario. Despite these developments, specific challenges remain mostly: Model Interpretability, Data Diversity, and Clinical Integration. The present review discusses progress, challenges, and future avenues in using machine learning in predicting heart diseases, which focus on the critical need for explainable AI models, diverse datasets, and real-world validation for the optimum use of clinical applications to improve global healthcare outcomes eventually.

groups

Safa S. Abdul-Jabbar mail

link https://doi.org/10.54216/MOR.020203

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2024

Artificial Intelligence in Path Planning for Autonomous Robots: A Review

Automated motion planning is an essential component of any autonomous system that effectively and safely finds the route in different application areas such as industry, hospitals, and cars. New developments in artificial intelligence and machine learning have improved additional attributes of path-planning algorithms in dealing with the complexities of their environment. This review also covers traditional algorithms, including RRT and A*, integrated frameworks, and AI solutions encompassing reinforcement learning, deep neural networks, and the Large Language Model (LLM). This paper looks at these methods' essence, advantages and disadvantages, and use for flexibility, productivity, and feasibility. It also outlines practical problems such as real-world testing, multi-robot operation, and energy issues and finally describes research directions in both cross-disciplinary research and practical application. This review aims to present the current developments and possibilities for robotic path planning to the researcher and practitioner communities.

groups

Shahid Mahmood mail

link https://doi.org/10.54216/MOR.020204

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2024

A Review of Hybrid Machine Learning and Metaheuristics for Vehicle Routing Problems

Vehicle Routing Problem (VRP) variants and modifications are significant problems in combinatorial programming and logistics. They relate to efficient and optimal transport routing for customer demand fulfillment while monitoring operational costs. Traditional methods have been exact algorithms, heuristics, and metaheuristics; however, it has yet to be known to cater to the scalability, computational, efficiency, and adaptability challenges posed by dynamic and large-scale VRPs. Recent advances have shown enormous promise in combining this with learning approaches in hybrid forms: ML and metaheuristic and optimization techniques to overcome them. Such hybrid approaches now promise even better quality solutions, computational speeds, and real-world applicability for two actual ML methods: deep reinforcement learning and meta-learning. The present study surveys the current state of the art of hybrid methods applying to VRPs to find strengths, weaknesses, and directions that future research could intensify to enhance efficiency, scalability, and applicability to transportation and logistics systems.

groups

Ali Wagdy Mohamed mail

link https://doi.org/10.54216/MOR.020205

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025