Metaheuristic Optimization Review

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.

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.

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.

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.

The present review aims to describe the impact of machine learning techniques in health risk prediction, including the progress, drawbacks, and potential development. ML approaches in health care have become more effective in risk prediction than simple regression techniques because of their accuracy, scalability, and personalization. A Statistical tool like Decision trees, Support vector machines, and neural networks allows or examine non-linear genetic and environmental interactions with lifestyle factors. The review's main points are the increase in relevance of more complex types of models like ANN-PSO, a combination of two algorithms for feature selection, higher prediction accuracy, and other fields, including healthcare. These innovations have shown a unique success rate in identifying diseases, including obesity, diabetes, and any cardiovascular diseases, for better prevention measures and avenues of cure. Nevertheless, there are several difficulties: inferior quality of the data, the question of privacy, and explaining the decision-making of the modern complex models. Solving these issues calls for effective data governance, explainable artificial intelligence, and a multi-disciplinary approach to create and deliver transparency and fairness. As mentioned in the review, feature importance analysis like SHAP also carries plenty of significance for enhancing model interpretability to chase positive alterations. Concerning the outlook, implementing ML in the current HC system will require investment in data platforms, clinician expertise, and broader, suitable systems. As a result, new opportunities for using ML in connection with population health, patient and client outcomes, and receiving individualized care point out the further evolution of the transforming impact of technology. This paper offers an understanding of how health risk prediction and the public health strategy might benefit from new applications of ML and how the moral and practical issues of this new application of the technology may be dealt with.