Metaheuristic Optimization Review

FRT is acknowledged as one of the successful advancements of biometric applications in security, surveillance, health care and innovative solutions. More so, the past decade has seen improvements in deep learning, pre-trained Neural Network Convolutional Neural Networks (CNNs), and combining methods such as ensembles, which have highly improved the FRT's Accuracy and efficiency. Nonetheless, several issues remain – facial expression, illumination, demographic biases or adversarial and backdoor threats. Such limitations require new approaches and tools to enhance FRT's reliability and ethical use. The current review also presents ethical concerns and the social consequences of using FRT.

This review aims to demonstrate the effectiveness of the ML and DL approaches to demand forecasting in the retail supply chain, proving the superiority of the approaches over conventional statistical methods. Traditional models suit themselves poorly in the face of nonlinear dependencies, outside influences and fluctuating settings, especially in retail. At the same time, Machine Learning methodologies like RandomForest, SVMs, LSTM, and CNN provide astonishing accuracy once the temporal and spatial complexity characteristics of sales information are discovered. The review underlines the consideration of data fusion and feature construction, including macroeconomic indexes, weather, and promotions, in extending the forecasts. Issues like data quality, scalability and interpretability of the model are deliberated upon along with the solutions related to incorporating IoT and blockchain. These innovations imply real-time data capture, high-reliability levels and greater process transparency. On the same note, using enhanced value assessment indicators, usually MAE, RMSE, and MAPE, highlights that model engineering requires careful, distinct selection methods. Thus, this systematic review has put together and analyzed the most recent developments, issues, and trends in applying ML and DL in enhancing inventory management, pricing, and customer satisfaction in the retail industry to stimulate better performance and competitiveness in today's fast-growing market environment.

This embraces rehabilitation medicine as it significantly boosts the doctor's way of working and presents new ways or new tools that the doctor might consider to enhance or augment the results that the patients benefit from physically. This review focuses on applying AI technologies such as robotic systems, virtual reality (VR), machine learning algorithms, wearable devices and predictive analytics in different fields, including stroke recovery, neuromuscular disorder rehabilitation, orthopedic and critical care. AI utilization improves patient treatment, the accuracy of therapy, and the administration of evaluation to deal with issues such as a lack of therapists, comparative analysis, and the expensive nature of conventional treatment. Although the outlook for its progress is positive, there are twofold problems: ethical questions, data privacy and policy concerns, and regulatory challenges. Future directions indicate directions for research and practice and call for increased interdisciplinary cooperation, large-scale validation studies and appropriate ethical standards to unlock the full potential of AI in reinventing rehabilitation medicine and rendering patient-centered care possible.

Artificial Intelligence (AI) has become a revolutionary solution in drug discovery and development in aspects including high costs, long times, and high failure rates. This review describes the development and focuses on areas where AI has been used for target identification, lead optimization, design of new drugs from scratch and drug repurposing. Deep learning frameworks such as generative adversarial networks (GANs), variational autoencoders (VAEs), and explainable AI (XAI) approaches have been instrumental and comparative progress in enhancing the efficacy and specificity of drug discovery processes. AI has made advances in clinical trials, trial conduct, and participant selection, as well as enhanced patient-tailored therapies for personalized medicine. Issues such as data credibility, model explainability, and algorithmic biases are still present, and logical and social sciences' cooperation and code of conduct are needed. As such, this review aligns current developments with these challenges to demonstrate the possibilities of AI in revolutionizing pharma research and enhancing health solutions worldwide.

Today, new Artificial Intelligence (AI) techniques are utilized to help doctors forecast the occurrence of diseases because of the necessity of sustaining public health and early disease diagnosis. One significant kind of liver damage is liver cirrhosis, which typically results from long-term liver damage brought on by a variety of liver conditions and diseases, including hepatitis, persistent alcoholism, or heredity. We created this review to provide an overview of liver cirrhosis since it is essential to identify it early and prevent the damage from spreading throughout the liver tissues. In order to identify liver cirrhosis from biomedical markers rather than images, this study has recently conducted nine studies overlaying it with various artificial intelligence deep learning techniques. Our suggested approach used various Machine Learning (ML) models to predict the signs of cirrhosis in conjunction with other illnesses. Because this condition is so important, it is important to summarize these studies based on the methodology and findings of detection accuracy and precision.