Metaheuristic Optimization Review

The rapid expansion of digital data in financial markets and business environments has increased the need for intelligent, accessible, and integrated analytical systems. Traditional tools for trading analysis, marketing analytics, and user assistance often operate separately, forcing users to move between different platforms and making data interpretation more complex. This project presents InsightX, a web-based analytical platform that combines trading analysis, marketing analytics, and an AI-powered chatbot within a unified interface. The system is designed to support data-driven decision-making by transforming raw financial and marketing data into interactive visualizations, performance indicators, and user-oriented insights. The platform consists of three main modules. The Trading Analysis Module enables users to examine market data through real-time charts, historical comparisons, and technical indicators such as moving averages and RSI. The Marketing Analysis Module supports customer data analysis, segmentation, campaign performance evaluation, and KPI-based insight generation. The AI Chatbot Module enhances usability by allowing users to ask questions, receive explanations, and navigate analytical results through natural language interaction. InsightX is implemented using Python and Streamlit, supported by libraries such as Pandas, NumPy, Plotly, yfinance, and AI API integration for chatbot functionality.

Hantavirus remains an important zoonotic threat because of its association with severe human diseases, including hemorrhagic fever with renal syndrome and hantavirus pulmonary syndrome. Its transmission is strongly influenced by rodent reservoirs, environmental conditions, human exposure patterns, and regional ecological variability. Recent advances in artificial intelligence (AI) and deep learning have created new opportunities for improving Hantavirus detection, outbreak prediction, ecological risk mapping, diagnostic support, and public health surveillance. This review examines the role of AI-driven methods in Hantavirus research, with emphasis on how machine learning, deep learning, image-based analysis, epidemiological modeling, and data-driven surveillance can support earlier detection and more informed decision-making. The review also discusses the potential of AI to integrate heterogeneous data sources, including clinical records, environmental variables, remote sensing indicators, genomic information, and epidemiological reports. Despite these advances, several challenges remain, including limited datasets, geographic bias, model generalization, lack of clinical validation, data imbalance, interpretability concerns, and the need for real-time deployment. Overall, AI and deep learning offer promising tools for strengthening Hantavirus surveillance and response, but their practical value depends on transparent models, high-quality data, interdisciplinary validation, and integration into public health systems.

Machine learning and optimization techniques have significantly changed the healthcare industry, especially in finding and managing essential and dangerous diseases like lung cancer, breast cancer, diabetes as well as heart disease. Lung cancer, which is among the common fatal cancers, requires proper subtyping before proper management is made. This has been achieved through machine learning alongside radiomics, where detailed imaging characteristics of the tumor from CT scans are retrieved without invasive procedures. In the same way, machine learning has provided much higher detection, diagnosis and treatment levels of breast cancer, diabetes and heart disease. This literature review sums up the priorities of studies showing the benefits of using machine learning and bio-inspired optimization methods to address the challenges posed by disease classification and prediction. Such complications have proved great potential in improving the diagnostic methods used for early intervention and, thereby, accurate and efficient diagnosis of a problem, developing an appropriate treatment plan and, thus, improving the patient caring methods and scenario, which has played an imperative role determining the future of modern-day health care.

Metaheuristic Optimization in Engineering has gained much attention recently because of its application in solving challenging problems and nonlinear and constrained design often encountered in structural and mechanical design. These optimization techniques are derived from natural phenomena, including Bio-evolution, Animal instincts and the physical world, necessitating efficient and inexpensive design for engineers. In conventional design processes, the design process may be tiresome and often unable to cope with large and complex engineering endeavors; however, metaheuristic algorithms exhibit high effectiveness and functionality in optimizing designs in various sectors about reinforced concrete structures and steel reinforced frames, mechanical parts, among others. This literature review explains the current metaheuristic algorithms and their applicability to solving engineering problems, particularly regarding computational time, quality and physical solution constraints. Difficulties regarding mechanical properties, structural, and dynamic performances can effectively be resolved by utilizing metaheuristic algorithms such as harmony search, teaching-learning-based optimization and other useful hybrid strategies to elevate the engineering optimization field to another level. It also emphasizes CI application in improving the design processes and offers clues on the future application of both the hybrid and the multi-objective optimization strategies in engineering.

AI rapidly impacts the neurological and psychological sciences, both positive and negative, as more AI techniques are developed. This review will examine how reformative AI can improve mental health care and neuropsychological evaluations. Cognitive tools, including machine learning systems and therapeutic bots, are changing the approach to treatment and, in particular, the diagnosis of diseases. For example, early intervention in a mental health condition can be conducted when AI presents the results from a large data set where the disease may be concealed. Nevertheless, using AI also causes ethical concerns such as privacy, data pre-processing bias, and, obviously, the concern of validation. Since AI is a rapidly expanding field, it has essential consequences meant to be enacted to govern its safety and efficiency in usage in the clinical area. The future area of research should be aimed at minimizing the divergence between technical potentiality and clinical application of the developed AI so that the decision support is complementary to the existing stock of human expertise. The following review will discuss the current trends, issues, and prospects of neurological and psychological AI applications, focusing on the concept that interdisciplinary cooperation can fully unlock the potential of these developments while recognizing the potential issues. In this way, we must work together for a new era of mental health care that is evidence-based but also ethically sound.