Investment Optimization with Nonlinear Equation Solving

2025-09-30T12:51:27+00:00

Investment optimization is one of the important topics in the world of finance that aims to maximize profits with minimal risk. Mathematical approaches, particularly through the solution of nonlinear equations, have become an effective method of aiding investment decision-making. This article discusses the development of an investment optimization model that uses nonlinear equation solving techniques to determine optimal asset allocation. In this study, a nonlinear equation is used to describe the relationship between various investment variables, such as profit level, risk, and asset allocation. Using this approach, investors can find optimal solutions that meet their investment goals, whether in conservative, moderate, or aggressive scenarios. The methodology used involves historical data analysis, mathematical model formulation, and the application of numerical algorithms to solve the nonlinear equations. The results show that the solution of nonlinear equations is able to provide a more precise solution than traditional methods, such as linear programming or simple heuristic. This approach not only improves accuracy in determining the optimal portfolio, but also provides flexibility in dealing with dynamic market conditions. The proposed model allows sensitivity analysis to variable changes, allowing investors to make more informative and adaptive decisions. Investment optimization with the solution of nonlinear equations is a significant innovation in the field of finance, which not only supports investment efficiency but also opens up opportunities for the development of more complex investment models. This article is expected to be a reference for academics and practitioners in applying a mathematical approach for optimal portfolio management.

AI-Enabled Early Detection of Chronic Diseases Using Wearable Sensor Data

2026-03-27T09:59:26+00:00

Early detection of chronic diseases remains a major public health challenge, particularly in urban communities where lifestyle-related risk factors are increasingly prevalent. This study proposes and evaluates an artificial intelligence (AI)-enabled framework for the early detection of chronic diseases using wearable sensor data collected in Quezon City, Philippines. The research integrates multimodal physiological and behavioral signals, including heart rate, physical activity, sleep patterns, and other relevant biosensor indicators, to identify early risk patterns associated with chronic conditions such as hypertension, diabetes, and cardiovascular disease. A supervised machine learning approach was employed to preprocess, analyze, and classify wearable sensor data obtained from [number] participants. Feature engineering and model optimization techniques were applied to improve predictive performance and reduce noise from real-world sensor streams. The developed model was evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). The findings indicate that the proposed AI-based system achieved promising predictive performance in distinguishing individuals at elevated risk of chronic disease, demonstrating the feasibility of wearable technologies as scalable tools for preventive healthcare. In addition, the study highlights the potential of continuous, non-invasive monitoring to support earlier clinical intervention and personalized health management in resource-constrained urban settings. This research contributes to the growing field of digital health by offering an AI-driven, data-informed approach to chronic disease surveillance and early risk detection in the Philippine context.

International Journal of Smart Systems

Investment Optimization with Nonlinear Equation Solving

AI-Enabled Early Detection of Chronic Diseases Using Wearable Sensor Data