International Journal Of Mathematics And Computer Research

Prediction of Obesity among Adults and Adolescents Using a Machine Learning Approach

Md. Hamidul Islam — 2025-05-01

Obesity has become one of the most significant public health issues of the 21st century. Obesity is a chronic, complex disease characterized by excessive fat accumulation that impairs health. It also has social and psychological dimensions, impacting individuals across all age groups and socioeconomic strata. It is associated with a range of risk factors, including diabetes, depression, and cancer. It presents a significant challenge to both developed and developing countries worldwide. This study aims to explore the factors contributing to obesity and develop a predictive model to identify individuals at risk. Using a secondary dataset, three machine learning algorithms were implemented for both interpretation and prediction. Logistic regression was used to identify significant associations between obesity and key factors such as family history of overweight and high caloric food intake. Among the predictive models, the random forest classifier demonstrated superior performance. The model was evaluated using metrics such as accuracy, specificity, and the Receiver Operating Characteristic (ROC) curve. The results indicated that the random forest classifier was the most effective model for predicting obesity, achieving the highest accuracy and ROC values. In conclusion, the findings of this study suggest that machine learning models, particularly the random forest classifier, can be effectively used to identify at-risk individuals and may offer valuable insights for the healthcare sector. The integration of such models could improve targeted interventions and support public health initiatives aimed at mitigating the obesity epidemic.

Video Dehazing Using Denoising Diffusion Probabilistic Model with Trilateral Filtering and Multi-Scale Boosted Dehazing Network

Nisha Amin — 2025-05-05

Outdoor videos captured in poor weather conditions, such as haze or fog, often suffer from reduced visibility, affecting both cinematography and surveillance applications. The light scattering and absorption caused by aerosols in the atmosphere and airlight reflection result in images with faded colors and decreased contrast. This paper presents a novel approach to video dehazing by leveraging Deep Learning (DL), specifically utilizing the Denoising Diffusion Probabilistic Model (DDPM) for haze removal and a Multi-Scale Boosted Dehazing Network (MSBDN) with Dense Feature Fusion for enhanced image quality. We refine the transmission map using trilateral filtering to achieve smooth edge transitions and improve dehazing performance. Our method is evaluated on both synthetic and real-world datasets, demonstrating its robustness and effectiveness compared to state-of-the-art dehazing algorithms.

Spatial Analysis of Poverty in Central Java Indonesia Through Multiscale Geographically Weighted Regression

Prajna Pramita Izati — 2025-05-05

Multiple linear regression is a statistical analysis method used to measure the influence of two or more independent variables on the dependent variable. Violation of the assumption of homoscedasticity in multiple linear regression analysis modeling can occur due to spatial heterogeneity. Therefore, it is necessary to analyze by providing spatial weights for each observation location. Multiscale Geographically Weighted Regression (MGWR) is a method developed to analyze spatial data with different scales of influence for each independent variable. This study aims to model the number of poor people in districts/cities in Central Java Province in 2023 using MGWR with the fixed gaussian kernel function. Average Wage of Laborers/Employees/Staff (X_1 ), Average Wage/Net Salary of Informal Workers (X_2 ), Number of TKI AKAN (X_3 ), Number of Unemployed Workers (X_4 ), APK SLTA (X_5 ), Number of people aged 15+ who have smoked in the past month (X_6 ), Percentage of Households Having Access to Clean Drinking Water Sources (X_7 ), and Percentage of Households Having Access to Proper Sanitation (X_8 ) each have an effect on the Number of Poor Population (Y) in at least one observation location. The MGWR model has an R^2 value of 0.8780482 and an R_adjusted^2 of 0.8200639.

Development of Learning Media in Elementary Schools Assisted by Storyline Application

Sukmawarti . — 2025-05-07

The development of information and communication technology has had a significant impact on education, demanding innovation in the learning process to meet the demands of 21st-century skills. This research aims to develop interactive learning media based on the Articulate Storyline application for elementary school students. The research uses the Borg and Gall development model through the Define, Design, and Develop stages. In the Define stage, an analysis of student needs, basic competencies, and technology integration in learning is conducted. The Design stage includes the design of learning media involving competency design, learning objectives, and student activities. In the Develop stage, the product is validated by media and material experts, followed by revisions based on the trial results. The research results in a valid and feasible learning media assisted by the Storyline application for use in elementary school mathematics, particularly on the topic of fractions. This media offers advantages such as an attractive visual display, interactive features, and the integration of multimedia elements like text, animations, and videos. The use of this media has been proven to increase students' motivation and understanding of the learning material. This research also produced outputs in the form of educational media products, journal articles, and monographs registered with an ISBN. The next steps include product trials on a wider population and the dissemination of research results.