International Journal Of Mathematics And Computer Research

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

Md. Hamidul Islam, Md. Shohel Rana, (PhD) — Thu, 01 May 2025 11:43:22 +0000

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, Geeta B, R. L. Raibagkar, G. G. Rajput — Mon, 05 May 2025 10:30:12 +0000

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, Dira Raina Agustina, Agus Rusgiyono — Mon, 05 May 2025 10:32:27 +0000

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 ., Lia Afrianti Nst, Noorihan Abdul Rahman, Hidayat . — Wed, 07 May 2025 09:36:19 +0000

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.

The Three Machines Sequencing Problems

Dr. A. Sridhar, Swathi . — Mon, 12 May 2025 10:43:15 +0000

Sequencing problems have found numerous applications across various domains due to their ability to understand patterns and relationships within sequential data. A sequencing model refers to tasks where you need to determine the optimal order or arrangement of items or events according to certain criteria. They find applications in various fields. Sequencing models are powerful tools that require a solid understanding of mathematical concepts, problem formulation, and appropriate solution techniques to effectively address complex real-world challenges

On Isomorphism Classes of Right Loops

Sushmita Kushwaha, A. C. Yadav — Tue, 13 May 2025 11:13:08 +0000

In this article, we find formula for non-isomorphic right loops of order n by operation ◦_g will be termed as the deformation of ◦ through the map g: S → Sym(S) with g(e) = I.

Geographic Information System Shortest Route Recommendation for Base Transceiver Station Location in Semarang Regency

Agung Rahmad Hidayat, Rahmat Gernowo, Aris Sugiharto — Wed, 14 May 2025 00:00:00 +0000

Base transceiver station (BTS) towers serve to connect mobile devices with operator networks by sending and receiving radio signals to and from communication devices. The strength of the received signal is influenced by the distance between the BTS tower location and the device, the farther the weaker. In addition, the quality of the signal is affected by disturbances on the BTS tower itself such as infrastructure damage, blackspots, and extreme weather conditions. There are 288 BTS towers scattered in Semarang Regency, owned by telecommunication tower providers and mobile operator networks. Each BTS tower requires regular maintenance to ensure the signal quality remains stable. With the large number of towers, a shortest route-finding system is needed to facilitate maintenance staff to get to the tower location. This study aims to examine the geographic information system (GIS) in recommending the closest path to the tower that needs to be repaired using Dijkstra and A* (A-star) algorithms. Dijkstra finds the shortest path by calculating the total weight of all points that need to be passed to reach the destination. A-star searches for a path based on the estimated value of the total weight of the points passed using Euclidean Distance. The route search process by determining the starting point, then selecting the location of the BTS tower, and looking for the shortest route based on possible major roads to the tower using Dijkstra and A-star algorithms. The two algorithms will be compared based on the speed in getting the closest route. GIS testing is carried out using the Black-Box Testing method with Boundary Value Testing techniques to ensure conformity between software and requirements specifications. System testing 82 routes to BTS towers using Dijkstra and A-star, with the average result that Dijkstra is 10 times faster in finding the shortest route than A-star. The result of this research is a geographic information system for finding the closest route to the BTS tower in Semarang Regency. The recommended route is based on major roads in Semarang Regency.

On Fixed Points in B-Rectangular Metric Spaces

A.S. Saluja, Asmita Yadav — Thu, 15 May 2025 00:00:00 +0000

This paper presents fixed point results in b-rectangular metric spaces. The results obtained expand and generalize several well-established finding in the existing literature.

2020 Mathematics Subject Classification: Primary: 54H25; Secondary: 54E50, 47H10.

A Result on Fixed Points in Rectangular S-Metric Spaces

Asmita Yadav, A. S. Saluja — Thu, 15 May 2025 00:00:00 +0000

In this paper, the notion of rectangular S-metric which extends rectangular metric spaces introduced by Branciari. The results obtained expand and generalize several well-established findings in the existing literature.

MATHEMATICS SUBJECT CLASSIFICATION (2020) : Primary: 54H25;

Secondary:54E50, 47H10.

Computational-Reumathological CAD Clinical Diagnosis with Lumbar Vertebral Cadaveric Specimens and Spine Sub-Units Mathematical Modeling. Part III

Francisco Casesnoves — Thu, 15 May 2025 00:00:00 +0000

Continuing this biomechanical research line, and based on previous series, a subsequent reumathologically-directed clinical study with 3D-CAD imaging-processing was done. In this part with GNU Octave and Matlab programming—comparing both. The lumbar cadaveric specimen is a different one related to previous publications specimens of bioengineering laboratory. The objective of the research is the clinical finding of lumbar spine degeneration signs, arthrosis, osteoarthritis, deformations, and disk herniations. The software method is presented in series of 3D-CAD lumbar vertebral imaging processing and software-camera with Matlab. Results demonstrate that, from scanned cloud data of cadaveric specimens, a clinical necropsia and diagnosis database can be taken out. Clinical applications on computational-forensics, computational-imaging intelligence, medical physics, and biomedical engineering are obtained from these cadaveric specimens 3D imaging-processing evaluation. A number of epidemiological/statistical data is shown. What is more, the author’s mathematical spine sub-unit model biomechanical equations and model is initially developed. The article is an advance and shows review of necessary parts for better understanding.

A Quantitative Analysis of the Joint Dynamics of the Interconnected Spread of Cholera and Typhoid Diseases

Vanenchii Peter Ayoo, Akpan Collins Emmanue, Ibrahim Babuje — Wed, 21 May 2025 11:56:45 +0000

In this paper, a mathematical model that captures the spread of Cholera and Typhoid is considered. The system of equations was solved using Laplace Adomian Decomposition Method (LADM) and was implemented using MATLAB. The analysis showed that an increase in the burden or cases of Cholera will result to an increase of Typhoid fever and vise-versa indicating that there is a symbiotic nature of the relationship between the typhoid disease and the cholera disease.

Downhill Product Connectivity Indices of Graphs

V.R. Kulli — Wed, 21 May 2025 00:00:00 +0000

In this study, we introduce the downhill product connectivity index and reciprocal downhill product connectivity index and their corresponding exponentials of a graph. Furthermore, we compute these indices for some standard graphs, wheel graphs.

Implementation of Fuzzy Possibilistic C-Means with Optimal Membership of Fuzzy C-Means for Stunting Management in Central Java Province

Falidazia Hasanah Faizana, Puspita Kartikasari, Deby Fakhriyana — Mon, 26 May 2025 11:35:29 +0000

Stunting is a serious problem makes children vulnerable disease and reduced productivity. According to Indonesian Health Survey (2023), stunting rate Indonesia in 2023 was 21.5%. The target set in 2020-2024 National Medium-Term Development Plan of 14% and WHO standard below 20% have still not been achieved. Based on Indonesian Health Survey (2023), prevalence stunting in Central Java Province in 2023 has decreased only 0.1% to 20.7%. Therefore, it is necessary to evaluate acceleration stunting handling from achievement more focused and targeted Special Index for Stunting Management (IKPS) indicators, one of which is through clustering analysis. The data used is the indicators IKPS of districts/cities in Central Java Province from the official website of the Central Bureau of Statistics (BPS) in 2023. The data contains outliers because the acceleration rate of stunting reduction varies in each region. Fuzzy Possibilistic C-Means algorithm with optimal membership of Fuzzy C-Means which is able to handle outlier data, is used in this research. The clustering results using the algorithm will be validated using the Davies Bouldin Index (DBI) to find the most optimal cluster. The validated shows the optimal cluster with 5 clusters and a DBI value of 1.520291.

Variance Gamma Model for Daily Stock Price Prediction (Case Study: Daily Stock Price of PT Industri Jamu dan Farmasi Sido Muncul Tbk)

Izzati Khairina Putri, Abdul Hoyyi, Agus Rusgiyono — Mon, 26 May 2025 00:00:00 +0000

Stocks have high volatility, with price fluctuations that are difficult to predict and often do not meet the assumption of a normal distribution. The Brownian Motion method is widely used in stock price modeling, but is unable to capture sharp spikes in leptokurtic data. The Variance Gamma model is applied by evaluating a modified Brownian Motion process using a random time following a Gamma process. This model has three parameters to control volatility, kurtosis, and skewness. This research analyzes the daily stock return data of PT Industri Jamu dan Farmasi Sido Muncul from December 2023 to December 2024. TheVariance Gamma parameter estimation using the Moment Method and Maximum Likelihood Method. Accuracy calculation uses Mean Absolute Percentage Error (MAPE). The results showed that the Variance Gamma model with the normal standard process approach of a gamma process produced a MAPE of 4.150345%. While the different approach of two independent Gamma processes has a MAPE of 4.515595%. Both methods are more accurate when compared to the Geometric Brownian Motion with Jump model, which has a MAPE of 6.866523%. Variance Gamma has a smaller MAPE value, so it is more suitable for modeling stock prices with jumps and not normally distributed.

Learning Mathematics Using Core Learning Model to Support Numeracy Skills and Learning Achievement of Junior High School Students

Maryam Pratiwi Azra, Dhoriva Urwatul Wustqa — Tue, 27 May 2025 11:49:11 +0000

Numeracy skills and learning achievement are important aspects in mathematics learning that support students' readiness to face the challenges of the modern era. However, in reality, many junior high school students still show low and suboptimal numeracy skills and learning achievement. Therefore, a learning model is needed that can facilitate students' numeracy skills and learning achievement.The objectives of this study were to 1) analyse the effect of mathematics learning using CORE model on students' numeracy skills, 2) analyse the effect of mathematics learning using CORE model on students' learning achievement. This type of research is a pseudo-experimental research with quantitative approach. The subjects of this research were 23 students of SMP 3 Muhammadiyah Yogyakarta. Data collection techniques in this study used tests which included: 1) numeracy skill test, 2) mathematics learning achievement test. The data analysis technique in this research is hypothesis testing using paired sample t-test. The results of this study showed that: 1) learning mathematics using CORE model can support numeracy skills, 2) learning using CORE model can improve students' mathematics learning achievement.

Identification of Factors Influencing the Preferences of College Students in Choosing SIM Cards Using the Orthogonal Factor Model

Wed, 28 May 2025 11:15:46 +0000

A Subscriber Identity Module (SIM) card is a key component in mobile telecommunication systems that stores identity information and enables access to mobile networks. Students' choice of SIM card is important given their need for efficient communication, fast and stable internet access, and other additional services. This study aims to identify factors that influence the preferences in choosing a SIM card using the orthogonal factor model. This model is applied to simplify a large number of variables into several main factors that are not correlated with each other, making interpretation easier. Data was collected through a questionnaire involving 257 respondents and analyzed using the principal component factoring (PCF) technique for parameter estimation. PCF was chosen due to its ability to extract principal components that represent the largest variance of the data, so that the resulting factors are more accurate in describing student preferences. The study results indicate that six key factors affect students' preferences when selecting a SIM card: price, service, network quality, promotional offers, extra features, and bonuses. Understanding these factors, service providers can design more effective marketing strategies to attract and retain student customers.

Support Vector Machine Implementation for Classifying Public Sentiment on Electronic Stamps use in Civil Servant Registration

Nilna Adiba Kamal, Suparti ., Puspita Kartikasari — Thu, 29 May 2025 00:00:00 +0000

Civil Servants play a crucial role in government administration and national development. The selection process for Civil Servant Candidates is a critical stage in civil servant management, as it has long-term implications for organizational effectiveness. With the advancement of digital technology, the Indonesian government has implemented electronic stamps in the civil servant candidate registration process to enhance efficiency and transparency. However, this policy has received various responses from the public, particularly on the social media platform Twitter. Many users express positive opinions regarding the convenience of administrative digitalization, while others report technical issues in using electronic stamps. Therefore, sentiment analysis is needed to understand public responses to this policy. One of the effective methods for sentiment classification is Support Vector Machine (SVM), which can optimally separate positive and negative opinions. This study utilizes a dataset comprising 1,249 reviews collected from Twitter/X. The best SVM model is selected through hyperparameter tuning using the GridSearchCV technique. The findings indicate that the SVM model with cost = 100 and gamma = 0.01 achieves the best performance, with an accuracy of 92%, precision of 86.48%, recall of 86.48%, F1-score of 86.48%, and a Kappa-Statistic of 81%.

Sentiment Classification of Livin by Mandiri App Reviews with Support Vector Machine Based on Modified Particle Swarm Optimization

Salsa Febriliana Sandita, Iut Tri Utami, Masithoh Yessi Rochayani — Thu, 29 May 2025 11:00:56 +0000

This research aims to evaluate the quality of the Livin by Mandiri mobile banking application based on user reviews from the Google Play Store. A Support Vector Machine (SVM) classifier is applied to distinguish between positive and negative sentiments. Modified Particle Swarm Optimization (MPSO) is used for parameter tuning because SVM performance is sensitive to parameter selection. The baseline SVM attains 90.30% accuracy, 90.64% precision, 89.86% recall, and 90.25% F1-score with a linear kernel with C = 1 and a 90:10 training-testing split. Accuracy is increased to 91.33% by the SVM-MPSO model, with precision of 90.87%, recall of 91.71%, and F1-Score of 91.29%. The Streamlit framework for interactive sentiment classification is used to deploy the model.

AI's First World War: A Silent Technological War with Global Strategic Stakes

Augustin NYEMBO MPAMPI, Shadrack MBAYO LUKASU — Sat, 31 May 2025 10:34:15 +0000

Artificial intelligence (AI) has become a major strategic issue, giving rise to a First Technological World War where major powers compete for computational and algorithmic supremacy. This competition is based on the mastery of advanced hardware infrastructures (GPUs, TPUs, ASICs) and increasingly powerful learning models, notably Transformers and LLMs . However, this rise in power is accompanied by new threats in cybersecurity , where AI is both an offensive weapon and a defensive shield. From adversarial attacks to autonomous malware, vulnerabilities in AI systems are becoming prime targets, forcing cybersecurity players to develop adversarial training and automatic threat detection techniques .

In the face of these advances, AI regulation is becoming a crucial challenge. Europe is imposing strict rules with the AI Act , while the United States favors a sector-specific approach and China is centralizing control of AI. However, the absence of a harmonized international framework could lead to technological fragmentation, accentuating the AI race between geopolitical blocs. In this First World War of AI, dominance will not only be determined by the performance of algorithms, but also by the ability of nations to balance innovation, security, and governance to shape the digital future.