Science, Technology, and Communication Journal

Toddler nutritional status identification: Support vector machine (SVM) algorithm adoption

2025-01-27T09:21:00+07:00

Inadequate nutrition in toddlers can lead to health issues and adversely affect their growth, development, and cognitive capabilities. Consequently, it is essential to assess the nutritional status of toddlers to ascertain their health level. This study seeks to ascertain the nutritional health of toddlers utilizing the support vector machine (SVM) methodology, taking into account body weight (BB), height (TB), age, BB/TB ratio, Z-scores for BB/U, Z-scores for TB/U, and Z-scores for BB/TB. The data of 1458 toddlers were evaluated using the knowledge data discovery methodology. This study effectively categorized toddler nutrition into six classifications including malnutrition, undernutrition, adequate nutrition, overnutrition, risk of overnutrition, and obesity. Utilizing the confusion matrix methodology with an 80% training data to 20% test data ratio yields an accuracy of 89.04%. The SVM method is effectively utilized to ascertain the nutritional condition of toddlers, hence enhancing their growth and development.

Plasma argon particle interactions in a non-equilibrium state through the Maxwell-Boltzmann kinetic equation

2024-12-12T14:41:25+07:00

Non-thermal argon plasmas serve multiple functions, particularly in healthcare and industrial applications. Numerous particles of the same species exhibit varying velocities, referred to as a population. The distribution function is a standard method for characterizing a population. The speed and energy distribution functions in the Maxwell-Boltzmann equation are simulated utilizing MATLAB. The density of each species was numerically calculated using the Runge-Kutta method. This research reviews various argon species, including Ar*, Ar⁺, Ar(1s⁵), Ar(1s⁴), Ar(1s³), Ar(1s²), Ar, and electrons. The parameters utilized include a pressure of 10 mTorr, an argon temperature about 400 K, and an electron temperature about 30,000 K. The maximum velocity probability density value is observed in the Ar⁺ species at 6.18 × 10⁷ (m/s)^-1, while the minimum value is found in electrons at 1.93 (m/s)^-1. The maximum energy probability density value is observed in the Ar⁺ species at 2.13 × 10²⁹ (Joule)^-1, while the minimum value is found in the Ar(1s³) species at 1.40 × 10²⁵ (Joule)^-1. The time evolution of the distribution function, independent of the coordinates r, is associated with v, at t = 10^-8 s. The velocity distribution function is significantly affected by the density value, while the distribution function is contingent upon the velocity.

Optimization of plantar foot thermogram for diabetic foot ulceration early detection: An image enhancement approach

2025-01-14T10:09:06+07:00

Diabetes mellitus (DM) is a critical health condition caused by insulin production failure, leading to elevated blood glucose levels. DM often results in severe complications such as heart disease, stroke, and diabetic foot ulcers (DFU), which pose risks of infection and potential amputation. This study developed a machine learning model for early detection of diabetic foot ulcers, using thermogram images and the thermo dataset containing detailed foot temperature data. The multi-classifier model integrates CNNs for processing thermogram images and an ANN for tabular data analysis. Various image enhancement techniques were applied, including solarize, CLAHE, posterize, and gamma adjustment, to improve the visibility of key temperature distribution patterns. The results demonstrate that solarize consistently emerged as the most effective image enhancement method, significantly improving model performance across all evaluation metrics. Models enhanced with solarize achieved an impressive accuracy of 97.06%, alongside a perfect AUC score of 1,000. Additionally, the application of image enhancement techniques proved instrumental in reducing training and inference times, indicating computational efficiency. The integration of temperature data with enhanced thermogram images further boosted predictive accuracy while maintaining critical thermal information. This study underscores the transformative potential of image enhancement techniques, particularly solarize, in advancing the accuracy and efficiency of early detection models for diabetic foot ulcers. These findings contribute meaningfully to the development of medical imaging technologies, offering a robust framework for improving disease diagnosis and management.

Final assignment exam scheduling optimization using genetic algorithms with tournament selection techniques and violated directed mutation (VDM)

2025-01-14T10:00:32+07:00

Scheduling the final assignment exam is an important process that requires careful planning to ensure smooth implementation for each student. This process involves the stages of archiving final assignment submission files, determining supervisors and examiners, as well as preparing seminar and trial schedules. However, obstacles that often arise include conflicting schedules, long execution times, and low fitness values. To overcome this problem, the genetic algorithm approach is used to optimize scheduling. This algorithm can handle complex problems with a wide search space, although it has weaknesses in selecting appropriate parameters and the time required to reach the optimal solution. Genetic algorithm optimization techniques such as violated directed mutation (VDM) and tournament selection are used in this research. Previous research shows that VDM provides better results than other methods, while tournament selection improves the desired solution. It is hoped that the use of genetic algorithms with VDM and tournament selection will overcome the problem of conflicting schedules and increase the execution speed in final project exam scheduling.

Analysis of anemia disease in Pakistan using logistic regression

2025-01-27T09:03:18+07:00

Anemia, a global health issue affecting over two billion individuals, is characterized by a deficiency in red blood cells or hemoglobin, impairing oxygen transport in the body. Early detection of anemia is critical, particularly in resource-constrained regions. This research aims to develop a robust anemia prediction model leveraging machine learning techniques and non-invasive data inputs, including red, green, and blue (RGB) pixel intensities and hemoglobin levels. The research focuses on three objectives which are to analyze the relationship between predictor variables and anemia status using a correlation heatmap, to assess the contribution of RGB pixel intensities and hemoglobin levels in predicting anemia using feature importance analysis, and to identify significant predictors through recursive feature elimination. The model, developed using logistic regression, achieved an exceptional accuracy of 99.33% and an AUC score of 1.00. The hemoglobin level emerged as the most significant predictor, showing a strong negative correlation of -0.84 with anemia status. This approach not only enhances understanding of anemia's determinants but also provides actionable insights for healthcare professionals to devise targeted therapies and public health measures. Addressing these risk factors is vital to improving health outcomes, particularly for vulnerable populations at higher risk of anemia.