Optimizing Type II Diabetes Prediction Through Hybrid Big Data Analytics and H-SMOTE Tree Methodology

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 10, 2025

In the last few years, Type II diabetes has become much more common worldwide, presenting major problems for both healthcare systems and individuals. Utilizing big data analytics shown potential as a means of forecasting managing persistent illnesses, like diabetes. This paper proposes novel hybrid approach that combines techniques with an H-SMOTE tree algorithm prediction The suggested method addresses class imbalance present in medical datasets improves accuracy by combining steps feature selection, preprocessing, classification. order to prepare raw analysis, it must first be cleaned, standardised, transformed. Then, selection are used identify most important factors help predict streamlines predictive model lowers its dimensionality. classification phase, called is used. two existing techniques: Hoeffding Adaptive Tree (HAT) Synthetic Minority Oversampling Technique (SMOTE). tackles imbalanced creating synthetic samples under-represented class, while also adapting decision structure receives new data. Experiments show this effective accurately predicting researchers found outperformed other machine learning methods, classic recent ones. words, was accurate T2DM cases. evident terms several metrics, including how well identified true positives (sensitivity), avoided false (specificity), overall performance captured AUC-ROC score. Additionally, proposed displays resilience scalability, rendering apt extensive frequently encountered within domains.

Language: Английский

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Social and Cognitive Predictors of Collaborative Learning in Music Ensembles

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 13, 2025

There have been many attempts to find ways make music education more relevant and useful for pupils. Learning theories, performance-based learning, contract-learning, discovery-learning, cooperative daily clocking, stage practice, music-focused required elective courses are all part of the implementation these methods. Since high vocational students tend lower GPAs, it is imperative that they discover strategies enhance their academic performance. Reform, rather than relying on theoretical frameworks, should be grounded practical, innovative human actions. Both instructors pupils possess capacity comprehend what learnt, according humanistic perspective. This paper provides evidence collaborative learning beneficial first-year practice in a popular program at Chinese institution. The work small, diverse groups. Data was collected analyzed from over course one year with grades 4-6.. Collaboration powerful tool has applications, including but not limited degree programs, which implemented this using machine techniques. It zeroed down seven important characteristics, had obvious applications educational process. Another online could use method predict students' performance, real-time tracking progress risk dropping out, after adjusted capture features corresponding different contexts. also applied other management platforms.

Language: Английский

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Rainfall Forecasting in India Using Combined Machine Learning Approach and Soft Computing Techniques : A HYBRID MODEL

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 9, 2025

Accurate rainfall prediction in India is crucial for agriculture, water management, and disaster preparedness, particularly due to the reliance on southwest monsoon. This paper examines historical trends from 1901 2022, highlighting significant anomalies changes identified through Pettitt test. The effectiveness of advanced machine learning techniques explored Artificial Neural Network-Multilayer Perceptron (ANN-MLP) enhancing forecasting accuracy compared with statistical methods. By integrating important climate variables—temperature, humidity, wind speed, precipitation into ANN-MLP model, its ability capture complex nonlinear relationships demonstrated. Additionally, analysis employs geo-statistical techniques, specifically Kriging, visualize spatial-temporal variability across different regions India. findings emphasize potential modern computational methods overcome traditional challenges, ultimately improving decision-making agricultural planning resource management face variability.

Language: Английский

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A Context-Aware Content Recommendation Engine for Personalized Learning using Hybrid Reinforcement Learning Technique

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 5, 2025

In the evolving landscape of e-learning, delivering personalized content that aligns with learners' needs and preferences is crucial. This study proposes a Context-Aware Content Recommendation Engine (CACRE) utilizes Hybrid Reinforcement Learning (HRL) technique to optimize learning experiences. The engine incorporates contextual data, such as pace, preferences, performance, deliver tailored recommendations. proposed HRL model combines Deep Q-Learning for dynamic selection Policy Gradient Methods adapt individual trajectories. Experimental results demonstrate significant improvements in learner engagement, relevance, knowledge retention. approach underscores potential context-aware recommendation systems revolutionize education by fostering adaptive interactive environments.

Language: Английский

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