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

---

Towards Smarter E-Learning: Real-Time Analytics and Machine Learning for Personalized Education

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

Published: Jan. 2, 2025

E-Learning platforms change fast, and real-time behavioural analytics with machine learning provides the most powerful means to enhance learner outcomes. The datasets undergo preprocessing techniques like Z-score outlier detection, Min-Max scaling for feature normalization, Ridge-RFE (Ridge regression Recursive Feature Elimination) selection in order improve accuracy reliability of predictions. Applying Gradient Boosting Machine, classification up a 94% level respect model about predictions on outcomes was achievable. Thus, applying this, feedback systems may offer timely recommendations or directions class that propel students toward better understanding how raise participation success percentages. However, this approach has some potential benefits but there are still various challenges such as managing data imbalance models generalize dynamic environment. Though hybrid methods mitigate problem, pipelines behaviour incorporation call significant computer-intensive resources infrastructure. This integration very high paybacks. It makes possible more responsive individual needs almost met manners, thus giving instantaneous feedback, content suggestions, interventions. Finally, convergence ML culminates adaptive environments which student engagement, retention, quality academic results.

Language: Английский

---

Enhancing Predictive Accuracy of Renewable Energy Systems and Sustainable Architectural Design Using PSO Algorithm

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

Published: Jan. 12, 2025

This paper formulates and examines the approach of integrating PSO into tune DNNs for boosting predictive capability in renewable energy systems green building designs. The method was then employed to select Key features such as; Solar Irradiance, Ambient Temperature, Panel Efficiency Energy Output. PSO-based feature selection resulted significant enhancements across a set four metrics, there an improvement accuracy from previous 0.82 0.87, precision 0.78 0.83, as well recall 0.76 0.81, F1-Score 0.77 current score 0.82. Moreover, RMSE values reduced 0.27 0.23, AUC enriched 0.74 0.85. Thus, results study support PSO’s role improving selection, which, return, improves models management. presented emphasizes possibility use enhanced optimization algorithms enhancing best performing, less resource-intensive, environmentally friendly solutions architecture.

Language: Английский

---

Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization

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

Published: Jan. 9, 2025

The rapid advancement of computational intelligence (CI) techniques has enabled the development highly efficient frameworks for solving complex optimization problems across various domains, including engineering, healthcare, and industrial systems. This paper presents innovative that integrate advanced algorithms such as Quantum-Inspired Evolutionary Algorithms (QIEA), Hybrid Metaheuristics, Deep Learning-based models. These aim to address challenges by improving convergence rates, solution accuracy, efficiency. In context a framework was successfully used predict optimal treatment plans cancer patients, achieving 92% accuracy rate in classification tasks. proposed demonstrate potential addressing broad spectrum problems, from resource allocation smart grids dynamic scheduling manufacturing integration cutting-edge CI methods offers promising future optimizing performance real-world wide range industries.

Language: Английский

---

Enhancing Trading Strategies: Mandani Fuzzy Logic Forecasting for Borsa Istanbul Stocks Using Important Indicators

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

Published: Jan. 14, 2025

Recent years have seen significant financial market advancements, predicting stock or crypto exchange prices is a complex and risky process. Developments in the world are becoming increasingly interesting, especially for traders investors who want to maximise profits. Nowadays, forecasting analysis changing as conditions change popular methods preferred instead of traditional methods. Current changes developments markets become very important with fuzzy logic method selection indicators. In this study, contrary existing indicators, success was achieved 6 most indicators (RSI, SO, MACD, OBV, BB, CCI). Since each indicator has its pros cons, these aspects balanced mandani method. This study provides facilitate operation 655 companies listed Borsa Istanbul (BIST). FROTO data belonging Ford Otosan company on BIST used data. aims enable maximize their profits increase portfolios. The accurate results were obtained using membership functions created 34 rules Mamdani

Language: Английский

---

Towards Precision Medicine with Genomics using Big Data Analytics

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

Published: Jan. 23, 2025

Precision medicine is considered to be the future of healthcare. It allows doctors select treatments based on patient's genetic information. being adapted a few typical complicated like cancer at an intermediate level. As information in large volumes, Big data analytics showing reliable promise modern-day health care revolution. Extremely and continuous collection volumes Genomics, Proteomics, Glycomics etc. creating challenge analysis interpretation, which addressed effectively by analytics. This research work reviews highlights evolution medicine, Data Analytics its significance related work. Also detailed Machine learning perspectives Precise with genomic models along Challenges.

Language: Английский

---

IntelliFuzz: An Advanced Fuzzy Logic Framework for Dynamic Evaluation of Student Performance in Open-Ended Learning Tasks

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

Published: Feb. 5, 2025

This study presents IntelliFuzz, an advanced fuzzy logic-based assessment system designed for the dynamic evaluation of student performance in open-ended tasks. The proposed leverages logic to address inherent subjectivity and ambiguity evaluating tasks such as essays, project work, case studies. IntelliFuzz incorporates multiple criteria, including task relevance, critical thinking, creativity, presentation quality, generate a comprehensive score. Experimental results on dataset 500 submissions demonstrate effectiveness IntelliFuzz. achieved 95% accuracy aligning with expert assessments reduced time by 30% compared traditional manual grading methods. inference was calibrated using 150 feedback samples, yielding average correlation coefficient 0.92 between system-generated scores evaluations. Furthermore, rated 85% satisfactory instructors its ability provide consistent fair evaluations.The highlights potential educational assessment, offering scalable efficient solution subjective Future research will focus integrating machine learning further enhance adaptability precision system.

Language: Английский

---

A Graph Neural Network Assisted Reverse Polymers Engineering to Design Low Bandgap Benzothiophene Polymers for Light Harvesting Applications

Materials Chemistry and Physics, Journal Year: 2025, Volume and Issue: unknown, P. 130747 - 130747

Published: March 1, 2025

Language: Английский

---

Unlocking Youth Athletic Potential: Predicting Triple Jump Outcomes from Anthropometric Profiles in U-17 Male Athletes

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 13, 2025

Understanding the role of anthropometric characteristics in athletic performance is essential for identifying and nurturing young talent. This study explores predictive relationship between key variables triple jump among under-17 male athletes. A total 60 participants were assessed parameters including height, weight, leg length, arm span, thigh circumference, body mass index (BMI). Triple was evaluated under standardized field conditions. Using multiple linear regression analysis, identified length height as most significant predictors distance, while BMI showed a negative association. The developed model demonstrated strong accuracy, accounting 68% variance outcomes. These findings emphasize importance incorporating physical profiling into youth training programs, allowing coaches sports scientists to design data-driven strategies athlete development. contributes optimization talent identification frameworks athletics.

Language: Английский

---

Deep Learning Algorithm Design for Discovery and Dysfunction of Landmines

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2024, Volume and Issue: 10(4)

Published: Dec. 21, 2024

Deep Learning is a cutting-edge technology which has noteworthy impact in the real-world applications. The multi-layer neural nets involved blueprint of deep learning enables it to deliver comprehensive decision-making system with quality “think alike human cerebrum”. assumes an essential part various fields like horticulture, medication, substantial business and so forth. can be well prompted remote sensing applications especially perilous military location land mines detected using algorithm design technique aided distinctive machine tools techniques. intelligent designed by process involves massive dataset including assorted features landmines size, sort, dampness, ground profundity on. Incorporation Geographical Information System give prevalent statistical analysis varied landmines. multiple layers present schema may increase feature extraction knowledge representation through complexities landmines’ input sets. likelihood brokenness increased utilization prediction model enormously helps survival militaries, creating social effect.

Language: Английский

---