Predicting Student Performance in a Blended Learning Course Using Machine Learning

Sustainable civil infrastructures, Год журнала: 2025, Номер unknown, С. 1251 - 1263

Опубликована: Янв. 1, 2025

Язык: Английский

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Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review

BMC Musculoskeletal Disorders, Год журнала: 2025, Номер 26(1)

Опубликована: Янв. 4, 2025

Abstract Background To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview these models. Methods Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception February 6, 2024, identify literature on use predict injury (e.g. (ACL) meniscus), reoperation ACLR. The authors adhered PRISMA R-AMSTAR guidelines as well Cochrane Handbook for Systematic Reviews Interventions. Demographic data specifics recorded. Model was recorded using discrimination, area under curve (AUC), concordance, calibration, Brier score. Factors deemed predictive also extracted. MINORS criteria used methodological quality assessment. Results Nine studies comprising 125,427 patients with mean follow-up 5.82 (0.08–12.3) years included this review. Two nine (22.2%) served external validation analyses. Five (55.6%) reported AUC (strongest model range 0.77–0.997). Four (44.4%) concordance range: 0.67–0.713). score, calibration intercept, slope, values ranging 0.10 0.18, 0.0051–0.006, 0.96–0.97 amongst highest performing models, respectively. error, all four demonstrating significant miscalibration at either two five-year follow-ups 10 14 models assessed. Conclusion Machine designed risk revision demonstrate variable discriminatory when evaluated metrics. Furthermore, there is several evidence marks. lack existing limits generalizability findings. Future research should focus validating current addition developing new multimodal neural networks improve accuracy reliability.

Язык: Английский

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Assessment of Water Hydrochemical Parameters Using Machine Learning Tools

Sustainability, Год журнала: 2025, Номер 17(2), С. 497 - 497

Опубликована: Янв. 10, 2025

Access to clean water is a fundamental human need, yet millions of people worldwide still lack access safe drinking water. Traditional quality assessments, though reliable, are typically time-consuming and resource-intensive. This study investigates the application machine learning (ML) techniques for analyzing river in Barnaul area, located on Ob River Altai Krai. The research particularly highlights use Water Quality Index (WQI) as key factor feature engineering. WQI, calculated using Horton model, integrates nine hydrochemical parameters: pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, turbidity. primary objective was demonstrate contribution WQI enhancing predictive performance analysis. A dataset 2465 records analyzed, with missing values parameters (pH, trihalomethanes) addressed imputation via neural network (NN) architectures optimized genetic algorithms (GAs). Models trained without achieved moderate accuracy, but incorporating dramatically improved across all tasks. For trihalomethanes R2 score increased from 0.68 (without WQI) 0.86 (with WQI). Similarly, 0.35 0.74, 0.27 0.69 after including set.

Язык: Английский

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Artificial Intelligence in Healthcare: Current Trends and Future Directions

Current Medical Issues, Год журнала: 2025, Номер 23(1), С. 53 - 60

Опубликована: Янв. 1, 2025

Abstract Artificial intelligence (AI) is a milestone technological advancement that enables computers and machines to simulate human problem-solving capabilities. This article serves give broad overview of the application AI in medicine including current applications future. shows promise changing field medical practice although its practical implications are still their infancy need further exploration. However, not without limitations this also tries address them along with suggesting solutions by which can advance healthcare for betterment mass benefit.

Язык: Английский

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Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 15, 2025

Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer reliable approach to obtaining this property involve time-consuming procedures. Recent advancements artificial neural networks (ANNs) have shown promise simplifying task by estimating it with high accuracy. Nevertheless, conventional ANNs often require deep achieve acceptable results cases large datasets where generalization required for variety of mixtures. This leads increased training durations susceptibility noise, causing reduced accuracy potential information loss networks. In order address these limitations, study introduces novel multi-lobar network (MLANN) architecture inspired the brain's lobar processing sensory information, aiming improve efficiency concrete strength. The MLANN framework employs various architectures hidden layers, referred as "lobes," each unique arrangement neurons optimize data processing, reduce expedite time. Within context, an developed, its performance evaluated predict concrete. Moreover, compared against two traditional cases, ANN ensemble learning (ELNN). indicated significantly improves estimation performance, reducing root mean square error up 32.9% absolute 25.9% while also enhancing A20 index 17.9%, ensuring more robust generalizable model. advancement model refinement can ultimately enhance design analysis processes civil engineering, leading cost-effective practices.

Язык: Английский

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