Application of Even Span Greenhouse Solar Dryer (ESGSD) for drying Persian shallot; Kinetic analysis, machine learning modeling and quality evaluation

Case Studies in Thermal Engineering, Год журнала: 2025, Номер unknown, С. 105880 - 105880

Опубликована: Фев. 1, 2025

Язык: Английский

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Prediction of Student Academic Performance Utilizing a Multi-Model Fusion Approach in the Realm of Machine Learning

Applied Sciences, Год журнала: 2025, Номер 15(7), С. 3550 - 3550

Опубликована: Март 24, 2025

The digitization of college student management is a crucial approach for training institutions to decrease costs while enhancing the quality students’ development. In this study, we focused on students majoring in Computer Science certain university and conducted an exploration using their scores multiple undergraduate courses. Initially, selected basic core academic courses based program identified four groups course combinations with strong positive correlations through correlation cluster analysis. This finding helped optimize arrangement structure major’s system. Next, organized overall performance data sequential format semester order. Multiple machine learning models were utilized perform regression prediction classification tasks determine student’s level. Finally, integrated create practical framework predicting performance, which can be applied digital management. also provide effective decision support early warning guide

Язык: Английский

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Recommendation Model of Additional Fuel Based on Feature Optimization and Machine Learning

Lecture notes in mechanical engineering, Год журнала: 2025, Номер unknown, С. 155 - 163

Опубликована: Янв. 1, 2025

Язык: Английский

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Are the same parameters measured at admission and in the ICU comparable in their predictive values for complication and mortality in severely injured patients?

European journal of medical research, Год журнала: 2025, Номер 30(1)

Опубликована: Апрель 2, 2025

Numerous studies have investigated variables that predict mortality and complications following severe trauma. These studies, however, mainly focus on admission values or a single variable. The aim of this study was to investigate the predictive quality multiple routine clinical measurements (at in ICU). Retrospective cohort severely injured patients treated at one Level 1 academic trauma centre. injury (ISS ≥ 16 points), primary complete data set. Exclusion criteria end-of-life treatment based advanced directive, secondary transferred patients. mortality, pneumonia, sepsis. Routine parameters were stratified measurement timepoint into Group TB (Trauma Bay, admission) intensive care unit (ICU, 72 h after admission). Prediction calculated using two prediction methods: adaptive boosting (AdaBoost, artificial intelligence, AI) LASSO regression analysis. Inclusion 3668 cases. Overall mean age 45.5 ± 20 years, ISS 28.2 15.1 points, incidence pneumonia 19.0%, sepsis 14.9%, death from haemorrhagic shock 4.1%, organ failure 1.9%, overall rate 26.8%. Highest value for include abbreviated scale (AIS), new severity score (NISS) systemic Inflammatory Response Syndrome (SIRS) score. ICU late lactate values, haematocrit, leukocytes, CRP. Sensitivity specificity models 25% cutoff 73.61% 76.24%, respectively. strongly depends measurement. Upon admission, affected anatomical regions are more predictive, while during stay, laboratory better predictor adverse outcomes. Therefore, dynamics pathophysiologic responses should be taken consideration, especially decision making definitive surgical interventions. III (retrospective study).

Язык: Английский

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Supervised Machine learning and Molecular docking modeling to Identify Potential Anti-Parkinson’s Agents

Journal of Molecular Graphics and Modelling, Год журнала: 2025, Номер 139, С. 109073 - 109073

Опубликована: Май 9, 2025

Язык: Английский

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