Results 91 to 100 of about 7,680 (217)

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, Volume 2, Issue 4, August 2026.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Classification of tau status with machine learning models in amyloid‐positive cohorts

Alzheimer's &Dementia, Volume 22, Issue 8, August 2026.
Abstract INTRODUCTION Although tau positron emission tomography (PET) imaging is effective for staging tau pathology, it is limited clinically by cost and availability. Machine learning models based on magnetic resonance imaging (MRI)– and amyloid PET–derived features may serve as useful screening tools for tau pathology.
Yun‐Chi Lin, Sheng‐Chieh Chiu, Rebecca Massey, Yu‐Hua Dean Fang, for the Alzheimer's Disease Neuroimaging Initiative   +4 more
wiley   +1 more source

Prediksi Kualitas Udara Menggunakan Metode CatBoost

JISKA (Jurnal Informatika Sunan Kalijaga)
Air is important for life, but industrial activities, forest burning, cigarette smoke and transportation increase air pollution. AirVisual AQI 2024 data places Jakarta in 11th place in the world with the highest level of pollution, reaching 127 which is unhealthy for sensitive groups, and poses a risk of causing serious illnesses such as skin and ...
Mohamad Arif Abdul Syukur Syukur, Suhartono Suhartono, Totok Chamidy   +2 more
openaire   +2 more sources

Machine Learning‐Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta‐Analysis

Brain and Behavior, Volume 16, Issue 8, August 2026.
ABSTRACT Background Spontaneous intracerebral hemorrhage (ICH) is associated with high risks of mortality and disability, yet early and accurate outcome prediction remains challenging. This study systematically evaluated the performance of machine learning (ML) models in predicting key adverse outcomes (hematoma expansion [HE], poor functional outcome,
Qi Deng, Weitao Cheng, Hui Lu, Nan Fei, Rong He, Yuanyuan Chen, Jianlong Bi, Wenluo Zhang   +7 more
wiley   +1 more source

Machine Learning Methods for Forecasting Intermittent Tin Ore Production

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Effective production forecasting is important for resource planning and management in the mining industry. Tin ore production from Cutter Section Dredges (CSD) may fluctuate due to a variety of factors, in which there are periods when the production is ...
Nabila Dhia Alifa Rahmah, Budhi Handoko, Anindya Apriliyanti Pravitasari   +2 more
doaj   +1 more source

Execution Time Optimization Through Feature and Temporal Reduction in Asset Pricing

Concurrency and Computation: Practice and Experience, Volume 38, Issue 15, August 2026.
ABSTRACT High‐dimensional financial machine learning (ML) pipelines are computational workloads as much as predictive models: their practical value depends on runtime, memory footprint, scalability, and the ability to retrain under resource constraints. This paper treats empirical asset pricing as a demanding real‐world workload and proposes Cost‐Aware
Umit Demirbaga, Yue Xu, Evrim Guler
wiley   +1 more source

Rectangular Concrete-Filled Steel Tube Rational Dimensions under Uniaxial Eccentric Compression

Structural Mechanics of Engineering Constructions and Buildings
An algorithm for generating the training dataset and the machine learning model for selecting the cross-sectional dimensions of eccentrically compressed concrete filled steel tubular (CFST) columns have been developed.
Anton S. Chepurnenko, Samir H. Al-Zgul, Batyr M. Yazyev   +2 more
doaj   +1 more source

Categorical Boosting‐PSO: A SHAP‐Interpretable Hybrid Framework for High‐Fidelity Wax Appearance Temperature Prediction in Subsea Pipelines

Engineering Reports, Volume 8, Issue 8, August 2026.
CatBoost–PSO accurately predicts wax appearance temperature from oil density, wax content, and pour point temperature, achieving R2 = 0.9760 and RMSE = 1.8106 K. External validation, SHAP interpretation, and low computational cost support rapid wax‐risk screening for subsea pipeline flow assurance.
H. M. Rayhan Rifat, Abdullah Abduljabbar, Mysara Eissa Mohyaldinna   +2 more
wiley   +1 more source

An interpretable machine learning-based framework for fatal traffic accident prediction and causal analysis

Digital Transportation and Safety
Traffic accidents are a leading cause of unnatural human fatalities. Numerous researchers have investigated ways to predict traffic fatalities and interpret the prediction results.
Zhuopeng Xie, Yongfeng Ma, Aemal J. Khattak, Shuyan Chen, Hongru Liu   +4 more
doaj   +1 more source

Explainable Machine Learning With Hybrid Feature Selection for Thyroid Disease Classification: A Case Study in Bangladesh

Engineering Reports, Volume 8, Issue 8, August 2026.
Our research establishes a hybrid feature selection and ensemble machine learning pipeline for the classification of euthyroid, hyperthyroid, hypothyroid, and subclinical hypo‐ and hyperthyroid disorders. Variance Threshold with Backward Feature Elimination attained nearly 100% accuracy, while SHAP and LIME clarified the significance of features and ...
Md. Minhajul Abedin, Arka Chakraborty, Sawrav Das, Manoshi Das Turjo, Aditta Chowdhury, Mahdy Rahman Chowdhury   +5 more
wiley   +1 more source