Results 81 to 90 of about 7,680 (217)

An Innovative Approach for Forecasting Hydroelectricity Generation by Benchmarking Tree-Based Machine Learning Models

Applied Sciences
Hydroelectricity, one of the oldest and most potent forms of renewable energy, not only provides low-cost electricity for the grid but also preserves nature through flood control and irrigation support.
Bektaş Aykut Atalay, Kasım Zor
doaj   +1 more source

Machine Learning‐Based Prediction of Dental Caries in 12‐Year‐Old Adolescents: A Nationwide Cross‐Sectional Study in Korea

International Journal of Dental Hygiene, EarlyView.
ABSTRACT Objective We aimed to develop and evaluate machine learning models to support population‐level risk stratification for dental caries in the permanent dentition of 12‐year‐old Korean children and to visualise the relative importance of key predictors.
Gahyun Cho, Hae‐Young Kim, Ki‐Bong Choi, Sunmi Song, Junesun Kim   +4 more
wiley   +1 more source

Intelligent design of artificial biocatalyst for biomedical diseases

Journal of Intelligent Medicine, Volume 3, Issue 3, Page 220-249, September 2026.
This review summarizes recent advances in the intelligent design of artificial biocatalysts for biomedical diseases. By leveraging tailored design strategies, including environment‐responsive engineering and rational/artificial intelligence‐aided optimization, these biocatalysts enable precise modulation of pathological microenvironments and targeted ...
Lijie Zhang, Nan Song, Huanhuan Qiao, Si Sun   +3 more
wiley   +1 more source

PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE

Barekeng
Accurately predicting stock market trends remains a significant challenge for investors due to its dynamic nature. This study explores the performance of Gradient Boosting models, including XGBoost, XGBoost Random Forest, CatBoost, and Gradient Boosting ...
Delvian Christoper Kho, Hindriyanto Dwi Purnomo, Hendry Hendry   +2 more
doaj   +1 more source

EnsCL-CatBoost: A Strategic Framework for Software Requirements Classification

IEEE Access
Accurate classification of software requirements, distinguishing between functional and non-functional aspects, is crucial for developing reliable and efficient software systems. However, existing methods often struggle with insufficient semantic understanding and managing diverse software requirements.
Jalil Abbas, Cheng Zhang 0010, Bin Luo 0001   +2 more
openaire   +2 more sources

Multi‐Modal AI Approach in Depression Detection and Treatment: A Systematic Review of Last Decade

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.
Overview of multimodal approaches for depression detection and treatment. ABSTRACT Depression is a common and devastating mental health illness with serious personal and societal consequences. Despite advancing treatment techniques, there are still hurdles in the effective diagnosis and treatment of depression, such as prompt diagnosis, personalized ...
Smith K. Khare, Esmaeil S. Nadimi, U. Rajendra Acharya, Victoria Blanes‐Vidal   +3 more
wiley   +1 more source

PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS

Barekeng
Time series forecasting often faces challenges in producing reliable predictions due to inherent uncertainty in dynamic systems. While point predictions are commonly used, they may not adequately capture this uncertainty, especially in financial systems ...
Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo   +2 more
doaj   +1 more source

Molecular descriptor driven QSPR modeling of Papp, TEER and Efflux Ratio from Caco‐2 cells using machine learning for various phytochemicals

Journal of the Science of Food and Agriculture, Volume 106, Issue 11, Page 6606-6616, 30 August 2026.
Abstract BACKGROUND The present study aimed to develop and validate quantitative structure–property relationship (QSPR) models for predicting permeability related bioavailability indicators including apparent permeability (Papp), trans‐epithelial electrical resistance (TEER) and efflux ratio (ER) based on molecular descriptors (n = 5003) of 83 ...
Jin‐Woo Kim, Rixing Cong, Jin‐Soo Park, Keunwan Park, Kyungsu Kang, Soon‐Mi Shim   +5 more
wiley   +1 more source

Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption

Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls
This research investigates how to accurately predict electrical energy consumption to address growing global energy demands. The study employs three Machine Learning (ML) models: k-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost.
Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil   +2 more
doaj   +1 more source

A stacking based deep learning framework integrating random search neural architecture search for meniscus tear diagnosis

Journal of Applied Clinical Medical Physics, Volume 27, Issue 8, August 2026.
Abstract Background Accurate and rapid diagnosis of meniscal tears is crucial for effective management of sports‐related injuries and degenerative knee disorders. Magnetic resonance imaging (MRI) is widely used for meniscus evaluation; however, manual interpretation is time‐consuming and subject to inter‐observer variability.
Ebubekir Seyyarer, Hasan Genç, Faruk Ayata   +2 more
wiley   +1 more source