Results 81 to 90 of about 7,493 (200)

Joint‐specific measures improve risk adjustment in total knee arthroplasty: A machine learning approach

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose Accurate risk adjustment in total knee arthroplasty (TKA) is essential for outcome prediction and quality assessment. Most existing prediction models rely solely on patient demographics and comorbidities and do not account for joint‐specific pathology.
Dirk Müller, Amna Gillani, Michael T. Hirschmann, Igor Lazic, Florian Hinterwimmer, Anabel Arber, Florian Krüger, Georg Matziolis, Rüdiger von Eisenhart‐Rothe   +8 more
wiley   +1 more source

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

From Prediction to Prevention: An Explainable GeoAI Framework for Flood Susceptibility and Urban Exposure Assessment Using Machine and Deep Learning Models

Sustainable Development, EarlyView.
ABSTRACT Rapid urbanisation and intensifying rainfall have increased cities' vulnerability to flooding, posing major challenges to sustainable development. Although machine learning models have improved flood prediction accuracy, most remain limited by their black‐box nature and lack of actionable insights.
Abdulwaheed Tella, Izni Zahidi, Chow Ming Fai, Chukwuma Okolie, Quoc Bao Pham   +4 more
wiley   +1 more source

Explainable artificial intelligence (XAI)‐powered design framework for lightweight strain‐hardening ultra‐high‐performance composites (SH‐UHPC)

Structural Concrete, EarlyView.
Abstract Lightweight strain‐hardening ultra‐high‐performance concrete composite (SH‐UHPC) is an outstanding alternative for engineering applications and infrastructure thanks to its outstanding strength, toughness, ductility, and low density. The integration of artificial intelligence (AI)‐based modeling strategies into engineering problems can ...
Metin Katlav, Kazim Turk
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

Short‐Term Multi‐Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention‐GCN‐LSTM

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level ...
Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng   +4 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

Developing Predictive and Explainable Models for Cryptocurrency Delistings: A Case Study of Binance Exchange

Asia-Pacific Journal of Financial Studies, EarlyView.
Abstract This study develops an explainable machine learning model to predict cryptocurrency delistings using Binance data. It combines quantitative indicators (price, volume) with qualitative data from real‐time news and Reddit. Latent Dirichlet Allocation (LDA) is used to extract topic trends and community reactions, which are transformed into time ...
Sungju Yang, Hunyeong Kwon
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 Unified Machine Learning Model for Relapse Prediction in Clinical Stage I Testicular Cancer

Andrology, EarlyView.
ABSTRACT Background Approximately one‐fourth of patients with clinical stage I testicular cancer relapse. For decades, risk stratification has been based on different tumor characteristics for seminomas and non‐seminomas. Previous studies primarily used Cox proportional‐hazards models and included only a limited number of variables.
Thomas Wagner, Ramtin Zargari Marandi, Jakob Lauritsen, Daniel Dawson Murray, Mikkel Bandak, Birgitte Grønkær Toft, Dan Berney, Gedske Daugaard   +7 more
wiley   +1 more source