Results 71 to 80 of about 7,680 (217)

Analysis of SMOTE and Random Search on Machine Learning Algorithms for Stroke Disease Diagnosis

Journal of Applied Informatics and Computing
Stroke is a critical medical condition in which false negative predictions may lead to delayed treatment and increased mortality. Therefore, predictive models in the medical domain should prioritize sensitivity (recall) in addition to overall accuracy ...
Ubaid Khoir Julio Dn, Majid Rahardi
doaj   +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

FORECASTING OVERALL EQUIPMENT EFFICIENCY IN ELECTRONIC INDUSTRY USING INDUSTRIAL INTERNET OF THINGS AND MACHINE LEARNING METHODS

Измерение, мониторинг, управление, контроль
Background. The article considers modern methods for forecasting the overall efficiency of equipment, allowingto identify productivity reserves and manage production losses at industrial enterprises in the contextof digitalization and the implementation ...
Vitaly R. Aleksandrov, Aleksey A. Shchetkin, Aleksandr S. Bevz, Olga A. Zemskova, Sergey S. Sakhnenko   +4 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

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

Peningkatan Akurasi Prediksi Curah Hujan menggunakan Gradient Boosting dan CatBoost dengan Pendekatan Voting Classifier

Edumatic
Accurate rainfall prediction is essential for agriculture, disaster mitigation, and water resource management, especially in the face of climate change impacts. This research aims to improve the accuracy of rainfall prediction using gradient boosting and
Dina Fudhlatina, Fikri Budiman
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

A Hybrid Methodology for Predicting the Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns Under Eccentric Compression

Вестник Дагестанского государственного технического университета: Технические науки
Objective. To develop a hybrid methodology for predicting the load-bearing capacity of rectangular concrete-filled steel tube (CFST) columns under eccentric compression, based on the integration of analytical calculation models, nonlinear finite-element ...
S. Kh. Al-Zgul, T. N. Kondratieva, B. M. Yazyev, A. S. Chepurnenko   +3 more
doaj   +1 more source

Artificial intelligence prediction algorithms for refractive error onset and progression in children and adolescents: A systematic review and meta‐analysis

Acta Ophthalmologica, EarlyView.
Abstract This systematic review and meta‐analysis evaluates the performance of artificial intelligence (AI)‐based models for predicting the onset and progression of refractive error (RE) in children and adolescents and quantitatively synthesizes their prediction accuracy.
Athanasia Sandali, Anna Nikolaidou, Theodora Gianni, Andreas Katsimpris, Ioannis D. Apostolopoulos, Lampros Lamprogiannis, Eirini Maliagkani   +6 more
wiley   +1 more source

Automating Archaeological Discovery: Assessing Geospatial Artificial Intelligence (GeoAI) Tools for Stone Wall Identification in Kweneng, South Africa

Archaeometry, EarlyView.
ABSTRACT The discovery of archaeological sites traditionally entails the utilisation of physically demanding exploration methodologies, including terrain surveying and the analysis of historical records. Recent technological developments have led to an increased use of non‐invasive remote sensing techniques, including Google Earth, LiDAR and aerial ...
Mncedisi J. Siteleki
wiley   +1 more source