Results 61 to 70 of about 16,703 (255)

Artificial Intelligence Tools for Carbon Nanotube Research: Opportunities From Synthesis to Applications

Carbon and Hydrogen, EarlyView.
Artificial intelligence tools are reshaping carbon nanotube research by connecting synthesis, characterization, and application‐oriented design. This review outlines how supervised learning, deep learning, Bayesian optimization, and large language models accelerate data extraction, experiment planning, and structure–property discovery for carbon ...
Yanlong Zhao, Fei Wang, Qixuan Cai, Aike Xi, Kangkang Wang, Khaixien Leu, Rufan Zhang   +6 more
wiley   +1 more source

Confusion matrix for LightGBM model.

This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver’s age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as
Fayez Alanazi (17508657), Abdulaziz H. Alshehri (17508654), Muhammad Yasir (3555896), Ahmed. M. Yosri (18510054)   +3 more
core   +1 more source

LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination

, 2022
his article presents an intelligent and accurate framework for fault diagnosis of induction motors using light gradient boosting machine (LightGBM). The proposed framework offers promising generalization ability when the testing data contains new unseen ...
Vaimann, Toomas, Toomas Vaimann, Šobra, Jan, Jan Sobra, Nemat Saberi, Alireza, Saberi, Alireza Nemat, Alireza Nemat Saberi, Anouar Belahcen, Belahcen, Anouar   +8 more
core   +1 more source

ПОРІВНЯЛЬНИЙ АНАЛІЗ МЕТОДІВ ГЛИБОКОГО ТА МАШИННОГО НАВЧАННЯ ДЛЯ ВИЯВЛЕННЯ МЕРЕЖЕВИХ ВТОРГНЕНЬ

Кібербезпека: освіта, наука, техніка
У статті представлено результати комплексного порівняльного дослідження шести методів машинного та глибокого навчання для задачі багатокласової класифікації мережевих атак.
Володимир Рихва, Ганна Солодовник   +1 more
doaj   +1 more source

Application of LightGBM in the Chinese stock market

Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network
This study employs LightGBM, a gradient boosting decision tree model, to predict stock returns and identify key pricing factors in the Chinese A-share market. The empirical analysis yields two main findings. First, LightGBM demonstrates superior predictive performance, achieving a monthly out-of-sample R² of 2.13%, more than doubling the 0.95%
openaire   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Comparison of LightGBM and CatBoost Algorithms for Diabetes Prediction Based on Clinical Data

Journal of Applied Informatics and Computing
Diabetes Mellitus presents a global health challenge necessitating accurate early detection to prevent fatal complications. However, clinical data often exhibit imbalanced class distributions, hindering standard prediction models from effectively ...
Muhammad Sidik Latuconsina, Majid Rahardi   +1 more
doaj   +1 more source

Explainable hybrid stacking ensemble method for hard rock pillar stability prediction and engineering applications

Deep Underground Science and Engineering, EarlyView.
This research proposes an interpretable hybrid stacking ensemble framework, optimized by the Sparrow Search Algorithm, to enhance hard rock pillar stability prediction. By integrating six machine learning models—k‐nearest neighbors, support vector machines, random forests, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and Light Gradient ...
Ning Wang, Guoyan Zhao, Meng Wang, Taraka Pattela   +3 more
wiley   +1 more source

Analysis of Gradient Boosted Trees Algorithm in Breast Cancer Classification

Journal of Applied Informatics and Computing
Early and accurate classification of breast cancer is essential to support clinical diagnostic processes and improve patient outcomes. This study proposes a comprehensive machine learning pipeline based on Gradient Boosted Tree algorithms to classify ...
Cantika Okzen Suryaputri, Majid Rahardi
doaj   +1 more source