Results 51 to 60 of about 13,208 (247)

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

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

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

Hybrid Simulation–Machine Learning Surrogates for Coordinate‐Based Solar and Wind Energy Yield Assessment in Iraq: A Streamlit Decision‐Support Tool

Energy Science &Engineering, EarlyView.
This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 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

An Optimized LightGBM Model for Fraud Detection

Journal of Physics: Conference Series, 2020
Abstract The rapid development of e-commerce and the growing popularity of credit cards have made online transactions smooth and convenient. However, large numbers of online transactions are also the targets of online credit card fraud, which aggregate to enormous losses annually. In response to this trend, many machine learning and deep
openaire   +1 more source

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

Journal of Forecasting, EarlyView.
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly, Pooja Sengupta, Divya Aggarwal   +2 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

Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data sparsity undermine the ...
Hanbyeol Park, Sunghyun Sim, Kikun Park, Yongjae Lee, Eunhee Park, Hyerim Bae   +5 more
wiley   +1 more source

Assessing climate anomalies in the strait of Hormuz using gradient boosting and remote sensing–based environmental parameters

Frontiers in Remote Sensing
Strait of Hormuz is a climatically sensitive marine transition zone in which interplay of complex air sea interactions, monsoonal forcing, and land ocean thermal contrasts produces a strong impact on the variability of environment in the region. The long
Priya Vijayan, Suraj Kumar Singh, Gowhar Meraj, Shruti Kanga   +3 more
doaj   +1 more source