Results 71 to 80 of about 16,703 (255)
A Comprehensive Review of AI‐Powered Energy Systems
Energy Science &Engineering, EarlyView.The role of Artificial Intelligence (AI) in developing next‐generation energy systems is getting more day by day. Therefore, incorporating AI enables real‐time decision‐making and advanced grid management, which are essential for optimizing the use of intermittent renewable sources like wind and solar power.
Armin Razmjoo +5 more
wiley +1 more source
Lithology prediction of tight sandstone formation using GS-LightGBM hybrid machine learning model
, 2021 Classic lithology predictors, represented by crossplot, are generally ineffective for tight sandstone formation, mainly due to a point that most lithologies present extremely similar logging responses and thus are rather difficult to be analyzed ...
Jihong Ren +5 more
core +1 more source
Frontiers in Remote SensingStrait 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 +3 more
doaj +1 more source
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 +3 more
wiley +1 more source
Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
, 2023 A method of establishing a prediction model of the greenhouse temperature based on time-series analysis and the boosting tree model is proposed, aiming at the problem that the temperature of a greenhouse cannot be accurately predicted owing to nonlinear ...
Jing Yin, Qiong Cao, Jia Yang, Yihang Wu
core +1 more source
Enhanced Botnet Detection and Neutralization through Machine Learning: A Synergistic Analysis of Host Activity, Network Patterns with Explainable Insights [PDF]
Computer Science Journal of MoldovaBotnets continue to be one of the biggest cybersecurity risks since they provide a platform for a number of unlawful operations. The growing sophistication and stealth of contemporary botnet networks, which frequently elude conventional detection tools ...
B. Gomathy +4 more
doaj +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 +2 more
wiley +1 more source
Прогнозна модель товару без передісторії з використанням LightGBM
, 2023 Метою роботи є встановлення ціни на товар без історії за його характеристиками та даними про сусідні схожі товари з використанням ...
Толокнова, Варвара
core
Frontiers in MedicineBackgroundHepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a life-threatening syndrome, the condition can deteriorate rapidly, and the 90-day mortality rate is high.
Yijun Zhang +6 more
doaj +1 more source
Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries
InfoScience, EarlyView.Artificial intelligence can efferently accelerate the high‐throughput screening of battery materials, the analysis of multiphase mechanisms, and the precise prediction of capacity and cycle life. This review systematically summarizes the applications of machine learning (ML) in decoupling the complex structure‐activity relationships of lithium‐ion ...
Tao Wang +6 more
wiley +1 more source