Results 111 to 120 of about 31,169 (266)

Tree‐Boost–Guided CNN–BiLSTM–Transformer for Solar Irradiance Forecasting: Cross‐Regional Evidence for Sustainable Energy Planning

Energy Science &Engineering, EarlyView.
This graphical abstract illustrates a reproducible pipeline that combines gradient‐boosting‐based feature selection with a CNN–BiLSTM–Transformer model to forecast solar irradiance across multi‐site satellite and ground datasets, delivering robust, high‐accuracy predictions that support sustainable grid planning and reliable PV integration.
Muhammad Farhan Hanif, Usama Hassan, Javid Hussain, Irfan Ali Khan, Yuldasheva Gulora, Syed Masood Arif Bukhari   +5 more
wiley   +1 more source

Paradoxical noise preference in RNNs

CoRR
In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biological variability and regularize learning. The expectation is that removing the noise at test time should preserve or improve performance.
Noah Eckstein, Manoj Srinivasan
openaire   +2 more sources

Graph Neural Network‐Based Prediction of Building Energy Consumption

Energy Science &Engineering, EarlyView.
A graph neural network that encodes a multi‐zone building as a graph accurately predicts hourly cooling and heating loads across three distinct climates, outperforming Random Forest and XGBoost baselines and serving as a fast surrogate to EnergyPlus simulations for scalable building energy management.
Ali Maboudi Reveshti, Jamal Dabbagh, Jhila Nasiri Reveshti, Karim Farajeyan Bonab, Farid Hosseini Mansoub   +4 more
wiley   +1 more source

Detecting Ineffective Efforts during Expiration for Neonates with Attention RNNs

Current Directions in Biomedical Engineering
Patient-ventilator asynchronies occur during mechanical ventilation when there is a mismatch between the patient’s needs and the ventilator’s settings.
Oprea Camelia, Stegemann Lara, Olivier Lena Sophie, Buglowski Mateusz, Becker Sabine, Orlikowsky Thorsten, Kowalewski Stefan, Schoberer Mark, Stollenwerk André   +8 more
doaj   +1 more source

A Probabilistic Fractional Order Physics Informed Mamba Kolmogorov‐Arnold Network for State‐of‐Charge Estimation in Grid‐Connected Battery Energy Storage Systems

Energy Science &Engineering, EarlyView.
This paper introduces the probabilistic fractional‐order Mam‐KAN (PFO‐Mam‐KAN) controller, a physics‐informed gray‐box framework for real‐time battery state‐of‐charge estimation. By unifying efficient Mamba encoders with uncertainty‐aware fractional physics, it achieves superior 0.31% RMSE accuracy and robust grid‐support operation under dynamic ...
Arun Kumar Rawat, Subhash Chandra, Vinay Kumar Deolia   +2 more
wiley   +1 more source

Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Energy Science &Engineering, EarlyView.
Workflow of the PV power estimation and ML forecasting methodology. ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine
Abdoalateef Alzhrani, Abdullahi Abubakar Mas'ud, Mohd Khairul Amri Kamarudin, Firdaus Muhammad‐Sukki, Abdullahi Mohammad   +4 more
wiley   +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

Research progress on the depth of anesthesia monitoring based on the electroencephalogram

Ibrain, Volume 11, Issue 1, Page 32-43, Spring 2025.
Electroencephalogram (EEG) can noninvasive, continuous, and real‐time monitor the state of brain electrical activity, and the monitoring of EEG can reflect changes in the depth of anesthesia (DOA). The development of artificial intelligence can enable anesthesiologists to extract, analyze, and quantify DOA from complex EEG data.
Xiaolan He, Tingting Li, Xiao Wang
wiley   +1 more source

Intelligent diagnosis‐related group classification for 134,189 patients in eastern China using machine learning

Journal of Intelligent Medicine, EarlyView.
Intelligent DRG Classification with ELGWO‐LightGBM. Abstract Diagnosis‐related group (DRG) classification is crucial for healthcare cost management and resource allocation, but traditional manual classification by physicians is inefficient and error‐prone, especially for large‐scale medical data.
Yanxi Zhang, Yihan Cheng, Yuhui Ruan, Kun Wang   +3 more
wiley   +1 more source

Channel and model selection for multi-channel EEG input to neural networks

SICE Journal of Control, Measurement, and System Integration
Studies employing neural networks to classify emotions from brain waves and other biological signals provide a quantitative perspective on understanding human physiological phenomena.
Kento Harachi, Yusuke Yamamoto, Ayumi Muramatsu, Hajime Nagahara, Noriko Takemura, Shinji Shimojo, Daisuke Furihata, Yuko Mizuno-Matsumoto   +7 more
doaj   +1 more source