Results 151 to 160 of about 212,585 (307)

Predicting Parameters Affecting Building Energy Consumption Using Machine Learning Models

Energy Science &Engineering, EarlyView.
This framework integrates long‐term weather forecasting with building energy simulation to predict energy demand. The CNN–LSTM model achieved the highest accuracy (R2 ≈ 0.95), supporting smart HVAC control with ~ 15% energy savings. ABSTRACT The rapid growth of population and the construction industry have led to an increase in energy demand and an ...
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 more
wiley   +1 more source

Machine Learning and Artificial Intelligence Techniques for Intelligent Control and Forecasting in Energy Storage‐Based Power Systems

Energy Science &Engineering, EarlyView.
A new energy paradigm assisted by AI. ABSTRACT The tremendous penetration of renewable energy sources and the integration of power electronics components increase the complexity of the operation and power system control. The advancements in Artificial Intelligence and machine learning have demonstrated proficiency in processing tasks requiring ...
Balasundaram Bharaneedharan, Suresh Panchanathan, Chan Choon Kit, Elumalai Perumal Venkatesan, Nasim Hasan   +4 more
wiley   +1 more source

Amélioration de la gestion des centrales d'énergie nucléaire par l'introduction de l'intelligence artificielle et synthétique

Revue des Sciences de la Santé
L'énergie nucléaire est une source de production d'énergie à haut rendement, mais sa gestion présente des défis techniques et sécuritaires majeurs. Le Centre de Recherche en Énergie Nucléaire de l'Université de Kinshasa (C.RE.N.K - UNIKIN) se trouve à la
Vincent KASUENDE NTAMBWE NGANDU, Leonard WOTO MAKONTSHI, Justin KHUALA MVUMBI   +2 more
doaj   +1 more source

A Scoping Review and Bibliometric Analysis on Smart Firefighting in Buildings and Infrastructures

Fire and Materials, EarlyView.
ABSTRACT Smart Firefighting is a concept that has emerged within the fire engineering and fire science disciplines in recent years. It can enable informed decision making and improved fire safety. However, its scope, definition, outcomes, and value to emergency management remain unclear. To investigate this, a scoping review and a bibliometric analysis
José Antonio Morales Mere, Nils Johansson, Enrico Ronchi   +2 more
wiley   +1 more source

Comparison of RNN and LSTM Classifiers for Sentiment Analysis of Airline Tweets

Journal of Information Systems and Informatics
This study examines the application of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for sentiment analysis of airline-related tweets, focusing on customer feedback directed at U.S. airlines on the X platform (formerly Twitter).
Rogaia Yousif Ahmed, Noon Fahmi Yuosif, Sarmed Awad Ahmed, Al-Baraa Ali Mohammed   +3 more
doaj   +1 more source

Mortality Forecasting Using Variational Inference

Journal of Forecasting, EarlyView.
ABSTRACT This paper considers the problem of forecasting mortality rates. A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two‐step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use.
Patrik Andersson, Mathias Lindholm
wiley   +1 more source

PREDICTING COVID-19 TRENDS: A DEEP DIVE INTO TIME- DEPENDENT SIRSD WITH DEEP LEARNING TECHNIQUE

Malaysian Journal of Computing
he COVID-19 pandemic, also known as Coronavirus Disease 2019, has affected over 700 million people globally, resulting in approximately 7 million deaths. Research has proposed multiple mathematical models to institute a disease transmission framework and
Abdul Basit, Jasni Mohamad Zain, Abdul Kadir Jumaat, Nur’Izzati Hamdan, Hafiza Zoya Mojahid   +4 more
doaj   +1 more source

A Deep Neural Network Based on Two‐Stage Training for Estimating Heart Rate Variability From Camera Videos

Health Care Science, EarlyView.
Heart Rate Variability Estimation from Camera Videos ABSTRACT Background Studies have shown that heart rate variability (HRV) is a predictor of the prognosis of cardiovascular diseases. Contact heartbeat monitoring equipment is widely used, especially in hospitals, and benefits from the rapidity and accuracy of the detection of physiological health ...
Lan Lan, Jin Yin, Haohan Zhang, Hua Jiang, Rui Qin, Xia Zhao, Yu Zhang, Yilong Wang, Jiajun Qiu   +8 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

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