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AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures

Frontiers
The primary objective of this paper was to evaluate different statistical, neural, and ensemble techniques in their ability to predict patients' weekly average...
3 months ago

Deep learning-driven hybrid model for short-term load forecasting and smart grid information management

Nature
This paper proposes an innovative approach to improve the accuracy and reliability of short-term electricity load forecasting in smart grids.
3 months ago

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

Wiley Online Library
The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and...
4 months ago

(PDF) Comparative analysis of novel data‐driven techniques for remaining useful life estimation of wind turbine high‐speed shaft bearings

ResearchGate
FIGURE 1 Flow chart of the proposed methodology. PANDIT ET AL. |. 3. 3|METHODOLOGIES AND. EXPERIMENTAL SETUP. 3.1 |LSTM theoretical...
2 weeks ago

ESG guidance and artificial intelligence support for power systems analytics in the energy industry

Nature
In order to increase the precision and effectiveness of power system analysis and fault diagnosis, this study aims to assess the power...
4 months ago

Research on renewable energy power demand forecasting method based on IWOA-SA-BILSTM modeling

Frontiers
This paper introduces a novel coupling method to enhance the precision of short- and medium-term renewable energy power load demand...
8 months ago

Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network

ScienceDirect.com
This study proposes a transfer learning-based stacked bidirectional long short term memory (TLS-BLSTM) network to predict air quality for the new stations that...
55 months ago

Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network

Nature
This article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the...
10 months ago

An electricity load forecasting model based on multilayer dilated LSTM network and attention mechanism

Frontiers
From national development to daily life, electric energy is integral to people's lives. Although the development of electricity should be...
16 months ago

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Nature
Smart grids and smart homes are getting people's attention in the modern era of smart cities. The advancements of smart technologies and...
21 months ago