[HTML][HTML] A comprehensive review of the current status of smart grid technologies for renewable energies integration and future trends: The role of machine learning …
The integration of renewable energy sources (RES) into smart grids has been considered
crucial for advancing towards a sustainable and resilient energy infrastructure. Their …
crucial for advancing towards a sustainable and resilient energy infrastructure. Their …
Machine learning to assess and support safe drinking water supply: A systematic review
Drinking water is essential to public health and socioeconomic growth. Therefore, assessing
and ensuring drinking water supply is a critical task in modern society. Conventional …
and ensuring drinking water supply is a critical task in modern society. Conventional …
Forecasting the productivity of a solar distiller enhanced with an inclined absorber plate using stochastic gradient descent in artificial neural networks
Solar distillers are of significant importance in advanced technologies, as they provide a
sustainable approach to address the issue of water purification. The utilization of innovative …
sustainable approach to address the issue of water purification. The utilization of innovative …
Fault diagnosis of actuator damage in UAVs using embedded recorded data and stacked machine learning models
Unmanned aerial vehicles (UAVs) have gained significant importance due to their wide
applicability in modern life. Fault diagnosis plays a crucial role in ensuring their safe and …
applicability in modern life. Fault diagnosis plays a crucial role in ensuring their safe and …
[HTML][HTML] Comparative analysis of feature selection and extraction methods for student performance prediction across different machine learning models
Education is at the core of developmental progress, necessitating the exploration and
implementation of diverse contemporary methods to ensure the success of students across …
implementation of diverse contemporary methods to ensure the success of students across …
Advancing sub-seasonal to seasonal multi-model ensemble precipitation prediction in east asia: Deep learning-based post-processing for improved accuracy
The growing interest in Subseasonal to Seasonal (S2S) prediction data across different
industries underscores its potential use in comprehending weather patterns, extreme …
industries underscores its potential use in comprehending weather patterns, extreme …
Effective lane detection on complex roads with convolutional attention mechanism in autonomous vehicles
Autonomous Vehicles (AV's) have achieved more popularity in vehicular technology in
recent years. For the development of secure and safe driving, these AV's help to reduce the …
recent years. For the development of secure and safe driving, these AV's help to reduce the …
Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning
This paper investigates the application of three nature-inspired optimisation algorithms–
SHO, MFO, and GOA–combined with four machine learning methods–Gaussian Processes …
SHO, MFO, and GOA–combined with four machine learning methods–Gaussian Processes …
Adaptive penalty method with an Adam optimizer for enhanced convergence in optical waveguide mode solvers
PJ Chiang - Optics Express, 2023 - opg.optica.org
We propose a cutting-edge penalty method for optical waveguide mode solvers, integrating
the Adam optimizer into pseudospectral frequency-domain (PSFD) frameworks. This …
the Adam optimizer into pseudospectral frequency-domain (PSFD) frameworks. This …
A comparative study on ensemble soft-computing methods for geothermal power production potential forecasting
Many developed countries are increasingly interested in renewable energy sources (RESs)
as a result of environmental changes and the depletion of fossil fuels in recent years. Since …
as a result of environmental changes and the depletion of fossil fuels in recent years. Since …