Precise forecasting of solar power output is crucial for integrating renewable energy into
power networks, improving efficiency and dependability. This study assesses the efficacy of …

For effective use of renewable energy sources, accurate forecasting of solar power output is
crucial. This study investigates how machine learning techniques, such as Support Vector …

The primary objective of this research is to enhance the accuracy of identifying cardiac
conditions through machine learning applied to medical images. To achieve this goal, a …

The main goal of this investigation is to enhance the precision of crop forecasting by
transitioning from the Adaboost method to Naive Bayes analysis. The current inadequacy in …

The aim of this study is an analysis on obesity levels prediction based on smoking habits
using stepwise linear regression algorithm in comparison with random forest classifier for …

The main objective of this work is to perform Cirrhosis Stage using Novel CatBoost Classifier
Algorithm and Compare it with the Adaboost Classifier Algorithm to improve the accuracy. It …

In response to the escalating demand for energy, leveraging renewable, sustainable, and
cost-efficient sources is paramount to address the depletion of conventional resources and …

Solar power prediction is crucial for integrating renewable energy into the grid, but current
methods often struggle with accuracy due to the limitations of machine learning algorithms …

This study utilizes machine learning and optimization techniques to lower energy costs in
factory operations. By incorporating weather forecast data and machine learning techniques …

The continuing rise in electricity consumption in the Philippines combined with the global
issue of climate change calls for the need of a more sustainable source of electricity. Solar …