Abstract: The need for renewable resource growth stems from the world's ever-growing energy consumption rate, finite supply of fossil fuels, and pollution. In order to accomplish the 2030 agenda and ...
Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...
The model is built using a Machine Learning algorithm, namely Linear Regression. The model is trained using training data to learn the linear relationship between variable X (Age, Station, Stores, ...
Hybrid Quantum-Classical Algorithm for an Integrated Feature Selection and Logistic Regression Model
Abstract: Feature selection is a pivotal step in machine learning, aimed at reducing feature dimensionality and improving model performance. Conventional feature selection methods, typically framed as ...
This project addresses the problem of predicting water levels in fish ponds - a critical factor in aquaculture management. Using Machine Learning, we can: Predict water levels based on environmental ...
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