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 ...

Abstract: This full research paper presents a systematic literature review (SLR) to evaluate different Machine Learning (ML) algorithms used in predicting student success. As educational institutions ...

Abstract: This paper analyzes the performance of different LDA combinations with machine learning algorithms in predicting diabetes based on clinical data. The analysis involves patient records with ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

Data Science Program, University of Delaware, Newark, Delaware 19716, United States Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States ...

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The epidural-related maternal fever (ERMF) induced by patient-controlled epidural analgesia (PCEA) remains unpredictable. Our objective is to develop ERMF prediction models using real-world data, ...

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already ...

Quantum cryptography has emerged as a radical research field aimed at mitigating various security threats in modern communication systems. The integration of Quantum Machine Learning (QML) protocols ...

Researchers from MIT, Microsoft, and Google have introduced a “periodic table of machine learning” that stands to unify many different machine learning techniques using a single framework. Their ...