The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...

Abstract: This study presents a comprehensive benchmarking of 33 machine learning (ML) algorithms for bearing fault classification using vibration data, with a focus on real-world deployment in ...

Unsupervised learning is a branch of machine learning that focuses on analyzing unlabeled data to uncover hidden patterns, structures, and relationships. Unlike supervised learning, which requires pre ...

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 ...

Kidney cancer is a highly heterogeneous oncologic disease with historically poor prognosis. Precise assessment of the risk of distal metastasis can facilitate risk stratification and improve prognosis ...

A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

Postpartum depression (PPD) is a common and serious mental health complication after childbirth, with potential negative consequences for both the mother and her infant. This study aimed to develop an ...

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