Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...

Abstract: This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...

Background Transcatheter aortic valve replacement (TAVR) has increasingly emerged as one of the primary treatments for ...

According to the latest market research study published by MarkNtel Advisors, the Global Autonomous Driving Software Market is projected to grow at a CAGR of around 11.9% during 2026–2032. The market ...

A collaborative research group has shown that biological neurons can be trained to perform a temporal pattern learning task ...

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 ...

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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 ...