BMJ

Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and ...

2 State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, China 3 Chongqing Key Laboratory of Hematology and Microenvironment, Chongqing, China Participants This study analysed 471 newly ...
IEEE

Hybrid Deep Machine Learning Feature Selection for High-Dimensional Cybersecurity Data

Abstract: The rapid increase in cyber threats has heightened the demand for Intrusion Detection Systems (IDS) that are both accurate and efficient. While deep learning models outperform traditional ...
IEEE

Optimal Channel Selection for FY-4B GIIRS Explainable Machine Learning Cloud Detection Algorithm

Abstract: Cloud detection is a crucial preliminary step for assimilating meteorological satellite observation and retrieving other atmospheric parameters. This article presents an explainable machine ...
Frontiers

Postpartum depression risk prediction using explainable machine learning algorithms

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 ...
Live Science

'Quantum AI' algorithms already outpace the fastest supercomputers, study says

It's quick and easy to access Live Science Plus, simply enter your email below. We'll send you a confirmation and sign you up for our daily newsletter, keeping you up to date with the latest science ...
Frontiers

Machine learning-based prediction of diabetic peripheral neuropathy: model development and clinical validation

Diabetic peripheral neuropathy (DPN) is a common and debilitating complication of type 2 diabetes mellitus (T2DM), significantly impacting patients’ quality of life and increasing healthcare burdens.
eLife

Dynamics of striatal action selection and reinforcement learning

The authors present a biologically plausible framework for action selection and learning in the striatum that is a fundamental advance in our understanding of possible neural implementations of ...