Q&A: How mathematics can reveal the depth of deep learning AI

Artificial intelligence (AI) is increasingly prevalent, integrated into phone apps, search engines and social media platforms ...
IEEE

CausalConf: Datasize-Aware Configuration Auto-Tuning for Recurring Big Data Processing Jobs via Adaptive Causal Structure Learning

Abstract: To ensure high-performance processing capabilities across diverse application scenarios, Big Data frameworks such as Spark and Flink usually provide a number of performance-related ...
IEEE

Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review

Abstract: Radiology reports, as a means of inter-physician and physician-to-patient communication, contain key findings and interpretations from imaging studies that guide diagnosis and treatment.
Microsoft

Pattern Recognition and Machine Learning

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as ...
Scientific Research Publishing

Application of Machine Learning Methods in Parameter Prediction of Deep Lacustrine Oil Shale Reservoirs—Taking the Triassic Chang 7 in the Longdong Area as an Example ()

1 School of Earth Science and Engineering, Xi’an Shiyou University, Xi’an, China. 2 Key Laboratory of Petroleum Geology and Reservoir, Xi’an Shiyou University, Xi’an, China. In the course of oil and ...
Frontiers

Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa

Department Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia Background: Pregnancy loss is a significant public health ...
Scientific Research Publishing

Advancing Material Stability Prediction: Leveraging Machine Learning and High-Dimensional Data for Improved Accuracy ()

Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional ...