GitHub

3rd Workshop on Causal Inference and Machine Learning in Practice

The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application of causal ...
IEEE

Defenses in Adversarial Machine Learning: A Systematic Survey From the Lifecycle Perspective

Abstract: Adversarial phenomena have been widely observed in machine learning (ML) systems, especially those using deep neural networks. These phenomena describe situations where ML systems may ...
Frontiers

Accurate intelligent modeling of mud loss while drilling wells via soft computing methods

Accurate prediction of mud loss volume in drilling operations is a critical challenge in industries such as petroleum engineering and geothermal well construction. Unforeseen mud loss leads to ...
Frontiers

Multi-feature integrated machine learning prediction model for early nephropathy in elderly living with type 2 diabetes mellitus

Aims: To develop and validate a multi-feature machine learning (ML) model for early diabetic nephropathy (DN) prediction in elderly living with type 2 diabetes mellitus (T2DM), incorporating clinical ...
Scientific Research Publishing

Bradley, A.P. (1997) The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms. Pattern Recognition, 30, 1145-1159.

ABSTRACT: Cognitive impairment is a frequent and debilitating outcome of stroke, profoundly affecting patient independence, recovery trajectories, and long-term quality of life. Despite its prevalence ...
IEEE

Quantum Machine Learning: Recent Advances, Challenges, and Perspectives

Abstract: This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning ...