IEEE

GuardianML: Anatomy of Privacy-Preserving Machine Learning Techniques and Frameworks

Abstract: Machine learning has become integral to our lives, finding applications in nearly every aspect of our daily routines. However, using personal information in machine learning applications has ...
Frontiers

Comparative analysis of machine learning techniques on the BraTS dataset for brain tumor classification

1 Information Statistics Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 School of Computer Science and Technology, Hubei Business ...
Scientific Research Publishing

Explainable Machine Learning in Risk Management: Balancing Accuracy and Interpretability ()

1 School of Taxation and Public Administration, Shanghai Lixin University of Accounting and Finance, Shanghai, China. 2 School of Business, Computing and Social Sciences, University of Gloucestershire ...
Scientific Research Publishing

Al-Shehari, T. and Alsowail, R.A. (2021) An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques. Entropy, 23 ...

ABSTRACT: Evaluating drug safety during pregnancy remains an ongoing clinical and pharmacological challenge due to ethical, practical, and regulatory barriers, resulting in scarce human clinical trial ...
Frontiers

Classification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review

Department of Preventive Medicine, College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang, China Background: Effective connectivity (EC) refers to the ...
IEEE

Boosting Machine Learning Techniques with Positional Encoding for Robot Collision Checking

Abstract: Self-collision checking plays an important role in robot motion planning. In sampling-based motion planning methods, collision checking is performed multiple times, so this procedure should ...
GitHub

Filter Encoding Standard extension for Imagery (supported by Machine Learning)

An extension to the FES that allows raster datasets to be filtered according to how well they match objects detected by a specific Machine Learning model. This potentially could support WCS and CSW.