Control of metabolic syndrome (MS) is still a major challenge for patients suffering from Non-communicable diseases (NCDs) in Bangladesh. In this study, the status of MS control was determined, and ...

Abstract: An automated, robust, noncontact sleep posture recognition technique is proposed in this letter, which uses optimizable (Bayesian hyperparameter tuning) machine learning (ML) classifiers ...

Abstract: Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by ...

Many scientific problems entail labeling data items with one of a given, finite set of classes based on features of the data items. For example, oncologists classify tumors as different known cancer ...

Three funds filed to let software run the portfolio. The sales pages promise a lot. The risk pages quietly take most of it back.

Linear Classifiers provide one of the earliest and most important foundations of Machine Learning. They teach us how models learn patterns, optimize decisions, and generalize to unseen data.

Buy-in-Bulk Active Learning. Liu Yang and Jaime Carbonell. Advances in Neural Information Processing Systems 26 (NIPS), 2013. Liu Yang, Avrim Blum and Jaime Carbonell. Learnability of DNF with ...

When modelling the impact of events that have never happened before, historical data fails us. No time series can tell you what happens when the Strait of Hormuz — through which 20% of the world's oil ...

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Machine learning models in this domain often operate in low-data regimes, with training set sizes as low as 20 and a median dataset size of around 600 records—conditions that hinder model ...

This limitation motivates the Bayesian framework developed in the present study. Hybrid approaches that combine statistical ranking methods with machine learning have also gained traction. Groll et al ...

Yi Chen Liu, Jiawei Yu, Kexin Cao, Syed Irfan Ali Meerza, Trishika Movva, Jian Liu A Full-Pipeline Framework for Evaluating Membership Inference Attacks in Machine Learning Ding Chen, Xinwen Cheng, ...