Abstract Integrating Machine Learning (ML) in medicine has unlocked many opportunities to
harness complex medical data, enhancing patient outcomes and advancing the field …

Support vector machines (SVMs) are well-known machine learning algorithms for
classification and regression applications. In the healthcare domain, they have been used …

Predictive maintenance harnesses statistical analysis to preemptively identify equipment
and system faults, facilitating cost-effective preventive measures. Machine learning …

NIR sensors, in conjunction with advanced chemometric algorithms, have proven to be a
powerful and efficient tool for intelligent quality evaluation of sweetpotato roots throughout …

Accurate estimation of reference evapotranspiration (ET0) is a crucial parameter in
implementing precise irrigation strategies and managing regional water resources …

Natural gas transportation predominantly utilizes pipelines, and the safety of these systems
largely hinges on the integrity of girth welds. However, once pipelines are buried post …

Accurate detection is still a challenge in machine learning (ML) for Alzheimer's disease (AD).
Class imbalance in imbalanced AD data is another big challenge for machine-learning …

Lending platforms operating on a peer-to-peer (P2P) basis encounter the intricate challenge
of assessing borrower creditworthiness to minimize the risk of defaults. This study addresses …

The performance of predicted models is greatly affected when the dataset is highly
imbalanced and the sample size increases. Imbalanced training data have a major negative …

As artificial intelligence (AI) and machine learning (ML) advance, concerns about their
sustainability impact grow. The emerging field" Sustainability of AI" addresses this issue …