IEEE

Machine Learning Framework for Industrial Machine Sound Classification in Predictive Maintenance

Abstract: Predictive maintenance, utilising anomalous sound classification, demonstrates a strong potential to identify mechanical faults in industrial machinery. This research proposes a machine ...
Technology Networks

AI Applications in Phenotypic Screening

Explore how AI phenotypic screening transforms image-based drug discovery through advanced phenotypic data analysis and ...
Memeburn

What Is Machine Learning? How It Works, Types & Real-World Uses (2026)

Learn what machine learning is, how it works, its types, the algorithms it uses, and its real-world uses in this complete ...
IEEE

Multi-Dimensional Feature Extraction and Classification Method for Industrial Noise Integrating Wavelet Transform and Machine Learning

Abstract: Industrial noise classification plays a crucial role in equipment health monitoring and predictive maintenance, yet existing methods suffer from inadequate feature extraction, limited ...
The Lancet

Risk-stratified classification of pulmonary nodule malignancy via a machine learning model integrating imaging and cell-free DNA: a model development and validation study ...

aDepartment of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for ...
Scientific Research Publishing

Development of Machine Learning Models for Kiswahili Text Classification ()

Text classification plays a critical role in numerous natural language processing applications, yet limited work has addressed the unique linguistic structure of African languages such as Kiswahili.
Frontiers

Migraine triggers, phases, and classification using machine learning models

In many countries, patients with headache disorders such as migraine remain under-recognized and under-diagnosed. Patients affected by these disorders are often unaware of the seriousness of their ...
C&EN

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.