IEEE

Machine Learning Techniques to Improve the Cognitive Workload Classification Using Multimodal Sensors’ Data

Abstract: Using machine learning applied to multimodal physiological data allows the classification of cognitive workload (low, moderate, or high load) during task performance. However, current ...

Mr.Chongwei Shi Enhances Genomic Analysis Through Signal Processing and Machine Learning Integration for Gene Identification

A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral ...

Unveiling schizophrenia's neural and mental signatures with machine learning

Schizophrenia is a severe and often highly debilitating psychiatric disorder characterized by distorted emotions, thinking patterns and altered perceptions of reality, as well as mental impairments.
IEEE

An Explainable Machine Learning Network for Classification of Autism Spectrum Disorder Using Optimal Frequency Band Identification From Brain EEG

Abstract: Autism Spectrum Disorder (ASD) is characterized by repetitive and restricted behavior, involving challenges for subjective detection methods. There is a need for an objective diagnostic ...
Proceedings of the National Academy of Sciences (PNAS)

Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...