IEEE

Depression Analysis and Detection Using Machine Learning: Incorporating Gender Differences in a Comparative Study

Abstract: Depression is a significant mental health problem and presents a challenge for the machine learning field in the detection of this illness. This study explores automated depression ...
Frontiers

High-Dimensional Representations in Brain and Machine: Vector Symbolic Architectures for Neural Computation

Recent advances in neuroscience, cognitive science, and artificial intelligence are converging on the need for representations that are at once distributed, ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter ...
GitHub

Radiation-Tolerant Machine Learning Framework

Space-Radiation-Tolerant is a research project by Rishab Nuguru with core principles focused around sustainability in space. RadML was designed to help provide cost efficient solution for COTS ...
GitHub

Automated Neural Architecture Design for Industrial Defect Detection

This is an official implementation of our paper AutoNAD [IEEE/ASME Transactions on Mechatronics, 2025]. The datasets used in this project need to be download from here (The datasets have been ...
IEEE

Intrusion Detection in IoT and IIoT: Comparing Lightweight Machine Learning Techniques Using TON_IoT, WUSTL-IIOT-2021, and EdgeIIoTset Datasets

Abstract: The security of Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems has been significantly enhanced through the integration of effective intrusion detection systems ...