Topology-aware deep learning model enhances EEG-based motor imagery decoding

Electroencephalography (EEG) is a fascinating noninvasive technique that measures and records the brain's electrical activity ...
IEEE

Sentiment Analysis Algorithm Based on Deep Transfer Learning for Multi-Source Data Fusion

Abstract: To tackle the challenge of data diversity in sentiment analysis and improve the accuracy and generalization ability of sentiment analysis, this study first cleans, denoises, and standardizes ...

ZAC Cognitive Explainable-AI for Situational Awareness recognized as far superior algorithm to achieve Self-Driving Level-5 than Neural Nets

Z Advanced Computing, Inc. (ZAC), the pioneer CognitiveExplainable-AI (Artificial Intelligence) (Cognitive XAI or CXAI) software startup, has been recognized as far superior to the current algorithms ...
Mirage News

Deep Learning Boosts High-Res BOLD fMRI Imaging

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) is a cornerstone of non-invasive brain function investigation, yet its ...

Which Machine Learning Models Are Most Used In Crypto Signal Generation?

Machine learning is transforming how crypto traders create and understand signals. From supervised models such as Random Forests and Gradient Boosting Machines to sophisticated deep learning hybrids ...
IEEE

Algorithms on Electrical Impedance Tomography, Focusing on Deep Learning Architectures and Their Implementations: A Review

Abstract: Electrical impedance tomography (EIT) has garnered increasing attention in recent years, across different domains, as a promising alternative to traditional imaging techniques like X-rays ...
IEEE

A Hybrid Framework for Acute Lymphoblastic Leukemia Identification Utilizing VIT-CNN Fusion and Immunologically Inspired Deep Feature Selection

Abstract: Acute Lymphoblastic Leukemia (ALL) is a serious blood cancer characterized by the abnormal growth of progenitor white blood cells, which interferes with normal blood cell production. Early ...
IEEE

Feature-Enhanced Faster R-CNN for Small Object Detection in Complex Scenes

Abstract: This study proposes a feature-enhanced Faster R-CNN algorithm to address the challenges of dense targets, varying object shapes, and complex backgrounds in remote sensing image detection.