Deep learning reveals how auditory cortex neurons split up natural sound coding

Over the past decades, computer scientists have introduced numerous artificial intelligence (AI) systems designed to emulate the organization and functioning of networks of neurons in the brain.
IEEE

Human Daily Activity Recognition using Spectrogram and 2D-CNN Through Doppler Radar

Abstract: The identification and analysis of human daily activities has garnered substantial attention in recent years, driven by its expansive applications in areas including healthcare, surveillance ...
IEEE

TifinNet: CNN–Transformer Hybrid Architecture for Tifinagh Handwritten Recognition

Abstract: Handwritten Optical Character Recognition (OCR) for the Tifinagh script remains a challenging task due to the script’s geometric complexity, high similarity among rotated characters, and the ...
IEEE

Detecting Deep Fake Audio: A Hybrid CNN-LSTM Approach Leveraging Spectrogram Analysis

Abstract: The rapid progress in audio synthesis technologies allows the generating of artificial audio deep-fakes that sound convincingly real, thus causing security, privacy, and authenticity ...
CNN

Masterpiece or cheap copy? Art historians and AI may not agree

To the untrained eye, there is very little difference between the three known versions of “The Lute Player.” Almost identical in composition, the paintings all depict a young, doe-eyed subject in ...
IEEE

Deep Spectrogram-Based EEG Decoding of Visual Stimuli Using a Novel CNN Architecture

Abstract: Electroencephalography EEG - based decoding of visual stimuli has gained traction in computational neuroscience and brain-computer interface applications. This study explores the feasibility ...
IEEE

A Progressive Fusion Framework of Rotationally-Enhanced 3D CNN and GCN-DTW for Table Tennis Action Recognition and Quality Evaluation

Abstract: Most existing studies on table tennis training focus on either action recognition or scoring alone, lacking a systematic way to model both tasks together, which limits the ability to provide ...
IEEE

CKADisor: Central Kernel-Aligned Distillation for Efficient Event-Driven Object Recognition

Abstract: Spiking neural networks (SNNs) are one of the best practices for efficient event-driven object recognition. To achieve high recognition accuracy, existing methods generally accumulate ...
IEEE

Clinically Oriented CNN–Transformer Architectures for Reliable Bronchoscopic Recognition of Lung Lesions and Anatomical Structures

Abstract: Bronchoscopy is central to diagnosing central lung cancers but remains limited by reliance on operator expertise and variability in visual interpretation. In this work, we adapt and evaluate ...
IEEE

Lightweight and Personalized Single-Eye Emotion Recognition via CNN-SNN Spatiotemporal Learning and Memory-Inferred Event Features

Abstract: Emotion recognition is essential for improving user experience and interaction quality in human-centered applications. While recent studies have leveraged both event and traditional cameras ...