IEEE

A comparative analysis of Alzheimer’s Disease by Pre-Trained CNN Classifiers using Two Palnes of MRI Images

Abstract: Alzheimer’s Disease (AD) is a progressive neurological disorder that leads to significant deterioration in cognitive functions and memory. AD is accompanied by many symptoms like memory loss ...
IEEE

Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers

Abstract: Handwritten Amharic character recognition presents significant challenges due to the script’s syllabic nature and variations in handwriting styles. This study investigates a hybrid approach ...
IEEE

Deepfakes in Visual Art: Differentiating AI-Generated Art From Human Art Using Convolutional Neural Networks (CNN)

Abstract: As AI technology evolves, seeing is not believing. The boundary between human and machine creativity is increasingly blurred, presenting challenges for the art industry. This is more ...
IEEE

Alcoholic EEG Signal Feature Extraction Based on Relative Wavelet Bispectrum and Bispectrum-Gaussian Using CNN and ANN Classifier

Abstract: A person who is addicted to alcohol is most likely to have problems related to health and brain function, such as in doing cognitive tasks. Thus, detecting the alcoholic condition is ...
Computer Weekly

Nightfall heralds dawn of first AI-powered file classifiers

The latest trends in software development from the Computer Weekly Application Developer Network. Yes, data loss prevention tools i.e. cybersecurity services built to detect, monitors and protects ...
IEEE

Advanced Glaucoma detection using CNN models integrated with random forest classifier for enhanced diagnosis

Abstract: Glaucoma is a prevalent retinal disease worldwide, leading to irreversible blindness if not diagnosed early. This research aims to distinguish glaucomatous eyes from normal eyes using an ...
GitHub

[BUG] sigmoid hidden activation in CNN network, classifier & regressor

In the hidden layers of a CNN, it is non-standard to use sigmoid as an activation. Sigmoid outputs are in (0,1) and not zero-centred, which can slow convergence. Also, in general sigmoid is used a ...
IEEE

ECG Heartbeat Classification Using CNN Autoencoder Feature Extraction and Attention-Augmented BiLSTM Classifier

Abstract: The electrocardiogram (ECG) is a primary non-invasive technique for recording the heart’s electrical activity, but the complexity and subjectivity of manual interpretation have driven the ...
Frontiers

LLM-based multi-agent system for neuro-ophthalmic diagnosis and personalized treatment planning

Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Introduction: Ophthalmic findings can non-invasively reflect nervous-system status. We present ...
IEEE

Attention-Based Convolutional Neural Network Model for Skin Cancer Classification

Abstract: Early detection of skin cancer (SC) is paramount for effective treatment. Although convolutional neural networks (CNN) have facilitated automated learning of high-level features from ...