WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving classification and segmentation tasks.

Detecting concealed explosives and chemical threats constitutes a critical challenge in global security, yet current ...

Abstract: This study explores image classification models using previously constructed models on the Fashion MNIST dataset, containing 70,000 grayscale images divided into ten clothing classes. This ...

Introduction: Image emotion classification (IEC), which predicts human emotional perception from images, is a research highlight for its wide applications. Recently, most existing methods have focused ...

🔍 AI Image Classifier A powerful web-based image classification application built with Streamlit and TensorFlow's MobileNetV2 model. Upload any image and get instant AI-powered predictions with ...

Classifying corn varieties presents a significant challenge due to the high-dimensional characteristics of hyperspectral images and the complexity of feature extraction, which hinder progress in ...

Abstract: Aerial image detection is a crucial technology for a variety of applications, including but not limited to urban planning, environmental monitoring, and disaster management. In this project, ...

A new AI model, H-CAST, groups fine details into object-level concepts as attention moves from lower to high layers, outputting a classification tree—such as bird, eagle, bald eagle—rather than ...

ABSTRACT: In this study, multi-source remote sensing data and machine learning algorithms were used to delineate the prospect area of remote sensing geological prospecting in eastern Botswana. Landsat ...