来自MSN

Image classification with CNNs in Keras | Easy guide

In this video, we will implement Image Classification using CNN Keras. We will build a Cat or Dog Classification model using CNN Keras. Keras is a free and open-source high-level API used for neural ...
IEEE

Wafer Defect Image Classification Optimization Using CNN with Cyclical Learning Rate

Abstract: Wafer defect classification is important for yield optimization and quality assurance in semiconductor manufacturing. Convolutional Neural Network (CNN) has demonstrated strong performance ...
GitHub

Traffic Sign Classification Model Using CNN

A deep learning project that uses Convolutional Neural Networks (CNN) to classify Indian traffic signs. This project includes a trained model and two different user interface options (Streamlit and ...
CNN

Scientist turns people’s mental images into text using ‘mind-captioning’ technology

A scientist in Japan has developed a technique that uses brain scans and artificial intelligence to turn a person’s mental images into accurate, descriptive sentences. While there has been progress in ...
IEEE

Facial Shape Image-Based Stunting Classification using CNN with Sliced Feature Extraction

Abstract: We explore the use of Convolutional Neural Networks (CNNs) for stunting classification by comparing two methods: direct classification and sliced feature extraction. The aim is to assess ...
Frontiers

A hybrid framework for enhanced segmentation and classification of colorectal cancer ...

Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, India Introduction: Colorectal cancer (CRC) remains one of the leading causes of cancer-related deaths ...
CNN

CNN speaks with the man behind the viral ‘DJ Obama’ AI-generated images

Can AI create a professional headshot for your LinkedIn profile? See CNN producer's results ...
Frontiers

Multi-Scale graph wavelet convolutional network for hyperspectral image classification

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...