Que.com on MSN

Physical Intelligence unveils robot brain that learns untaught tasks

Revolutionizing Robotics with Physical Intelligence’s Adaptive Brain In the ever-evolving landscape of robotics, a ...
GitHub

supervised-learning-classification

Machine Learning project to predict water potability using supervised learning algorithms with data preprocessing, model comparison, and deployment using Gradio. Gradio. data preprocessing, model ...
Scientific Research Publishing

Prototype-Based Dynamic Autoencoders for Real-Time Anomaly Detection and Explainability ()

Real-time detection of anomalies in data streams is a foundation of modern applied analysis in complex systems. It enables experts to design rapid, efficient, reliable, and high-performance decision ...
GitHub

fahimehorvatinia/GAN-LSTM-Anomaly-Detection

This project implements a GAN-based approach for detecting anomalies in smart meter readings using the Large-scale Energy Anomaly Detection (LEAD) dataset. The model uses LSTM-based Generator and ...
Visual Studio Magazine

Anomaly Detection Using K-Means Clustering with JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...
Scientific Research Publishing

An, J. and Cho, S. (2015) Variational Autoencoder Based Anomaly Detection Using ...

ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...
IEEE

Semi-Supervised Learning for Foetal Anomaly Detection Using Limited Annotated Ultrasound

Abstract: Foetal anomaly detection is a critical task in prenatal care, requiring early diagnosis for appropriate medical intervention. However, the scarcity of annotated ultrasound data limits the ...