GitHub

Prometheus Time Series Anomaly Detection with LSTM Autoencoder

This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...
The New York Times

In China, A.I. Is Finding Deadly Tumors That Doctors Might Miss

A tool for spotting pancreatic cancer in routine CT scans has had promising results, one example of how China is racing to apply A.I. to medicine’s tough problems. Self-service kiosks at the ...
ecommercefastlane

7 Most Reliable AI Code Detection Tools for Agencies

Strengthen your agency’s edge by using AI code detection to spot risky AI-generated sections early and protect quality, security, and client trust. Build a repeatable review process by scanning repos, ...
CNN

‘Grey’s Anatomy’ star James Pickens Jr. opens up about his prostate cancer diagnosis, touts early detection

“Grey’s Anatomy” actor James Pickens Jr. saves lives on screen, but says early detection saved his. The actor, who has portrayed Dr. Richard Webber on the ABC hit for more than 20 years, appeared in ...
PC Gamer

Great, now even malware is using LLMs to rewrite its code, says Google, as it documents new phase of 'AI abuse'

I'm sorry Dave, I'm afraid I can't do that. When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. Google has recently warned "adversaries are no longer ...
VentureBeat

From prototype to production: What vibe coding tools must fix for enterprise adoption

Vibe coding — the fast-growing trend of using generative AI to spin up code from plain-language prompts — is quick, creative, and great for instant prototypes. But many argue that it's not cut out for ...
Hacker

An Overview of Log-Based Anomaly Detection Techniques

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?
IEEE

AdaptHAD: Adaptive One-step Hybrid Network for Hyperspectral Anomaly Detection

Abstract: Deep learning for anomaly detection is one of the current hot topics in remote sensing. However, most deep learning-based methods require retraining or parameter fine-tuning when detecting ...
GitHub

SmartAnom: Low-Code/No-Code Anomaly Detection Framework

SmartAnom is a modular, low-code/no-code anomaly detection framework designed for researchers, engineers, and practitioners. It enables rapid benchmarking, hyperparameter optimization, explainability ...
northpennnow

Real Skin Cancer Examples: Types and What They Teach Us About Early Detection

Skin cancer is one of the most common types of cancer worldwide, yet it’s also among the most preventable when caught early. Every year, millions of people are diagnosed with skin cancer, and while ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...