Demonstrating academic excellence and a strong interest in AI-related domains, seven students of Computer Science Engineering ...

By Dr. Richmond Atuahene1.0 Introduction/ BackgroundGlobalization and digitization are two major trends that will shape the future of nations. Despite the many challenges associated with adapting to ...

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 ...

Google Cloud has expanded its enterprise cybersecurity push with AI Threat Defense, as the market shifts towards AI-native threat detection, contextual triage, and supervised remediation. The launch ...

This software is a research prototype, solely developed for and published as part of the publication MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot ...

Artificial intelligence detectors are increasingly used to check the veracity of content online. We ran more than 1,000 tests and found several strengths and plenty of weaknesses. By Stuart A.

Abstract: Anomaly detection plays a crucial role in real-world applications by identifying instances that deviate significantly from the norm. In the context of road anomalies, deep learning models ...

Abstract: The use of encrypted traffic to protect user privacy has surged in response to the network security threats posed by the rapid advances in IoT, data science, and online services. Encryption ...

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, ...

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 ...