Prem Kireet Chowdary Nimmalapudi Announces New AI Research, ML Role at JPMorgan Chase, and Social-Impact Platform Launch

COLUMBUS, MD, UNITED STATES, January 27, 2026 /EINPresswire.com/ -- Prem Kireet Chowdary Nimmalapudi, an AI engineer, ...
IEEE

A Hybrid Approach to Transaction Anomaly Detection: Integrating Aquila Optimization with Machine Learning Algorithms

Abstract: A strong anomaly-detection solution in financial transactions serves to detect fraud and build protected financial platforms. This research develops a mixed method which unites Aquila ...
Security Boulevard

Anomaly Detection in Post-Quantum Encrypted MCP Metadata Streams

Secure your MCP metadata streams with post-quantum encryption and AI-driven anomaly detection. Learn to stop puppet attacks and tool poisoning in AI infrastructure.

10 Everyday Devices Using Hidden AI Technology to Simplify Daily Life in 2026

AI in daily life powers hidden features in smartphones, appliances, navigation, and fitness devices, enhancing efficiency, personalization, and predictive performance seamlessly.
GitHub

Flash LiDAR Vehicle Detection with MATLAB Dataset and Deep Learning Benchmarks

This repository provides code and workflows to test several state-of-the-art vehicle detection deep learning algorithms —including YOLOX, SalsaNext, RandLA-Net, and VoxelRCNN— on a Flash Lidar dataset ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...
Medical Xpress

Deep-learning algorithms enhance mutation detection in cancer and RNA sequencing

Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have developed two innovative deep-learning algorithms, ClairS-TO and Clair3-RNA, that significantly advance genetic ...
IEEE

DPMN: Deep Prior Mamba Network for Hyperspectral Anomaly Detection

Abstract: Recent advancements in hyperspectral anomaly detection (HAD) utilizing deep learning have garnered significant attention due to their superior performance. However, most existing methods ...