federated learning

Anomaly detection of internet of medical things cyberattacks using federated learning

Devices connected to the Internet of Medical Things (IoMT) handle sensitive patient data under strict privacy and resource constraints. Centralized intrusion detection introduces privacy risks and communication bottlenecks, while existing Federated Learning (FL) solutions struggle with class imbalance and data hete...

Internet of things-enabled biomedical engineering: current applications, challenges, and emerging directions

The internet of medical things (IoMT) is a structured integrative review of the internet of things (IoT) that focuses on the medical field, specifically on the integration of physiological sensors, medical devices, communication networks, and the clinical information systems to monitor and track patients' health and co...

AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions

Intelligent transportation systems (ITSs), Vehicle-to-Everything (V2X) communication and autonomous driving technologies have brought about significant changes in the modern vehicular network. At the same time, the cyber-attack surface has grown, leading to new and existing advanced security threats for Vehicular Ad ho...

FraudGraph-X: Privacy-Preserving Temporal Graph Learning for Scalable and Explainable Financial Fraud Detection

Review of bearing fault diagnosis based on neural networks

Bearings are critical components in rotating machinery, and their condition directly determines the operational efficiency and safety of industrial equipment. Traditional bearing fault diagnosis methods rely heavily on manual feature extraction, making it difficult to deal with complex industrial scenarios such as stro...

Reconfigurable trustless digital twins for continuous patient monitoring: A federated learning-blockchain architecture in healthcare 5.0

Poster: CMA-FL: Cognitive Multi-Agent Federated Learning for Resource-Aware Drone Communication Attack Detection

CMA-FL is proposed, a cognitive multi-agent assisted FL framework for resource-aware intrusion detection in drone communication networks that achieves 99.6% accuracy with 0.99 precision, recall, and F1-score and reduces response time by more than 80% relative to the evaluated FL, edge-cloud, and cloud-only alternatives...

An Analysis of Object Detection in Bad Weather Conditions using Deep Learning Models

Object detection, a task, in the field of computer vision faces obstacles when dealing with weather conditions such as fog, rain, snow, and low light situations. This paper provides an overview of advancements in the realm of object detection under challenging weather conditions. It delves into groundbreaking research...

Exploring the Landscape of Cloud Robotics: A Comprehensive Review

Cloud robotics is an innovative field that leverages cloud technologies-including cloud computing (CC), cloud storage, deep learning, big data, and the Internet of Things to augment the capabilities of robotics. This integration facilitates the execution of robotic functions through a converged infrastructure and share...

Future Trends in AI, Machine Learning, and Big Data: Implications for Technical Leadership

The study finds that the technical leader must be prepared to constantly learn, adapt, and understand the technological and human factors to be effective in the AI era.

AI Agentic Architectures for Autonomous Data Engineering Pipelines

This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.

Policy Drift in Learning AI Agents: A Dynamical Systems Perspective on Security Degradation

Policy drift takes shape through a nonlinear differential equation - framed within the policy state space - with support from Lyapunov stability concepts alongside bifurcation methods alongside bifurcation methods, and the Intent Drift Rate appears: a concrete number per dialogue turn built as the time-based change in...