Internet-of-Medical-Things
PulseAugur coverage of Internet-of-Medical-Things — every cluster mentioning Internet-of-Medical-Things across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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AI in medical devices introduces new cybersecurity risks, regulators warn
The integration of AI into medical devices is creating new cybersecurity vulnerabilities, according to experts. A recent report found that a vast majority of healthcare organizations have internet-exposed medical device…
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New framework enhances IoMT service orchestration in 6G-edge environments
Researchers have developed a novel framework for orchestrating Internet of Medical Things (IoMT) services in a 6G-edge-continuum environment. This system integrates 5G network slicing with resource-aware and mobility-aw…
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AI framework enhances early cardiovascular disease risk assessment
Researchers have developed a novel framework that combines machine learning and deep neural networks for the early detection of cardiovascular disease. This system utilizes data from Internet-of-Medical-Things devices, …
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New framework enhances IoMT security with AI and privacy preservation
A new framework has been proposed to enhance the security and privacy of Internet of Medical Things (IoMT) systems. This framework utilizes Artificial Neural Networks for intrusion detection and incorporates Federated L…
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New intrusion detection system for medical IoT environments
Researchers have developed a novel intrusion detection system for Internet of Medical Things (IoMT) environments, focusing on feature selection to overcome resource limitations. The system employs a Pearson correlation …
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New framework secures medical IoT with post-quantum AI
This research paper proposes a framework for securing Internet of Medical Things (IoMT) devices against quantum computing threats. It integrates Post-Quantum Cryptography (PQC) with federated learning (FL) and edge-nati…
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AI framework enhances wearable health monitoring in harsh underwater conditions
Researchers have developed a memory-efficient framework for denoising electrodermal activity (EDA) signals, crucial for wearable health monitoring systems. The method employs knowledge distillation to train a lightweigh…