World PulseNow
Newsletter

SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection

arXiv — cs.LG•Thursday, November 6, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
A recent study highlights the importance of securing IoT devices in healthcare by introducing a machine learning framework designed to detect cyberattacks and device anomalies. With the increasing reliance on IoT technology in medical settings, this research is crucial as it addresses the growing security threats and operational challenges faced by healthcare providers. By evaluating eight machine learning models, the study aims to enhance the reliability of healthcare systems, ensuring patient safety and data integrity.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended apps based on your readingExplore all apps

One More Thing in AI

Master AI with curated tools and tutorials for practical, real-world applications.

Sponsored
AI & DataVisit website

LangWatch

Monitor and improve your AI applications for quality, safety, and reliability.

AI & DataView app details

AI Best Tools

Discover top AI tools to stay current with the latest in development and innovation.

Business & ProductivityView app details

Octofy

Access all top AI models with one subscription, automatically optimized for your needs.

AI & DataView app details

AskTuring

Private AI that protects your data and never trains on it.

Business & ProductivityView app details

SecureVibing

Secure AI coding with automated vulnerability detection and real-time threat protection.

Business & ProductivityView app details
Continue Readings
SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning
arXiv — cs.CV2 days ago
SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning
PositiveArtificial Intelligence
A new study introduces Semantic Orthogonal Calibration (SoC), a method aimed at improving the calibration of uncertainty estimates in vision-language models (VLMs) during test-time prompt tuning. This approach addresses the challenge of overconfidence in models by enforcing smooth prototype separation while maintaining semantic proximity.
Read full article
via arXiv — cs.CV
Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classification
arXiv — cs.CV2 days ago
Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classification
PositiveArtificial Intelligence
A new framework for cascading multi-agent anomaly detection in surveillance systems has been introduced, utilizing vision-language models and embedding-based classification to enhance real-time performance and semantic interpretability. This approach integrates various methodologies, including reconstruction-gated filtering and object-level assessments, to address the complexities of detecting anomalies in dynamic visual environments.
Read full article
via arXiv — cs.CV
Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma
arXiv — cs.CV2 days ago
Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma
PositiveArtificial Intelligence
A recent study has developed predictive and robust radiomics models aimed at assessing chemotherapy response in patients with high-grade serous ovarian carcinoma (HGSOC), a cancer typically diagnosed at an advanced stage. The research utilizes machine learning techniques to analyze computed tomography imaging data, enhancing the prediction of neoadjuvant chemotherapy response.
Read full article
via arXiv — cs.CV
Application of Ideal Observer for Thresholded Data in Search Task
arXiv — cs.CV2 days ago
Application of Ideal Observer for Thresholded Data in Search Task
PositiveArtificial Intelligence
A recent study has introduced an anthropomorphic thresholded visual-search model observer, enhancing task-based image quality assessment by mimicking the human visual system. This model selectively processes high-salience features, improving discrimination performance and diagnostic accuracy while filtering out irrelevant variability.
Read full article
via arXiv — cs.CV
Global 3D Reconstruction of Clouds & Tropical Cyclones
arXiv — cs.CV2 days ago
Global 3D Reconstruction of Clouds & Tropical Cyclones
PositiveArtificial Intelligence
Recent advancements in machine learning have led to the development of a new framework for the 3D reconstruction of clouds and tropical cyclones (TCs) from satellite imagery, addressing the challenges of accurate TC forecasting. This framework utilizes a pre-training and fine-tuning pipeline to convert 2D satellite images into detailed 3D cloud maps, significantly enhancing the understanding of TC structures.
Read full article
via arXiv — cs.CV
Tuberculosis Screening from Cough Audio: Baseline Models, Clinical Variables, and Uncertainty Quantification
arXiv — cs.LG2 days ago
Tuberculosis Screening from Cough Audio: Baseline Models, Clinical Variables, and Uncertainty Quantification
NeutralArtificial Intelligence
A new standardized framework for automatic tuberculosis (TB) detection from cough audio and clinical data has been proposed, aiming to establish a reproducible baseline for TB prediction. This framework addresses inconsistencies in previous studies, which varied in datasets, cohort definitions, and evaluation metrics, making it challenging to compare results.
Read full article
via arXiv — cs.LG
On the Sample Complexity of Differentially Private Policy Optimization
arXiv — cs.LG2 days ago
On the Sample Complexity of Differentially Private Policy Optimization
NeutralArtificial Intelligence
A recent study on differentially private policy optimization (DPPO) has been published, focusing on the sample complexity of policy optimization (PO) in reinforcement learning (RL). This research addresses privacy concerns in sensitive applications such as robotics and healthcare by formalizing a definition of differential privacy tailored to PO and analyzing the sample complexity of various PO algorithms under DP constraints.
Read full article
via arXiv — cs.LG
On the use of graph models to achieve individual and group fairness
arXiv — stat.ML2 days ago
On the use of graph models to achieve individual and group fairness
NeutralArtificial Intelligence
A new theoretical framework utilizing Sheaf Diffusion has been proposed to enhance fairness in machine learning algorithms, particularly in critical sectors such as justice, healthcare, and finance. This method aims to project input data into a bias-free space, thereby addressing both individual and group fairness metrics.
Read full article
via arXiv — stat.ML

Ready to build your own newsroom?

Subscribe to unlock a personalised feed, podcasts, newsletters, and notifications tailored to the topics you actually care about