securehealthkey-integrating-explainable-tinyml-anomaly-detection-into

SecureHealthKey+: Integrating Explainable TinyML Anomaly Detection Into Lightweight Authentication Protocols in IoT-Enabled Healthcare Systems

The Internet of Medical Things (IoMT) and Wireless Body Area Networks (WBANs) enable real-time, remote, and continuous patient monitoring but remain ...

Bharati Vidyapeeth's College of Engineering for Women

The Natural Pothole Dataset within River Environments is an extensive collection of 3992 high-resolution images [1] documenting various natural potholes located in riverine settings. Each image has been rigorously annotated utilizing the YOLO (You Only Look Once) object detection framework, which ensures precise bounding box coordinates and accurate class labels for identified potholes. The annotations are provided in XML format, facilitating seamless integration with machine learning algorithms and computer vision applications. This dataset is particularly valuable for researchers and professionals in Geomorphology, Hydrology, River Science, Machine Learning, Environmental Science, and geospatial analysis, offering a robust foundation for tasks such as pothole detection, classification, and predictive modelling. By focusing exclusively on the natural occurrence of potholes, the dataset captures the diversity in shapes, sizes, and environmental contexts, thereby enriching the study and understanding of riverine geomorphological processes.

Shital Pawar

Biography

JoVE Journal Publications

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