Machine Learning

This dataset consists of “.csv” files of 4 different routing attacks (Blackhole Attack, Flooding Attack, DODAG Version Number Attack, and Decreased Rank Attack) targeting the RPL protocol, and these files are taken from Cooja (Contiki network simulator). It allows researchers to develop IDS for RPL-based IoT networks using Artificial Intelligence and Machine Learning methods without simulating attacks. Simulating these attacks by mimicking real-world attack scenarios is essential to developing and testing protection mechanisms against such attacks.

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2638 Views

The dataset contains a collection of V2X (Vehicle-to-Everything) messages for classification, prioritization, and spam message detection. It comprises 1,000 messages with varying message types, content, priorities, and spam labels. The messages are sourced from different vehicles with specific destination vehicles or broadcast to all vehicles. They cover various message types, including traffic updates, emergency alerts, weather notifications, hazard warnings, roadwork information, and spam messages. The priority of the messages is categorized as either high, medium, or low.

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798 Views

The rapid growth of interconnected IoT devices has introduced complexities in their monitoring and management. Autonomous and intelligent management systems are essential for addressing these challenges and achieving self-healing, self-configuring, and self-managing networks. Intelligent agents have emerged as a powerful solution for autonomous network design, but their dynamic and intelligent management requires processing large volumes of data for training network function agents.

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58 Views

The Inverter Fault Diagnosis dataset is a comprehensive collection of data aimed at facilitating research and development in the field of fault diagnosis for solar integrated grid-side three-phase inverters. This dataset includes three key features, namely Ea, Eb, and Ec, representing the energy calculated from the fault currents for phases A, B, and C, respectively.

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780 Views

Cattle health monitoring is essential in the modern world, because of the high demand for dairy products. Regular monitoring is essential to extend the lifecycle of cattle and maintain the quality of dairy products. Unfortunately, Observing the health of cattle regularly is difficult in large farms where workers do not have enough time to do so. This paper described IoT devices such as skin temperature, heart rate, and motion sensor. Using this device, you can monitor cattle’s heart rate, activity level, heat stress, the surrounding temperature, and sleep tracking.

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1910 Views

This data set is from a macroscale molecular communication testbed and provides a set of experimental measurement data.

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256 Views

This dataset contains simulation data of the LightGBM controller for spacecraft attitude control. The data were generated using a closed-loop system of spacecraft attitude dynamics under an exact feedback linearization-based controller.

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82 Views

The recording data include the following anthropometries: age (AG), weight (WE), height (HE), body mass index (BMI), waist circumference (WA), waist/height ratio (WHT), arm circumference (AR), hip circumference (HP), systolic blood pressure (BSY), diastolic blood pressure (DSY), heart rate (HR); the health indicator: glucose (DX); and the following functional fitness parameters: muscle (MM), visceral fat (VF), body fat (BF), and body age (BA). Ageing (AGG) is the ratio AG/BA.

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5 Views

CucumberFlower

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130 Views

The DeepVerse 6G Machine Learning Challenge is a student competition organized by the Student and Outreach Subcommittee (SOSC) of the IEEE Information Theory Society, in collaboration with the Wireless Intelligence Lab at Arizona State University (ASU) and the Information Theory Labs at National Yang Ming Chiao Tung University.

Last Updated On: 
Sun, 06/18/2023 - 23:51

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