Machine Learning
This Data set was obtained from a Hospital in Karaikudi, Tamilnadu Iindia, and has 400 insstances with 25 attributes, intended for classification problems.
The Data Set has medical relevant variables that can be associated to the presence of CKD (Chronical Kidney Diasease). Some of the variables can be arguably more relevant for the model, and after analysis some of them can be correlated, so it's recommended to analyze the dataset and decide the best approach based on individual needs.
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Microwave active antenna sensor based MDBM meter is developed for continuous glucose measurement in human body. MDBM meter consists of Antiallergic Abdominal belt with radar IC (RCWL0516) mixer IC (BFR520) and HSIS antenna sensor. Microwave active antenna sensor is H-shaped patch with I-shaped slot, which is placed on human pancreas and acquires reflected microwave signals from dielectric materials in pancreas. Pancreas dielectric materials radiation property changes during insulin secretion.
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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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Unmanned aerial vehicles (UAVs) are being used for various applications, but the associated cyber risks are also increasing. Machine learning techniques have been successfully adopted to develop intrusion detection systems (IDSs). However, none of the existing works published the cyber or physical datasets that have been used to develop the IDS, which hinders further research in this field.
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The extended bandit learning game algorithm can search the best solution for the hybrid discrete-continuous strategy space. At each learning time, the player can quickly decide based on a finite discrete strategy pool, thereby improving the learning efficiency.
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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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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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SYPHAX dataset was collected from Tunisia in “Sfax” city, the second largest Tunisian city after the capital. A total of 2008 images were gathered through manual collection one by one, with each image energizing text detection challenges in nature according to real existing complexity of 15 different routes (downtown, Nasryia, Sidi Mansour, Sakiet Ezziet, Sakiet Eddayer, Mahdia road, Tunis main road, Chehia, Taniour, Lafran, Elayn, Gremda, Manzel Chaker, Matar route, Gabes road) along with ring roads, intersections and roundabouts.
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278 Views
The e-nose device used in this study was constructed using a gas sensor array, LCD display, micro air pumps for inhalation and exhalation, a microcontroller, and a mini-PC. Gas samples from the sample chamber were periodically drawn into the device through a hose. Each sample underwent a 30-hour sampling process at room temperature (25°C). The sampling frequency was 15 times per hour, resulting in 60 records per sample.
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