Artificial Intelligence
As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge.The deployment of IoT applications makes protection more challenging with the increased attack surfaces as well as the vulnerable and resource-constrained devices. Anomaly detection is a crucial procedure in protecting IoT. A promising way to perform anomaly detection on IoT is through the use of machine learning algorithms. There is a lack in the literature to identify the optimal (with regard to both effectiveness and efficiency) anomaly detection models for IoT.
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This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.
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4821 Views
STP dataset is a dataset for Arabic text detection on traffic panels in the wild. It was collected from Tunisia in “Sfax” city, the second largest Tunisian city after the capital. A total of 506 images were gathered through manual collection one by one, with each image energizing Arabic text detection challenges in natural scene images according to real existing complexity of 15 different routes in addition to ring roads, roundabouts, intersections, airport and highways.
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The enhanced dataset is a sophisticated collection of simulated data points, meticulously designed to emulate real-world data as collected from wearable Internet of Things (IoT) devices. This dataset is tailored for applications in safety monitoring, particularly for women, and is ideal for developing machine learning models for distress or danger detection.
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1680 Views
Our paper presents RespiroDynamics: A Comprehensive Multimodal Respiratory Dataset, compiled from 60 participants, recorded in two sessions labelled ’rest’ and ’exercise’. This dataset incorporates a variety of data types, including Red-Green-Blue (RGB) and Thermal videos, Heart Rate (HR), ECG readings and metadata, all synchronized with observed respiratory activities. Additionally, these data are enriched with reference values from the NHANES III (Hankinson- 1999) distribution.
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The dataset, developed at the National Institute of Neurology and Neurosurgery in Mexico, encapsulates crucial gait biomarkers associated with neurodegenerative diseases. This invaluable compilation serves as a comprehensive resource for understanding and analyzing the distinctive gait patterns exhibited by patients grappling with neurological disorders. By delving into these intricate biomarkers, researchers gain insights into the nuanced manifestations of conditions impacting the nervous system.
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We obtained this dataset as part of a project to generate a realistic speed profile on a trip specified by GPS coordinates. Specifically, we focused on generating the speed profile for a passenger car traveling on an unfamiliar route, i.e., a route the machine-learning model has yet to see.
The dataset contains 5973 rides of five different passenger cars, with a total length of 9049.3 km. The data was collected during 2021 in the Czech Republic and includes municipal and non-municipal trips.
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2921 Views
Egocentric video and Inertial sensor data Kitchen activity dataset is the first V-S-S interaction-focused dataset for the ego-HAR task.
It consists of sequences of everyday kitchen activities involving rich interactions among the subject's body, object, and environment.
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The evaluations are modified with the feed back mechanism based on optimal model in Large Scale Group Decision Making (LSGDM) usually, the intelligent decision making cannot be achieved with end-to-end. The application of LSGDM is limited, such as the customer evaluation to sales factors, the most customers would not modify the provided evaluations. A novel method combining Conditional Variational Auto-Encoder (CVAE) and self attention mechanism is developed to conduct the intelligent decision making with end-to-end.
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