Artificial Intelligence
Optical Coherence Tomography (OCT) is a non-invasive imaging technology widely used in endoscopic examinations. Saturation artifacts occur when the intensity of the light signal received by the detector exceeds its dynamic range, causing image distortion. This distortion can manifest as excessive brightness or blurriness in specific areas of the image, thereby affecting the quality of the imaging.
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Image inpainting is a great challenge when reconstructed with realistic textures and required to enhance the consistency of semantic structures in large-scale missing regions. However, popular structural-prior guided methods rely mainly on the structural features, which directly accumulate and propagate random noise, causing inconsistencies in contextual semantics within the flled regions and poor network robustness.
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To conduct a comprehensive evaluation of MotifMDA's effectiveness, HMDD V2.0 \cite{li2014hmdd} is employed as the benchmark dataset. It encompasses 495 miRNAs, 383 diseases, and 5,430 human MDAs that have been confirmed through experimental validation. Moreover, another well-regarded database, namely miR2Disease, is also employed as a benchmark dataset in our study.
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64 Views
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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275 Views
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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4919 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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228 Views
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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1703 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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192 Views
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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2957 Views