Machine Learning
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Here we collected data through social media such as Youtube, because the best method to obtain data from a variety of wild and diverse acoustic environments is to use a freely available source. Otherwise, manually creating such volatility would take a long time. Even after that, we will not be able to share the data collected with other researchers.
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Currently, Internet applications running on mobile devices generate a massive amount of data that can be transmitted to a Cloud for processing. However, one fundamental limitation of a Cloud is the connectivity with end devices. Fog computing overcomes this limitation and supports the requirements of time-sensitive applications by distributing computation, communication, and storage services along the Cloud to Things (C2T) continuum, empowering potential new applications, such as smart cities, augmented reality (AR), and virtual reality (VR).
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Photo identification (photoID) is a non-invasive technique devoted to the identification of individual animals using photos, and it is based on the hypothesis that each specimen has unique features useful for its recognition. This technique is particularly suitable to study highly mobile and hard to detect marine species, such as cetaceans. These animals play a key role in marine biodiversity conservation because they maintain the stability and health of marine ecosystems due to their apical role as top predators in food webs.
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Link prediction and graph classification datasets for heterogeneous graphs in DGL format
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The dataset used in the study consists of different IoT network traffic data files each IoT traffic data has files containing benign, i.e. normal network traffic data, and malicious traffic data related to the most common IoT botnet attacks which are known as the Mirai botnet.
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<h1>It include Multi-source data that affect stock prices, such as stock historical trading data and stock forum sentiment indicator.<h1>
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Ovarian cancer is among the top health issues faced by women everywhere in the world . Ovarian tumours have a wide range of possible causes. Detecting and tracking down these cancers in their early stages is difficult which adds to the difficulty of treatment. In most cases, a woman finds out she has ovarian cancer after it has already spread. In addition, as technology in the field of artificial intelligence advances, detection can be done at an earlier level. Having this data will assist the gynaecologist in treating these tumours as soon as possible.
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The I Scan 2 scanner from Cross-Match Technologies was used to acquire all data. Iris images are captured in near-infrared wavelength band (700-900 nm) of the electromagnetic spectrum. All images were acquired in SAP laboratory of computer science and engineering department of Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. The subject images were acquired during the span of 7 to 8 months in years 2017 and 2018. GMBAMU-IRIS dataset contains total 5616 images from 312 subjects.
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Most of the existing human action datasets are common human actions in daily scenes（e.g. NTU RGB+D series, Kinetics series）, not created for Human-Robot Interaction(HRI), and most of them are not collected based on the perspective of the service robot, which can not meet the needs of vision-based interactive action recognition.
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The Phoenix Contact Relay (PCR) dataset is provided for the development of algorithms for the analysis of lifetime data. It differs from previously published datasets in the number and complexity of measurements, as well as the scope of units tested and the practical relevance of the data. Thus, it provides an opportunity to develop and evaluate new concepts for predictive maintenance. The PCR data set presented includes the life data of 546 relays with a total of more than 106 million switching cycles, collected over a period of several years at a sampling rate of 10 kHz.
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