Detection

The dataset comprises of image file s of size 20 x 20 pixels for various types of metals and non-metal.The data collected has been augmented, scaled and modified to represent a number a training set dataset.It can be used to detect and identify object type based on material type in the image.In this process both training data set and test data set can be generated from these image files. 

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  • Artificial Intelligence
  • Last Updated On: 
    Thu, 06/25/2020 - 06:24

    This dataset is a collection of images and their respective labels containing examples of multiple Brazilian coins, the primary purpose is to support the development of Computer Vision techniques for automatic detection of such objects, i.e., localization and classification tasks. 

    208 views
  • Computer Vision
  • Last Updated On: 
    Wed, 05/06/2020 - 11:13

    Endoscopy is a widely used clinical procedure for the early detection of cancers in hollow-organs such as oesophagus, stomach, and colon. Computer-assisted methods for accurate and temporally consistent localisation and segmentation of diseased region-of-interests enable precise quantification and mapping of lesions from clinical endoscopy videos which is critical for monitoring and surgical planning. Innovations have the potential to improve current medical practices and refine healthcare systems worldwide.

  • Artificial Intelligence
  • Computer Vision
  • Image Processing
  • Machine Learning
  • Biomedical and Health Sciences
  • Medical Imaging
  • Last Updated On: 
    Sun, 03/29/2020 - 13:15

    This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Top-1000 imported functions extracted from the 'pe_imports' elements of Cuckoo Sandbox reports. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.

    1147 views
  • Machine Learning
  • Last Updated On: 
    Fri, 11/08/2019 - 05:43

    This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Raw PE byte stream rescaled to a 32 x 32 greyscale image using the Nearest Neighbor Interpolation algorithm and then flattened to a 1024 bytes vector. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.

    329 views
  • Machine Learning
  • Last Updated On: 
    Thu, 11/07/2019 - 11:45

    This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data (PE Section Headers of the .text, .code and CODE sections) extracted from the 'pe_sections' elements of Cuckoo Sandbox reports. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.

    639 views
  • Machine Learning
  • Last Updated On: 
    Wed, 11/06/2019 - 06:10

     

    Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma.

    513 views
  • Computer Vision
  • Last Updated On: 
    Thu, 12/12/2019 - 13:38

    This dataset is part of our research on malware detection and classification using Deep Learning. It contains 42,797 malware API call sequences and 1,079 goodware API call sequences. Each API call sequence is composed of the first 100 non-repeated consecutive API calls associated with the parent process, extracted from the 'calls' elements of Cuckoo Sandbox reports.

    1213 views
  • Machine Learning
  • Last Updated On: 
    Wed, 12/11/2019 - 20:28

    Two files are provided. In the first one, there are the power signals obtained from the current and voltage measurements made with our own acquisition system (with a sampling frequency of 5 kHz). They correspond to the switching on and off of 12 home electrical appliances randomly switched on and off during 1 hour by using relay modules and resulting in 1200 events.

    In the second file, the time instants of these events are all reported.

    118 views
  • Smart Grid
  • Last Updated On: 
    Wed, 05/01/2019 - 09:35