Machine Learning

Dataset for Telugu Handwritten Gunintam

47 views
  • Computer Vision
  • Last Updated On: 
    Fri, 11/29/2019 - 07:25

    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.

    131 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.

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

    This dataset comes up as a benchmark dataset for machines to automatically recognizing the handwritten assamese digists (numerals) by extracting useful features by analyzing the structure. The Assamese language comprises of a total of 10 digits from 0 to 9. We have collected a total of 516 handwritten digits from 52 native assamese people irrespective of their age (12-86 years), gender, educational background etc. The digits are captured in .jpeg format using a paint mobile application developed by us which automatically saves the images in the internal storage of the mobile.

    162 views
  • Computer Vision
  • Last Updated On: 
    Wed, 11/06/2019 - 04:14

    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.

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

    An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device.

    519 views
  • Artificial Intelligence
  • Last Updated On: 
    Tue, 11/12/2019 - 10:46

    This dataset includes the measurements of a simulated vehicle inside a Gazebo simulation using different sensors: a simulated UWB tag, a IMU and a PX4Flow. 

    54 views
  • Machine Learning
  • Last Updated On: 
    Mon, 10/28/2019 - 13:54

    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.

    261 views
  • Machine Learning
  • Last Updated On: 
    Wed, 11/06/2019 - 06:18

    7200 .csv files, each containing a 10 kHz recording of a 1 ms lasting 100 hz sound, recorded centimeterwise in a 20 cm x 60 cm locating range on a table. 3600 files (3 at each of the 1200 different positions) are without an obstacle between the loudspeaker and the microphone, 3600 RIR recordings are affected by the changes of the object (a book). The OOLA is initially trained offline in batch mode by the first instance of the RIR recordings without the book. Then it learns online in an incremental mode how the RIR changes by the book.

    136 views
  • Artificial Intelligence
  • Last Updated On: 
    Mon, 11/04/2019 - 07:37

    As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the 

    256 views
  • Artificial Intelligence
  • Last Updated On: 
    Sun, 10/13/2019 - 17:07

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