Machine Learning

A SAR ship dataset for detection, discrimination and analysis


Synthetic Aperture Radar is a monitoring solution which is especially well fitted to maritime surveillance. The large swath widths, time-independent, weather resistant observations can be very useful for the detection of ships typically invisible to other forms of monitoring. A dataset which contains 43 Sentinel-1 Extended Wide Swath images and 3 RADARSAT-2 ScanSAR Narrow images between the 6th October 2014 to the 22 July 2015 is described and presented here.

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Citation Author(s):
Submitted by:
Colin Schwegmann
Last updated:
Wed, 03/28/2018 - 10:53
DOI:
10.21227/H2RK82
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[1] , "A SAR ship dataset for detection, discrimination and analysis", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2RK82. Accessed: Apr. 21, 2018.
@data{h2rk82-17,
doi = {10.21227/H2RK82},
url = {http://dx.doi.org/10.21227/H2RK82},
author = { },
publisher = {IEEE Dataport},
title = {A SAR ship dataset for detection, discrimination and analysis},
year = {2017} }
TY - DATA
T1 - A SAR ship dataset for detection, discrimination and analysis
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2RK82
ER -
. (2017). A SAR ship dataset for detection, discrimination and analysis. IEEE Dataport. http://dx.doi.org/10.21227/H2RK82
, 2017. A SAR ship dataset for detection, discrimination and analysis. Available at: http://dx.doi.org/10.21227/H2RK82.
. (2017). "A SAR ship dataset for detection, discrimination and analysis." Web.
1. . A SAR ship dataset for detection, discrimination and analysis [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2RK82
. "A SAR ship dataset for detection, discrimination and analysis." doi: 10.21227/H2RK82

Machine Learning: A Science Mapping Analysis


Machine learning is becoming increasingly important for companies and the scientific community. It allows us to generate solutions for several problems faced by society. In this study, we perform a science mapping analysis on the machine learning research, in order to provide an overview of the scientific work during the last decade in this area and to show trends that could be the basis for future developments in the field of computer science. This study was carried out using the CiteSpace and SciMAT tools based on results from Scopus and Clarivate Web of Science.

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Citation Author(s):
Submitted by:
Juan Rincon-Patino
Last updated:
Fri, 04/20/2018 - 10:10
DOI:
10.21227/H2337Z
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[1] , "Machine Learning: A Science Mapping Analysis", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2337Z. Accessed: Apr. 21, 2018.
@data{h2337z-18,
doi = {10.21227/H2337Z},
url = {http://dx.doi.org/10.21227/H2337Z},
author = { },
publisher = {IEEE Dataport},
title = {Machine Learning: A Science Mapping Analysis},
year = {2018} }
TY - DATA
T1 - Machine Learning: A Science Mapping Analysis
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2337Z
ER -
. (2018). Machine Learning: A Science Mapping Analysis. IEEE Dataport. http://dx.doi.org/10.21227/H2337Z
, 2018. Machine Learning: A Science Mapping Analysis. Available at: http://dx.doi.org/10.21227/H2337Z.
. (2018). "Machine Learning: A Science Mapping Analysis." Web.
1. . Machine Learning: A Science Mapping Analysis [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2337Z
. "Machine Learning: A Science Mapping Analysis." doi: 10.21227/H2337Z

Dataset malware/beningn permissions Android


This dataset is a result of my research production into machine learning in android security. The data was obtained by a process that consisted to map a binary vector of permissions used for each application analyzed {1=used, 0=no used}. Moreover, the samples of malware/benign were devided by "Type"; 1 malware and 0 non-malware.

When I did my research, the datasets of malware and benign Android applications were not available, then I give to the community a part of my research results for the future works.

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Dataset Details

Citation Author(s):
Submitted by:
Christian Urcuqui
Last updated:
Thu, 11/17/2016 - 10:43
DOI:
10.21227/H26P4M
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[1] , "Dataset malware/beningn permissions Android", IEEE Dataport, 2016. [Online]. Available: http://dx.doi.org/10.21227/H26P4M. Accessed: Apr. 21, 2018.
@data{h26p4m-16,
doi = {10.21227/H26P4M},
url = {http://dx.doi.org/10.21227/H26P4M},
author = { },
publisher = {IEEE Dataport},
title = {Dataset malware/beningn permissions Android},
year = {2016} }
TY - DATA
T1 - Dataset malware/beningn permissions Android
AU -
PY - 2016
PB - IEEE Dataport
UR - 10.21227/H26P4M
ER -
. (2016). Dataset malware/beningn permissions Android. IEEE Dataport. http://dx.doi.org/10.21227/H26P4M
, 2016. Dataset malware/beningn permissions Android. Available at: http://dx.doi.org/10.21227/H26P4M.
. (2016). "Dataset malware/beningn permissions Android." Web.
1. . Dataset malware/beningn permissions Android [Internet]. IEEE Dataport; 2016. Available from : http://dx.doi.org/10.21227/H26P4M
. "Dataset malware/beningn permissions Android." doi: 10.21227/H26P4M