NDLI: Nuclear detection using higher order learning

Content Provider	IEEE Xplore Digital Library
Author	Nelson, C. Pottenger, W.M.
Copyright Year	2011
Description	Author affiliation: RUTCOR, Rutgers University, Piscataway, New Jersey (Nelson, C.) \|\| DIMACS and Computer Science, Rutgers University, Piscataway, New Jersey (Pottenger, W.M.)
Abstract	The detection of potentially threatening nuclear materials is a challenging homeland security problem. This research reports on the application of a novel statistical relational learning algorithm, Higher Order Naïve Bayes (HONB), to improve the detection and identification of nuclear isotopes. When classifying nuclear detection data, distinguishing potentially threatening from harmless radioisotopes is critical. These also must be distinguished from the naturally occurring radioactive background. This research applied Higher Order Learning to nuclear detection data to improve the detection and identification of four isotopes: Ga67, I131, In111, and Tc99m. In the research traditional IID machine learning methods are applied to the area of nuclear detection, and the results compared with the performance of leveraging higher-order dependencies between feature values using HONB. The findings give insight about the performance of higher-order classifiers (described in [2]) on datasets with small numbers of positive instances. In the initial study, Naïve Bayes was compared with its higher-order counterpart, Higher Order Naïve Bayes. HONB was found to perform statistically significantly better for isotope Ga67 when using a preprocessing methodology of discretizing then binarizing the input sensor data. Similar results were seen for different amounts of training data for I131, In111, and Tc99m. HONB was also found to perform statistically significantly better for isotopes I131 and Tc99m when the preprocessing involved normalization, discretization then binarization. This study shows that Higher Order Learning techniques can be very useful in the arena of nuclear detection.
Starting Page	319
Ending Page	324
File Size	491496
Page Count	6
File Format	PDF
ISBN	9781457713750
e-ISBN	9781457713767
DOI	10.1109/THS.2011.6107890
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement nuclear detection machine learning Training Radioactive materials Accuracy Weapons higher-order learning Machine learning naïve bayes HONB higher order naïve bayes Isotopes
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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