skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Neural machine learning algorithms and hardware for image analysis and data science applications.

Abstract

Abstract not provided.

Authors:
; ; ; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1398356
Report Number(s):
SAND2016-9781C
647886
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the 2016 International Conference on Brain Informatics and Health, International Workshop on Neuromorphic Computing and Algorithms held October 13-16, 2016 in Omaha, Nebraska.
Country of Publication:
United States
Language:
English

Citation Formats

James, Conrad D., Severa, William Mark, Draelos, Timothy John, Aimone, James Bradley, Vineyard, Craig Michael, Agarwal, Sapan, Hsia, Alexander H., Hughart, David Russell, Finnegan, Patrick Sean, Jacobs-Gedrim, Robin B, Fuller, Elliot James, Talin, Albert Alec, Marinella, Matthew, Schiek, Richard, and Plimpton, Steven J. Neural machine learning algorithms and hardware for image analysis and data science applications.. United States: N. p., 2016. Web.
James, Conrad D., Severa, William Mark, Draelos, Timothy John, Aimone, James Bradley, Vineyard, Craig Michael, Agarwal, Sapan, Hsia, Alexander H., Hughart, David Russell, Finnegan, Patrick Sean, Jacobs-Gedrim, Robin B, Fuller, Elliot James, Talin, Albert Alec, Marinella, Matthew, Schiek, Richard, & Plimpton, Steven J. Neural machine learning algorithms and hardware for image analysis and data science applications.. United States.
James, Conrad D., Severa, William Mark, Draelos, Timothy John, Aimone, James Bradley, Vineyard, Craig Michael, Agarwal, Sapan, Hsia, Alexander H., Hughart, David Russell, Finnegan, Patrick Sean, Jacobs-Gedrim, Robin B, Fuller, Elliot James, Talin, Albert Alec, Marinella, Matthew, Schiek, Richard, and Plimpton, Steven J. 2016. "Neural machine learning algorithms and hardware for image analysis and data science applications.". United States. doi:. https://www.osti.gov/servlets/purl/1398356.
@article{osti_1398356,
title = {Neural machine learning algorithms and hardware for image analysis and data science applications.},
author = {James, Conrad D. and Severa, William Mark and Draelos, Timothy John and Aimone, James Bradley and Vineyard, Craig Michael and Agarwal, Sapan and Hsia, Alexander H. and Hughart, David Russell and Finnegan, Patrick Sean and Jacobs-Gedrim, Robin B and Fuller, Elliot James and Talin, Albert Alec and Marinella, Matthew and Schiek, Richard and Plimpton, Steven J.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 9
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • In this article we report initial, quantitative results on application of simple neutral networks, and simple machine learning methods, to two problems in DNA sequence analysis. The two problems we consider are: (1) determination of whether procaryotic and eucaryotic DNA sequences segments are translated to protein. An accuracy of 99.4% is reported for procaryotic DNA (E. coli) and 98.4% for eucaryotic DNA (H. Sapiens genes known to be expressed in liver); (2) determination of whether eucaryotic DNA sequence segments containing the dinucleotides ''AG'' or ''GT'' are transcribed to RNA splice junctions. Accuracy of 91.2% was achieved on intron/exon splice junctionsmore » (acceptor sites) and 92.8% on exon/intron splice junctions (donor sites). The solution of these two problems, by use of information processing algorithms operating on unannotated base sequences and without recourse to biological laboratory work, is relevant to the Human Genome Project. A variety of neural network, machine learning, and information theoretic algorithms are used. The accuracies obtained exceed those of previous investigations for which quantitative results are available in the literature. They result from an ongoing program of research that applies machine learning algorithms to the problem of determining biological function of DNA sequences. Some predictions of possible new genes using these methods are listed -- although a complete survey of the H. sapiens and E. coli sections of GenBank will be given elsewhere. 36 refs., 6 figs., 6 tabs.« less
  • Knowledge-based remote sensing image analysis with GIS data is acknowledged as a promising technique. However, the difficulty in knowledge acquisition, a well-known bottleneck in building knowledge-based systems, impedes the adoption of this technique. Automating knowledge acquisition is therefore in demand. This paper presents a machine learning approach to automated construction of knowledge bases for image analysis expert systems integrating remotely sensed and GIS data. The methodology applied in the study is based on inductive learning techniques in machine learning, a subarea of artificial intelligence. It involves training with examples from remote sensing and GIS data, learning using the inductive principles,more » decision tree generating, rule generating from the decision tree, and knowledge base building for an image analysis expert system. This method was used to construct a knowledge base for wetland classification of Par Pond on the Savannah River Site, SC, using SPOT image data and GIS data. The preliminary results show that this method can provide an effective approach to integration of remotely sensed and GIS data in geographic information processing.« less