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Title: ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists

Journal Article · · Journal of Open Source Software
ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Cornell Univ., Ithaca, NY (United States)
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Recently, advances in machine learning and artificial intelligence have been playing more and more critical roles in a wide range of areas. For the last several years, industries have shown that how learning from data, identifying patterns and making decisions with minimal human intervention can be extremely useful to their business (e.g., image classification, recommending a product to a customer, finding friends in a social network, predicting customers actions, etc.). These success stories have been motivating scientists who study physics, chemistry, materials, medicine, and many other subjects, to explore a new pathway of utilizing machine learning techniques like regression and classification for their scientific activities. However, most existing machine learning tools, systems, and methodologies have been developed for programming experts but not for scientists (or any users) who have no or little knowledge of programming. ASCENDS is a toolkit that is developed to assist scientists (or any persons) who want to use their data for machine learning tasks, more specifically, correlation analysis, regression, and classification. ASCENDS does not require programming skills. Instead, it provides a set of simple but powerful CLI (Command Line Interface) and GUI (Graphic User Interface) tools for non-data scientists to be able to intuitively perform advanced data analysis and machine learning techniques. ASCENDS has been implemented by wrapping around opensource software including Keras, TensorFlow, and scikit-learn.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1615818
Journal Information:
Journal of Open Source Software, Journal Name: Journal of Open Source Software Journal Issue: 46 Vol. 5; ISSN 2475-9066
Publisher:
Open Source Initiative - NumFOCUSCopyright Statement
Country of Publication:
United States
Language:
English

References (9)

Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology journal January 2019
Modern data analytics approach to predict creep of high-temperature alloys journal April 2019
Petascale supercomputing to accelerate the design of high-temperature alloys journal January 2017
Learning a classification model for segmentation conference January 2003
Detecting Novel Associations in Large Data Sets journal December 2011
Pearson's correlation coefficient journal July 2012
Modern Data Analytics Approach to Predict Creep of High-Temperature Alloys journal January 2018
Petascale supercomputing to accelerate the design of high-temperature alloys [Supplemental Data] figure October 2017
Petascale supercomputing to accelerate the design of high-temperature alloys dataset October 2017

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97 MATHEMATICS AND COMPUTING
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