scholarly journals General Purpose Data-Driven Monitoring for Space Operations

2012 ◽  
Vol 9 (2) ◽  
pp. 26-44 ◽  
Author(s):  
David L. Iverson ◽  
Rodney Martin ◽  
Mark Schwabacher ◽  
Lilly Spirkovska ◽  
William Taylor ◽  
...  
Keyword(s):  
General Purpose ◽  
Data Driven ◽  
Space Operations

General Purpose Data-Driven System Monitoring for Space Operations

2009 ◽  
Author(s):  
David Iverson ◽  
Rodney Martin ◽  
Mark Schwabacher ◽  
Liljana Spirkovska ◽  
William Taylor ◽  
...  
Keyword(s):  
General Purpose ◽  
Data Driven ◽  
System Monitoring ◽  
Space Operations ◽  
Driven System

scholarly journals Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Abdul Majeed ◽  
Seong Oun Hwang
Keyword(s):  
Artificial Intelligence ◽  
Personal Data ◽  
General Purpose ◽  
Data Driven ◽  
Healthcare Sector ◽  
Future Research ◽  
Recent Developments ◽  
Technological Developments ◽  
Novel Coronavirus

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Gaurav Vishwakarma ◽  
Doaa Altarawy ◽  
Ramachandran Subramanian ◽  
Bhargava Urala Kota ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Data Science ◽  
General Purpose ◽  
Data Driven ◽  
Research Paradigm ◽  
User Friendliness ◽  
Methodological Innovation ◽  
Software Ecosystem ◽  
Key Features

<div><i>ChemML</i> is an open machine learning and informatics program suite that is designed to support and advance the data-driven research paradigm that is currently emerging in the chemical and materials domain. <i>ChemML</i> allows its users to perform various data science tasks and execute machine learning workflows that are adapted specifically for the chemical and materials context. Key features are automation, general-purpose utility, versatility, and user-friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. <i>ChemML</i> is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data-driven <i>in silico</i> research outlined in our recent publication<sup>1</sup>.</div>

A TOOL THAT SUPPORTS CONSTRUCTING CHINESE EXPERT SYSTEMS

1987 ◽  
Vol 01 (03n04) ◽  
pp. 393-404
Author(s):  
LI-MIN FU
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
General Purpose ◽  
Data Driven ◽  
Input Output ◽  
Design Decisions ◽  
Rule Based ◽  
System Building ◽  
Control Schemes ◽  
And Control

This paper describes EJAUNDICE, which is designed to be a general-purpose expert system building tool. Considerations behind a number of design decisions for purposes of generality are examined. EJAUNDICE provides several control schemes, including biphasical control with goal-directed reasoning, data-driven processing, and control blocks, and integrates rule-based, frame-based, and logic-based reasoning paradigms in its framework. The issues of knowledge representation and input/output in developing a Chinese expert system are also investigated.

scholarly journals Outsmarting Quantum Chemistry Through Transfer Learning

2018 ◽  
Author(s):  
Justin S Smith ◽  
Benjamin T. Nebgen ◽  
Roman Zubatyuk ◽  
Nicholas Lubbers ◽  
Christian Devereux ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Gold Standard ◽  
Materials Science ◽  
Chemical Space ◽  
General Purpose ◽  
Theoretical Chemistry ◽  
Data Driven ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Large Systems

<div>Computer simulations are foundational to theoretical chemistry. Quantum-mechanical (QM) methods provide the highest accuracy for simulating molecules but have difficulty scaling to large systems. Empirical interatomic potentials (classical force fields) are scalable, but lack transferability to new systems and are hard to systematically improve. Automated, data-driven machine learning is close to achieving the best of both approaches. Here we use transfer learning to retrain a general purpose neural network potential, ANI-1x, on a dataset of gold standard QM calculations (CCSD(T)/CBS level) that is relatively small but designed to optimally span chemical space. The resulting potential, ANI-1ccx, approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. ANI-1ccx is broadly applicable to materials science, biology and chemistry, and billions of times faster than the parent CCSD(T)/CBS calculations.</div>

scholarly journals Data-driven simulation for general-purpose multibody dynamics using Deep Neural Networks

2020 ◽  
Author(s):  
Hee-Sun Choi ◽  
Junmo An ◽  
Seongji Han ◽  
Jin-Gyun Kim ◽  
Jae-Yoon Jung ◽  
...  
Keyword(s):  
Neural Networks ◽  
Multibody Dynamics ◽  
Deep Neural Networks ◽  
General Purpose ◽  
Data Driven

ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Gaurav Vishwakarma ◽  
Doaa Altarawy ◽  
Ramachandran Subramanian ◽  
Bhargava Urala Kota ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Data Science ◽  
General Purpose ◽  
Data Driven ◽  
Research Paradigm ◽  
User Friendliness ◽  
Methodological Innovation ◽  
Software Ecosystem ◽  
Key Features

<div><i>ChemML</i> is an open machine learning and informatics program suite that is designed to support and advance the data-driven research paradigm that is currently emerging in the chemical and materials domain. <i>ChemML</i> allows its users to perform various data science tasks and execute machine learning workflows that are adapted specifically for the chemical and materials context. Key features are automation, general-purpose utility, versatility, and user-friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. <i>ChemML</i> is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data-driven <i>in silico</i> research outlined in our recent publication<sup>1</sup>.</div>

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