scholarly journals Micropulse Lidar Cloud Mask Machine-Learning Value-Added Product Report

2021 ◽  
Author(s):  
Donna Flynn ◽  
◽  
Erol Cromwell ◽  
Damao Zhang
Keyword(s):  
Machine Learning ◽  
Value Added ◽  
Product Report ◽  
Cloud Mask

scholarly journals Oklahoma Mesonet Soil Moisture (OKMSOIL) Value-Added Product Report

2019 ◽  
Author(s):  
L Gregory ◽  
Alice Cialella ◽  
Scott Giangrande
Keyword(s):  
Soil Moisture ◽  
Value Added ◽  
Product Report ◽  
Oklahoma Mesonet

scholarly journals Point Cloud of Cloud Points (PCCP) Value-Added Product Report

2020 ◽  
Author(s):  
David Romps ◽  
Rusen Oktem
Keyword(s):  
Point Cloud ◽  
Value Added ◽  
Product Report ◽  
Cloud Points

scholarly journals The next step for Artificial Intelligence in a globally connected economy

2021 ◽  
Vol 129 ◽  
pp. 04001
Author(s):  
Dumitru Alexandru Bodislav ◽  
Florina Bran ◽  
Carol Cristina Gombos ◽  
Amza Mair
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Factor ◽  
Value Added ◽  
Point Of View ◽  
Business Analytics ◽  
Economic Point ◽  
Technological Breakthrough ◽  
Big Picture ◽  
The Difference

Research background: This research paper represents an overview of what artificial intelligence is, what are its roots, and what is the next big thing regarding the domain. In this paper we try to highlight how the domain is growing and what is the difference between the ideology, the business factor and the human factor. We try to create a big picture on the entire phenomenon by creating a parallel between machine learning, artificial intelligence and the influence of technological breakthrough from a hardware perspective. Purpose of the article: The paper is built as a tool in understanding technology, globalization and the pathway to success and scientific glory for what can be seen as the industry of artificial intelligence. The tools presented in the research have the purpose to create an easier path to how we can develop this domain by accelerating theoretical processing and business analytics that come together to form the next level of machine learning/artificial intelligence; research and development, everything being filtered from an economic point of view. Methods: The used research method is based on fundamental analysis of the artificial intelligence domain and its purpose in the complexity of globalization and economic development. Findings & Value added: The paper tries to offer a tool for building a better understanding of the next decade in the domain of artificial intelligence.

scholarly journals Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach

BMJ Open ◽  
2017 ◽  
Vol 7 (9) ◽  
pp. e017199 ◽  
Author(s):  
Thomas Desautels ◽  
Ritankar Das ◽  
Jacob Calvert ◽  
Monica Trivedi ◽  
Charlotte Summers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Tertiary Care Hospital ◽  
Tertiary Care ◽  
Value Added ◽  
Secondary Outcome ◽  
Hospital Death ◽  
Care Hospital ◽  
Icu Discharge

ObjectivesUnplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event.SettingA single academic, tertiary care hospital in the UK.ParticipantsA set of 3326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who were ≤16 years of age; visited ICUs other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2018 outcome-labelled episodes remained.Primary and secondary outcome measuresArea under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48 hours of first ICU discharge.ResultsIn 10-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the Medical Information Mart for Intensive Care (MIMIC-III) database and tested on the target hospital’s data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built Stability and Workload Index for Transfer (SWIFT) score (AUROC=0.6082, SE 0.0249; p=0.014, pairwise t-test).ConclusionsDespite the inherent difficulties, we demonstrate that a novel machine learning algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.

scholarly journals Aerosol Optical Depth Value-Added Product Report

2013 ◽  
Author(s):  
A Koontz ◽  
G Hodges ◽  
J Barnard ◽  
C Flynn ◽  
J Michalsky
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Value Added ◽  
Product Report

scholarly journals Epidemiology and forecasting of insect-pests and diseases for value-added agro-advisory

MAUSAM ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 267-276
Author(s):  
AMRENDER KUMAR ◽  
A. K. JAIN ◽  
B. K. BHATTACHARYA ◽  
VINOD KUMAR ◽  
A. K. MISHRA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Insect Pests ◽  
Plant Protection ◽  
Crop Protection ◽  
Value Added ◽  
Accurate Estimation ◽  
Learning Approach ◽  
Pests And Diseases ◽  
Functional Relationships ◽  
Machine Learning Approach

Models are means to capture, condense and organize knowledge. These are expressions, which represent relationship between various components of a system. A well-tested weather-based model can be an effective scientific tool for forewarning insect-pests and diseases in advance so that timely plant protection measures could be taken up. Various types of techniques have been developed for the purpose. The simplest technique forms the class of thumb rules, which are based on experience. Though these do not have much scientific background but are extensively used to provide quick forewarning of the menace. Another tool in practice is regression model that represents relationship between two or more variables so that one variable can be predicted from the other (s). Linear and non-linear regression models have been widely used in studying relationship of insect-pests and diseases with time and weather variables (as such or in some transformed forms). With the advent of computers more sophisticated techniques such as simulation modelling and machine learning approach such as decision tree induction algorithms, genetic algorithms, neural networks, rough sets, etc. have been explored. A number of simulation models have been developed all over the world for quantifying effects of various factors including weather on agriculture.  These may provide a good forecast but require detailed data base, which may not be available. Machine learning approach has recently received some attention. As opposed to traditional model-based methods, machine learning approach is self adaptive methods in that there are a few a priori assumptions about the models for problem(s) under study. This technique learns more from examples and captures subtle functional relationships among the data even if the underlying relationships are unknown or hard to describe.  This modelling approach with ability to learn from experience is very useful for many practical problems provided enough data are available. Remotely sensed data can provide useful information relating to area under the crop and also the condition thereof. It has certain advantages over land use statistics due to multi-spectral, synoptic and repetitive coverage. An attempt has been made for accurate estimation of area affected by insect-pests and diseases in crops along with accurate assessment of damage due to the same are possible for providing compensation to farmers. In this study, an Integrated Decision Support System (IDSS) for Crop Protection Services is also discussed.  

scholarly journals Navigation and Meteorological Data from Multiple Sensors on Airborne Platform (NAVMET-AIR) Value-Added Product Report

2020 ◽  
Author(s):  
Lexie Goldberger ◽  
◽  
Fan Mei ◽  
John Hubbe ◽  
C Kluzek
Keyword(s):  
Meteorological Data ◽  
Value Added ◽  
Product Report ◽  
Multiple Sensors

scholarly journals Classification of Astrophysics Journal Articles with Machine Learning to Identify Data for NED

2022 ◽  
Vol 134 (1031) ◽  
pp. 014501
Author(s):  
Tracy X. Chen ◽  
Rick Ebert ◽  
Joseph M. Mazzarella ◽  
Cren Frayer ◽  
Scott Terek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Journal Article ◽  
Value Added ◽  
Journal Articles ◽  
Online Service ◽  
Production Pipeline ◽  
Multi Wavelength ◽  
Data Content ◽  
Number Of Publications

Abstract The NASA/IPAC Extragalactic Database (NED) is a comprehensive online service that combines fundamental multi-wavelength information for known objects beyond the Milky Way and provides value-added, derived quantities and tools to search and access the data. The contents and relationships between measurements in the database are continuously augmented and revised to stay current with astrophysics literature and new sky surveys. The conventional process of distilling and extracting data from the literature involves human experts to review the journal articles and determine if an article is of extragalactic nature, and if so, what types of data it contains. This is both labor intensive and unsustainable, especially given the ever-increasing number of publications each year. We present here a machine learning (ML) approach developed and integrated into the NED production pipeline to help automate the classification of journal article topics and their data content for inclusion into NED. We show that this ML application can successfully reproduce the classifications of a human expert to an accuracy of over 90% in a fraction of the time it takes a human, allowing us to focus human expertise on tasks that are more difficult to automate.

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