EventsMOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.
Published
This submission belongs to the session 03. USEDAT-03: USA-EU Data Analysis Training Prog. Work., Cambridge, UK-Bilbao, Spain-Duluth, USA, 2017 of the event MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.
Published date
28 Nov, 2017
Citation
Humbert Gonzalez-Diaz, Viviana F. Quevedo-Tumailli, Bernabe Ortega-Tenezaca, FRAMA 1.0: Framework for Moving Average Operators Calculation in Data Analysis, in Proceedings of MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed., 15 January–15 December 2017, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-03-05044
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FRAMA 1.0: Framework for Moving Average Operators Calculation in Data Analysis

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Viviana F. Quevedo-Tumailli 1
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1. RNASA-IMEDIR, Computer Science Faculty, University of Coruña, Spain
2. Department of Organic Chemistry II, University of the Basque Country (UPV/EHU), Biscay, Spain, Spain
3. IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain.
Abstract

Moving Average (MA) operators are used in Box-Jenkins’s ARIMA models in time series analysis (1). We can used MA operators of structural descriptors are useful to quantify multiple conditions or parameters in complex datasets in Omics, Medicinal Chemistry, Nanotechnology, etc. (2-7). Speck-Planche and Cordeiro have also used this kind of models in multiple problems (8-11). In this work, we develop a desktop application that allows applying mathematical and statistical calculations in batches, on input and output variables selected by the user. From the obtained result a percentage sample of data is taken with a random contrast on which Machine Learning algorithms are applied.

Keywords
Biga Data
Data Fusion
Data Analysis
Moving Average
Machine Learning
Poster
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