Hybrid Generative/Discriminative Approaches for Proportional Data Modeling and Classification

2012 ◽  
Vol 24 (12) ◽  
pp. 2184-2202 ◽  
Author(s):  
Nizar Bouguila
Keyword(s):  
Data Modeling ◽  
Proportional Data

Proportional data modeling via entropy-based variational bayes learning of mixture models

2017 ◽  
Vol 47 (2) ◽  
pp. 473-487 ◽  
Author(s):  
Wentao Fan ◽  
Faisal R. Al-Osaimi ◽  
Nizar Bouguila ◽  
Jixiang Du
Keyword(s):  
Mixture Models ◽  
Data Modeling ◽  
Variational Bayes ◽  
Proportional Data

Proportional data modeling via selection and estimation of a finite mixture of scaled Dirichlet distributions

2020 ◽  
Vol 36 (2) ◽  
pp. 459-485 ◽  
Author(s):  
Nuha Zamzami ◽  
Rua Alsuroji ◽  
Oboh Eromonsele ◽  
Nizar Bouguila
Keyword(s):  
Data Modeling ◽  
Finite Mixture ◽  
Dirichlet Distributions ◽  
Proportional Data

Agile Development with 3D Data Modeling

2015 ◽  
Vol 10 (6) ◽  
pp. 558 ◽  
Author(s):  
Kristian Sestak ◽  
Zdenek Havlice
Keyword(s):  
Data Modeling ◽  
Agile Development ◽  
3D Data

Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers

2017 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
First Principles ◽  
Data Modeling ◽  
Accurate Prediction ◽  
Index Of Refraction ◽  
Structure Theory ◽  
Organic Polymers ◽  
Number Density ◽  
Lorenz Equation ◽  
In Silico Studies ◽  
Wide Range

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3–1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that an guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and highly economical path to determining the RI values for a wide range of organic polymers.

METHOD FOR SYNTHESIS OF CONCEPTUAL DATA MODELS BASED ON PROCESS DOMAIN MODELS

2020 ◽  
pp. 80-87
Author(s):  
С.И. Рябухин
Keyword(s):  
Data Modeling ◽  
Database Design ◽  
Data Models ◽  
Process Models ◽  
Domain Models ◽  
Conceptual Data

Процессные модели предметной области широко применяются при проектировании баз данных, а именно в ходе концептуального моделирования данных. Предлагается решение проблемы неоднозначности преобразования процессных доменных моделей типа SADT в концептуальные модели данных. Domain process models are widely used in database design, namely in conceptual data modeling. The solution of the problem of ambiguity of transformation of process domain models of the SADT type into conceptual data models is proposed.

Advance in Moving Object Data Modeling under Geographic Network Environment

2013 ◽  
Vol 15 (3) ◽  
pp. 328
Author(s):  
Hengcai ZHANG ◽  
Feng LU ◽  
Jie CHEN
Keyword(s):  
Data Modeling ◽  
Moving Object ◽  
Network Environment
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