Open Access. Powered by Scholars. Published by Universities.®

Data Science Commons

Open Access. Powered by Scholars. Published by Universities.®

86 Full-Text Articles 230 Authors 0 Downloads 39 Institutions

All Articles in Data Science

Faceted Search

86 full-text articles. Page 5 of 5.

Special Issue: Neutrosophic Theories Applied In Engineering, Florentin Smarandache, Jun Ye 2017 University of New Mexico

Special Issue: Neutrosophic Theories Applied In Engineering, Florentin Smarandache, Jun Ye

Mathematics and Statistics Faculty and Staff Publications

Neutrosophic sets and logic are generalizations of fuzzy and intuitionistic fuzzy sets and logic. Neutrosophic sets and logic are gaining significant attention in solving many real life decision making problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. They have been applied in computational intelligence, multiple criteria decision making, image processing, medical diagnoses, etc. This Special Issue presents original research papers that report on state-of-the-art and recent advancements in neutrosophic sets and logic in soft computing, artificial intelligence, big and small data mining, decision making problems, and practical achievements.


Visualization Of Multidimensional Data With Collocated Paired Coordinates And General Line Coordinates, Boris Kovalerchuk 2014 Central Washington University

Visualization Of Multidimensional Data With Collocated Paired Coordinates And General Line Coordinates, Boris Kovalerchuk

All Faculty Scholarship for the College of the Sciences

Often multidimensional data are visualized by splitting n-D data to a set of low dimensional data. While it is useful it destroys integrity of n-D data, and leads to a shallow understanding complex n-D data. To mitigate this challenge a difficult perceptual task of assembling low-dimensional visualized pieces to the whole n-D vectors must be solved. Another way is a lossy dimension reduction by mapping n-D vectors to 2-D vectors (e.g., Principal Component Analysis). Such 2-D vectors carry only a part of information from n-D vectors, without a way to restore n-D vectors exactly from it. An alternative way ...


Rigidity Analysis Of Protein Biological Assemblies And Periodic Crystal Structures, Filip Jagodzinski, Pamela Clark, Jessica Grant, Tiffany Liu, Samantha Monastra, Ileana Streinu 2013 Central Washington University

Rigidity Analysis Of Protein Biological Assemblies And Periodic Crystal Structures, Filip Jagodzinski, Pamela Clark, Jessica Grant, Tiffany Liu, Samantha Monastra, Ileana Streinu

All Faculty Scholarship for the College of the Sciences

Background

We initiate in silico rigidity-theoretical studies of biological assemblies and small crystals for protein structures. The goal is to determine if, and how, the interactions among neighboring cells and subchains affect the flexibility of a molecule in its crystallized state. We use experimental X-ray crystallography data from the Protein Data Bank (PDB). The analysis relies on an effcient graph-based algorithm. Computational experiments were performed using new protein rigidity analysis tools available in the new release of our KINARI-Web server http://kinari.cs.umass.edu.

Results

We provide two types of results: on biological assemblies and on crystals. We found ...


A Conservation And Rigidity Based Method For Detecting Critical Protein Residues, Bahar Akbal-Delibas, Filip Jagodzinski, Nurit Haspel 2013 University of Massachusetts Boston

A Conservation And Rigidity Based Method For Detecting Critical Protein Residues, Bahar Akbal-Delibas, Filip Jagodzinski, Nurit Haspel

All Faculty Scholarship for the College of the Sciences

Background

Certain amino acids in proteins play a critical role in determining their structural stability and function. Examples include flexible regions such as hinges which allow domain motion, and highly conserved residues on functional interfaces which allow interactions with other proteins. Detecting these regions can aid in the analysis and simulation of protein rigidity and conformational changes, and helps characterizing protein binding and docking. We present an analysis of critical residues in proteins using a combination of two complementary techniques. One method performs in-silico mutations and analyzes the protein's rigidity to infer the role of a point substitution to ...


Mapsnap System To Perform Vector-To-Raster Fusion, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Jerry Tagestad, Sergei Kovalerchuk, Brian Graff 2011 Central Washington University

Mapsnap System To Perform Vector-To-Raster Fusion, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Jerry Tagestad, Sergei Kovalerchuk, Brian Graff

All Faculty Scholarship for the College of the Sciences

As the availability of geospatial data increases, there is a growing need to match these datasets together. However, since these datasets often vary in their origins and spatial accuracy, they frequently do not correspond well to each other, which create multiple problems. To accurately align with imagery, analysts currently either: 1) manually move the vectors, 2) perform a labor-intensive spatial registration of vectors to imagery, 3) move imagery to vectors, or 4) redigitize the vectors from scratch and transfer the attributes. All of these are time consuming and labor-intensive operations. Automated matching and fusing vector datasets has been a subject ...


Extreme Data Mining: Inference From Small Datasets, Răzvan Andonie 2010 Central Washington University

Extreme Data Mining: Inference From Small Datasets, Răzvan Andonie

All Faculty Scholarship for the College of the Sciences

Neural networks have been applied successfully in many fields. However, satisfactory results can only be found under large sample conditions. When it comes to small training sets, the performance may not be so good, or the learning task can even not be accomplished. This deficiency limits the applications of neural network severely. The main reason why small datasets cannot provide enough information is that there exist gaps between samples, even the domain of samples cannot be ensured. Several computational intelligence techniques have been proposed to overcome the limits of learning from small datasets.

We have the following goals: i. To ...


Digital Commons powered by bepress