Data integration of exploration data using colour space on an image processor

1989 ◽  
Vol 20 (2) ◽  
pp. 31 ◽  
Author(s):  
G.A. Spencer ◽  
D.F. Pridmore ◽  
D.J. Isles
Keyword(s):  
Data Integration ◽  
Effective Means ◽  
Hard Copy ◽  
Data Sets ◽  
Independent Data ◽  
Vector Data ◽  
Integration Process ◽  
Colour Space ◽  
Airborne Geophysical Data ◽  
Data Management Software

lmage processing in exploration has rapidly evolved into the field of data integration, whereby independent data sets which coincide in space are displayed concurrently. Interrelation-ships between data sets which may be crucial to exploration can thus be identified much more effectively than with conventional hard copy overlays. The use of perceptual colour space; hue, saturation and luminosity (HSL) provides an effective means for integrating raster data sets, as illustrated with the multi-spectral scanner and airborne geophysical data from the Kambalda area in Western Australia. The integration process must also cater for data in vector format, which is more appropriate for geological, topographic and cultural information, but to date, image processing systems have poorly captured and managed such data. As a consequence, the merging of vector data management software such as GIS (geographic information system) with existing advanced image enhancement packages is an area of active development in the exploration industry.

The visual arrays task…visual storage capacity or attention control?

2019 ◽  
Author(s):  
Jessie Martin ◽  
Jason S. Tsukahara ◽  
Christopher Draheim ◽  
Zach Shipstead ◽  
Cody Mashburn ◽  
...  
Keyword(s):  
Working Memory ◽  
Storage Capacity ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Data Sets ◽  
Independent Data ◽  
Visual Storage ◽  
Design Selection ◽  
The Relationship

**The uploaded manuscript is still in preparation** In this study, we tested the relationship between visual arrays tasks and working memory capacity and attention control. Specifically, we tested whether task design (selection or non-selection demands) impacted the relationship between visual arrays measures and constructs of working memory capacity and attention control. Using analyses from 4 independent data sets we showed that the degree to which visual arrays measures rely on selection influences the degree to which they reflect domain-general attention control.

scholarly journals Towards Application of One-Class Classification Methods to Medical Data

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Itziar Irigoien ◽  
Basilio Sierra ◽  
Concepción Arenas
Keyword(s):  
State Of The Art ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Biomedical Data ◽  
Vector Data ◽  
Target Class ◽  
Tumor Recognition ◽  
One Class Classification

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

scholarly journals Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

BMC Cancer ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
James E Korkola ◽  
Ekaterina Blaveri ◽  
Sandy DeVries ◽  
Dan H Moore ◽  
E Shelley Hwang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Signature ◽  
Data Sets ◽  
Independent Data ◽  
Breast Cancer Outcome ◽  
Cancer Outcome

Scrutinizing the Barriers That Impede Industry 4.0 Projects

2019 ◽  
pp. 294-314
Author(s):  
Ömür Yaşar Saatçioğlu ◽  
Nergis Özispa ◽  
Gökçe T. Kök
Keyword(s):  
Decision Making ◽  
Data Integration ◽  
Industry 4.0 ◽  
Implementation Process ◽  
Structural Modeling ◽  
The Other ◽  
Research Institutions ◽  
Integration Process ◽  
Interpretive Structural Modeling ◽  
Utmost Care

The concept of Industry 4.0 has recently attracted attention from academics, research institutions, and companies. In order for projects to achieve success in Industry 4.0, project specifications must be known and they must be conducted with utmost care. While Industry 4.0 projects ensure lots of advantages, they encounter many risks such as data integration, process flexibility, and security problems. Identification of barriers to Industry 4.0 is important for the success of the projects. The aim of the chapter is to determine the Industry 4.0 barriers in implementation process in Turkey's conditions investigate the interrelations among them and develop a model that can measure the interacting effects of the barriers on the other barriers in the Industry 4.0 implementation process. To reach that aim, interpretive structural modeling (ISM) and decision-making trail and evaluation laboratory (DEMATEL) are used. According to results, one of the most important findings is the lack of digital vision which found as the only affecting barrier and it affects all the other barriers.

Analysis and Integration of Biological Data

2010 ◽  
pp. 287-314
Author(s):  
Diego Milone ◽  
Georgina Stegmayer ◽  
Matías Gerard ◽  
Laura Kamenetzky ◽  
Mariana López ◽  
...  
Keyword(s):  
Data Integration ◽  
New Technologies ◽  
Biological Significance ◽  
Neural Model ◽  
Biological Data ◽  
Data Sets ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Internal Coherence ◽  
Biological Data Integration

The volume of information derived from post genomic technologies is rapidly increasing. Due to the amount of involved data, novel computational methods are needed for the analysis and knowledge discovery into the massive data sets produced by these new technologies. Furthermore, data integration is also gaining attention for merging signals from different sources in order to discover unknown relations. This chapter presents a pipeline for biological data integration and discovery of a priori unknown relationships between gene expressions and metabolite accumulations. In this pipeline, two standard clustering methods are compared against a novel neural network approach. The neural model provides a simple visualization interface for identification of coordinated patterns variations, independently of the number of produced clusters. Several quality measurements have been defined for the evaluation of the clustering results obtained on a case study involving transcriptomic and metabolomic profiles from tomato fruits. Moreover, a method is proposed for the evaluation of the biological significance of the clusters found. The neural model has shown a high performance in most of the quality measures, with internal coherence in all the identified clusters and better visualization capabilities.

Synthetic Taxonomy of Rosa Races Using ACT-STATIS

2000 ◽  
Vol 55 (5-6) ◽  
pp. 399-409 ◽  
Author(s):  
Olivier Raymond ◽  
Jean-Louis Fiasson ◽  
Maurice Jay
Keyword(s):  
Artificial Selection ◽  
Contingency Tables ◽  
Principal Component ◽  
Data Sets ◽  
Independent Data ◽  
Selection Pressures ◽  
Principal Component Analyses ◽  
History Of ◽  
Rv Coefficient ◽  
Good Agreement

Fifteen Rosa cultivated races were described by means of phenotypic frequencies (11 tables). Two groups of correlated contingency tables were identified by ACT-STATIS (Analyse Conjointe de Tableaux - Structuration de Tableaux à Trois Indices de la Statistique) interstructure analysis. Three data sets appeared to be independent from the others. Typologies of races were obtained after ACT-STATIS compromise analyses for the two groups of correlated tables, and after Principal Component Analyses for the independent data sets. Each typology was original and variously influenced by genealogical structure, mutation or artificial selection pressures. A weighted synthesis was attempted in order to build a taxonomy of races taking into account these diversity factors. The good agreement between the resulting classification and the assumptions about the history of Rosa domestication advocated for a wider utilization of ACT-STATIS and RV coefficient when the relationships between individuals or populations have to be studied on the basis of their similarities.

scholarly journals Weighted Gene Coexpression Network Analysis Reveals the Dynamic Transcriptome Regulation and Prognostic Biomarkers of Hepatocellular Carcinoma

2020 ◽  
Vol 16 ◽  
pp. 117693432092056
Author(s):  
Shuping Qu ◽  
Qiuyuan Shi ◽  
Jing Xu ◽  
Wanwan Yi ◽  
Hengwei Fan
Keyword(s):  
Hepatocellular Carcinoma ◽  
Differentially Expressed ◽  
Prognostic Biomarkers ◽  
Coexpression Network ◽  
Data Sets ◽  
Independent Data ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Gene Modules ◽  
Coexpression Network Analysis

This study was aimed at revealing the dynamic regulation of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in hepatocellular carcinoma (HCC) and to identify HCC biomarkers capable of predicting prognosis. Differentially expressed mRNAs (DEmRNAs), lncRNAs, and miRNAs were acquired by comparing expression profiles of HCC with normal samples, using an expression data set from The Cancer Genome Atlas. Altered biological functions and pathways in HCC were analyzed by subjecting DEmRNAs to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Gene modules significantly associated with disease status were identified by weighted gene coexpression network analysis. An lncRNA-mRNA and an miRNA-mRNA coexpression network were constructed for genes in disease-related modules, followed by the identification of prognostic biomarkers using Kaplan-Meier survival analysis. Differential expression and association with the prognosis of 4 miRNAs were verified in independent data sets. A total of 1220 differentially expressed genes were identified between HCC and normal samples. Differentially expressed mRNAs were significantly enriched in functions and pathways related to “plasma membrane structure,” “sensory perception,” “metabolism,” and “cell proliferation.” Two disease-associated gene modules were identified. Among genes in lncRNA-mRNA and miRNA-mRNA coexpression networks, 9 DEmRNAs and 7 DEmiRNAs were identified to be potential prognostic biomarkers. MIMAT0000102, MIMAT0003882, and MIMAT0004677 were successfully validated in independent data sets. Our results may advance our understanding of molecular mechanisms underlying HCC. The biomarkers may contribute to diagnosis in future clinical practice.

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