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Analysis of application of distributed multi-node, multi-GPU heterogeneous system for acceleration of image reconstruction in Electrical Capacitance Tomography

Measurement Data Visualization: Developments Towards 3D, proc of 5th World Congress on Industrial Process Tomography, Bergen, Norway, 2006 [15] P. Russek, K.Wiatr, Dedicated architecture for double precision matrix multiplication in supercomputing environment, Proceedings of the 2007 IEEE Workshop on Design and diagnostics of Electronic Circuits and Systems, DDECS, pp. 321-324, 2007 [16] M. Soleimani, Three-dimensional electrical capacitance tomography imaging, Insight, Non- Destructive Testing and Condition Monitoring, vol. 48, no. 10, pp

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Relief visualization techniques using free and open source GIS tools

−479. Doneus M., 2013, Openness as a visualization technique for interpretative mapping of airborne LiDAR derived digital terrain models. “Remote Sensing” Vol. 5, no. 12, pp. 6427−6442. Doneus M., Kühteiber T., 2013, Airborne laser scanning and archaeological interpretation – bringing back the people. In: Opitz R., Cowley D. Interpreting Archaeological Topography: Lasers, 3D Data, Visualisation and Observation . Oxford, UK: Oxbow Books, pp. 32−50. Drachal J., 2017, Combined shading used for small scale photographic maps. “Unbounded Mapping of Mountains

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Data Analytics in CRM Processes: A Literature Review

.02.021 [6] N. R. Mabroukeh and C. I. Ezeife, “A Taxonomy of Sequential Pattern Mining Algorithms,” ACM Computing Surveys, vol. 43, no. 1, pp. 1-41, Nov. 2010. https://doi.org/10.1145/1824795.1824798 [7] M. Friendly, Milestones in the history of thematic cartography, statistical graphics, and data visualization, 2009 [Online]. Available: http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf [8] M. Aparicio and C. J. Costa, “Data visualization,” Communication Design Quarterly Review, vol. 3, no. 1, pp. 7-11, Jan. 2015. https

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Cloud Service for Numerical Calculations and Visualizations of Photonic Dissipative Systems

of Light via Photon Blockade in Optical Nanocavities. - Phys. Rev., Vol. A81, 2010, 033838. 25. Abhijit, J. Data Visualization with the D3. JS JavaScript library. - Journal of Computing Sciences in Colleges, Vol. 30, 2014, No 2, pp. 139-141. 26. Scott, W. Adding Visualization with JSON. Pro j Query in Oracle Application Express. Apress, 2015, pp. 133-147.

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Territorial Division and Income Affluence – Analysis Using Two-Level Logit Models

of Epidemiology, vol. 161, no. 1, pp. 81-88. Larsen K., Petersen J.H., Budtz-Jorgensen E., Endahl L., 2000, Interpreting parameters in the logistic regression model with random effects , Biometrics, vol. 56, no. 3, pp. 909-914. Lüdecke D., 2018a, Sjmisc: Miscellaneous data management tools , R package version 2.7.0, https://CRAN.R-project.org/package=sjmisc . Lüdecke D., 2018b, SjPlot: Data visualization for statistics in social science , R package version 2.4.1, https://CRAN.R-project.org/package=sjPlot . Moineddin R., Matheson F

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Linear Regression Diagnostics in Cluster Samples

References Atkinson, A.C., and M. Riani. 2000. Robust Diagnostic Regression Analysis. New York: Springer-Verlag. Atkinson, A.C., and M. Riani. 2004. “The Forward Search and Data Visualization.” Computational Statistics 19: 29-54. Bates, D., M. Maechler, B. Bolker and S. Walker. 2014. “lme4: Linear Mixed-Effects Models Using Eiqen and S4. R package version 1.1-7.” Available at: http://CRAN.R-project.org/package= lme4 (accessed February 2, 2015). Belsley, D.A., R. E. Kuh, and R. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and

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On Explainable Fuzzy Recommenders and their Performance Evaluation

mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system, Journal of Artificial Intelligence and Soft Computing Research 7 (1): 33–46. Riid, A. and Preden, J.-S. (2017). Design of fuzzy rule-based classifiers through granulation and consolidation, Journal of Artificial Intelligence and Soft Computing Research 7 (2): 137–147. Rutkowska, D. (2002). Neuro-Fuzzy Architectures and Hybrid Learning , Studies in Fuzziness and Soft Computing, Springer Verlag, New York, NY. Rutkowski, L. (2004). Flexible Neuro

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Application of Chemometric Analysis to the Study of Snow at the Sudety Mountains, Poland

Appennines. Atmos Res. 2002;61:311-334. DOI: 10.1016/S0169-8095(01)00139-9. [18] Stanimirova I, Walczak B, Massart DL. Multiple factor analysis in environmental chemistry. Anal Chim Acta. 2005;545:1-12. DOI: 10.1016/j.aca.2005.04.054. [19] Mellinger M. Chemometr Intell Lab Syst. 1987;2(29):29-36. DOI: 10.1016/0169-7439(87)80083-7. [20] Kohonen T. Self-organizing Maps. Berlin: Springer; 2001. www.springer.com/cn/book/9783540679219 . [21] Jin H, Shum WH, Leung KS, Wong ML. Expanding self-organizing map for data visualization and cluster analysis. Inf

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On-Line Signature Partitioning Using a Population Based Algorithm

.M., Lewis, A. Grey wolf optimizer. Advances in engineering software, vol. 69, pp. 46-61 (2014) [19] Nanni, L., Maiorana, E., Lumini, A. and Campisi, P. Combining local, regional and global matchers for a template protected on-line signature verification system. Expert Systems with Applications, 37, 3676-3684 (2010) [20] Pedersen, M.E.H. Good parameters for differential evolution. Hvass Laboratories Technical Report, vol. HL1002 (2010) [21] Prasad, M., Liu, Y.T., Li, D.L., Lin, Ch.T., Shah, R.R., Kaiwartya, O.P. A New Mechanism for Data Visualization with

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A Coupled Insulin and Meal Effect Neuro-Fuzzy Model for The Prediction of Blood Glucose Level in Type 1 Diabetes Mellitus Patients.

, J., 2017, Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods, Journal of Medical Systems, 41 (9):142. International Diabetes Federation, 2013, IDF Diabetes Atlas – 6 th Edition, Brussels Belgium, Retrieved September 2, 2016, from http://www.idf.org/diabetesatlas/data-visualisations . International Diabetes Federation, 2016, IDF Diabetes Atlas – 7 th Edition. Retrieved September 2, 2016, from http://www.idf.org/diabetesatlas . Juan, Li. and Chandima, F., 2016, Smartphone-based personalized blood glucose

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