LEADER 03448nam 2200481 450 001 996465367103316 005 20210211234015.0 010 $a3-030-36375-9 024 7 $a10.1007/978-3-030-36375-8 035 $a(CKB)4100000011445386 035 $a(DE-He213)978-3-030-36375-8 035 $a(MiAaPQ)EBC6348885 035 $a(PPN)250222558 035 $a(EXLCZ)994100000011445386 100 $a20210211d2020 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplying data science $ehow to create value with artificial intelligence /$fArthur K. Kordon 205 $a1st ed. 2020. 210 1$aCham, Switzerland :$cSpringer,$d[2020] 210 4$dİ2020 215 $a1 online resource (XXXII, 494 p. 262 illus., 195 illus. in color.) 311 $a3-030-36374-0 327 $aPart I, From Business Problems to Data Science -- Data Science Based on Artificial Intelligence -- Business Problems Dependent on Data -- Artificial Intelligence-Based Data Science Solutions -- Integrate and Conquer -- The Lost-in-Translation Trap -- Part II, The AI-Based Data Science Toolbox -- The AI-Based Data Science Workflow -- Problem Knowledge Acquisition -- Data Preparation -- Data Analysis -- Model Development -- The Model Deployment Life Cycle -- Part III, AI-Based Data Science in Action -- Infrastructure -- People -- Applications of AI-Based Data Science in Manufacturing -- Applications of AI-Based Data Science in Business -- How to Operate AI-Based Data Science in a Business -- How to Become an Effective Data Scientist -- Glossary. 330 $aThis book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline. 606 $aArtificial intelligence 606 $aBusiness$xData processing 606 $aBig data 615 0$aArtificial intelligence. 615 0$aBusiness$xData processing. 615 0$aBig data. 676 $a658.0563 700 $aKordon$b Arthur K.$0994821 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465367103316 996 $aApplying data science$92278658 997 $aUNISA LEADER 05248nam 2200613 a 450 001 9910830743203321 005 20230721030037.0 010 $a1-280-93536-7 010 $a9786610935369 010 $a0-470-17563-X 010 $a0-470-17562-1 035 $a(CKB)1000000000356827 035 $a(EBL)309755 035 $a(OCoLC)476092034 035 $a(SSID)ssj0000125108 035 $a(PQKBManifestationID)11146542 035 $a(PQKBTitleCode)TC0000125108 035 $a(PQKBWorkID)10026198 035 $a(PQKB)10918662 035 $a(MiAaPQ)EBC309755 035 $a(EXLCZ)991000000000356827 100 $a20070621d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aColorimetry$b[electronic resource] $eunderstanding the CIE system /$fedited by Ja?nos Schanda 210 $a[Vienna, Austria] $cCIE/Commission internationale de l'eclairage ;$aHoboken, N.J. $cWiley-Interscience$dc2007 215 $a1 online resource (500 p.) 300 $aDescription based upon print version of record. 311 $a0-470-04904-9 320 $aIncludes bibliographical references and index. 327 $aCOLORIMETRY; CONTENTS; Preface; Contributors and Referees; Part I Historic retrospection; 1 Translation of CIE 1931 Resolutions on Colorimetry; Decision 1; Decision 2; Appendix to Decision 2; Decision 3; Decision 3a; Decision 4; Decision 5; 2 Professor Wright's Paper from the Golden Jubilee Book: The Historical and Experimental Background to the 1931 CIE System of Colorimetry; Color mixture and measurement in the Nineteenth Century; American contributions to photometry and colorimetry, 1900-24; The run-up to the 1931 observer: 1924-30; The drama of 1931; Postscript to 1931 327 $aNote added in proofReferences; Part II Colorimetric fundamentals; 3 CIE Colorimetry; Introduction; CIE standard colorimetric observers; The CIE 1931 standard colorimetric observer; Determination of the r(?), g(?), b(?) color-matching functions; Derivation of the CIE XYZ trichromatic system from the CIE RGB trichromatic system; Tristimulus values and chromaticity coordinates; CIE 1964 standard colorimetric observer; k(10) in the tristimulus values of self-luminous objects for the 10° Observer; k(10) in the tristimulus values of non-self-luminous objects for the 10° Observer 327 $aChromaticity coordinates for the 10° observerNotes on the use of the CIE 1964 standard colorimetric observer; CIE illuminants and sources; CIE standard illuminant A and Planckian radiators; Daylight illuminants; CIE standard illuminant D65; CIE illuminants; CIE sources and simulators for colorimetry; Source A; Sources B and C; Source D65; Standards and recommendations for measuring reflecting/transmitting materials; Terms used in conjunction with transmission and reflection measurement; Phenomena; Quantities to describe reflection and transmission; Measuring geometries 327 $aThe sample plane and influx geometryDirectional geometries; Quantities using different measuring geometries; Nonstandard geometries; Recommended geometry for transmission measurements; Standards of reflectance; Uniform chromaticity diagram and uniform color spaces; Uniform chromaticity diagram, CIE 1976 UCS diagram; CIE 1976 uniform color spaces; CIE 1976 (L*a*b*) color space, CIELAB color space; CIE 1976 (L*u*v*) color space, CIELUV color space; Descriptors of chromaticity; Dominant/complementary wavelength and purity; Correlated color temperature; Whiteness; Metamerism 327 $aSpecial metamerism index: change in illuminantSpecial metamerism index: change in observer; Summary; Appendix A; Appendix B; References; 4 CIE Color Difference Metrics; Introduction; MacAdam's experiments on variable stimuli; Adams' and Nickerson's contribution to color difference evaluation; Constant stimuli experiments; CIE 1976 color difference formulas; Testing and improving CIELAB; Collection of new datasets; Development of CIEDE2000; Further developments; References; 5 Spectral Color Measurement; Introduction; General practice in spectral color measurements; Type of instruments 327 $aUse of spectroradiometers for light source color measurement 330 $aColorimetry: Understanding the CIE System summarizes and explains the standards of CIE colorimetry in one comprehensive source.Presents the material in a tutorial form, for easy understanding by students and engineers dealing with colorimetry.Provides an overview of the area of CIE colorimetry, including colorimetric principles, the historical background of colorimetric measurements, uncertainty analysis, open problems of colorimetry and their possible solutions, etc.Includes several appendices, which provide a listing of CIE colorimetric tables as well as an annotated list 606 $aColorimetry 615 0$aColorimetry. 676 $a535.60287 676 $a543.55 676 $a543/.55 701 $aSchanda$b Ja?nos$01658854 712 02$aInternational Commission on Illumination. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830743203321 996 $aColorimetry$94013139 997 $aUNINA