LEADER 04063nam 2200481za 450 001 9910810999603321 005 20240513094848.0 010 $a1-00-301985-4 010 $a1-000-09467-7 010 $a1-003-01985-4 010 $a1-000-09465-0 035 $a(MiAaPQ)EBC6264228 035 $a(CKB)4100000011351538 035 $a(EXLCZ)994100000011351538 100 $a20200312d2020 uy 0 101 0 $aeng 135 $aur|n|nnn||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aData analytics and AI /$fedited by Jay Liebowitz 205 $a1st ed. 210 $aBoca Raton, Fla. $cCRC P.$d2020 215 $a1 online resource xv, (242 p.) $cill 225 1 $aData analytics applications 300 $aAn Auerbach Book. 327 $a1 Unraveling Data Science, Artificial Intelligence, and Autonomy -- 2 Unlock the True Power of Data Analytics with Artificial -- 3 Machine Intelligence and Managerial Decision-Making -- 4 Measurement Issues in the Uncanny Valley: The Interaction between Artificial Intelligence and Data Analytics -- 5 An Overview of Deep Learning in Industry -- 6 Chinese AI Policy and the Path to Global Leadership: Competition, Protectionism, and Security -- 7 Natural Language Processing in Data Analytics -- 8 AI in Smart Cities Development: A Perspective of Strategic Risk Management -- 9 Predicting Patient Missed Appointments in the Academic Dental Clinic -- 10 Machine Learning in Cognitive Neuroimaging -- 11 People, Competencies, and Capabilities Are Core Elements in Digital Transformation: A Case Study of a Digital Transformation Project at ABB -- 12 AI-Informed Analytics Cycle: Reinforcing Concepts -- Index. 330 $aAnalytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data. 410 0$aData analytics applications 606 $aStatistics$xData processing 606 $aArtificial intelligence 615 0$aStatistics$xData processing. 615 0$aArtificial intelligence. 676 $a001.422028563 676 $a001.422028563 701 $aLiebowitz$b Jay$0993187 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910810999603321 996 $aData analytics and AI$93971243 997 $aUNINA