LEADER 03788nam 2200541 450 001 9910830819303321 005 20231110223609.0 010 $a1-119-79243-6 010 $a1-119-79241-X 035 $a(MiAaPQ)EBC6976103 035 $a(Au-PeEL)EBL6976103 035 $a(CKB)21957530700041 035 $a(OCoLC-P)1314853913 035 $a(OCoLC)1314853913 035 $a(CaSebORM)9781119791751 035 $a(EXLCZ)9921957530700041 100 $a20221128d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvanced analytics and deep learning models /$fShaveta Malik, Amit Kumar Tyagi and Archana Mire, editors 210 1$aHoboken, NJ :$cJohn Wiley & Sons, Inc.,$d[2022] 210 4$dİ2022 215 $a1 online resource (375 pages) 225 1 $aNext Generation Computing and Communication Engineering 311 08$aPrint version: Mire, Archana Advanced Analytics and Deep Learning Models Newark : John Wiley & Sons, Incorporated,c2022 9781119791751 320 $aIncludes bibliographical references and index. 330 $aAdvanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning. 410 0$aNext Generation Computing and Communication Engineering 606 $aDeep learning (Machine learning) 606 $aArtificial intelligence 606 $aBig data 615 0$aDeep learning (Machine learning) 615 0$aArtificial intelligence. 615 0$aBig data. 676 $a006.31 702 $aTyagi$b Amit Kumar$f1988- 702 $aMire$b Archana 702 $aMalik$b Shaveta$f1987- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830819303321 996 $aAdvanced analytics and deep learning models$94028387 997 $aUNINA