LEADER 03495nam 22006975 450 001 996466300803316 005 20200702135253.0 010 $a3-030-05090-4 024 7 $a10.1007/978-3-030-05090-0 035 $a(CKB)4100000007334846 035 $a(DE-He213)978-3-030-05090-0 035 $a(MiAaPQ)EBC6281646 035 $a(PPN)232963754 035 $a(EXLCZ)994100000007334846 100 $a20181228d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Data Mining and Applications$b[electronic resource] $e14th International Conference, ADMA 2018, Nanjing, China, November 16?18, 2018, Proceedings /$fedited by Guojun Gan, Bohan Li, Xue Li, Shuliang Wang 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XIV, 532 p. 199 illus., 137 illus. in color.) 225 1 $aLecture Notes in Artificial Intelligence ;$v11323 311 $a3-030-05089-0 327 $aData Mining Foundations -- Big Data -- Text and Multimedia Mining -- Miscellaneous Topics. 330 $aThis book constitutes the refereed proceedings of the 14th International Conference on Advanced Data Mining and Applications, ADMA 2018, held in Nanjing, China in November 2018. The 23 full and 22 short papers presented in this volume were carefully reviewed and selected from 104 submissions. The papers were organized in topical sections named: Data Mining Foundations; Big Data; Text and Multimedia Mining; Miscellaneous Topics. 410 0$aLecture Notes in Artificial Intelligence ;$v11323 606 $aArtificial intelligence 606 $aData mining 606 $aDatabase management 606 $aInformation storage and retrieval 606 $aComputer organization 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aComputer Systems Organization and Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13006 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aDatabase management. 615 0$aInformation storage and retrieval. 615 0$aComputer organization. 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aDatabase Management. 615 24$aInformation Storage and Retrieval. 615 24$aComputer Systems Organization and Communication Networks. 676 $a006.312 702 $aGan$b Guojun$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLi$b Bohan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLi$b Xue$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWang$b Shuliang$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466300803316 996 $aAdvanced Data Mining and Applications$9771989 997 $aUNISA LEADER 03516nam 2200769 a 450 001 9910789997203321 005 20200520144314.0 010 $a1-283-16560-0 010 $a9786613165602 010 $a3-11-023606-0 024 7 $a10.1515/9783110236064 035 $a(CKB)2670000000088763 035 $a(EBL)690638 035 $a(OCoLC)723945514 035 $a(SSID)ssj0000539793 035 $a(PQKBManifestationID)12193302 035 $a(PQKBTitleCode)TC0000539793 035 $a(PQKBWorkID)10571841 035 $a(PQKB)10106047 035 $a(MiAaPQ)EBC690638 035 $a(DE-B1597)122632 035 $a(OCoLC)746480417 035 $a(DE-B1597)9783110236064 035 $a(Au-PeEL)EBL690638 035 $a(CaPaEBR)ebr10486410 035 $a(CaONFJC)MIL316560 035 $a(PPN)175563098 035 $a(EXLCZ)992670000000088763 100 $a20101118d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe origin of Ashkenazi Jewry$b[electronic resource] $ethe controversy unraveled /$fJits van Straten 210 $aNew York $cWalter de Gruyter$d2011 215 $a1 online resource (248 p.) 300 $aDescription based upon print version of record. 311 $a3-11-023605-2 320 $aIncludes bibliographical references and index. 327 $aThe controversy : Germany or Khazaria -- The Khazars -- The development of Ashkenazi Jewry by region : France, Germany, Bohemia, Moravia Silesia, and Hungary -- The development of Ashkenazi Jewry by region : Poland, Lithuania, and Russia from 1500 to 1900 : the Numerical increase -- Yiddish -- Genetic research (and Anthropology) -- The revised origin and development of East European Jewry. 330 $aWhere do East European Jews - about 90 percent of Ashkenazi Jewry - descend from? This book conveys new insights into a century-old controversy. Jits van Straten argues that there is no evidence for the most common assumption that German Jews fled en masse to Eastern Europe to constitute East European Jewry. Dealing with another much debated theory, van Straten points to the fact that there is no way to identify the descendants of the Khazars in the Ashkenazi population. Using a multidisciplinary approach, the author draws heavily on demographic findings which are vital to evaluate the conclusions of modern DNA research. Finally, it is suggested that East European Jews are mainly descendants of Ukrainians and Belarussians. UPDATE: The article "The origin of East European Ashkenazim via a southern route" (Aschkenas 2017; 27(1): 239-270) is intended to clarify the origin of East European Jewry between roughly 300 BCE and 1000 CE. It is a supplement to this book. 606 $aJews$zEurope$xHistory 606 $aJews$zEurope, Eastern$xHistory 606 $aEthnicity$zEurope 606 $aKhazars 606 $aJews$xOrigin 610 $aAshkenazi Jewry. 610 $aDNA Research. 610 $aDemography. 610 $aJewish History. 610 $aOrigin. 615 0$aJews$xHistory. 615 0$aJews$xHistory. 615 0$aEthnicity 615 0$aKhazars. 615 0$aJews$xOrigin. 676 $a940/.04924 686 $aNY 4700$2rvk 700 $aStraten$b Jits van$01470236 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910789997203321 996 $aThe origin of Ashkenazi Jewry$93681923 997 $aUNINA LEADER 04846nam 2201093z- 450 001 9910557148403321 005 20210501 035 $a(CKB)5400000000040574 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68306 035 $a(oapen)doab68306 035 $a(EXLCZ)995400000000040574 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aArtificial Neural Networks and Evolutionary Computation in Remote Sensing 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (256 p.) 311 08$a3-03943-827-1 311 08$a3-03943-828-X 330 $aArtificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification. 606 $aResearch and information: general$2bicssc 610 $aaerial images 610 $aAI on the edge 610 $aartificial neural networks 610 $aChina 610 $aclassification 610 $aclassification ensemble 610 $aCNN 610 $aCNNs 610 $aconvolutional neural network 610 $aconvolutional neural networks 610 $aconvolutional neural networks (CNNs) 610 $adeep learning 610 $adense network 610 $adigital terrain analysis 610 $adilated convolutional network 610 $aearth observation 610 $aend-to-end detection 610 $aFaster RCNN 610 $afeature fusion 610 $aFeicheng 610 $afew-shot learning 610 $aGaofen 6 610 $aGaofen-2 imagery 610 $ageographic information system (GIS) 610 $ahyperspectral image classification 610 $ahyperspectral images 610 $aimage downscaling 610 $aimage segmentation 610 $aland-use 610 $aLiDAR 610 $alight detection and ranging 610 $amachine learning 610 $amask R-CNN 610 $amask regional-convolutional neural networks 610 $amicrosat 610 $amission 610 $amixed forest 610 $amixed-inter nonlinear programming 610 $amodel generalization 610 $amulti-label segmentation 610 $amulti-scale feature fusion 610 $ananosat 610 $aon-board 610 $aoptical remote sensing images 610 $apost-processing 610 $aquadruplet loss 610 $aremote sensing 610 $aresource extraction 610 $asemantic features 610 $asemantic segmentation 610 $aSentinel-2 610 $aship detection 610 $asingle shot multi-box detector (SSD) 610 $aspatial distribution 610 $aSRGAN 610 $astatistical features 610 $asuper-resolution 610 $asuperstructure optimization 610 $aTai'an 610 $atransfer learning 610 $aunmanned aerial vehicles 610 $awinter wheat 610 $aYou Look Only Once-v3 (YOLO-v3) 615 7$aResearch and information: general 700 $aKavzoglu$b Taskin$4edt$01288742 702 $aKavzoglu$b Taskin$4oth 906 $aBOOK 912 $a9910557148403321 996 $aArtificial Neural Networks and Evolutionary Computation in Remote Sensing$93020968 997 $aUNINA