LEADER 02786oam 2200769 450 001 9910712014403321 005 20190412133348.0 035 $a(CKB)5470000002489962 035 $a(OCoLC)963246714 035 $a(EXLCZ)995470000002489962 100 $a20161118d1964 ua 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aQuality of surface waters of the United States, 1962$hParts 3-4$iOhio River basin and St. Lawrence River basin /$fprepared under the direction of S.K. Love 210 1$a[Washington, D.C.] :$cUnited States Department of the Interior, Geological Survey,$d1964. 210 2$aWashington :$cUnited States Government Printing Office. 215 $a1 online resource (x, 322 pages) $cmap 225 1 $aGeological Survey water-supply paper ;$v1942 300 $aPrepared in cooperation with the States of Georgia, Illinois, Indiana, Kentucky, Minnesota, New York, North Carolina, Ohio, Pennsylvania, West Virginia, and with other agencies. 320 $aIncludes bibliographical references (pages 30-31) and index. 517 3 $aOhio River basin and St. Lawrence River basin 606 $aWater quality$zNortheastern States$vStatistics 606 $aWater quality$zOhio River Watershed$vStatistics 606 $aWater quality$zSaint Lawrence River Watershed$vStatistics 606 $aWater-supply$zNortheastern States$vStatistics 606 $aWater-supply$zOhio River Watershed$vStatistics 606 $aWater-supply$zSaint Lawrence River Watershed$vStatistics 606 $aWater quality$2fast 606 $aWater-supply$2fast 607 $aNorth America$zSaint Lawrence River Watershed$2fast 607 $aNortheastern States$2fast 607 $aOhio River Watershed$2fast 608 $aStatistics.$2fast 608 $aStatistics.$2lcgft 615 0$aWater quality 615 0$aWater quality 615 0$aWater quality 615 0$aWater-supply 615 0$aWater-supply 615 0$aWater-supply 615 7$aWater quality. 615 7$aWater-supply. 700 $aLove$b S. K$g(Samuel Kenneth),$f1903-1995,$01385984 712 02$aGeological Survey (U.S.), 712 02$aGeorgia. 712 02$aIllinois. 712 02$aIndiana. 712 02$aKentucky. 712 02$aMinnesota. 712 02$aNew York (State) 712 02$aNorth Carolina. 712 02$aOhio. 712 02$aPennsylvania. 712 02$aWest Virginia. 801 0$bCOP 801 1$bCOP 801 2$bOCLCO 801 2$bOCLCF 801 2$bOCLCA 801 2$bGPO 906 $aBOOK 912 $a9910712014403321 996 $aQuality of surface waters of the United States, 1962$93434520 997 $aUNINA LEADER 04213nam 22007215 450 001 9910632476503321 005 20230810180303.0 010 $a3-031-16237-4 024 7 $a10.1007/978-3-031-16237-4 035 $a(MiAaPQ)EBC7145519 035 $a(Au-PeEL)EBL7145519 035 $a(CKB)25456506400041 035 $a(DE-He213)978-3-031-16237-4 035 $a(PPN)266353487 035 $a(EXLCZ)9925456506400041 100 $a20221123d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence for Cyber-Physical Systems Hardening /$fedited by Issa Traore, Isaac Woungang, Sherif Saad 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (241 pages) 225 1 $aEngineering Cyber-Physical Systems and Critical Infrastructures,$x2731-5010 ;$v2 311 08$aPrint version: Traore, Issa Artificial Intelligence for Cyber-Physical Systems Hardening Cham : Springer International Publishing AG,c2022 9783031162367 320 $aIncludes bibliographical references. 327 $aIntroduction -- Machine Learning Construction: implications to cybersecurity -- Machine Learning Assessment: implications to cybersecurity -- A Collection of Datasets for Intrusion Detection in MIL-STD-1553 Platforms -- Unsupervised Anomaly Detection for MIL-STD-1553 Avionic Platforms using CUSUM -- Secure Design of Cyber-Physical Systems at the Radio Frequency Level: Machine and Deep Learning-Driven Approaches, Challenges and Opportunities -- Attack Detection by Using Deep Learning for Cyber-Physical System -- Security and privacy of IoT devices for ageing in place -- Detecting Malicious Attacks Using Principal Component Analysis in Medical Cyber-Physical Systems -- Activity and Event Network Graph and Application to Cyberphysical Security. 330 $aThis book presents advances in security assurance for cyber-physical systems (CPS) and report on new machine learning (ML) and artificial intelligence (AI) approaches and technologies developed by the research community and the industry to address the challenges faced by this emerging field. Cyber-physical systems bridge the divide between cyber and physical-mechanical systems by combining seamlessly software systems, sensors, and actuators connected over computer networks. Through these sensors, data about the physical world can be captured and used for smart autonomous decision-making. This book introduces fundamental AI/ML principles and concepts applied in developing secure and trustworthy CPS, disseminates recent research and development efforts in this fascinating area, and presents relevant case studies, examples, and datasets. We believe that it is a valuable reference for students, instructors, researchers, industry practitioners, and related government agencies staff. 410 0$aEngineering Cyber-Physical Systems and Critical Infrastructures,$x2731-5010 ;$v2 606 $aCooperating objects (Computer systems) 606 $aEngineering$xData processing 606 $aComputational intelligence 606 $aBig data 606 $aArtificial intelligence 606 $aCyber-Physical Systems 606 $aData Engineering 606 $aComputational Intelligence 606 $aBig Data 606 $aArtificial Intelligence 615 0$aCooperating objects (Computer systems) 615 0$aEngineering$xData processing. 615 0$aComputational intelligence. 615 0$aBig data. 615 0$aArtificial intelligence. 615 14$aCyber-Physical Systems. 615 24$aData Engineering. 615 24$aComputational Intelligence. 615 24$aBig Data. 615 24$aArtificial Intelligence. 676 $a060 676 $a006.3 702 $aTraore?$b Issa Baba 702 $aWoungang$b Isaac 702 $aSaad$b Sherif 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910632476503321 996 $aArtificial intelligence for cyber-physical systems hardening$93085455 997 $aUNINA