LEADER 01019nam 2200349 4500 001 990000729010203316 010 $a88-15-01157-9 035 $a0072901 035 $aUSA010072901 035 $a(ALEPH)000072901USA01 035 $a0072901 100 $a20011112d1986----km-y0ENGy0103----ba 101 $aita 102 $aIT 200 1 $aOspedale e società in età liberale$fPaolo Frascani 210 $aBologna$cIl mulino$d1986 215 $a268 p.$d22 cm 225 2 $aCollana di storia contemporanea 410 $12001$aCollana di storia contemporanea 606 $aOspedali$xStoria$zSec. 19.-20. 676 $a362.11 700 1$aFRASCANI,$bPaolo$0122799 801 0$aIT$bsalbc$gISBD 912 $a990000729010203316 951 $aCOLL. HNH 1$b650 EC$cCOLL. 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[227]-256) and index. 330 $aWhen environmental health problems arise in a community, policymakers must be able to reconcile the first-hand experience of local residents with recommendations by scientists. In this highly original look at environmental health policymaking, Jason Corburn shows the ways that local knowledge can be combined with professional techniques to achieve better solutions for environmental health problems. He traces the efforts of a low-income community in Brooklyn to deal with environmental health problems in its midst and offers a framework for understanding "street science"--decision making that draws on community knowledge and contributes to environmental justice.Like many other low-income urban communities, the Greenpoint/Williamsburg neighborhood of Brooklyn suffers more than its share of environmental problems, with a concentration of polluting facilities and elevated levels of localized air pollutants. Corburn looks at four instances of street science in Greenpoint/Williamsburg, where community members and professionals combined forces to address the risks from subsistence fishing from the polluted East River, the asthma epidemic in the Latino community, childhood lead poisoning, and local sources of air pollution. These episodes highlight both the successes and the limits of street science and demonstrate ways residents can establish their own credibility when working with scientists. Street science, Corburn argues, does not devalue science; it revalues other kinds of information and democratizes the inquiry and decision making processes. 410 0$aUrban and industrial environments. 606 $aEnvironmental health$xPublic opinion 606 $aEnvironmental health$xCitizen participation 606 $aEnvironmental policy$xCitizen participation 606 $aEnvironmental justice 606 $aCommunities 610 $aARCHITECTURE/Urban Design 610 $aENVIRONMENT/Environmental Politics & Policy 615 0$aEnvironmental health$xPublic opinion. 615 0$aEnvironmental health$xCitizen participation. 615 0$aEnvironmental policy$xCitizen participation. 615 0$aEnvironmental justice. 615 0$aCommunities. 676 $a362.196/98 700 $aCorburn$b Jason$0899278 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910777569603321 996 $aStreet science$93701740 997 $aUNINA LEADER 04753nam 22007935 450 001 9910731481703321 005 20251008163501.0 010 $a9789819921157 010 $a9819921155 024 7 $a10.1007/978-981-99-2115-7 035 $a(CKB)26946777000041 035 $a(MiAaPQ)EBC30593528 035 $a(Au-PeEL)EBL30593528 035 $a(DE-He213)978-981-99-2115-7 035 $a(PPN)272263745 035 $a(EXLCZ)9926946777000041 100 $a20230613d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence and Cyber Security in Industry 4.0 /$fedited by Velliangiri Sarveshwaran, Joy Iong-Zong Chen, Danilo Pelusi 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (374 pages) 225 1 $aAdvanced Technologies and Societal Change,$x2191-6861 311 08$a9789819921140 311 08$a9819921147 327 $aIntroduction to Artificial Intelligence and Cyber Security for Industry -- Role of AI and its impact on the development of cyber security applications -- AI and IoT in Manufacturing and related Security Perspectives for Industry 4.0 -- IoT Security Vulnerabilities and Defensive Measures in Industry 4.0 -- Adopting Artificial Intelligence in ITIL for Information Security Management - Way forward in Industry 4.0 -- Intelligent Autonomous Drones in Industry 4.0 -- A review on automatic generation of attack trees and its application to automotive cybersecurity -- Malware Analysis using Machine Learning Tools and Techniques in IT Industry -- USE OF MACHINE LEARNING IN FORENSICS AND COMPUTER SECURITY -- Control of feed drives in CNC machine tools using artificial immune adaptive strategy -- Efficient Anomaly Detection for Empowering Cyber Security by Using Adaptive Deep Learning Model -- Intrusion Detection in IoT based Healthcare Using ML and DL approaches: A Case Study -- War Strategy Algorithm based GAN model for Detecting the Malware Attacks in Modern Digital Age -- ML algorithms for providing financial security in banking sectors with the prediction of loan risks -- Machine Learning based DDoS Attack Detection using Support Vector Machine -- Artificial Intelligence based Cyber Security Applications. 330 $aThis book provides theoretical background and state-of-the-art findings in artificial intelligence and cybersecurity for industry 4.0 and helps in implementing AI-based cybersecurity applications. Machine learning-based security approaches are vulnerable to poison datasets which can be caused by a legitimate defender's misclassification or attackers aiming to evade detection by contaminating the training data set. There also exist gaps between the test environment and the real world. Therefore, it is critical to check the potentials and limitations of AI-based security technologies in terms of metrics such as security, performance, cost, time, and consider how to incorporate them into the real world by addressing the gaps appropriately. This book focuses on state-of-the-art findings from both academia and industry in big data security relevant sciences, technologies, and applications. 410 0$aAdvanced Technologies and Societal Change,$x2191-6861 606 $aArtificial intelligence 606 $aInternet of things 606 $aBig data 606 $aMachine learning 606 $aComputational intelligence 606 $aWireless communication systems 606 $aMobile communication systems 606 $aArtificial Intelligence 606 $aInternet of Things 606 $aBig Data 606 $aMachine Learning 606 $aComputational Intelligence 606 $aWireless and Mobile Communication 615 0$aArtificial intelligence. 615 0$aInternet of things. 615 0$aBig data. 615 0$aMachine learning. 615 0$aComputational intelligence. 615 0$aWireless communication systems. 615 0$aMobile communication systems. 615 14$aArtificial Intelligence. 615 24$aInternet of Things. 615 24$aBig Data. 615 24$aMachine Learning. 615 24$aComputational Intelligence. 615 24$aWireless and Mobile Communication. 676 $a658.4038028563 700 $aSarveshwaran$b Velliangiri$01368935 701 $aChen$b Joy Iong-zong$01362048 701 $aPelusi$b Danilo$01368936 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910731481703321 996 $aArtificial Intelligence and Cyber Security in Industry 4.0$93561459 997 $aUNINA