LEADER 02051nam 2200565 a 450 001 9910455768603321 005 20200520144314.0 010 $a0-309-51079-1 035 $a(CKB)111069351123228 035 $a(SSID)ssj0000166602 035 $a(PQKBManifestationID)11161633 035 $a(PQKBTitleCode)TC0000166602 035 $a(PQKBWorkID)10161200 035 $a(PQKB)11196380 035 $a(MiAaPQ)EBC3375427 035 $a(Au-PeEL)EBL3375427 035 $a(CaPaEBR)ebr10038701 035 $a(OCoLC)923256360 035 $a(EXLCZ)99111069351123228 100 $a20011115d2001 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aGulf War veterans$b[electronic resource] $etreating symptoms and syndromes /$fCommittee on Identifying Effective Treatments for Gulf War Veterans' Health Problems, Board on Health Promotion and Disease Prevention ; Bernard M. Rosof and Lyla M. Hernandez, editors ; Institute of Medicine 210 $aWashington, D.C. $cNational Academy Press$dc2001 215 $ax, 152 p 225 1 $aThe compass series 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-309-07587-4 320 $aIncludes bibliographical references (p. 125-136). 410 0$aCompass series (Washington, D.C.) 606 $aPersian Gulf syndrome$xTreatment$zUnited States 606 $aPersian Gulf War, 1991$xHealth aspects$zUnited States 606 $aPersian Gulf War, 1991$xVeterans$xHealth and hygiene$zUnited States 608 $aElectronic books. 615 0$aPersian Gulf syndrome$xTreatment 615 0$aPersian Gulf War, 1991$xHealth aspects 615 0$aPersian Gulf War, 1991$xVeterans$xHealth and hygiene 676 $a616.9/8023 701 $aRosof$b Bernard M$01028152 701 $aHernandez$b Lyla M$0907766 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455768603321 996 $aGulf War veterans$92444020 997 $aUNINA LEADER 02888nam 2200397 450 001 9910645946003321 005 20230516112927.0 035 $a(CKB)5860000000285465 035 $a(NjHacI)995860000000285465 035 $a(EXLCZ)995860000000285465 100 $a20230516d2022 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApplications /$fedited by Katharina Morik, Jo?rg Rahnenfu?hrer, Christian Wietfeld 210 1$aBerlin :$cDe Gruyter,$d2022. 215 $a1 online resource (478 pages) $cillustrations 225 1 $aDe Gruyter STEM 300 $aIncludes index. 311 $a3-11-078614-1 330 $aMachine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel. 410 0$aDe Gruyter STEM. 606 $aInformation technology 615 0$aInformation technology. 676 $a004 702 $aRahnenfu?hrer$b Jo?rg 702 $aMorik$b Katharina 702 $aWietfeld$b Christian 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910645946003321 996 $aApplications$9879593 997 $aUNINA