LEADER 10170nam 22012735 450 001 996503570503316 005 20230103011142.0 010 $a3-11-078598-6 024 7 $a10.1515/9783110785982 035 $a(CKB)5580000000492072 035 $a(DE-B1597)617928 035 $a(DE-B1597)9783110785982 035 $a(MiAaPQ)EBC7156271 035 $a(Au-PeEL)EBL7156271 035 $a(OCoLC)1357017469 035 $a(EXLCZ)995580000000492072 100 $a20230103h20222023 fg 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning under Resource Constraints. $iApplications /$fed. by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer 205 $a1st ed. 210 1$aBerlin ;$aBoston : $cDe Gruyter, $d[2022] 210 4$d©2023 215 $a1 online resource (VIII, 470 p.) 225 0 $aDe Gruyter STEM ;$vVolume 3 311 $a3-11-078597-8 327 $tFrontmatter -- $tContents -- $t1 Editorial -- $t2 Health / Medicine -- $t2.1 Machine Learning in Medicine -- $t2.2 Virus Detection -- $t2.3 Cancer Diagnostics and Therapy from Molecular Data -- $t2.4 Bayesian Analysis for Dimensionality and Complexity Reduction -- $t2.5 Survival Prediction and Model Selection -- $t2.6 Protein Complex Similarity -- $t3 Industry 4.0 -- $t3.1 Keynote on Industry 4.0 -- $t3.2 Quality Assurance in Interlinked Manufacturing Processes -- $t3.3 Label Proportion Learning -- $t3.4 Simulation and Machine Learning -- $t3.5 High-Precision Wireless Localization -- $t3.6 Indoor Photovoltaic Energy Harvesting -- $t3.7 Micro-UAV Swarm Testbed for Indoor Applications -- $t4 Smart City and Traffic -- $t4.1 Inner-City Traffic Flow Prediction with Sparse Sensors -- $t4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows -- $t4.3 Green Networking and Resource Constrained Clients for Smart Cities -- $t4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing -- $t4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata -- $t4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms -- $t5 Communication Networks -- $t5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum -- $t5.2 Resource-Efficient Vehicle-to-Cloud Communications -- $t5.3 Mobile-Data Network Analytics Highly Reliable Networks -- $t5.4 Machine Learning-Enabled 5G Network Slicing -- $t5.5 Potential of Millimeter Wave Communications -- $t6 Privacy -- $t6.1 Keynote: Construction of Inference-Proof Agent Interactions -- $tBibliography -- $tIndex -- $tList of Contributors 330 $aMachine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel. 410 3$aDe Gruyter STEM Series 606 $aSCIENCE / Chemistry / General$2bisacsh 610 $aArtificial Intelligence. 610 $aBig Data and Machine Learning. 610 $aCyber-physical systems. 610 $aData mining for Ubiquitous System Software. 610 $aEmbedded Systems and Machine Learning. 610 $aHighly Distributed Data. 610 $aML on Small devices. 610 $aMachine learning for knowledge discovery. 610 $aMachine learning in high-energy physics. 610 $aResource-Aware Machine Learning. 610 $aResource-Constrained Data Analysis. 615 7$aSCIENCE / Chemistry / General. 676 $a006.31 702 $aArendt$b Christian, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aAwasthi$b Shrutarv, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBektas$b Caner, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBiskup$b Joachim, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBorgwardt$b Karsten, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBöcker$b Stefan, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBüscher$b Jan, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aCheng$b Liang, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aDeuse$b Jochen, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aDing$b Zeyu, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aFalkenberg$b Robert, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aFinkeldey$b Felix, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aGramse$b Nils, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aHaferkamp$b Marcus, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aHeimann$b Karsten, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aHergenröder$b Roland, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aHompel$b Michael ten, 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$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aMorik$b Katharina, $4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMunteanu$b Alexander, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aMutzel$b Petra, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aOverbeck$b Dennis, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aPanusch$b Thorben, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aRahmann$b Sven, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aRahnenführer$b Jörg, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aRahnenführer$b Jörg, $4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRan$b Ran, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aReining$b Christopher, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aRichter$b Jakob, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aRoidl$b Moritz, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aSaadallah$b Amal, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aSchramm$b Alexander, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aSchreckenberg$b Michael, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aSchäfer$b Till, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aShpacovitch$b Victoria, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aSliwa$b Benjamin, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aStolpe$b Marco, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aStöcker$b Bianca K, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aTiemann$b Janis, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aVranken$b Tim, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aWeichert$b Frank, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aWiederkehr$b Petra, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aWietfeld$b Christian, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aWietfeld$b Christian, $4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWüstefeld$b Konstantin, $4ctb$4https://id.loc.gov/vocabulary/relators/ctb 801 0$bDE-B1597 801 1$bDE-B1597 906 $aBOOK 912 $a996503570503316 996 $aMachine Learning under Resource Constraints$93011774 997 $aUNISA