Machine Learning under Resource Constraints : Applications
Machine 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 a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Morik, Katharina [herausgeberIn] Rahnenführer, Jörg [herausgeberIn] Wietfeld, Christian [herausgeberIn] Arendt, Christian [mitwirkender] Awasthi, Shrutarv [mitwirkender] Bektas, Caner [mitwirkender] Biskup, Joachim [mitwirkender] Borgwardt, Karsten [mitwirkender] Böcker, Stefan [mitwirkender] Büscher, Jan [mitwirkender] Cheng, Liang [mitwirkender] Deuse, Jochen [mitwirkender] Ding, Zeyu [mitwirkender] Falkenberg, Robert [mitwirkender] Finkeldey, Felix [mitwirkender] Gramse, Nils [mitwirkender] Haferkamp, Marcus [mitwirkender] Heimann, Karsten [mitwirkender] Hergenröder, Roland [mitwirkender] Hompel, Michael ten [mitwirkender] Häger, Simon [mitwirkender] Ickstadt, Katja [mitwirkender] Jutzeler, Catherine [mitwirkender] Jörke, Pascal [mitwirkender] Kriege, Nils [mitwirkender] Krieger, Cedrik [mitwirkender] Kurtz, Fabian [mitwirkender] Köster, Johannes [mitwirkender] Lang, Michel [mitwirkender] Liebig, Thomas [mitwirkender] Machado, Maximillian [mitwirkender] Masoudinejad, Mojtaba [mitwirkender] Munteanu, Alexander [mitwirkender] Mutzel, Petra [mitwirkender] Overbeck, Dennis [mitwirkender] Panusch, Thorben [mitwirkender] Rahmann, Sven [mitwirkender] Ran, Ran [mitwirkender] Reining, Christopher [mitwirkender] Richter, Jakob [mitwirkender] Roidl, Moritz [mitwirkender] Saadallah, Amal [mitwirkender] Schramm, Alexander [mitwirkender] Schreckenberg, Michael [mitwirkender] Schäfer, Till [mitwirkender] Shpacovitch, Victoria [mitwirkender] Sliwa, Benjamin [mitwirkender] Stolpe, Marco [mitwirkender] Stöcker, Bianca K [mitwirkender] Tiemann, Janis [mitwirkender] Vranken, Tim [mitwirkender] Weichert, Frank [mitwirkender] Wiederkehr, Petra [mitwirkender] Wüstefeld, Konstantin [mitwirkender] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Berlin Boston: De Gruyter ; 2022 ©2023 |
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| Rechteinformationen: |
Open Access Namensnennung 4.0 International ; CC BY 4.0 |
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| Systematik: |
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| Umfang: |
1 Online-Ressource (VIII, 470 Seiten) |
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| Weitere Ausgabe: |
Erscheint auch als Druck-Ausgabe: Machine learning under resource constraints ; Volume 3: Applications - Berlin : De Gruyter, 2023 |
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| Reihe: |
Machine Learning under Resource Constraints ; Volume 3 De Gruyter STEM |
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| Links: | |
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| ISBN: |
978-3-11-078598-2 978-3-11-078614-9 |
| DOI / URN: |
10.1515/9783110785982 |
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| Katalog-ID: |
1831669501 |
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| 245 | 1 | 0 | |a Machine Learning under Resource Constraints |b Applications |c edited by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer |
| 264 | 1 | |a Berlin |a Boston |b De Gruyter |c 2022 | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 Online-Ressource (VIII, 470 Seiten) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Machine Learning under Resource Constraints |v Volume 3 | |
| 490 | 0 | |a De Gruyter STEM | |
| 505 | 8 | 0 | |t Frontmatter |
| 505 | 8 | 0 | |t Contents |
| 505 | 8 | 0 | |t 1 Editorial |
| 505 | 8 | 0 | |t 2 Health / Medicine |
| 505 | 8 | 0 | |t 2.1 Machine Learning in Medicine |
| 505 | 8 | 0 | |t 2.2 Virus Detection |
| 505 | 8 | 0 | |t 2.3 Cancer Diagnostics and Therapy from Molecular Data |
| 505 | 8 | 0 | |t 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction |
| 505 | 8 | 0 | |t 2.5 Survival Prediction and Model Selection |
| 505 | 8 | 0 | |t 2.6 Protein Complex Similarity |
| 505 | 8 | 0 | |t 3 Industry 4.0 |
| 505 | 8 | 0 | |t 3.1 Keynote on Industry 4.0 |
| 505 | 8 | 0 | |t 3.2 Quality Assurance in Interlinked Manufacturing Processes |
| 505 | 8 | 0 | |t 3.3 Label Proportion Learning |
| 505 | 8 | 0 | |t 3.4 Simulation and Machine Learning |
| 505 | 8 | 0 | |t 3.5 High-Precision Wireless Localization |
| 505 | 8 | 0 | |t 3.6 Indoor Photovoltaic Energy Harvesting |
| 505 | 8 | 0 | |t 3.7 Micro-UAV Swarm Testbed for Indoor Applications |
| 505 | 8 | 0 | |t 4 Smart City and Traffic |
| 505 | 8 | 0 | |t 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors |
| 505 | 8 | 0 | |t 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows |
| 505 | 8 | 0 | |t 4.3 Green Networking and Resource Constrained Clients for Smart Cities |
| 505 | 8 | 0 | |t 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing |
| 505 | 8 | 0 | |t 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata |
| 505 | 8 | 0 | |t 4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms |
| 505 | 8 | 0 | |t 5 Communication Networks |
| 505 | 8 | 0 | |t 5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum |
| 505 | 8 | 0 | |t 5.2 Resource-Efficient Vehicle-to-Cloud Communications |
| 505 | 8 | 0 | |t 5.3 Mobile-Data Network Analytics Highly Reliable Networks |
| 505 | 8 | 0 | |t 5.4 Machine Learning-Enabled 5G Network Slicing |
| 505 | 8 | 0 | |t 5.5 Potential of Millimeter Wave Communications |
| 505 | 8 | 0 | |t 6 Privacy |
| 505 | 8 | 0 | |t 6.1 Keynote: Construction of Inference-Proof Agent Interactions |
| 505 | 8 | 0 | |t Bibliography |
| 505 | 8 | 0 | |t Index |
| 505 | 8 | 0 | |t List of Contributors |
| 506 | 0 | |a Open Access |e Controlled Vocabulary for Access Rights |u https://purl.org/coar/access_right/c_abf2 |f unrestricted online access |2 star | |
| 520 | |a Machine 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 | ||
| 530 | |a Issued also in print | ||
| 533 | |n Issued also in print | ||
| 540 | |a Namensnennung 4.0 International |f CC BY 4.0 |2 cc |u https://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a In English | ||
| 650 | 4 | |a Computer aided manufacture (CAM) | |
| 650 | 4 | |a Computeranwendungen in Industrie und Technologie | |
| 650 | 4 | |a Data Mining | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Information technology: general issues | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Maschinelles Lernen | |
| 650 | 4 | |a Praktische Anwendung für Informationstechnologien | |
| 650 | 4 | |a SCIENCE / Chemistry / General | |
| 653 | |a Artificial Intelligence | ||
| 653 | |a Big Data and Machine Learning | ||
| 653 | |a Cyber-physical systems | ||
| 653 | |a Data mining for Ubiquitous System Software | ||
| 653 | |a Embedded Systems and Machine Learning | ||
| 653 | |a Highly Distributed Data | ||
| 653 | |a ML on Small devices | ||
| 653 | |a Machine learning for knowledge discovery | ||
| 653 | |a Machine learning in high-energy physics | ||
| 653 | |a Resource-Aware Machine Learning | ||
| 653 | |a Resource-Constrained Data Analysis | ||
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9783110785982 PDF 978-3-11-078598-2 9783110786149 EPUB 978-3-11-078614-9 10.1515/9783110785982 doi (DE-627)1831669501 (DE-599)KEP08380689X (OCoLC)1433066834 (DE-B1597)617928 (EBP)08380689X DE-627 ger DE-627 rda eng XA-DE-BE XD-US SCI013000 bisacsh COM032000;BISACSCI013000 bisacsh ST 300 DE-Ofb1/23 rvk (DE-625)rvk/143650: 54.72 bkl 54.80 bkl Machine Learning under Resource Constraints Applications edited by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer Berlin Boston De Gruyter 2022 ©2023 1 Online-Ressource (VIII, 470 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine Learning under Resource Constraints Volume 3 De Gruyter STEM Frontmatter Contents 1 Editorial 2 Health / Medicine 2.1 Machine Learning in Medicine 2.2 Virus Detection 2.3 Cancer Diagnostics and Therapy from Molecular Data 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction 2.5 Survival Prediction and Model Selection 2.6 Protein Complex Similarity 3 Industry 4.0 3.1 Keynote on Industry 4.0 3.2 Quality Assurance in Interlinked Manufacturing Processes 3.3 Label Proportion Learning 3.4 Simulation and Machine Learning 3.5 High-Precision Wireless Localization 3.6 Indoor Photovoltaic Energy Harvesting 3.7 Micro-UAV Swarm Testbed for Indoor Applications 4 Smart City and Traffic 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows 4.3 Green Networking and Resource Constrained Clients for Smart Cities 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata 4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms 5 Communication Networks 5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum 5.2 Resource-Efficient Vehicle-to-Cloud Communications 5.3 Mobile-Data Network Analytics Highly Reliable Networks 5.4 Machine Learning-Enabled 5G Network Slicing 5.5 Potential of Millimeter Wave Communications 6 Privacy 6.1 Keynote: Construction of Inference-Proof Agent Interactions Bibliography Index List of Contributors Open Access Controlled Vocabulary for Access Rights https://purl.org/coar/access_right/c_abf2 unrestricted online access star Machine 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 Issued also in print Issued also in print Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ In English Computer aided manufacture (CAM) Computeranwendungen in Industrie und Technologie Data Mining Data mining Information technology: general issues Machine learning Maschinelles Lernen Praktische Anwendung für Informationstechnologien SCIENCE / Chemistry / General Artificial Intelligence Big Data and Machine Learning Cyber-physical systems Data mining for Ubiquitous System Software Embedded Systems and Machine Learning Highly Distributed Data ML on Small devices Machine learning for knowledge discovery Machine learning in high-energy physics Resource-Aware Machine Learning Resource-Constrained Data Analysis s (DE-588)4033447-8 (DE-627)106257188 (DE-576)209002050 Künstliche Intelligenz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4396978-1 (DE-627)191533580 (DE-576)212005545 Eingebettetes System gnd s (DE-588)4802620-7 (DE-627)472310364 (DE-576)216543657 Big Data gnd (DE-627) Morik, Katharina herausgeberin edt Rahnenführer, Jörg herausgeberin edt Wietfeld, Christian herausgeberin edt Arendt, Christian mitwirkender ctb Awasthi, Shrutarv mitwirkender ctb Bektas, Caner mitwirkender ctb Biskup, Joachim mitwirkender ctb Borgwardt, Karsten mitwirkender ctb Böcker, Stefan mitwirkender ctb Büscher, Jan mitwirkender ctb Cheng, Liang mitwirkender ctb Deuse, Jochen mitwirkender ctb Ding, Zeyu mitwirkender ctb Falkenberg, Robert mitwirkender ctb Finkeldey, Felix mitwirkender ctb Gramse, Nils mitwirkender ctb Haferkamp, Marcus mitwirkender ctb Heimann, Karsten mitwirkender ctb Hergenröder, Roland mitwirkender ctb Hompel, Michael ten mitwirkender ctb Häger, Simon mitwirkender ctb Ickstadt, Katja mitwirkender ctb Jutzeler, Catherine mitwirkender ctb Jörke, Pascal mitwirkender ctb Kriege, Nils mitwirkender ctb Krieger, Cedrik mitwirkender ctb Kurtz, Fabian mitwirkender ctb Köster, Johannes mitwirkender ctb Lang, Michel mitwirkender ctb Liebig, Thomas mitwirkender ctb Machado, Maximillian mitwirkender ctb Masoudinejad, Mojtaba mitwirkender ctb Munteanu, Alexander mitwirkender ctb Mutzel, Petra mitwirkender ctb Overbeck, Dennis mitwirkender ctb Panusch, Thorben mitwirkender ctb Rahmann, Sven mitwirkender ctb Ran, Ran mitwirkender ctb Reining, Christopher mitwirkender ctb Richter, Jakob mitwirkender ctb Roidl, Moritz mitwirkender ctb Saadallah, Amal mitwirkender ctb Schramm, Alexander mitwirkender ctb Schreckenberg, Michael mitwirkender ctb Schäfer, Till mitwirkender ctb Shpacovitch, Victoria mitwirkender ctb Sliwa, Benjamin mitwirkender ctb Stolpe, Marco mitwirkender ctb Stöcker, Bianca K mitwirkender ctb Tiemann, Janis mitwirkender ctb Vranken, Tim mitwirkender ctb Weichert, Frank mitwirkender ctb Wiederkehr, Petra mitwirkender ctb Wüstefeld, Konstantin mitwirkender ctb 9783110785975 Erscheint auch als Druck-Ausgabe Machine learning under resource constraints ; 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9783110785982 PDF 978-3-11-078598-2 9783110786149 EPUB 978-3-11-078614-9 10.1515/9783110785982 doi (DE-627)1831669501 (DE-599)KEP08380689X (OCoLC)1433066834 (DE-B1597)617928 (EBP)08380689X DE-627 ger DE-627 rda eng XA-DE-BE XD-US SCI013000 bisacsh COM032000;BISACSCI013000 bisacsh ST 300 DE-Ofb1/23 rvk (DE-625)rvk/143650: 54.72 bkl 54.80 bkl Machine Learning under Resource Constraints Applications edited by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer Berlin Boston De Gruyter 2022 ©2023 1 Online-Ressource (VIII, 470 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine Learning under Resource Constraints Volume 3 De Gruyter STEM Frontmatter Contents 1 Editorial 2 Health / Medicine 2.1 Machine Learning in Medicine 2.2 Virus Detection 2.3 Cancer Diagnostics and Therapy from Molecular Data 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction 2.5 Survival Prediction and Model Selection 2.6 Protein Complex Similarity 3 Industry 4.0 3.1 Keynote on Industry 4.0 3.2 Quality Assurance in Interlinked Manufacturing Processes 3.3 Label Proportion Learning 3.4 Simulation and Machine Learning 3.5 High-Precision Wireless Localization 3.6 Indoor Photovoltaic Energy Harvesting 3.7 Micro-UAV Swarm Testbed for Indoor Applications 4 Smart City and Traffic 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows 4.3 Green Networking and Resource Constrained Clients for Smart Cities 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata 4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms 5 Communication Networks 5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum 5.2 Resource-Efficient Vehicle-to-Cloud Communications 5.3 Mobile-Data Network Analytics Highly Reliable Networks 5.4 Machine Learning-Enabled 5G Network Slicing 5.5 Potential of Millimeter Wave Communications 6 Privacy 6.1 Keynote: Construction of Inference-Proof Agent Interactions Bibliography Index List of Contributors Open Access Controlled Vocabulary for Access Rights https://purl.org/coar/access_right/c_abf2 unrestricted online access star Machine 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 Issued also in print Issued also in print Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ In English Computer aided manufacture (CAM) Computeranwendungen in Industrie und Technologie Data Mining Data mining Information technology: general issues Machine learning Maschinelles Lernen Praktische Anwendung für Informationstechnologien SCIENCE / Chemistry / General Artificial Intelligence Big Data and Machine Learning Cyber-physical systems Data mining for Ubiquitous System Software Embedded Systems and Machine Learning Highly Distributed Data ML on Small devices Machine learning for knowledge discovery Machine learning in high-energy physics Resource-Aware Machine Learning Resource-Constrained Data Analysis s (DE-588)4033447-8 (DE-627)106257188 (DE-576)209002050 Künstliche Intelligenz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4396978-1 (DE-627)191533580 (DE-576)212005545 Eingebettetes System gnd s (DE-588)4802620-7 (DE-627)472310364 (DE-576)216543657 Big Data gnd (DE-627) Morik, Katharina herausgeberin edt Rahnenführer, Jörg herausgeberin edt Wietfeld, Christian herausgeberin edt Arendt, Christian mitwirkender ctb Awasthi, Shrutarv mitwirkender ctb Bektas, Caner mitwirkender ctb Biskup, Joachim mitwirkender ctb Borgwardt, Karsten mitwirkender ctb Böcker, Stefan mitwirkender ctb Büscher, Jan mitwirkender ctb Cheng, Liang mitwirkender ctb Deuse, Jochen mitwirkender ctb Ding, Zeyu mitwirkender ctb Falkenberg, Robert mitwirkender ctb Finkeldey, Felix mitwirkender ctb Gramse, Nils mitwirkender ctb Haferkamp, Marcus mitwirkender ctb Heimann, Karsten mitwirkender ctb Hergenröder, Roland mitwirkender ctb Hompel, Michael ten mitwirkender ctb Häger, Simon mitwirkender ctb Ickstadt, Katja mitwirkender ctb Jutzeler, Catherine mitwirkender ctb Jörke, Pascal mitwirkender ctb Kriege, Nils mitwirkender ctb Krieger, Cedrik mitwirkender ctb Kurtz, Fabian mitwirkender ctb Köster, Johannes mitwirkender ctb Lang, Michel mitwirkender ctb Liebig, Thomas mitwirkender ctb Machado, Maximillian mitwirkender ctb Masoudinejad, Mojtaba mitwirkender ctb Munteanu, Alexander mitwirkender ctb Mutzel, Petra mitwirkender ctb Overbeck, Dennis mitwirkender ctb Panusch, Thorben mitwirkender ctb Rahmann, Sven mitwirkender ctb Ran, Ran mitwirkender ctb Reining, Christopher mitwirkender ctb Richter, Jakob mitwirkender ctb Roidl, Moritz mitwirkender ctb Saadallah, Amal mitwirkender ctb Schramm, Alexander mitwirkender ctb Schreckenberg, Michael mitwirkender ctb Schäfer, Till mitwirkender ctb Shpacovitch, Victoria mitwirkender ctb Sliwa, Benjamin mitwirkender ctb Stolpe, Marco mitwirkender ctb Stöcker, Bianca K mitwirkender ctb Tiemann, Janis mitwirkender ctb Vranken, Tim mitwirkender ctb Weichert, Frank mitwirkender ctb Wiederkehr, Petra mitwirkender ctb Wüstefeld, Konstantin mitwirkender ctb 9783110785975 Erscheint auch als Druck-Ausgabe Machine learning under resource constraints ; 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9783110785982 PDF 978-3-11-078598-2 9783110786149 EPUB 978-3-11-078614-9 10.1515/9783110785982 doi (DE-627)1831669501 (DE-599)KEP08380689X (OCoLC)1433066834 (DE-B1597)617928 (EBP)08380689X DE-627 ger DE-627 rda eng XA-DE-BE XD-US SCI013000 bisacsh COM032000;BISACSCI013000 bisacsh ST 300 DE-Ofb1/23 rvk (DE-625)rvk/143650: 54.72 bkl 54.80 bkl Machine Learning under Resource Constraints Applications edited by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer Berlin Boston De Gruyter 2022 ©2023 1 Online-Ressource (VIII, 470 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine Learning under Resource Constraints Volume 3 De Gruyter STEM Frontmatter Contents 1 Editorial 2 Health / Medicine 2.1 Machine Learning in Medicine 2.2 Virus Detection 2.3 Cancer Diagnostics and Therapy from Molecular Data 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction 2.5 Survival Prediction and Model Selection 2.6 Protein Complex Similarity 3 Industry 4.0 3.1 Keynote on Industry 4.0 3.2 Quality Assurance in Interlinked Manufacturing Processes 3.3 Label Proportion Learning 3.4 Simulation and Machine Learning 3.5 High-Precision Wireless Localization 3.6 Indoor Photovoltaic Energy Harvesting 3.7 Micro-UAV Swarm Testbed for Indoor Applications 4 Smart City and Traffic 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows 4.3 Green Networking and Resource Constrained Clients for Smart Cities 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata 4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms 5 Communication Networks 5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum 5.2 Resource-Efficient Vehicle-to-Cloud Communications 5.3 Mobile-Data Network Analytics Highly Reliable Networks 5.4 Machine Learning-Enabled 5G Network Slicing 5.5 Potential of Millimeter Wave Communications 6 Privacy 6.1 Keynote: Construction of Inference-Proof Agent Interactions Bibliography Index List of Contributors Open Access Controlled Vocabulary for Access Rights https://purl.org/coar/access_right/c_abf2 unrestricted online access star Machine 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 Issued also in print Issued also in print Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ In English Computer aided manufacture (CAM) Computeranwendungen in Industrie und Technologie Data Mining Data mining Information technology: general issues Machine learning Maschinelles Lernen Praktische Anwendung für Informationstechnologien SCIENCE / Chemistry / General Artificial Intelligence Big Data and Machine Learning Cyber-physical systems Data mining for Ubiquitous System Software Embedded Systems and Machine Learning Highly Distributed Data ML on Small devices Machine learning for knowledge discovery Machine learning in high-energy physics Resource-Aware Machine Learning Resource-Constrained Data Analysis s (DE-588)4033447-8 (DE-627)106257188 (DE-576)209002050 Künstliche Intelligenz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4396978-1 (DE-627)191533580 (DE-576)212005545 Eingebettetes System gnd s (DE-588)4802620-7 (DE-627)472310364 (DE-576)216543657 Big Data gnd (DE-627) Morik, Katharina herausgeberin edt Rahnenführer, Jörg herausgeberin edt Wietfeld, Christian herausgeberin edt Arendt, Christian mitwirkender ctb Awasthi, Shrutarv mitwirkender ctb Bektas, Caner mitwirkender ctb Biskup, Joachim mitwirkender ctb Borgwardt, Karsten mitwirkender ctb Böcker, Stefan mitwirkender ctb Büscher, Jan mitwirkender ctb Cheng, Liang mitwirkender ctb Deuse, Jochen mitwirkender ctb Ding, Zeyu mitwirkender ctb Falkenberg, Robert mitwirkender ctb Finkeldey, Felix mitwirkender ctb Gramse, Nils mitwirkender ctb Haferkamp, Marcus mitwirkender ctb Heimann, Karsten mitwirkender ctb Hergenröder, Roland mitwirkender ctb Hompel, Michael ten mitwirkender ctb Häger, Simon mitwirkender ctb Ickstadt, Katja mitwirkender ctb Jutzeler, Catherine mitwirkender ctb Jörke, Pascal mitwirkender ctb Kriege, Nils mitwirkender ctb Krieger, Cedrik mitwirkender ctb Kurtz, Fabian mitwirkender ctb Köster, Johannes mitwirkender ctb Lang, Michel mitwirkender ctb Liebig, Thomas mitwirkender ctb Machado, Maximillian mitwirkender ctb Masoudinejad, Mojtaba mitwirkender ctb Munteanu, Alexander mitwirkender ctb Mutzel, Petra mitwirkender ctb Overbeck, Dennis mitwirkender ctb Panusch, Thorben mitwirkender ctb Rahmann, Sven mitwirkender ctb Ran, Ran mitwirkender ctb Reining, Christopher mitwirkender ctb Richter, Jakob mitwirkender ctb Roidl, Moritz mitwirkender ctb Saadallah, Amal mitwirkender ctb Schramm, Alexander mitwirkender ctb Schreckenberg, Michael mitwirkender ctb Schäfer, Till mitwirkender ctb Shpacovitch, Victoria mitwirkender ctb Sliwa, Benjamin mitwirkender ctb Stolpe, Marco mitwirkender ctb Stöcker, Bianca K mitwirkender ctb Tiemann, Janis mitwirkender ctb Vranken, Tim mitwirkender ctb Weichert, Frank mitwirkender ctb Wiederkehr, Petra mitwirkender ctb Wüstefeld, Konstantin mitwirkender ctb 9783110785975 Erscheint auch als Druck-Ausgabe Machine learning under resource constraints ; 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9783110785982 PDF 978-3-11-078598-2 9783110786149 EPUB 978-3-11-078614-9 10.1515/9783110785982 doi (DE-627)1831669501 (DE-599)KEP08380689X (OCoLC)1433066834 (DE-B1597)617928 (EBP)08380689X DE-627 ger DE-627 rda eng XA-DE-BE XD-US SCI013000 bisacsh COM032000;BISACSCI013000 bisacsh ST 300 DE-Ofb1/23 rvk (DE-625)rvk/143650: 54.72 bkl 54.80 bkl Machine Learning under Resource Constraints Applications edited by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer Berlin Boston De Gruyter 2022 ©2023 1 Online-Ressource (VIII, 470 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine Learning under Resource Constraints Volume 3 De Gruyter STEM Frontmatter Contents 1 Editorial 2 Health / Medicine 2.1 Machine Learning in Medicine 2.2 Virus Detection 2.3 Cancer Diagnostics and Therapy from Molecular Data 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction 2.5 Survival Prediction and Model Selection 2.6 Protein Complex Similarity 3 Industry 4.0 3.1 Keynote on Industry 4.0 3.2 Quality Assurance in Interlinked Manufacturing Processes 3.3 Label Proportion Learning 3.4 Simulation and Machine Learning 3.5 High-Precision Wireless Localization 3.6 Indoor Photovoltaic Energy Harvesting 3.7 Micro-UAV Swarm Testbed for Indoor Applications 4 Smart City and Traffic 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows 4.3 Green Networking and Resource Constrained Clients for Smart Cities 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata 4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms 5 Communication Networks 5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum 5.2 Resource-Efficient Vehicle-to-Cloud Communications 5.3 Mobile-Data Network Analytics Highly Reliable Networks 5.4 Machine Learning-Enabled 5G Network Slicing 5.5 Potential of Millimeter Wave Communications 6 Privacy 6.1 Keynote: Construction of Inference-Proof Agent Interactions Bibliography Index List of Contributors Open Access Controlled Vocabulary for Access Rights https://purl.org/coar/access_right/c_abf2 unrestricted online access star Machine 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 Issued also in print Issued also in print Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ In English Computer aided manufacture (CAM) Computeranwendungen in Industrie und Technologie Data Mining Data mining Information technology: general issues Machine learning Maschinelles Lernen Praktische Anwendung für Informationstechnologien SCIENCE / Chemistry / General Artificial Intelligence Big Data and Machine Learning Cyber-physical systems Data mining for Ubiquitous System Software Embedded Systems and Machine Learning Highly Distributed Data ML on Small devices Machine learning for knowledge discovery Machine learning in high-energy physics Resource-Aware Machine Learning Resource-Constrained Data Analysis s (DE-588)4033447-8 (DE-627)106257188 (DE-576)209002050 Künstliche Intelligenz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4396978-1 (DE-627)191533580 (DE-576)212005545 Eingebettetes System gnd s (DE-588)4802620-7 (DE-627)472310364 (DE-576)216543657 Big Data gnd (DE-627) Morik, Katharina herausgeberin edt Rahnenführer, Jörg herausgeberin edt Wietfeld, Christian herausgeberin edt Arendt, Christian mitwirkender ctb Awasthi, Shrutarv mitwirkender ctb Bektas, Caner mitwirkender ctb Biskup, Joachim mitwirkender ctb Borgwardt, Karsten mitwirkender ctb Böcker, Stefan mitwirkender ctb Büscher, Jan mitwirkender ctb Cheng, Liang mitwirkender ctb Deuse, Jochen mitwirkender ctb Ding, Zeyu mitwirkender ctb Falkenberg, Robert mitwirkender ctb Finkeldey, Felix mitwirkender ctb Gramse, Nils mitwirkender ctb Haferkamp, Marcus mitwirkender ctb Heimann, Karsten mitwirkender ctb Hergenröder, Roland mitwirkender ctb Hompel, Michael ten mitwirkender ctb Häger, Simon mitwirkender ctb Ickstadt, Katja mitwirkender ctb Jutzeler, Catherine mitwirkender ctb Jörke, Pascal mitwirkender ctb Kriege, Nils mitwirkender ctb Krieger, Cedrik mitwirkender ctb Kurtz, Fabian mitwirkender ctb Köster, Johannes mitwirkender ctb Lang, Michel mitwirkender ctb Liebig, Thomas mitwirkender ctb Machado, Maximillian mitwirkender ctb Masoudinejad, Mojtaba mitwirkender ctb Munteanu, Alexander mitwirkender ctb Mutzel, Petra mitwirkender ctb Overbeck, Dennis mitwirkender ctb Panusch, Thorben mitwirkender ctb Rahmann, Sven mitwirkender ctb Ran, Ran mitwirkender ctb Reining, Christopher mitwirkender ctb Richter, Jakob mitwirkender ctb Roidl, Moritz mitwirkender ctb Saadallah, Amal mitwirkender ctb Schramm, Alexander mitwirkender ctb Schreckenberg, Michael mitwirkender ctb Schäfer, Till mitwirkender ctb Shpacovitch, Victoria mitwirkender ctb Sliwa, Benjamin mitwirkender ctb Stolpe, Marco mitwirkender ctb Stöcker, Bianca K mitwirkender ctb Tiemann, Janis mitwirkender ctb Vranken, Tim mitwirkender ctb Weichert, Frank mitwirkender ctb Wiederkehr, Petra mitwirkender ctb Wüstefeld, Konstantin mitwirkender ctb 9783110785975 Erscheint auch als Druck-Ausgabe Machine learning under resource constraints ; 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9783110785982 PDF 978-3-11-078598-2 9783110786149 EPUB 978-3-11-078614-9 10.1515/9783110785982 doi (DE-627)1831669501 (DE-599)KEP08380689X (OCoLC)1433066834 (DE-B1597)617928 (EBP)08380689X DE-627 ger DE-627 rda eng XA-DE-BE XD-US SCI013000 bisacsh COM032000;BISACSCI013000 bisacsh ST 300 DE-Ofb1/23 rvk (DE-625)rvk/143650: 54.72 bkl 54.80 bkl Machine Learning under Resource Constraints Applications edited by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer Berlin Boston De Gruyter 2022 ©2023 1 Online-Ressource (VIII, 470 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Machine Learning under Resource Constraints Volume 3 De Gruyter STEM Frontmatter Contents 1 Editorial 2 Health / Medicine 2.1 Machine Learning in Medicine 2.2 Virus Detection 2.3 Cancer Diagnostics and Therapy from Molecular Data 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction 2.5 Survival Prediction and Model Selection 2.6 Protein Complex Similarity 3 Industry 4.0 3.1 Keynote on Industry 4.0 3.2 Quality Assurance in Interlinked Manufacturing Processes 3.3 Label Proportion Learning 3.4 Simulation and Machine Learning 3.5 High-Precision Wireless Localization 3.6 Indoor Photovoltaic Energy Harvesting 3.7 Micro-UAV Swarm Testbed for Indoor Applications 4 Smart City and Traffic 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows 4.3 Green Networking and Resource Constrained Clients for Smart Cities 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata 4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms 5 Communication Networks 5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum 5.2 Resource-Efficient Vehicle-to-Cloud Communications 5.3 Mobile-Data Network Analytics Highly Reliable Networks 5.4 Machine Learning-Enabled 5G Network Slicing 5.5 Potential of Millimeter Wave Communications 6 Privacy 6.1 Keynote: Construction of Inference-Proof Agent Interactions Bibliography Index List of Contributors Open Access Controlled Vocabulary for Access Rights https://purl.org/coar/access_right/c_abf2 unrestricted online access star Machine 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 Issued also in print Issued also in print Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ In English Computer aided manufacture (CAM) Computeranwendungen in Industrie und Technologie Data Mining Data mining Information technology: general issues Machine learning Maschinelles Lernen Praktische Anwendung für Informationstechnologien SCIENCE / Chemistry / General Artificial Intelligence Big Data and Machine Learning Cyber-physical systems Data mining for Ubiquitous System Software Embedded Systems and Machine Learning Highly Distributed Data ML on Small devices Machine learning for knowledge discovery Machine learning in high-energy physics Resource-Aware Machine Learning Resource-Constrained Data Analysis s (DE-588)4033447-8 (DE-627)106257188 (DE-576)209002050 Künstliche Intelligenz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd s (DE-588)4396978-1 (DE-627)191533580 (DE-576)212005545 Eingebettetes System gnd s (DE-588)4802620-7 (DE-627)472310364 (DE-576)216543657 Big Data gnd (DE-627) Morik, Katharina herausgeberin edt Rahnenführer, Jörg herausgeberin edt Wietfeld, Christian herausgeberin edt Arendt, Christian mitwirkender ctb Awasthi, Shrutarv mitwirkender ctb Bektas, Caner mitwirkender ctb Biskup, Joachim mitwirkender ctb Borgwardt, Karsten mitwirkender ctb Böcker, Stefan mitwirkender ctb Büscher, Jan mitwirkender ctb Cheng, Liang mitwirkender ctb Deuse, Jochen mitwirkender ctb Ding, Zeyu mitwirkender ctb Falkenberg, Robert mitwirkender ctb Finkeldey, Felix mitwirkender ctb Gramse, Nils mitwirkender ctb Haferkamp, Marcus mitwirkender ctb Heimann, Karsten mitwirkender ctb Hergenröder, Roland mitwirkender ctb Hompel, Michael ten mitwirkender ctb Häger, Simon mitwirkender ctb Ickstadt, Katja mitwirkender ctb Jutzeler, Catherine mitwirkender ctb Jörke, Pascal mitwirkender ctb Kriege, Nils mitwirkender ctb Krieger, Cedrik mitwirkender ctb Kurtz, Fabian mitwirkender ctb Köster, Johannes mitwirkender ctb Lang, Michel mitwirkender ctb Liebig, Thomas mitwirkender ctb Machado, Maximillian mitwirkender ctb Masoudinejad, Mojtaba mitwirkender ctb Munteanu, Alexander mitwirkender ctb Mutzel, Petra mitwirkender ctb Overbeck, Dennis mitwirkender ctb Panusch, Thorben mitwirkender ctb Rahmann, Sven mitwirkender ctb Ran, Ran mitwirkender ctb Reining, Christopher mitwirkender ctb Richter, Jakob mitwirkender ctb Roidl, Moritz mitwirkender ctb Saadallah, Amal mitwirkender ctb Schramm, Alexander mitwirkender ctb Schreckenberg, Michael mitwirkender ctb Schäfer, Till mitwirkender ctb Shpacovitch, Victoria mitwirkender ctb Sliwa, Benjamin mitwirkender ctb Stolpe, Marco mitwirkender ctb Stöcker, Bianca K mitwirkender ctb Tiemann, Janis mitwirkender ctb Vranken, Tim mitwirkender ctb Weichert, Frank mitwirkender ctb Wiederkehr, Petra mitwirkender ctb Wüstefeld, Konstantin mitwirkender ctb 9783110785975 Erscheint auch als Druck-Ausgabe Machine learning under resource constraints ; 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ind1="8" ind2="0"><subfield code="t">2.6 Protein Complex Similarity</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3 Industry 4.0</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.1 Keynote on Industry 4.0</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.2 Quality Assurance in Interlinked Manufacturing Processes</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.3 Label Proportion Learning</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.4 Simulation and Machine Learning</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.5 High-Precision Wireless Localization</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.6 Indoor Photovoltaic Energy Harvesting</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.7 Micro-UAV Swarm Testbed for Indoor Applications</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4 Smart City and Traffic</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.1 Inner-City Traffic Flow Prediction with Sparse Sensors</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.3 Green Networking and Resource Constrained Clients for Smart Cities</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5 Communication Networks</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.2 Resource-Efficient Vehicle-to-Cloud Communications</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.3 Mobile-Data Network Analytics Highly Reliable Networks</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.4 Machine Learning-Enabled 5G Network Slicing</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.5 Potential of Millimeter Wave Communications</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">6 Privacy</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">6.1 Keynote: Construction of Inference-Proof Agent Interactions</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">Bibliography</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">Index</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">List of Contributors</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">https://purl.org/coar/access_right/c_abf2</subfield><subfield code="f">unrestricted online access</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Machine 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. 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tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Rav1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-941</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Hed2</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2159</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-984</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-D117</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-1141</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">ST 300</subfield><subfield code="b">Allgemeines</subfield><subfield code="k">Informatik</subfield><subfield code="k">Monografien</subfield><subfield code="k">Künstliche Intelligenz</subfield><subfield code="k">Allgemeines</subfield><subfield code="0">(DE-627)1271119005</subfield><subfield code="0">(DE-625)rvk/143650:</subfield><subfield code="0">(DE-576)201119005</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">SEPA</subfield><subfield code="0">(DE-627)10641240X</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.80</subfield><subfield code="j">Angewandte Informatik</subfield><subfield code="q">SEPA</subfield><subfield code="0">(DE-627)106417967</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">006.31</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">11</subfield><subfield code="1">01</subfield><subfield code="x">0001</subfield><subfield code="b">4272616579</subfield><subfield code="c">00</subfield><subfield code="f">5:LIZFREI</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="y">k</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">22</subfield><subfield code="1">01</subfield><subfield code="x">0018</subfield><subfield code="b">4272620436</subfield><subfield code="h">OLR-ZDB-23-GOA</subfield><subfield code="k">De Gruyter Open Access</subfield><subfield code="y">zu</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="b">4272624210</subfield><subfield code="h">olr-degruyter-oa</subfield><subfield code="u">f</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">24</subfield><subfield code="1">01</subfield><subfield code="x">0008</subfield><subfield code="b">4272627864</subfield><subfield code="h">olr-dgoafrei</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">30</subfield><subfield code="1">01</subfield><subfield code="x">0104</subfield><subfield code="b">427262914X</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">DeGruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">31</subfield><subfield code="1">01</subfield><subfield code="x">0027</subfield><subfield code="b">4272632574</subfield><subfield code="h">OLR-FREE</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">34</subfield><subfield code="1">01</subfield><subfield code="x">3551</subfield><subfield code="b">4272636367</subfield><subfield code="h">OLR-DG-OA</subfield><subfield code="k">De Gruyter Open Access</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">39</subfield><subfield code="1">01</subfield><subfield code="x">0547</subfield><subfield code="b">4272637630</subfield><subfield code="h">OLR-ZDB-23-GOA</subfield><subfield code="y">ke</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">40</subfield><subfield code="1">01</subfield><subfield code="x">0007</subfield><subfield code="b">4272638904</subfield><subfield code="h">OLR-deGruyter-OA</subfield><subfield code="y">xsn</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">50</subfield><subfield code="1">01</subfield><subfield code="x">0023</subfield><subfield code="b">4272640119</subfield><subfield code="h">EBOA21</subfield><subfield code="k">Das Werk ist lizenzfrei im Web zugänglich</subfield><subfield code="y">k</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="b">4272641883</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">s</subfield><subfield code="j">--%%--</subfield><subfield code="h">Olr-dggoa</subfield><subfield code="u">verv</subfield><subfield code="u">shib</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">62</subfield><subfield code="1">01</subfield><subfield code="x">0028</subfield><subfield code="b">4272643517</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">DeGruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">63</subfield><subfield code="1">01</subfield><subfield code="x">3401</subfield><subfield code="b">4272644726</subfield><subfield code="h">E-Books LF</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">65</subfield><subfield code="1">01</subfield><subfield code="x">0003</subfield><subfield code="b">4272646028</subfield><subfield code="c">03</subfield><subfield code="f">--%%--</subfield><subfield code="d">ebook</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-GBV-deGruyter-OA</subfield><subfield code="k">DeGruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">69</subfield><subfield code="1">01</subfield><subfield code="x">0009</subfield><subfield code="b">4272649779</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">De Gruyter Open Access</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden.</subfield><subfield code="y">x</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">70</subfield><subfield code="1">01</subfield><subfield code="x">0089</subfield><subfield code="b">4272651013</subfield><subfield code="h">OLR-DG-OA</subfield><subfield code="k">De Gruyter Open Access eBook</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">72</subfield><subfield code="1">01</subfield><subfield code="x">0035</subfield><subfield code="b">4272652427</subfield><subfield code="h">OLR-ZDB-23-GOA</subfield><subfield code="k">De Gruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">90</subfield><subfield code="1">01</subfield><subfield code="x">3090</subfield><subfield code="b">4272656104</subfield><subfield code="h">OLR-HILdegruyter</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">91</subfield><subfield code="1">01</subfield><subfield code="x">3091</subfield><subfield code="b">4272657380</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">92</subfield><subfield code="1">01</subfield><subfield code="x">0033</subfield><subfield code="b">4272658670</subfield><subfield code="h">OLR-DGOA</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Das Ebook ist frei zugänglich.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">100</subfield><subfield code="1">01</subfield><subfield code="x">3100</subfield><subfield code="b">4272662333</subfield><subfield code="c">09</subfield><subfield code="f">--%%--</subfield><subfield code="d">ebook deGruyter open access</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">101</subfield><subfield code="1">01</subfield><subfield code="x">3101</subfield><subfield code="b">4272663615</subfield><subfield code="c">09</subfield><subfield code="f">--%%--</subfield><subfield code="d">ebook deGruyter open access</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">105</subfield><subfield code="1">01</subfield><subfield code="x">0841</subfield><subfield code="b">4272664891</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">DeGruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">110</subfield><subfield code="1">01</subfield><subfield code="x">3110</subfield><subfield code="b">4272668641</subfield><subfield code="h">OLR-OA-DEGRUYTER</subfield><subfield code="k">Kostenloser Zugriff</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">120</subfield><subfield code="1">01</subfield><subfield code="x">0715</subfield><subfield code="b">4275135563</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">g</subfield><subfield code="j">--%%--</subfield><subfield code="h">alma</subfield><subfield code="y">z</subfield><subfield code="z">21-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">122</subfield><subfield code="1">01</subfield><subfield code="x">0897</subfield><subfield code="b">4272672428</subfield><subfield code="h">OLR-OA-deGruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">130</subfield><subfield code="1">01</subfield><subfield code="x">0700</subfield><subfield code="b">4272676067</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">Kostenloser Zugriff</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">131</subfield><subfield code="1">01</subfield><subfield code="x">3131</subfield><subfield code="b">4272677276</subfield><subfield code="h">OLR-OA-DeGruyter</subfield><subfield code="k">Bitte beachten Sie die Nutzungsbedingungen und Copyright-Bestimmungen des Verlages/Herausgebers</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="b">4272678531</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="u">Open Access</subfield><subfield code="y">x</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">136</subfield><subfield code="1">01</subfield><subfield code="x">3526</subfield><subfield code="b">4272679813</subfield><subfield code="h">OLR-degruyter-oa</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">140</subfield><subfield code="1">01</subfield><subfield code="x">0839</subfield><subfield code="b">4272683586</subfield><subfield code="h">OLR-OA-deGruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">147</subfield><subfield code="1">01</subfield><subfield code="x">3528</subfield><subfield code="b">4272687352</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">frei verfügbar für Europa-Universität Flensburg, Hochschule Flensburg und Zentrale Hochschulbibliothek</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">151</subfield><subfield code="1">01</subfield><subfield code="x">0546</subfield><subfield code="b">4272688634</subfield><subfield code="h">OLR-ZDB-23-GOA</subfield><subfield code="k">De Gruyter Open Access</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">161</subfield><subfield code="1">01</subfield><subfield code="x">0960</subfield><subfield code="b">4272689916</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">164</subfield><subfield code="1">01</subfield><subfield code="x">0916</subfield><subfield code="b">430309563X</subfield><subfield code="h">OLR-OA-DEGRUYTER</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">05-04-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">187</subfield><subfield code="1">01</subfield><subfield code="x">3520</subfield><subfield code="b">4272691201</subfield><subfield code="k">De Gruyter Open Access eBook</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">206</subfield><subfield code="1">01</subfield><subfield code="x">3350</subfield><subfield code="b">4272692488</subfield><subfield code="h">OLR-OA-DG</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">213</subfield><subfield code="1">01</subfield><subfield code="x">0551</subfield><subfield code="b">4272693786</subfield><subfield code="h">OLR-OA-Degruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">230</subfield><subfield code="1">01</subfield><subfield code="x">0552</subfield><subfield code="b">4272695061</subfield><subfield code="h">OLR-OA-Degruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">250</subfield><subfield code="1">01</subfield><subfield code="x">3250</subfield><subfield code="b">4272696343</subfield><subfield code="h">OLR-DEG</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">264</subfield><subfield code="1">01</subfield><subfield code="x">3264</subfield><subfield code="b">4272700138</subfield><subfield code="h">OLR-OA-deGruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">01</subfield><subfield code="x">0517</subfield><subfield code="b">4272702629</subfield><subfield code="h">OLR-GRUY-OA</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">293</subfield><subfield code="1">01</subfield><subfield code="x">3293</subfield><subfield code="b">4272701436</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="y">xf</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="b">4272706365</subfield><subfield code="h">OLR-FREE</subfield><subfield code="k">Kostenloser Zugriff</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">496</subfield><subfield code="1">01</subfield><subfield code="x">3496</subfield><subfield code="b">432321944X</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">Online</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-EBC</subfield><subfield code="k">Open Access</subfield><subfield code="y">z</subfield><subfield code="z">17-05-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">603</subfield><subfield code="1">01</subfield><subfield code="x">4603</subfield><subfield code="b">4272707639</subfield><subfield code="c">00</subfield><subfield code="f">Online-Publikation</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-deGruyter-OA</subfield><subfield code="k">Bitte beachten Sie die Nutzungsbedingungen und Copyright-Bestimmungen des Verlages/Herausgebers!</subfield><subfield code="k">Elektronischer Volltext, Link anklicken</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">635</subfield><subfield code="1">01</subfield><subfield code="x">3635</subfield><subfield code="b">4509333072</subfield><subfield code="h">OLR-GOA</subfield><subfield code="k">Open Access. Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">09-04-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2007</subfield><subfield code="1">01</subfield><subfield code="x">DE-352E</subfield><subfield code="b">4473952444</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="k">Open Access</subfield><subfield code="y">l01</subfield><subfield code="z">02-02-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2009</subfield><subfield code="1">01</subfield><subfield code="x">DE-180</subfield><subfield code="b">4250668177</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2009</subfield><subfield code="1">02</subfield><subfield code="x">DE-180</subfield><subfield code="b">4291227671</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="y">l01</subfield><subfield code="z">16-03-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2010</subfield><subfield code="1">01</subfield><subfield code="x">DE-15</subfield><subfield code="b">4250668185</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2011</subfield><subfield code="1">01</subfield><subfield code="x">DE-16</subfield><subfield code="b">4250654141</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2015</subfield><subfield code="1">01</subfield><subfield code="x">DE-93</subfield><subfield code="b">4250668193</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">p</subfield><subfield code="j">--%%--</subfield><subfield code="k">lizenzfrei</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2021</subfield><subfield code="1">01</subfield><subfield code="x">DE-289</subfield><subfield code="b">425065415X</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Open Access</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2034</subfield><subfield code="1">01</subfield><subfield code="x">DE-Rt2</subfield><subfield code="b">4250654168</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook</subfield><subfield code="e">n</subfield><subfield code="j">n</subfield><subfield code="k">frei zugänglich</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2037</subfield><subfield code="1">01</subfield><subfield code="x">DE-747</subfield><subfield code="b">4261659832</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook DeGruyter OpenAccess</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Im Internet frei verfügbar</subfield><subfield code="y">l01</subfield><subfield code="z">01-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2039</subfield><subfield code="1">01</subfield><subfield code="x">DE-He76</subfield><subfield code="b">4433162949</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">E-Book OA</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext</subfield><subfield code="y">l01</subfield><subfield code="z">12-12-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2044</subfield><subfield code="1">01</subfield><subfield code="x">DE-751</subfield><subfield code="b">4250654176</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook de Gruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Bitte beachten Sie auch die Nutzungshinweise auf unserer Homepage</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2045</subfield><subfield code="1">01</subfield><subfield code="x">DE-Mit1</subfield><subfield code="b">4250668207</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2050</subfield><subfield code="1">01</subfield><subfield code="x">DE-Zi4</subfield><subfield code="b">4250668215</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2055</subfield><subfield code="1">01</subfield><subfield code="x">DE-840</subfield><subfield code="b">4400845311</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">DeGruyter Technik Informatik</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Campuslizenz HHN</subfield><subfield code="y">l01</subfield><subfield code="z">02-11-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2059</subfield><subfield code="1">01</subfield><subfield code="x">DE-Kon4</subfield><subfield code="b">4250654214</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook deGruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Zugang über WWW</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2063</subfield><subfield code="1">01</subfield><subfield code="x">DE-951</subfield><subfield code="b">4250654222</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">De Gruyter (Open Access)</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2064</subfield><subfield code="1">01</subfield><subfield code="x">DE-953</subfield><subfield code="b">4250654230</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook OA</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Open Access</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2065</subfield><subfield code="1">01</subfield><subfield code="x">DE-958</subfield><subfield code="b">4250654249</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook DeGruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2068</subfield><subfield code="1">01</subfield><subfield code="x">DE-Fn1</subfield><subfield code="b">4250654257</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook DeGruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2089</subfield><subfield code="1">01</subfield><subfield code="x">DE-540</subfield><subfield code="b">4250668223</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">p</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Open 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5:LIZFREI --%%-- Online-Publikation |
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| abstract |
Machine 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 |
| abstractGer |
Machine 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 |
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Machine 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 |
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Machine Learning under Resource Constraints |
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https://doi.org/10.1515/9783110785982 https://www.degruyter.com/isbn/9783110785982 https://www.degruyter.com/document/cover/isbn/9783110785982/original |
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Morik, Katharina Rahnenführer, Jörg Wietfeld, Christian Arendt, Christian Awasthi, Shrutarv Bektas, Caner Biskup, Joachim Borgwardt, Karsten Böcker, Stefan Büscher, Jan Cheng, Liang Deuse, Jochen Ding, Zeyu Falkenberg, Robert Finkeldey, Felix Gramse, Nils Haferkamp, Marcus Heimann, Karsten Hergenröder, Roland Hompel, Michael ten Häger, Simon Ickstadt, Katja Jutzeler, Catherine Jörke, Pascal Kriege, Nils Krieger, Cedrik Kurtz, Fabian Köster, Johannes Lang, Michel Liebig, Thomas Machado, Maximillian Masoudinejad, Mojtaba Munteanu, Alexander Mutzel, Petra Overbeck, Dennis Panusch, Thorben Rahmann, Sven Ran, Ran Reining, Christopher Richter, Jakob Roidl, Moritz Saadallah, Amal Schramm, Alexander Schreckenberg, Michael Schäfer, Till Shpacovitch, Victoria Sliwa, Benjamin Stolpe, Marco Stöcker, Bianca K Tiemann, Janis Vranken, Tim Weichert, Frank Wiederkehr, Petra Wüstefeld, Konstantin |
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Morik, Katharina Rahnenführer, Jörg Wietfeld, Christian Arendt, Christian Awasthi, Shrutarv Bektas, Caner Biskup, Joachim Borgwardt, Karsten Böcker, Stefan Büscher, Jan Cheng, Liang Deuse, Jochen Ding, Zeyu Falkenberg, Robert Finkeldey, Felix Gramse, Nils Haferkamp, Marcus Heimann, Karsten Hergenröder, Roland Hompel, Michael ten Häger, Simon Ickstadt, Katja Jutzeler, Catherine Jörke, Pascal Kriege, Nils Krieger, Cedrik Kurtz, Fabian Köster, Johannes Lang, Michel Liebig, Thomas Machado, Maximillian Masoudinejad, Mojtaba Munteanu, Alexander Mutzel, Petra Overbeck, Dennis Panusch, Thorben Rahmann, Sven Ran, Ran Reining, Christopher Richter, Jakob Roidl, Moritz Saadallah, Amal Schramm, Alexander Schreckenberg, Michael Schäfer, Till Shpacovitch, Victoria Sliwa, Benjamin Stolpe, Marco Stöcker, Bianca K Tiemann, Janis Vranken, Tim Weichert, Frank Wiederkehr, Petra Wüstefeld, Konstantin |
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| doi_str |
10.1515/9783110785982 |
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2024-10-02T08:37:29.771Z |
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ind1="8" ind2="0"><subfield code="t">2.6 Protein Complex Similarity</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3 Industry 4.0</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.1 Keynote on Industry 4.0</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.2 Quality Assurance in Interlinked Manufacturing Processes</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.3 Label Proportion Learning</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.4 Simulation and Machine Learning</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.5 High-Precision Wireless Localization</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.6 Indoor Photovoltaic Energy Harvesting</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">3.7 Micro-UAV Swarm Testbed for Indoor Applications</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4 Smart City and Traffic</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.1 Inner-City Traffic Flow Prediction with Sparse Sensors</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.3 Green Networking and Resource Constrained Clients for Smart Cities</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5 Communication Networks</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.2 Resource-Efficient Vehicle-to-Cloud Communications</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.3 Mobile-Data Network Analytics Highly Reliable Networks</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.4 Machine Learning-Enabled 5G Network Slicing</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">5.5 Potential of Millimeter Wave Communications</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="t">6 Privacy</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield 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tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-He76</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-751</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2045</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Mit1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Zi4</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-840</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Kon4</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2063</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-951</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-953</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-958</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Fn1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2089</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-540</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-944</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-1033</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Mh35</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-943</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Ofb1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Rav1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-941</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Hed2</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2159</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-984</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-D117</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-1141</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">ST 300</subfield><subfield code="b">Allgemeines</subfield><subfield code="k">Informatik</subfield><subfield code="k">Monografien</subfield><subfield code="k">Künstliche Intelligenz</subfield><subfield code="k">Allgemeines</subfield><subfield code="0">(DE-627)1271119005</subfield><subfield code="0">(DE-625)rvk/143650:</subfield><subfield code="0">(DE-576)201119005</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">SEPA</subfield><subfield code="0">(DE-627)10641240X</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.80</subfield><subfield code="j">Angewandte Informatik</subfield><subfield code="q">SEPA</subfield><subfield code="0">(DE-627)106417967</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">006.31</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">11</subfield><subfield code="1">01</subfield><subfield code="x">0001</subfield><subfield code="b">4272616579</subfield><subfield code="c">00</subfield><subfield code="f">5:LIZFREI</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="y">k</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">22</subfield><subfield code="1">01</subfield><subfield code="x">0018</subfield><subfield code="b">4272620436</subfield><subfield code="h">OLR-ZDB-23-GOA</subfield><subfield code="k">De Gruyter Open Access</subfield><subfield code="y">zu</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="b">4272624210</subfield><subfield code="h">olr-degruyter-oa</subfield><subfield code="u">f</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">24</subfield><subfield code="1">01</subfield><subfield code="x">0008</subfield><subfield code="b">4272627864</subfield><subfield code="h">olr-dgoafrei</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">30</subfield><subfield code="1">01</subfield><subfield code="x">0104</subfield><subfield code="b">427262914X</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">DeGruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">31</subfield><subfield code="1">01</subfield><subfield code="x">0027</subfield><subfield code="b">4272632574</subfield><subfield code="h">OLR-FREE</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">34</subfield><subfield code="1">01</subfield><subfield code="x">3551</subfield><subfield code="b">4272636367</subfield><subfield code="h">OLR-DG-OA</subfield><subfield code="k">De Gruyter Open Access</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">39</subfield><subfield code="1">01</subfield><subfield code="x">0547</subfield><subfield code="b">4272637630</subfield><subfield code="h">OLR-ZDB-23-GOA</subfield><subfield code="y">ke</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">40</subfield><subfield code="1">01</subfield><subfield code="x">0007</subfield><subfield code="b">4272638904</subfield><subfield code="h">OLR-deGruyter-OA</subfield><subfield code="y">xsn</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">50</subfield><subfield code="1">01</subfield><subfield code="x">0023</subfield><subfield code="b">4272640119</subfield><subfield code="h">EBOA21</subfield><subfield code="k">Das Werk ist lizenzfrei im Web zugänglich</subfield><subfield code="y">k</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="b">4272641883</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">s</subfield><subfield code="j">--%%--</subfield><subfield code="h">Olr-dggoa</subfield><subfield code="u">verv</subfield><subfield code="u">shib</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">62</subfield><subfield code="1">01</subfield><subfield code="x">0028</subfield><subfield code="b">4272643517</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">DeGruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">63</subfield><subfield code="1">01</subfield><subfield code="x">3401</subfield><subfield code="b">4272644726</subfield><subfield code="h">E-Books LF</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">65</subfield><subfield code="1">01</subfield><subfield code="x">0003</subfield><subfield code="b">4272646028</subfield><subfield code="c">03</subfield><subfield code="f">--%%--</subfield><subfield code="d">ebook</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-GBV-deGruyter-OA</subfield><subfield code="k">DeGruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">69</subfield><subfield code="1">01</subfield><subfield code="x">0009</subfield><subfield code="b">4272649779</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">De Gruyter Open Access</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden.</subfield><subfield code="y">x</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">70</subfield><subfield code="1">01</subfield><subfield code="x">0089</subfield><subfield code="b">4272651013</subfield><subfield code="h">OLR-DG-OA</subfield><subfield code="k">De Gruyter Open Access eBook</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">72</subfield><subfield code="1">01</subfield><subfield code="x">0035</subfield><subfield code="b">4272652427</subfield><subfield code="h">OLR-ZDB-23-GOA</subfield><subfield code="k">De Gruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">90</subfield><subfield code="1">01</subfield><subfield code="x">3090</subfield><subfield code="b">4272656104</subfield><subfield code="h">OLR-HILdegruyter</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">91</subfield><subfield code="1">01</subfield><subfield code="x">3091</subfield><subfield code="b">4272657380</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">92</subfield><subfield code="1">01</subfield><subfield code="x">0033</subfield><subfield code="b">4272658670</subfield><subfield code="h">OLR-DGOA</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Das Ebook ist frei zugänglich.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">100</subfield><subfield code="1">01</subfield><subfield code="x">3100</subfield><subfield code="b">4272662333</subfield><subfield code="c">09</subfield><subfield code="f">--%%--</subfield><subfield code="d">ebook deGruyter open access</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">101</subfield><subfield code="1">01</subfield><subfield code="x">3101</subfield><subfield code="b">4272663615</subfield><subfield code="c">09</subfield><subfield code="f">--%%--</subfield><subfield code="d">ebook deGruyter open access</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">105</subfield><subfield code="1">01</subfield><subfield code="x">0841</subfield><subfield code="b">4272664891</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">DeGruyter Open Access Titel</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">110</subfield><subfield code="1">01</subfield><subfield code="x">3110</subfield><subfield code="b">4272668641</subfield><subfield code="h">OLR-OA-DEGRUYTER</subfield><subfield code="k">Kostenloser Zugriff</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">120</subfield><subfield code="1">01</subfield><subfield code="x">0715</subfield><subfield code="b">4275135563</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">g</subfield><subfield code="j">--%%--</subfield><subfield code="h">alma</subfield><subfield code="y">z</subfield><subfield code="z">21-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">122</subfield><subfield code="1">01</subfield><subfield code="x">0897</subfield><subfield code="b">4272672428</subfield><subfield code="h">OLR-OA-deGruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">130</subfield><subfield code="1">01</subfield><subfield code="x">0700</subfield><subfield code="b">4272676067</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">Kostenloser Zugriff</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">131</subfield><subfield code="1">01</subfield><subfield code="x">3131</subfield><subfield code="b">4272677276</subfield><subfield code="h">OLR-OA-DeGruyter</subfield><subfield code="k">Bitte beachten Sie die Nutzungsbedingungen und Copyright-Bestimmungen des Verlages/Herausgebers</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="b">4272678531</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="u">Open Access</subfield><subfield code="y">x</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">136</subfield><subfield code="1">01</subfield><subfield code="x">3526</subfield><subfield code="b">4272679813</subfield><subfield code="h">OLR-degruyter-oa</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">140</subfield><subfield code="1">01</subfield><subfield code="x">0839</subfield><subfield code="b">4272683586</subfield><subfield code="h">OLR-OA-deGruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">147</subfield><subfield code="1">01</subfield><subfield code="x">3528</subfield><subfield code="b">4272687352</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="k">frei verfügbar für Europa-Universität Flensburg, Hochschule Flensburg und Zentrale Hochschulbibliothek</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">151</subfield><subfield code="1">01</subfield><subfield code="x">0546</subfield><subfield code="b">4272688634</subfield><subfield code="h">OLR-ZDB-23-GOA</subfield><subfield code="k">De Gruyter Open Access</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">161</subfield><subfield code="1">01</subfield><subfield code="x">0960</subfield><subfield code="b">4272689916</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">164</subfield><subfield code="1">01</subfield><subfield code="x">0916</subfield><subfield code="b">430309563X</subfield><subfield code="h">OLR-OA-DEGRUYTER</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">05-04-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">187</subfield><subfield code="1">01</subfield><subfield code="x">3520</subfield><subfield code="b">4272691201</subfield><subfield code="k">De Gruyter Open Access eBook</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">206</subfield><subfield code="1">01</subfield><subfield code="x">3350</subfield><subfield code="b">4272692488</subfield><subfield code="h">OLR-OA-DG</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">213</subfield><subfield code="1">01</subfield><subfield code="x">0551</subfield><subfield code="b">4272693786</subfield><subfield code="h">OLR-OA-Degruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">230</subfield><subfield code="1">01</subfield><subfield code="x">0552</subfield><subfield code="b">4272695061</subfield><subfield code="h">OLR-OA-Degruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">250</subfield><subfield code="1">01</subfield><subfield code="x">3250</subfield><subfield code="b">4272696343</subfield><subfield code="h">OLR-DEG</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">264</subfield><subfield code="1">01</subfield><subfield code="x">3264</subfield><subfield code="b">4272700138</subfield><subfield code="h">OLR-OA-deGruyter</subfield><subfield code="k">Dieser Volltext ist kostenlos und unbeschränkt zur wissenschaftlichen Information im Internet zugänglich</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">01</subfield><subfield code="x">0517</subfield><subfield code="b">4272702629</subfield><subfield code="h">OLR-GRUY-OA</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">293</subfield><subfield code="1">01</subfield><subfield code="x">3293</subfield><subfield code="b">4272701436</subfield><subfield code="h">OLR-DGGOA</subfield><subfield code="y">xf</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="b">4272706365</subfield><subfield code="h">OLR-FREE</subfield><subfield code="k">Kostenloser Zugriff</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">496</subfield><subfield code="1">01</subfield><subfield code="x">3496</subfield><subfield code="b">432321944X</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">Online</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-EBC</subfield><subfield code="k">Open Access</subfield><subfield code="y">z</subfield><subfield code="z">17-05-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">603</subfield><subfield code="1">01</subfield><subfield code="x">4603</subfield><subfield code="b">4272707639</subfield><subfield code="c">00</subfield><subfield code="f">Online-Publikation</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-deGruyter-OA</subfield><subfield code="k">Bitte beachten Sie die Nutzungsbedingungen und Copyright-Bestimmungen des Verlages/Herausgebers!</subfield><subfield code="k">Elektronischer Volltext, Link anklicken</subfield><subfield code="y">z</subfield><subfield code="z">15-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">635</subfield><subfield code="1">01</subfield><subfield code="x">3635</subfield><subfield code="b">4509333072</subfield><subfield code="h">OLR-GOA</subfield><subfield code="k">Open Access. Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">09-04-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2007</subfield><subfield code="1">01</subfield><subfield code="x">DE-352E</subfield><subfield code="b">4473952444</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="k">Open Access</subfield><subfield code="y">l01</subfield><subfield code="z">02-02-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2009</subfield><subfield code="1">01</subfield><subfield code="x">DE-180</subfield><subfield code="b">4250668177</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2009</subfield><subfield code="1">02</subfield><subfield code="x">DE-180</subfield><subfield code="b">4291227671</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="y">l01</subfield><subfield code="z">16-03-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2010</subfield><subfield code="1">01</subfield><subfield code="x">DE-15</subfield><subfield code="b">4250668185</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2011</subfield><subfield code="1">01</subfield><subfield code="x">DE-16</subfield><subfield code="b">4250654141</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2015</subfield><subfield code="1">01</subfield><subfield code="x">DE-93</subfield><subfield code="b">4250668193</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">p</subfield><subfield code="j">--%%--</subfield><subfield code="k">lizenzfrei</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2021</subfield><subfield code="1">01</subfield><subfield code="x">DE-289</subfield><subfield code="b">425065415X</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Open Access</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2034</subfield><subfield code="1">01</subfield><subfield code="x">DE-Rt2</subfield><subfield code="b">4250654168</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook</subfield><subfield code="e">n</subfield><subfield code="j">n</subfield><subfield code="k">frei zugänglich</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2037</subfield><subfield code="1">01</subfield><subfield code="x">DE-747</subfield><subfield code="b">4261659832</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook DeGruyter OpenAccess</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Im Internet frei verfügbar</subfield><subfield code="y">l01</subfield><subfield code="z">01-02-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2039</subfield><subfield code="1">01</subfield><subfield code="x">DE-He76</subfield><subfield code="b">4433162949</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">E-Book OA</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext</subfield><subfield code="y">l01</subfield><subfield code="z">12-12-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2044</subfield><subfield code="1">01</subfield><subfield code="x">DE-751</subfield><subfield code="b">4250654176</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook de Gruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Bitte beachten Sie auch die Nutzungshinweise auf unserer Homepage</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2045</subfield><subfield code="1">01</subfield><subfield code="x">DE-Mit1</subfield><subfield code="b">4250668207</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2050</subfield><subfield code="1">01</subfield><subfield code="x">DE-Zi4</subfield><subfield code="b">4250668215</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2055</subfield><subfield code="1">01</subfield><subfield code="x">DE-840</subfield><subfield code="b">4400845311</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">DeGruyter Technik Informatik</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Campuslizenz HHN</subfield><subfield code="y">l01</subfield><subfield code="z">02-11-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2059</subfield><subfield code="1">01</subfield><subfield code="x">DE-Kon4</subfield><subfield code="b">4250654214</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook deGruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Zugang über WWW</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2063</subfield><subfield code="1">01</subfield><subfield code="x">DE-951</subfield><subfield code="b">4250654222</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">De Gruyter (Open Access)</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2064</subfield><subfield code="1">01</subfield><subfield code="x">DE-953</subfield><subfield code="b">4250654230</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook OA</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Open Access</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2065</subfield><subfield code="1">01</subfield><subfield code="x">DE-958</subfield><subfield code="b">4250654249</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook DeGruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2068</subfield><subfield code="1">01</subfield><subfield code="x">DE-Fn1</subfield><subfield code="b">4250654257</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook DeGruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2089</subfield><subfield code="1">01</subfield><subfield code="x">DE-540</subfield><subfield code="b">4250668223</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">p</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Open Access</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2111</subfield><subfield code="1">01</subfield><subfield code="x">DE-944</subfield><subfield code="b">4250654265</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">E-Book De Gruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2112</subfield><subfield code="1">01</subfield><subfield code="x">DE-1033</subfield><subfield code="b">4352158887</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">deGruyter OA</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="y">l01</subfield><subfield code="z">11-07-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2118</subfield><subfield code="1">01</subfield><subfield code="x">DE-Mh35</subfield><subfield code="b">4250654273</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2119</subfield><subfield code="1">01</subfield><subfield code="x">DE-943</subfield><subfield code="b">4340096601</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">eBook DeGruyter Oldenbourg</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">17-06-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2129</subfield><subfield code="1">01</subfield><subfield code="x">DE-Ofb1</subfield><subfield code="b">4255710058</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">E-Book DeGruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="y">l01</subfield><subfield code="z">31-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2143</subfield><subfield code="1">01</subfield><subfield code="x">DE-Rav1</subfield><subfield code="b">4250654281</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">E-Book OA</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2152</subfield><subfield code="1">01</subfield><subfield code="x">DE-941</subfield><subfield code="b">425065429X</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">E-Book deGruyter</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">23-01-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2153</subfield><subfield code="1">01</subfield><subfield code="x">DE-Hed2</subfield><subfield code="b">4250654303</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">e-Book De Gruyter OA</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Elektronischer Volltext - Open Access</subfield><subfield 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