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  • 1
    Online Resource
    Online Resource
    Cham : Springer International Publishing | Cham : Springer
    UID:
    b3kat_BV048885545
    Format: 1 Online-Ressource (XII, 252 p. 100 illus., 76 illus. in color)
    Edition: 1st ed. 2023
    ISBN: 9783031244537
    Series Statement: Studies in Computational Intelligence 1084
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-24452-0
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-24454-4
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-24455-1
    Language: English
    Subjects: Computer Science
    RVK:
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    b3kat_BV046451958
    Format: 1 Online-Ressource (xviii, 313 Seiten) , Illustrationen, Diagramme
    ISBN: 9783030392505
    Series Statement: Studies in computational intelligence 869
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-39249-9
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-39251-2
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-39252-9
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    URL: Volltext  (URL des Erstveröffentlichers)
    Author information: Kacprzyk, Janusz 1947-
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    UID:
    almahu_9948253962802882
    Format: XVIII, 313 p. 126 illus., 59 illus. in color. , online resource.
    Edition: 1st ed. 2020.
    ISBN: 9783030392505
    Series Statement: Studies in Computational Intelligence, 869
    Content: This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science. Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field. In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others. .
    Note: Object Detection in Aerial Photos Using Neural Networks -- Modelling and Control of Human Response to a Dynamic Virtual 3D Face -- Knowledge-Based Transformation Algorithms of UML Dynamic Models Generation from Enterprise Model -- An Approach for Networking of Wireless Sensors and Embedded Systems Applied for Monitoring of Environment Data -- Non-Standard Distances in High Dimensional Raw Data Stream Classification -- Data Analysis in Setting Action Plans of Telecom Operators -- Extending Model-Driven Development Process with Causal Modeling Approach -- Discrete Competitive Facility Location by Ranking Candidate Locations -- Investigating Feature Spaces for Isolated Word Recognition -- Developing Algorithmic Thinking Through Computational Making -- Improving Objective Speech Quality Indicators in Noise Conditions -- Investigation of User Vulnerability in Social Networking Site -- Zerocross Density Decomposition: a Novel Signal Decomposition Method -- DSS - A Class of Evolving Information Systems -- A Deep Knowledge-Based Evaluation of Enterprise Applications Interoperability -- Sentiment-Based Decision Making Model for Financial Markets.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783030392499
    Additional Edition: Printed edition: ISBN 9783030392512
    Additional Edition: Printed edition: ISBN 9783030392529
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    UID:
    almahu_BV049008990
    Format: xii, 252 Seiten : , Illustrationen, Diagramme (überwiegend farbig).
    ISBN: 978-3-031-24452-0
    Series Statement: Studies in computational intelligence volume 1084
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-031-24453-7
    Language: English
    Subjects: Computer Science
    RVK:
    Author information: Kacprzyk, Janusz, 1947-
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    UID:
    almahu_9949468715502882
    Format: XII, 252 p. 100 illus., 76 illus. in color. , online resource.
    Edition: 1st ed. 2023.
    ISBN: 9783031244537
    Series Statement: Studies in Computational Intelligence, 1084
    Content: This book provides an overview of a wide range of relevant applications and reveals how to solve them. Many of the latest applications in finance, technology, education, medicine and other important and relevant fields are data-driven. The volumes of data are enormous. Specific methods need to be developed or adapted to solve a particular problem. It illustrates data science in applications. These applications have in common the discovery of knowledge in data and the use of this knowledge to make real decisions. The set of examples presented serves as a recipe book for their direct application to similar problems or as a guide for the development of new, more sophisticated approaches. The intended readership is data scientists looking for appropriate solutions to their problems. In addition, the examples provided serves as material for lectures at universities.
    Note: Computational Thinking Design Application for STEAM Education -- Education Data for Science: Case of Lithuania -- Imbalanced Data Classification Approach Based on Clustered Training Set -- Baltic States in Global Value Chains: Quantifying International Production Sharing at Bilateral and Sectoral Levels -- The Soft Power Of Understanding Social Media Dynamics: A Data-Driven Approach -- Bootstrapping Network Autoregressive Models for Testing Linearity -- Novel data science methodologies for essential genes identification based on network analysis -- Acoustic Analysis for Vocal Fold Assessment - Challenges, Trends, and Opportunities -- The Paradigm of an Explainable Artificial Intelligence (XAI) and Data Science (DS) Based Decision Support System (DSS) -- Stock Portfolio Risk-Return Ratio Optimisation using Grey Wolf Model -- Towards Seamless Execution of Deep Learning Application on Heterogeneous HPC Systems.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031244520
    Additional Edition: Printed edition: ISBN 9783031244544
    Additional Edition: Printed edition: ISBN 9783031244551
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    UID:
    edoccha_9961046813902883
    Format: 1 online resource (260 pages)
    Edition: 1st ed. 2023.
    ISBN: 3-031-24453-2
    Series Statement: Studies in computational intelligence ; Volume 1084
    Content: This book provides an overview of a wide range of relevant applications and reveals how to solve them. Many of the latest applications in finance, technology, education, medicine and other important and relevant fields are data-driven. The volumes of data are enormous. Specific methods need to be developed or adapted to solve a particular problem. It illustrates data science in applications. These applications have in common the discovery of knowledge in data and the use of this knowledge to make real decisions. The set of examples presented serves as a recipe book for their direct application to similar problems or as a guide for the development of new, more sophisticated approaches. The intended readership is data scientists looking for appropriate solutions to their problems. In addition, the examples provided serves as material for lectures at universities.
    Note: Computational Thinking Design Application for STEAM Education -- Education Data for Science: Case of Lithuania -- Imbalanced Data Classification Approach Based on Clustered Training Set -- Baltic States in Global Value Chains: Quantifying International Production Sharing at Bilateral and Sectoral Levels -- The Soft Power Of Understanding Social Media Dynamics: A Data-Driven Approach -- Bootstrapping Network Autoregressive Models for Testing Linearity -- Novel data science methodologies for essential genes identification based on network analysis -- Acoustic Analysis for Vocal Fold Assessment - Challenges, Trends, and Opportunities -- The Paradigm of an Explainable Artificial Intelligence (XAI) and Data Science (DS) Based Decision Support System (DSS) -- Stock Portfolio Risk-Return Ratio Optimisation using Grey Wolf Model -- Towards Seamless Execution of Deep Learning Application on Heterogeneous HPC Systems.
    Additional Edition: Print version: Dzemyda, Gintautas Data Science in Applications Cham : Springer International Publishing AG,c2023 ISBN 9783031244520
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    UID:
    edoccha_9959767497902883
    Format: 1 online resource (325 pages) : , illustrations
    Edition: 1st ed. 2020.
    ISBN: 3-030-39250-3
    Series Statement: Studies in Computational Intelligence, 869
    Content: This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science. Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field. In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others. .
    Note: Includes index. , Object Detection in Aerial Photos Using Neural Networks -- Modelling and Control of Human Response to a Dynamic Virtual 3D Face -- Knowledge-Based Transformation Algorithms of UML Dynamic Models Generation from Enterprise Model -- An Approach for Networking of Wireless Sensors and Embedded Systems Applied for Monitoring of Environment Data -- Non-Standard Distances in High Dimensional Raw Data Stream Classification -- Data Analysis in Setting Action Plans of Telecom Operators -- Extending Model-Driven Development Process with Causal Modeling Approach -- Discrete Competitive Facility Location by Ranking Candidate Locations -- Investigating Feature Spaces for Isolated Word Recognition -- Developing Algorithmic Thinking Through Computational Making -- Improving Objective Speech Quality Indicators in Noise Conditions -- Investigation of User Vulnerability in Social Networking Site -- Zerocross Density Decomposition: a Novel Signal Decomposition Method -- DSS – A Class of Evolving Information Systems -- A Deep Knowledge-Based Evaluation of Enterprise Applications Interoperability -- Sentiment-Based Decision Making Model for Financial Markets.
    Additional Edition: ISBN 3-030-39249-X
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    UID:
    almahu_BV046627990
    Format: xviii, 313 Seiten : , Illustrationen, Diagramme (teilweise farbig).
    ISBN: 978-3-030-39249-9
    Series Statement: Studies in computational intelligence volume 869
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-030-39250-5
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    Author information: Kacprzyk, Janusz, 1947-
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 9
    UID:
    almafu_9959767497902883
    Format: 1 online resource (325 pages) : , illustrations
    Edition: 1st ed. 2020.
    ISBN: 3-030-39250-3
    Series Statement: Studies in Computational Intelligence, 869
    Content: This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science. Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field. In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others. .
    Note: Includes index. , Object Detection in Aerial Photos Using Neural Networks -- Modelling and Control of Human Response to a Dynamic Virtual 3D Face -- Knowledge-Based Transformation Algorithms of UML Dynamic Models Generation from Enterprise Model -- An Approach for Networking of Wireless Sensors and Embedded Systems Applied for Monitoring of Environment Data -- Non-Standard Distances in High Dimensional Raw Data Stream Classification -- Data Analysis in Setting Action Plans of Telecom Operators -- Extending Model-Driven Development Process with Causal Modeling Approach -- Discrete Competitive Facility Location by Ranking Candidate Locations -- Investigating Feature Spaces for Isolated Word Recognition -- Developing Algorithmic Thinking Through Computational Making -- Improving Objective Speech Quality Indicators in Noise Conditions -- Investigation of User Vulnerability in Social Networking Site -- Zerocross Density Decomposition: a Novel Signal Decomposition Method -- DSS – A Class of Evolving Information Systems -- A Deep Knowledge-Based Evaluation of Enterprise Applications Interoperability -- Sentiment-Based Decision Making Model for Financial Markets.
    Additional Edition: ISBN 3-030-39249-X
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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