東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Data science = new issues, challenge...

Dzemyda, Gintautas.

FindBook

Google Book

Amazon

博客來

Data science = new issues, challenges and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science/ edited by Gintautas Dzemyda, Jolita Bernataviciene, Janusz Kacprzyk.
其他題名:	new issues, challenges and applications /
其他作者:	Dzemyda, Gintautas.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xviii, 313 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-39250-5
ISBN:	9783030392505

Data science = new issues, challenges and applications /
Data sciencenew issues, challenges and applications /[electronic resource] :edited by Gintautas Dzemyda, Jolita Bernataviciene, Janusz Kacprzyk. - Cham :Springer International Publishing :2020. - xviii, 313 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8691860-949X ;. - Studies in computational intelligence ;v.869..

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.

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.

ISBN: 9783030392505

Standard No.: 10.1007/978-3-030-39250-5doiSubjects--Topical Terms:

595739
Computational intelligence.

LC Class. No.: Q342 / .D383 2020

Dewey Class. No.: 006.3

Data science = new issues, challenges and applications /
LDR:04366nmm a2200349 a 4500 001 2216367
003 DE-He213
005 20200714165904.0
006 m d
007 cr nn 008maaau
008 201120s2020 sz s 0 eng d
020 $a 9783030392505 $q (electronic bk.)
020 $a 9783030392499 $q (paper)
024 7 $a 10.1007/978-3-030-39250-5 $2 doi
035 $a 978-3-030-39250-5
040 $a GP $c GP
041 0 $a eng
050 4 $a Q342 $b .D383 2020
072 7 $a UN $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a UN $2 thema
072 7 $a TB $2 thema
082 0 4 $a 006.3 $2 23
090 $a Q342 $b .D232 2020
245 0 0 $a Data science $h [electronic resource] : $b new issues, challenges and applications / $c edited by Gintautas Dzemyda, Jolita Bernataviciene, Janusz Kacprzyk.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xviii, 313 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.869
505 0 $a 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.
520 $a 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.
650 0 $a Computational intelligence. $3 595739
650 0 $a Data mining. $3 562972
650 0 $a Big data. $3 2045508
650 1 4 $a Data Engineering. $3 3409361
650 2 4 $a Computational Intelligence. $3 1001631
700 1 $a Dzemyda, Gintautas. $3 753184
700 1 $a Bernataviciene, Jolita. $3 3448628
700 1 $a Kacprzyk, Janusz. $3 572477
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.869. $3 3448629
856 4 0 $u https://doi.org/10.1007/978-3-030-39250-5
950 $a Intelligent Technologies and Robotics (Springer-42732)