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|a 1225198797
|a 1225548415
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|a 10.1007/978-3-030-63007-2
|2 doi
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|a (OCoLC)1225069109
|z (OCoLC)1225198797
|z (OCoLC)1225548415
|z (OCoLC)1237465186
|z (OCoLC)1241727252
|z (OCoLC)1264794654
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|b I33 2020eb
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|a ICCCI (Conference)
|n (12th :
|d 2020 :
|c Online)
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|a Computational collective intelligence :
|b 12th international conference, ICCCI 2020 Da Nang, Vietnam, November 30-December 3, 2020 proceedings /
|c Ngoc Thanh Nguyen, Bao Hung Hoang, Cong Phap Huynh, Dosam Hwang, Bogdan Trawiński, Gottfried Vossen (eds.).
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|a ICCCI 2020
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|a Cham, Switzerland :
|b Springer,
|c 2020.
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|a 1 online resource
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
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|a text file
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|b PDF
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|a Lecture Notes in Computer Science ;
|v 12496
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|a Lecture notes in artificial intelligence
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|a LNCS sublibrary, SL 7, Artificial intelligence
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|a International conference proceedings.
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|a "The conference was held virtually due to the COVID-19 pandemic."
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|a This volume constitutes the refereed proceedings of the 12th International Conference on Computational Collective Intelligence, ICCCI 2020, held in Da Nang, Vietnam, in November 2020.* The 70 full papers presented were carefully reviewed and selected from 314 submissions. The papers are grouped in topical sections on: knowledge engineering and semantic web; social networks and recommender systems; collective decision-making; applications of collective intelligence; data mining methods and applications; machine learning methods; deep learning and applications for industry 4.0; computer vision techniques; biosensors and biometric techniques; innovations in intelligent systems; natural language processing; low resource languages processing; computational collective intelligence and natural language processing; computational intelligence for multimedia understanding; and intelligent processing of multimedia in web systems. *The conference was held virtually due to the COVID-19 pandemic.
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|a Includes author index.
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|a Online resource; title from PDF title page (SpringerLink, viewed February 3, 2021).
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|a Intro -- Preface -- Organization -- Contents -- Knowledge Engineering and Semantic Web -- Towards a Holistic Schema Matching Approach Designed for Large-Scale Schemas -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 The HMO Approach -- 4.1 Overlaps and Distinctions Generator -- 4.2 Schema Overlaps Matcher -- 4.3 Distinct Elements Matcher -- 5 Experimental Results -- 5.1 Experimental Settings -- 5.2 Results and Discussion -- 6 Conclusion and Future Work -- References -- Overcoming Local Optima for Determining 2-Optimality Consensus for Collectives -- 1 Introduction
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|a 2 Related Work -- 3 Basic Notions -- 4 Proposed Approach -- 5 Experiments and Evaluation -- 5.1 Consensus Quality -- 5.2 Running Time -- 6 Conclusions -- References -- Fundamentals of Generalized and Extended Graph-Based Structural Modeling -- 1 Introduction -- 2 Formal Definitions -- 3 Extended Graph Generalization Abstract Syntax -- 4 Extended Graph Generalization Features -- 5 Extended Graph Generalization Semantics -- 6 Extended Graph Generalization Concrete Syntax -- 6.1 Extended Graph Generalization Diagram Example -- 7 A Social Network Model Example Expressed in the EGG Categories
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| 505 |
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|a 8 Conclusions -- References -- Social Networks and Recommender Systems -- Topic Diffusion Prediction on Bibliographic Network: New Approach with Combination Between External and Intrinsic Factors -- 1 Introduction -- 2 Preliminaries -- 2.1 Meta-Path -- 3 Proposed Approach -- 3.1 Supervised Learning for Topic Diffusion on Bibliographic Network -- 3.2 Activation Probability Feature -- 3.3 Author's Interest Feature -- 4 Experiments and Results -- 4.1 Experiments -- 4.2 Results -- 5 Conclusion and Future Work -- References
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8 |
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|a Detecting the Degree of Risk in Online Market Based on Satisfaction of Twitter Users -- 1 Introduction -- 2 Related Works -- 3 Research Problem -- 3.1 Definition of Tweet and Types of Tweet Sentiments -- 3.2 Definition of Different Types of User Satisfaction -- 3.3 Definition of Different Degrees of Risk -- 3.4 Research Question -- 4 Proposed Method -- 4.1 Feature Extraction -- 4.2 Tweet Sentiment Analysis -- 4.3 Determining the User Satisfaction, Dissatisfaction, and Hesitation -- 4.4 Calculating the Degree of Risk -- 5 Experiment -- 5.1 Data Acquisition -- 5.2 Evaluation Results
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|a 5.3 Results and Discussion -- 6 Conclusion and Future Work -- References -- Method of Detecting Bots on Social Media. A Literature Review -- 1 Introduction and Background -- 2 Type of Bots -- 3 Method of Detecting Social Bots -- 3.1 Social Media Giant Model -- 3.2 Experts-Approach -- 3.3 Collective Intelligence-Approach -- 3.4 Machine Learning Model -- 3.5 Hybrid Method -- 3.6 Graphical Modeling to Detect Bots -- 3.7 Behavioral Based-Model -- 4 Discussion and Open Challenges -- 5 Conclusions and Future Directions -- References
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|a Expert systems (Computer science)
|v Congresses.
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|a Intelligent agents (Computer software)
|v Congresses.
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|a Artificial intelligence
|v Congresses.
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|a Semantic Web
|v Congresses.
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|a Human-computer interaction
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|a Semantic Web
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|a Optical data processing
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|a proceedings (reports)
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|a Conference papers and proceedings
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|a Conference papers and proceedings.
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|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Nguyen, Ngoc Thanh
|c (Computer scientist)
|1 https://id.oclc.org/worldcat/entity/E39PBJyrXJQyVj9XYCk9Q4X68C
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| 700 |
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|a Hoang, Bao Hung.
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|a Huynh, Cong Phap.
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|a Hwang, Dosam.
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|a Trawiński, Bogdan.
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|a Vossen, Gottfried.
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|i has work:
|a Computational collective intelligence (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFrpKMrrBH3jtvgjKyqwvd
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|a ICCCI (Conference) (12th : 2020 : Online).
|t Computational collective intelligence.
|d Cham, Switzerland : Springer, 2020
|z 3030630064
|z 9783030630065
|w (OCoLC)1198979289
|
| 830 |
|
0 |
|a Lecture notes in computer science ;
|v 12496.
|
| 830 |
|
0 |
|a Lecture notes in computer science.
|p Lecture notes in artificial intelligence.
|
| 830 |
|
0 |
|a LNCS sublibrary.
|n SL 7,
|p Artificial intelligence.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-63007-2
|y Click for online access
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| 903 |
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|a SPRING-COMP2020
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| 994 |
|
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|a 92
|b HCD
|