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Computational Collective Intelligence: 16th International Conference, ICCCI 2024, Leipzig, Germany, September 911, 2024, Proceedings, Part II [Paperback]

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  • Category: Books (Computers)
  • ISBN-10:  3031708180
  • ISBN-10:  3031708180
  • ISBN-13:  9783031708183
  • ISBN-13:  9783031708183
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • SKU:  3031708180-11-SPRI
  • SKU:  3031708180-11-SPRI
  • Pages:  395
  • Pages:  395
  • Item ID: 106793653
  • List Price: $74.99
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This two-volume set LNAI 14810-14811 constitutes the refereed proceedings of the 16th International Conference on Computational Collective Intelligence, ICCCI 2024, held in Leipzig, Germany, during September 911, 2024.

The 59 revised full papers presented in these proceedings were carefully reviewed and selected from 234 submissions. 

Part I:  collective intelligence and collective decision-making; deep learning techniques; natural language processing; data mining and machine learning

Part II: social networks and intelligent system; cybersecurity, blockchain technology, and internet of things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; knowledge engineering and application for industry 4.0 

 

 

.- Social Networks and Intelligent Systems.

.- A deep learning approach to fine-grained political ideology classification on social media texts.

.- Enhancing Social Network Trust with Improved EigenTrust Algorithm.

.- An Empirical Analysis of the Usage of Requirements Attributes in Requirements Engineering Research and Practice.

.- Experimental Study on Link Prediction in Unweighted and Weighted Time-Evolving Organizational Social Network.

.- Assessing Student Quality of Life: Analysis of Key Influential Factors.

.- An Adaptive Network Model for Interpersonal Emotion Regulation in Multimodal Human-Bot Interaction.

.- Cybersecurity, Blockchain Technology, and Internet of Things.

.- Strengthening Network Intrusion Detection in IoT Environments with Self-Supervisl³,

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