Computational Collective Intelligence (eBook, PDF)

16th International Conference, ICCCI 2024, Leipzig, Germany, September 9-11, 2024, Proceedings, Part II
Redaktion: Nguyen, Ngoc Thanh; Kozierkiewicz, Adrianna; Vossen, Gottfried; Treur, Jan; Núñez, Manuel; Ludwig, André; Franczyk, Bogdan

• Format: PDF

Produktbeschreibung

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 9-11, 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

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 395
• Erscheinungstermin: 5. September 2024
• Englisch
• ISBN-13: 9783031708190
• Artikelnr.: 72243091

Inhaltsangabe

.- 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-Supervised Learning and Few Shot Learning.

.- Daily activities forecasting for long-term elderly behavior change detection.

.- Detection of Fake Facial Images and Changes in Real Facial Images.

.- TabGAN-powered Data Augmentation and Explainable Boosting-based Ensemble Learning for Intrusion Detection in Industrial Control Systems.

.- Malware detection among contact tracing apps with deep learning.

.- Cooperative Strategies for Decision Making and Optimization.

.- Modeling the Functioning of Decision Trees Based on Decision Rule Systems by Greedy Algorithm.

.- Delays in computing with parallel metaheuristics on HPC infrastructure.

.- Reinforcement Learning-Based Cooperative Traffic Control System.

.- Discovering Spatial Prevalent Co-location Patterns by Once Scanning Datasets without Generating Candidates.

.- New results for some Tur´an problem instances obtained using the reinforcement learning technique.

.- Computational Intelligence for Digital Content Understanding.

.- Impact of acquisition parameters on the performance of radiomic systems.

.- Feature Explainability and Enhancement for Skin Lesion Image Analysis.

.- Toward Intelligent Ethnicity Recognition and Face Anonymization: An IncepX-Ensemble Model Approach.

.- Weak Supervised Asphalt Pavement Segmentation.

.- New Presence-Dependent Binary Similarity Measures for Pairwise Label Comparisons in Multi-label Classification.

.- Synergistic Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architectures.

.- Knowledge Engineering and Application for Industry 4.0.

.- High learning hierarchical neural networks.

.- A new method of detecting Alzheimer's disease.

.- GCC Aware Glaucoma Detection Using Macula OCT Image Analysis Based on Deep Convolutional Neural Networks.

.- Understanding Geometric Relationship Concepts in Few-Shot Learning.

.- Applicability criterion of the Non Overlapping Template Matching algorithm from NIST Statistical Test Suite SP800-22 for long aperiodic patterns.

.- Enhanced Activity Recognition through Joint utilization of Decimal Descriptors and Temporal Binary Motions.

.- Using Multilevel Temporal Factorisation to Analyse Structure and Dynamics for Higher-Order Adaptive and Evolutionary Processes.