Computational Collective Intelligence: 16th International Conference, ICCCI 2024, Leipzig, Germany, September 9-11, 2024, Proceedings, Part II

by Ngoc Thanh Nguyen (Editor), Bogdan Franczyk (Editor), André Ludwig (Editor), Manuel Núñez (Editor), Jan Treur (Editor)

Overview

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

Product Details

ISBN-13:	9783031708183
Publisher:	Springer Nature Switzerland
Publication date:	09/06/2024
Series:	Lecture Notes in Computer Science , #14811
Edition description:	2024
Pages:	395
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

.- 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.