
Advances in Computational Collective Intelligence
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This two-volume set CCIS 2165-2166 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 67 full papers included in this book were carefully reviewed and selected from 234 submissions.
The main track, covering the methodology and applications of CCI, included: collective decision-making, data fusion, deep learning techniques, natural language processing, data mining and machine learning, social networks and intelligent systems, optimization, computer vision, knowledge engineering and application, as well as Internet of Things: technologies and applications. The special sessions, covering some specific topics of particular interest, included: cooperative strategies for decision making and optimization, security and reliability of information, networks and social media, anomalies detection, machine learning, deep learning, digital image processing, artificial intelligence, speech communication, IOT applications, natural language processing, innovative applications in data science.
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Content
- Intro
- Preface
- Organization
- Contents - Part II
- Contents - Part I
- Cybersecurity, Blockchain Technology, and Internet of Things
- Enhanced Intrusion Detection Based Hybrid Meta-heuristic Feature Selection
- 1 Introduction
- 2 Related Works
- 3 Proposed Methodology
- 3.1 Datasets
- 3.2 Data Preparation Stage
- 3.3 Features Selection
- 3.4 Class Imbalance Processing
- 3.5 Machine Learning Algorithms
- 4 Experiment Results
- 5 Conclusion
- References
- Data Distribution-Based Change Detection Framework in SWaT Security Monitoring
- 1 Introduction
- 2 State-of-the-Art
- 2.1 K-Dimensional-Quad-Tree
- 2.2 CUmulative SUM
- 2.3 Crámer-von Mises
- 2.4 Principal Component Analysis-Change Detection
- 2.5 Kolmogorov-Smirnov WINdowing
- 2.6 One-Class Support Vector Machine
- 3 Proposed Framework and Dataset
- 3.1 Secure Water Treatment Dataset
- 3.2 Proposed Framework
- 4 Results and Discussion
- 4.1 Comparative Analysis of Change Detection Algorithms
- 4.2 Dashboards
- 5 Conclusion and Future Work
- References
- Fuzzy Rule-Based Anomaly Explanation in Micro-electromechanical Systems
- 1 Introduction
- 2 MEMS-Based Inertial Sensor
- 3 Anomaly Detection by Fuzzy Systems
- 3.1 Fuzzy Rule-Based System (FRBS)
- 3.2 Bacterial Memetic Algorithm (BMA)
- 3.3 BMA Applied on FRBS
- 3.4 Bacterial Mutation
- 3.5 Gene Transfer
- 3.6 Levenberg-Marquardt Method
- 4 Anomaly Detection by Fuzzy Systems in MEMS Based Sensor Production
- 4.1 Dataset
- 4.2 Rule Initialization by Exploiting Domain Knowledge
- 4.3 Training and Results
- 5 Conclusion
- References
- Analysis of Network Intrusion Detection and Potential Botnets Identification Using Selected Machine Learning Techniques
- 1 Introduction
- 2 Related Works
- 3 Methedology
- 3.1 Results and Analysis of Experiments
- 4 Conclusion and Future Work
- References
- MBMD-LoRa Scalable LoRaWAN for Internet of Things: A Multi-band Multi-data Rate Approach
- 1 Introduction
- 2 Background and Related Work
- 3 System Model for LoRaWAN
- 3.1 Propagation Model
- 3.2 Simulation Model
- 4 Multi-band Muti-data Rate for LoRaWAN
- 4.1 Slim Data Rate
- 4.2 Zone-Based MBMD Implementation (MBMZ-LoRa)
- 5 Performance Evaluation
- 5.1 Network Throughput
- 5.2 Energy Consumption
- 6 Conclusion
- References
- M2M Interface for IoT Traffic Light with Computer Vision and AnyLogic PLE
- 1 Introduction
- 2 Materials and Methods
- 2.1 Tasks Related to Integrating Traffic Lights with Computer Vision and a Server Based on M2M (Machine-to-Machine) Communication
- 2.2 The Design Stage
- 2.3 Implementation Stage
- 2.4 The Testing Stage
- 3 Discussion
- 4 Conclusion
- References
- An Automated and Verbose Approach for Detecting Anomalies in Cloud Computing Platform Using Logs
- 1 Introduction
- 2 Background and Related Work
- 3 Monilog Approach
- 3.1 Vectorizing Logs
- 3.2 Detecting Anomalies
- 3.3 Consolidation and Alerting
- 4 Experimental Setup
- 4.1 Dataset
- 4.2 Anomalies
- 4.3 Models
- 4.4 Error Metrics
- 5 Evaluation
- 5.1 Precision Evaluation
- 5.2 Critical Events Forecasting
- 6 Conclusion
- 7 Lesson Learned and Future Work
- References
- Cooperative Strategies for Decision Making and Optimization
- Efficiency of Specialized Genetic Operators in Non-dominated Tournament Genetic Algorithm (NTGA2) Applied to Multi-objective Multi-skill Resource Constrained Project Scheduling Problem
- 1 Introduction
- 2 Related Work
- 2.1 Semi-specialized Operators
- 2.2 Problem Specialized Operators
- 3 Multi-skill Resource Constrained Project Scheduling Problem
- 4 Method
- 4.1 Non-dominated Tournament Genetic Algorithm
- 4.2 Specialized Genetic Operators for MS-RCPSP
- 5 Experiments
- 5.1 Experimental Setup
- 5.2 Results
- 6 Summary and Future Work
- References
- Maximum Entropy Model of Synonym Selection in Post-editing Machine Translation into Kazakh Language
- 1 Introduction
- 2 Related works
- 3 Method
- 3.1 Semantic Cube Model and Algorithm Used to Correct Wrong Words
- 3.2 An Example of Choosing a High-Probability Synonym for a Wrongly Translated Kazakh Word
- 4 Experiment
- 5 Conclusion and future works
- References
- Cross-Domain Abbreviation Disambiguation on Vietnamese Clinical Texts in Online Processing
- 1 Introduction
- 2 A Cross-Domain Abbreviation Disambiguation Task on Vietnamese Clinical Texts in Online Processing
- 3 The Proposed Solution
- 3.1 Solution Details
- 3.2 The Proposed Nonparametric Self-training Method
- 3.3 The Characteristics of the Proposed Work
- 4 An Empirical Evaluation
- 4.1 Research Questions
- 4.2 Experiment Settings
- 4.3 Experimental Results and Discussions
- 5 Conclusion
- References
- Exploring the Potential of Generative Models in Promoting Local Language News Consumption
- 1 Introduction
- 2 Problem Description
- 3 The Evolutionary Trajectory of Generative Models
- 4 Related Work
- 5 Data Analysis and Preprocessing
- 6 Methods and Algorithms
- 7 Data Modelling and Training
- 8 Performance Measurement
- 9 Conclusion and Future Work
- References
- New Methodology for Attack Patterns Classification in Deep Brain Stimulation*-8pt
- 1 Introduction
- 2 Recent Works
- 3 Methodology
- 3.1 Signal Preprocessing
- 3.2 Convolutional Neural Network
- 3.3 Bidirectional Long Short-Term Memory Network
- 3.4 Convolutional Neural Network and Bidirectional Long Short-Term Memory for Different Attack Patterns Classification
- 4 Experimental Results
- 4.1 Dataset Description
- 4.2 Experiment Setup and Result Analysis
- 5 Conclusion
- References
- An Evolutionary Algorithm and a Clustering Technique to Select Good Subsets of Test for Finite State Machines
- 1 Introduction
- 2 Description of the Problem and Methods
- 2.1 The Formalism
- 2.2 Multi-objective Genetic Algorithm
- 2.3 Cluster Algorithm
- 3 Experiments
- 4 Conclusions and Future Work
- References
- Computational Intelligence for Digital Content Understanding
- Fusing Visual and Textual Representations via Multi-layer Fusing Transformers for Vietnamese Visual Question Answering
- 1 Introduction
- 2 Related Works
- 3 Our Model
- 3.1 Language Embedding Module
- 3.2 Visual Embedding Module
- 3.3 Cross-Attention Module
- 3.4 Answer Selector
- 4 Experiments
- 4.1 Dataset
- 4.2 Evaluation Metrics
- 4.3 Experimental Settings
- 4.4 Results
- 4.5 Ablation Studies
- 5 Conclusion
- References
- Analyzing the Publicization of Drought Debates in Arizona Newspapers
- 1 Introduction
- 2 Literature Review and Methodology
- 2.1 Literature Review
- 2.2 An Instrumented Methodology
- 3 Corpora
- 4 Analysis
- 5 Conclusion
- References
- Comparison of the Effectiveness of ANN and CNN in Image Classification
- 1 Introduction
- 2 Researching the Implementation of Artificial Neural Networks
- 3 Computational Experiment Assumptions
- 4 Results of the Experiment Obtained Using ANN
- 5 Results of the Experiment Obtained Using CNN
- 6 Summary
- References
- Study Neural Model for Recognition of Ancient Turkic Orkhon Runes
- 1 Introduction
- 2 Related Works
- 3 Method
- 3.1 Development of a Neural Model for Recognition of Ancient Turkic Orkhon Runes
- 3.2 Researching the Signs of the Ancient Turkic Orkhon Alphabet and Forming a Dataset
- 3.3 Creating a Script to Segment Input from an Image Containing Orkhon Runes
- 4 Experiments and Results
- 5 Discussion
- 6 Conclusion
- References
- Topic Modeling with Variable Neighborhood Search
- 1 Introduction
- 2 Related Work
- 3 Approach
- 3.1 Definition
- 3.2 Variable Neighborhood Search
- 3.3 Topic Modeling with VNS
- 4 Experiments
- 4.1 Results
- 5 Conclusion
- References
- A Neuro-Symbolic Classification Algorithm Using Neural Cell Assemblies
- 1 Introduction
- 2 Related Work
- 3 The Proposed Neuro-Symbolic System
- 3.1 A Brain-Inspired Representation
- 3.2 A (Somewhat) Equivalent Explicit Representation
- 3.3 Learning Prototypes
- 3.4 Decoding the Representations
- 3.5 Learning Rules
- 4 Case Studies
- 5 Conclusions
- References
- Transforming Challenges: Siamese-Based Vision Transformers for Robust Occluded Face Recognition
- 1 Introduction
- 2 Related Work
- 3 Proposed Approach
- 3.1 Siamese-Based ViT for Degraded Face Recognition
- 3.2 ViT Encoder
- 3.3 Recognition Stage
- 3.4 Optimization Process
- 4 Experiments AndDiscussion
- 4.1 Datasets
- 4.2 Results and Discussion
- 5 Conclusion
- References
- Recent Methods and Algorithms in Speech Segmentation Tasks
- 1 Introduction
- 2 Standard Speech Diarization System
- 3 Methodology
- 4 Metrics
- 5 Speaker Diarization Frameworks
- 6 Main Challenges Requiring Improvement in Speaker Diarization
- 7 Conclusion
- References
- Knowledge Engineering and Application for Industry 4.0
- Testing Usability of Different Implementations for VR Interaction Methods
- 1 Introduction
- 2 Related Works
- 3 Environment Setup
- 4 Experiment
- 5 Results
- 6 Conclusions
- References
- Retrofitting a Legacy Cutlery Washing Machine Using Computer Vision
- 1 Introduction
- 2 Related Studies
- 3 Methodology
- 3.1 Hardware Setup
- 3.2 Object Detection
- 3.3 YOLOv5 and Faster-RCNN
- 3.4 Color Image Segmentation (CIS)
- 4 Results and Discussion
- 4.1 Generalizability
- 4.2 Utilization and Speed Computation
- 4.3 Constraints
- 5 Conclusion
- References
- On Plagiarism and Software Plagiarism
- 1 Introduction
- 2 Comparison with Other Kinds of Plagiarism
- 3 Academia
- 4 Legal Aspects and Lawsuits
- 5 Classes
- 6 Techniques in Automatic Plagiarism Detection
- 7 Project Martial: Current and Future Work
- References
- Automatic Detection of Ambulance Vehicles in Day and Night Conditions in Surveillance Videos
- 1 Introduction
- 2 Related Work
- 3 Ambulance Vehicles
- 4 Ambulance Emergency Lights Detection
- 5 Conclusions and Further Research
- References
- Tools for Identifying and Preventing Loneliness in Older Adults
- 1 Introduction
- 2 Research Methodology
- 3 Related Work
- 4 The Digi-Ageing Digital Tools
- 4.1 Screening tool
- 4.2 Reminiscence tool
- 5 Evaluation
- 6 Discussion and Future Work
- References
- A Clustering Approach for Personalized Coaching Applications
- 1 Introduction
- 2 Preliminary Results
- 3 Description of the Proposed Solution
- 4 Clustering Procedure
- 4.1 Experimental Setup
- 4.2 Theoretical Context Setup
- 4.3 Clustering Definition
- 4.4 Resulting Clusters
- 5 Classification Results
- 5.1 Performance Evaluation : Accuracy and F1 Score
- 5.2 Fitting Time
- 6 Conslusions and Discussion
- References
- Addressing Initialization and Data Ordering Issues in Latent Factor-Based Recommendation Systems
- 1 Introduction
- 2 Related Work
- 2.1 Problem Statement
- 2.2 Latent Factor Model
- 2.3 Objective Function for Learning Latent Factor Matrices
- 2.4 Initialization of Latent Factor Matrices
- 3 Motivation
- 4 Proposed Method
- 4.1 Interpretation of Latent Factor Matrices
- 4.2 Initialization of Base User Matrix and User Coordinate Matrix (UCInit)
- 4.3 Data Ordering in the System Training
- 5 Experiment
- 5.1 Experiment Setup
- 5.2 Dataset
- 5.3 Measure
- 5.4 Experiment Result and Discussion
- 6 Conclusion
- References
- Usability Assessment of the Use-Case Model Textual Specification Language
- 1 Introduction
- 2 Related Works
- 3 Research Plan
- 3.1 Objectives
- 3.2 Participants
- 3.3 Research Methods
- 3.4 Data Analysis
- 4 Research Results
- 4.1 Syntax Learnability
- 4.2 Notation Applicability in the Business Context
- 4.3 Notation Efficiency
- 4.4 Tool Support
- 4.5 Perceived Satisfaction and Notation Effectiveness
- 4.6 Conclusions
- 5 Threats to Validity
- 5.1 Internal Validity
- 5.2 External Validity
- 6 Summary
- References
- Collective Intelligence in Healthcare
- A Multi-view Spatio-Temporal EEG Feature Learning for Cross-Subject Motor Imagery Classification
- 1 Introduction
- 2 Related Work
- 3 Materials and Methods
- 3.1 Considered Datasets
- 3.2 Preprocessing
- 3.3 Spatio-Temporal FocalNet Module
- 4 Experimental Results and Discussion
- 4.1 Experimental Setup
- 4.2 Scoring Performance
- 4.3 Comparative Study Using Different Classification Schemes
- 5 Discussion
- 6 Conclusion
- References
- A New MLEM Reconstruction Algorithm for Ultra-low Dose PET
- 1 Introduction
- 2 Forward Model Formulation
- 3 Statistical Considerations
- 4 Experimental Results
- 5 Conclusion
- References
- Cerebral Cortex Extraction Methods Based on a Priori Knowledge for T1-Weighted MRI Images
- 1 Introduction
- 2 Related Works
- 3 Proposed Methods
- 3.1 Phase 1: Pretreatment
- 3.2 Phase 2: Cerebral Cortex Extraction
- 3.3 CCE2
- 4 Experimental Results and Discussion
- 4.1 Evaluation Metrics
- 4.2 Results and Discussions
- 5 Conclusion
- References
- Modelling of Drug-Induced Liver Injury with Multiple Machine Learning Algorithms
- 1 Background
- 2 Methods
- 3 Results and Discussion
- 4 Conclusion
- References
- Temporal Focal Modulation Networks for EEG-Based Cross-Subject Motor Imagery Classification
- 1 Introduction
- 2 Related Work
- 2.1 Deep Learning Approaches for Motor Imagery (MI) Recognition
- 2.2 Transformer-Based Models
- 3 The Proposed Methodology
- 4 Experimental Results and Discussion
- 4.1 Considered Datasets
- 4.2 Implementation Details
- 4.3 Scoring Performance
- 4.4 Comparative Study with Established Models
- 4.5 Discussion
- 4.6 Conclusion
- References
- Blood Glucose Prediction in Type 1 Diabetes Based on Long Short-Term Memory
- 1 Introduction
- 2 Prediction Models
- 2.1 Long Short-Term Memory (LSTM)
- 2.2 Bidirectional Long Short-Term Memory
- 2.3 Stacked Long Short-Term Memory
- 3 System Architecture
- 3.1 Data Set
- 3.2 Clustering External Data
- 4 Results
- 5 Conclusions
- References
- Multi-method Analysis for Early Diagnosis of Alzheimer's Disease on Magnetic Resonance Imaging (MRI) Using Deep Learning and Hybrid Methods
- 1 Introduction
- 2 Methodology
- 2.1 Datasets Selection
- 2.2 Image Preprocessing
- 3 Data Augmentation
- 4 Data Characterization
- 5 Classification Model Training
- 6 Experimental Result and Discussion
- 6.1 Splitting Dataset
- 6.2 Model Evaluation
- 6.3 Analyzing the Outcomes of the OASIS Dataset
- 6.4 Analyzing Alzheimer's MRI Dataset Using CNN Models
- 6.5 Evaluating the Findings of the MRI Dataset Using Hybrid CNN Models with SVM
- 6.6 Comparing Performance: Deep Learning vs. Hybrid Deep and Machine Learning Approaches
- 7 Conclusion
- References
- Author Index
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