
Computational Science - ICCS 2024
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The 7-volume set LNCS 14832 - 14838 constitutes the proceedings of the 24th International Conference on Computational Science, ICCS 2024, which took place in Malaga, Spain, during July 2-4, 2024.
The 155 full papers and 70 short papers included in these proceedings were carefully reviewed and selected from 430 submissions.
They were organized in topical sections as follows:
Part I: ICCS 2024 Main Track Full Papers;
Part II: ICCS 2024 Main Track Full Papers;
Part III: ICCS 2024 Main Track Short Papers; Advances in High-Performance Computational Earth Sciences: Numerical Methods, Frameworks and Applications; Artificial Intelligence and High-Performance Computing for Advanced Simulations;
Part IV: Biomedical and Bioinformatics Challenges for Computer Science; Computational Health;
Part V : Computational Optimization, Modelling, and Simulation; Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine; Machine Learning and Data Assimilation for Dynamical Systems; Multiscale Modelling and Simulation;
Part VI: Network Models and Analysis: From Foundations to Artificial Intelligence; Numerical Algorithms and Computer Arithmetic for Computational Science; Quantum Computing;
Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks, and Artificial Intelligence; Solving Problems with Uncertainties; Teaching Computational Science
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Content
- Intro
- Preface
- Organization
- Contents - Part IV
- Biomedical and Bioinformatics Challenges for Computer Science
- Exploiting Medical-Expert Knowledge Via a Novel Memetic Algorithm for the Inference of Gene Regulatory Networks
- 1 Introduction
- 2 Related Work
- 3 Proposed Approach
- 4 Experimentation
- 4.1 Parameter Settings
- 4.2 Comparison with GENECI
- 5 Conclusions and Future Work
- References
- Human Sex Recognition Based on Dimensionality and Uncertainty of Gait Motion Capture Data
- 1 Introduction
- 2 Related Work
- 3 Correlation Dimension and Sample Entropy
- 4 Dataset
- 5 Experimental Setup
- 6 Results
- 7 Summary and Conclusions
- References
- A Multi-domain Multi-task Approach for Feature Selection from Bulk RNA Datasets
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 4 Data
- 5 Experiment
- 6 Results
- 7 Conclusions
- References
- Neural Dynamics in Parkinson's Disease: Integrating Machine Learning and Stochastic Modelling with Connectomic Data
- 1 Introduction
- 2 Methods
- 2.1 Discrete Brain Network Model of CBGTH
- 2.2 Stochastic Brain Network Model of CBGTH
- 3 Results
- 4 Discussion
- 5 Conclusions
- References
- Investigation of Energy-Efficient AI Model Architectures and Compression Techniques for ``Green'' Fetal Brain Segmentation
- 1 Introduction
- 1.1 Fetal Brain Segmentation
- 1.2 ``Green'' Deep Learning
- 1.3 Contribution
- 2 Methods
- 2.1 Setup and Hardware
- 2.2 Used Datasets
- 2.3 Energy Usage and Performance Measure
- 2.4 Experimental Design
- 2.5 Evaluated Techniques
- 3 Results and Discussion
- 3.1 Final Model Choice
- 3.2 Conclusions and Recommendations
- References
- Negation Detection in Medical Texts
- 1 Introduction
- 2 Basic Definition and Open Problems
- 3 Main Approaches to Negation Detection
- 3.1 Rule-Based Approaches
- 3.2 Machine Learning Approaches
- 3.3 Deep Learning Approaches
- 4 Discussions and Results
- References
- EnsembleFS: an R Toolkit and a Web-Based Tool for a Filter Ensemble Feature Selection of Molecular Omics Data
- 1 Introduction
- 2 Methods
- 2.1 Feature Selection and Classification Algorithms
- 2.2 Ensemble Feature Selection
- 3 EnsembleFS an R Toolkit
- 3.1 Web Application
- 3.2 R Package
- 4 Use Case
- 5 Computational Aspects
- 6 Summary
- References
- A Method for Inferring Candidate Disease-Disease Associations
- 1 Background
- 2 Materials and Methods
- 2.1 Datasets
- 2.2 Gene-Disease Associations
- 2.3 Disease-Disease Associations
- 2.4 Score Evaluation
- 3 Results and Discussion
- 4 Conclusion
- References
- Network Model with Application to Allergy Diseases
- 1 Introduction
- 2 Hierarchical Logistic Network Models
- 2.1 Generative Model
- 2.2 Misspecified Model
- 3 Application to Modelling Allergic Diseases
- 3.1 The Structure of the Model
- 3.2 Generative and Misspecified Models of Allergy Diseases
- 3.3 Comparison of Two Versions of Our Model
- 3.4 Estimation of Parameters and Evaluation of the Model
- 4 Discussion
- 5 Conclusions
- References
- TM-MSAligner: A Tool for Multiple Sequence Alignment of Transmembrane Proteins
- 1 Introduction
- 2 Software Description
- 2.1 Software Architecture
- 2.2 Transmembrane Proteins Features
- 2.3 Execution Modes
- 2.4 Output Results
- 3 Illustrative Example
- 4 Discussion
- 5 Conclusions
- References
- Determining Mouse Behavior Based on Brain Neuron Activity Data
- 1 Introduction
- 2 Background and Related Work
- 3 Classification of Mouse Position on a Circular Track
- 4 Regression of Mouse Position on a Circular Track
- 5 Conclusions
- References
- Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verification
- 1 Introduction
- 2 Methods
- 2.1 Prompt-Engineering ChatGPT for Simulated-Articles Generation
- 2.2 Feature Extraction and Biological Graph Construction
- 2.3 Fact-Checking ChatGPT Biological Graphs
- 3 Results
- 4 Discussion
- 5 Conclusion and Future Direction
- References
- Identification of Domain Phases in Selected Lipid Membrane Compositions
- 1 Introduction
- 2 Methods
- 2.1 System Preparation and Simulation
- 2.2 Lipid Features and Machine Learning Techniques
- 3 Results and Discussion
- 4 Conclustions
- References
- MonoWeb: Cardiac Electrophysiology Web Simulator
- 1 Introduction
- 2 Methods
- 2.1 Monodomain Model
- 2.2 Trame
- 2.3 Paraview
- 3 MonoWeb
- 3.1 Configuring the Cellular Model
- 3.2 Configuring the Stimuli
- 4 Case Studies
- 5 Conclusion
- References
- Enhancing Breast Cancer Diagnosis: A CNN-Based Approach for Medical Image Segmentation and Classification
- 1 Introduction
- 2 Related Works
- 3 Methods
- 3.1 Datasets of Breast Ultrasound Images and Preprocessing
- 3.2 Convolutional Neural Networks (CNNs) Architecture
- 3.3 Proposed Method
- 4 Results and Discussion
- 4.1 Breast Cancer Segmentation Results
- 4.2 Breast Cancer Classification Results
- 5 Conclusions
- References
- Integration of Self-supervised BYOL in Semi-supervised Medical Image Recognition
- 1 Introduction
- 2 Related Work
- 3 A Brief on BYOL
- 4 Proposed Method
- 4.1 Pre-training
- 4.2 Fine-Tuning
- 5 Experiments and Results
- 5.1 Datasets
- 5.2 Hyperparameter Tuning
- 5.3 Results
- 6 Conclusion
- References
- Computational Health
- Local Sensitivity Analysis of a Closed-Loop in Silico Model of the Human Baroregulation
- 1 Introduction
- 2 Methods
- 2.1 Baroreflex Model
- 2.2 Cardiovascular Circulation Model
- 2.3 Sensitivity and Orthogonality Analysis
- 3 Results
- 3.1 Local Sensitivity Analysis
- 3.2 Orthogonality Analysis
- 4 Discussion
- 5 Conclusions and Further Work
- References
- Healthcare Resilience Evaluation Using Novel Multi-criteria Method
- 1 Introduction
- 2 Methodology
- 3 Results
- 4 Conclusions
- References
- Plasma-Assisted Air Cleaning Decreases COVID-19 Infections in a Primary School: Modelling and Experimental Data
- 1 Introduction
- 2 Methods
- 2.1 Experiment Description and Experimental Results
- 2.2 Statistical Model for Data Interpretation
- 2.3 SIR Model Assumptions and Equations
- 2.4 Sensitivity Analysis on SIR Model
- 3 Results
- 3.1 Statistical Modelling Results
- 3.2 SIR Modelling Results
- 4 Uncertainty Quantification
- 5 Conclusion
- References
- Modelling Information Perceiving Within Clinical Decision Support Using Inverse Reinforcement Learning
- 1 Introduction
- 2 Related Works
- 3 Modelling Perceiving in Clinical Decision Process with CDSS Interaction
- 3.1 CDSS Data
- 3.2 Initializing MDP for CDSS Data
- 4 Case Study: T2DM Risk Prediction Perceiving
- 4.1 Simulating Risk Prediciton Using MDP
- 4.2 Inferring Reward Functions for Trajectories
- 5 Discussion
- 6 Conclusion and Future Work
- References
- Modelling of Practice Sharing in Complex Distributed Healthcare System
- 1 Introduction
- 2 Modelling Practice Sharing in Complex Healthcare System
- 2.1 Quantitative Medical Practice
- 2.2 Physician Practice Sharing Activity
- 2.3 Distributed Medical Network Structure.
- 2.4 Simulation
- 2.5 Evaluation Analysis Methods
- 3 Practice Sharing in Vertigo Treatment
- 3.1 Data Set and Processes
- 3.2 Model Identification, Validation, and Sensitivity Analysis Based on Actual Data
- 3.3 Simulation Scenario Expansion: Long-Term, Large-Scale, and Variant Studies
- 4 Discussion
- 5 Conclusion and Future Work
- References
- Simulation and Detection of Healthcare Fraud in German Inpatient Claims Data
- 1 Introduction
- 2 Related Work
- 3 Data Generation
- 3.1 Inpatient Claims Modeling
- 3.2 Evaluation of the Simulation Results
- 4 Fraud Detection
- 4.1 Results
- 4.2 Discussion
- 5 Conclusion
- References
- The Past Helps the Future: Coupling Differential Equations with Machine Learning Methods to Model Epidemic Outbreaks
- 1 Introduction
- 2 Methods
- 2.1 SIRD Model
- 2.2 Physics-Informed Neural Network
- 2.3 Data
- 3 Results
- 3.1 Forecasting of Post-peak Incidence
- 3.2 Peak Prediction
- 4 Discussion
- References
- Combining Convolution and Involution for the Early Prediction of Chronic Kidney Disease
- 1 Introduction
- 2 Related Work
- 3 Methods
- 3.1 Problem Definition
- 3.2 Model
- 3.3 Cohort Selection
- 3.4 Feature Engineering
- 3.5 Pre-processing
- 3.6 Machine Learning Model
- 4 Results and Discussion
- 4.1 Experimental Setup
- 4.2 Experimental Results
- 5 Conclusion
- References
- Segmentation of Cytology Images to Detect Cervical Cancer Using Deep Learning Techniques
- 1 Introduction
- 2 Materials and Methods
- 2.1 Dataset Collection
- 2.2 Pre-processing of the Data
- 2.3 Implementation Details
- 2.4 Training the Model
- 3 Results and Discussion
- 4 Conclusions
- References
- Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 3.1 Experimental Concept
- 3.2 Data Sets
- 3.3 Model Architectures
- 4 Experiments
- 4.1 Experimental Setup
- 4.2 Model Quality
- 4.3 Data Quality and Privacy
- 4.4 Computational Resources
- 5 Conclusions
- References
- Visual Explanations and Perturbation-Based Fidelity Metrics for Feature-Based Models
- 1 Introduction
- 2 Related Works
- 2.1 Evaluation Metrics for eXplainable AI - Challenges and Prospects
- 2.2 InceptionTime Deep Network for Time Series Classification
- 3 Methodology
- 3.1 Dataset
- 3.2 RNN-Autoencoder Extension
- 3.3 Visualisation of Importances
- 3.4 AUC Perturbational Accuracy Loss Metric
- 4 Results
- 4.1 Joint Evaluation of Normal and Anomaly classes
- 4.2 Insights from Individual Class Analysis
- 5 Discussion
- 6 Conclusion
- References
- Understanding Survival Models Through Counterfactual Explanations
- 1 Introduction
- 2 Related Works
- 2.1 Survival Analysis Background
- 2.2 Explainable AI and Counterfactual Explanations
- 3 Research Methods
- 3.1 Generation of Counterfactual Explanations
- 3.2 Particle Swarm Optimization
- 3.3 Datasets Description
- 4 Results and Discussion
- 4.1 Particle Swarm Optimization Vs. Simulated Annealing
- 4.2 Survival-Scores-Based Counterfactual Explanations
- 4.3 Survival-Patterns-Based Counterfactual Examples
- 4.4 Actionability of Counterfactual Explanations
- 5 Conclusion
- References
- Large Language Models for Binary Health-Related Question Answering: A Zero- and Few-Shot Evaluation
- 1 Introduction
- 2 Related Work
- 3 Experimental Design
- 3.1 Models
- 3.2 Datasets
- 3.3 Contexts
- 4 Zero-Shot Evaluation
- 5 Few-Shot Evaluation
- 6 Data Contamination
- 7 Error Analysis
- 8 Concluding Remarks
- References
- Brain Tumor Segmentation Using Ensemble CNN-Transfer Learning Models: DeepLabV3plus and ResNet50 Approach
- 1 Introduction
- 2 Related Works
- 3 Materials and Methods
- 3.1 Brain MRI Datasets and Preprocessing
- 3.2 CNN-Transfer Learning Approaches: DeepLabV3plus with ResNet50
- 3.3 Performance Evaluation Metrics for Segmentation Approach
- 4 Results and Discussion
- 4.1 Results of CNN-Transfer Learning for Brain Tumor Segmentation: DeepLabV3plus and ResNet50 Approach
- 4.2 Comparison of the Proposed Method with Other Approaches
- 4.3 Comparative Analysis of Segmentation Performance Across All Data
- 5 Conclusion
- References
- Focal-Based Deep Learning Model for Automatic Arrhythmia Diagnosis
- 1 Introduction
- 2 Material and Methods
- 2.1 Assumptions
- 2.2 ECG Database
- 2.3 Methods: Preprocessing
- 2.4 Methods: LSTM-Focal Classification Model
- 3 Results of Classification
- 4 Discussion
- 5 Conclusion
- References
- Graph-Based Data Representation and Prediction in Medical Domain Tasks Using Graph Neural Networks
- 1 Introduction
- 2 Related Work
- 2.1 Transformation Methods
- 2.2 Methods of Health Records Transformation to Graph
- 3 Creating EHR Representation Through Graphs
- 3.1 Data Description
- 3.2 Pipeline Description
- 4 Experiments
- 5 Conclusion
- References
- Global Induction of Oblique Survival Trees
- 1 Introduction
- 2 Preliminaries
- 3 Evolutionary Induction
- 3.1 Representation, Initialization, and Termination Condition
- 3.2 Genetic Operators
- 3.3 Fitness Function
- 4 Preliminary Experimental Validation
- 5 Conclusions
- References
- Development of a VTE Prediction Model Based on Automatically Selected Features in Glioma Patients
- 1 Introduction
- 2 Materials and Methods
- 3 Model Building
- 3.1 Metrics
- 3.2 Feature Selection for the Final Model
- 3.3 Model Building
- 3.4 Results
- 3.5 Comparison with Logistic Regression
- 4 Model Comparison
- 5 Conclusions
- References
- Author Index
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