Computational Science - ICCS 2024

Name: Computational Science - ICCS 2024 | 24th International Conference, Malaga, Spain, July 2-4, 2024, Proceedings, Part IV
Brand: Springer
Price: 149.79 EUR
Availability: OnlineOnly

24th International Conference, Malaga, Spain, July 2-4, 2024, Proceedings, Part IV

Leonardo Franco Clélia de Mulatier Maciej Paszynski Valeria V. Krzhizhanovskaya Jack J. Dongarra Peter M. A. Sloot(Editor)

Springer (Publisher)

Published on 27. June 2024

XXIV, 398 pages

E-Book

PDF with digital watermarking

System requirements

978-3-031-63772-8 (ISBN)

€149.79incl. 7% vat

System requirements

for PDF with digital watermarking

E-Book Single Licence

Available for download

Description

Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.

Alles über E-Books, Kopierschutz & Dateiformate finden Sie in unserem Info- & Hilfebereich.

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

More details

Other editions

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

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Computational Science - ICCS 2024

Description

More details

Other editions

Additional editions

Content

System requirements