Details

Subject(s)

• Computational intelligence [Browse]
• Artificial intelligence [Browse]

Related name

• Mahfouf, Mahdi [Browse]
• Mihaylova, Lyudmila S. [Browse]

Series

• Advances in Intelligent Systems and Computing, 1454 [More in this series]
• Advances in Intelligent Systems and Computing, 2194-5365 ; 1454 [More in this series]

Summary note

The scope of this book is to present the papers included at the 21st UK Workshop on Computational Intelligence (UKCI 2022), hosted by The University of Sheffield, between 7 and 9 September 2022, Sheffield, UK. This marks the first fully in-person UKCI conference, following the pandemic, a testament to the success and resilience of the UKCI community, as well as to the importance of computational intelligence (CI) research. The papers in this book are divided into five sections: fuzzy logic systems, machine learning, hybrid methods and network systems, deep learning and neural networks, and optimization and search.

Contents

• Intro
• Preface
• Contents
• Fuzzy Logic Systems
• Mixture Kernel-Based Fuzzy-Rough Feature Selection
• 1 Introduction
• 2 Fuzzy-Rough Sets
• 3 Mixture Kernel-Based Fuzzy-Rough Feature Selection
• 4 Experimental Evaluation
• 4.1 Experimental Set-Up
• 4.2 Subset Size
• 4.3 Classification Accuracy
• 5 Conclusion
• References
• Fuzzy Hidden Markov Chain Based Models for Time-Series Data
• 2 Model Structure
• 2.1 Multi-sequence HMM Structure
• 2.2 Fuzzy Inference Model Structure
• 3 Modelling
• 3.1 Input and Output Description
• 3.2 Hidden Markov Model Training
• 3.3 Interval Type-2 Fuzzy Logic Modelling
• 3.4 Fuzzy Rule Base Generation
• 4 Result and Discussion
• No Explanation Without (Fuzzy) Representation
• 2 AI and Explainability - Background
• 2.1 Black Box Models and Explainability
• 2.2 Symbolic AI and Explainability
• 3 A Conceptual Graph Approach to Collaborative Knowledge
• 3.1 Requirements
• 3.2 Ontologies and Knowledge Graphs
• 3.3 Conceptual Graphs
• 3.4 Reasoning with Conceptual Graphs
• 3.5 Fuzzy Conceptual Graphs
• 4 Experiments
• 4.1 Ontology Sources in Cyber-Security
• 4.2 Implementation Details
• 4.3 Limitations
• 5 Summary
• Noisy Instance Removal Using OWA-Based Fuzzy-Rough Sets
• 2 Theoretical Background
• 2.1 Rough Set Theory
• 2.2 Fuzzy-Rough Set Theory
• 2.3 The OWAFRDC Algorithm
• 3 Proposed Method
• 3.1 Measuring Noisiness
• 3.2 Quality Measures
• 4 Experimentation
• A Fuzzy Prescreening Tool to Assist in the Diagnosis of High Functioning Individuals on the Autism Spectrum Who Present with Mental Health Comorbidities
• 2 Design of the FIS
• 2.1 Input 1 and Input 2: Social_Communication and Restrictive_Repetative_Behaviour.
• 2.2 Input 3: Mental_Health
• 2.3 Input 4: Language
• 2.4 Input 5: Function
• 2.5 Output: Referral_Decision
• 3 Testing
• 3.1 Experimental Setup
• 3.2 Rule Base
• 3.3 Phase 1
• 3.4 Phase 2
• 4 Discussion
• 5 Conclusions
• 5.1 Future Work
• A Fuzzy Logic-Based Framework for Statistical Process Control in Additive Manufacturing
• 2 Multi-modal Monitoring for Additive Manufacturing
• 2.1 Calculation of the Features
• 2.2 Fuzzy C-Means Clustering
• 2.3 Multilinear Principal Component Analysis
• 2.4 Statistical Process Control
• 3 Experimental Results
• 3.2 Online Outlier Detection
• 4 Conclusion
• Towards Dynamic Fuzzy Rule Interpolation with Harmony Search
• 2 Background
• 2.1 Transformation-Based Fuzzy Rule Interpolation (T-FRI)
• 2.2 Harmony Search (HS)
• 3 Harmony Search-Based Dynamic Fuzzy Rule Interpolation
• 4 Simulation-Based Evaluation
• 4.1 Dataset and Experimental Setup
• 4.2 Experimental Results
• Fuzzy Inference for Well Log Lithology Classification
• 1.1 Problem Area
• 1.2 Background
• 2 Methods
• 2.1 Datasets
• 2.2 Input Well Logs
• 2.3 Fuzzy Inference System
• 2.4 Black-Box Learners
• 2.5 Evaluation
• 3 Results
• 4.1 Comparison with Force 2020 Results
• 4.2 Comparison Between Fuzzy Inference System and Black-Box Learners
• 4.3 Limitations and Future
• Machine Learning
• Laplacian Regularized Variational Few-Shot Learning for Image Classification
• 2 Related Work
• 3 Methodology
• 3.1 Variational Inference Using ELBO
• 3.2 Transductive Laplacian Regularized Prediction
• 4.1 Omniglot
• 4.2 miniImagenet
• 4.3 Results
• 5 Ablation Study.
• 5.1 With/without Transductive Laplacian Regularized Inference Module
• 5.2 Inference Time
• 6 Conclusion
• Modified Probabilistic Neural Networks LBP Classification Based on Distance Measures in Probability Space
• 2 Local Binary Pattern Preliminaries
• 3 Distance Measures
• 4 The Proposed Probabilistic Neural Network (PNN) with Probabilistic Distance Measures
• 4.1 PNN Based on Chi-Squared Distance Measure
• 4.2 PNN Based on Cosine Distance Measure
• 4.3 PNN Based on KLD Distance Measure
• 4.4 PNN Based on Bhattacharyya Distance Measure
• 5 Experiments
• Preliminary Introduction and Implementation of Novel Machine Learning Algorithm Utilising Pareto Principle: Classification of Small Biomedical Health-Related Datasets
• 2 Multi-objective Optimisation
• 3 Datasets
• 3.1 Immunotherapy
• 3.2 Cryotherapy
• 4 Pareto Distribution
• 5 Algorithmic Approach Building on Pareto Principle Application
• 5.1 Diagram
• 6 Experimental Classification
• 7 Comparison with Published Literature
• 8 Discussion and Results
• 9 Conclusion
• Nonlinear Model Combination Approach to Decentralised and Privacy-Preserving Classification
• 2 The Proposed Method
• 2.1 Global-Level Modelling
• 2.2 Local-Level Modeling
• 3 Experiments
• Classification of Imbalanced Immunotherapy and Health-Related Data Utilising Novel Machine Learning Experiments
• 2 Literature Review
• 4 Random Forest
• 5.1 Experiment 1: A Reproduced Immunotherapy Data Study
• 5.2 Experiment 2: Class Balancing
• 5.3 Experiment 3: Datasets Integration
• 5.4 Experiment 4: Datasets Comparison
• 5.5 Experiment 5: Effect of Minority Class.
• 6 Discussion and Results
• 7 Conclusion
• Automatic Scoring of Chair Sit-to-Stand Test Using a Smartphone
• 1 Background
• 2 Materials and Methods
• 2.1 Data Collection
• 2.2 Software System
• 2.3 Statistical Tests
• 3.1 Participant and CRS Classification
• 3.2 CST Characterisation Based on CRS
• Weight Management Programme: Use of Machine Learning Approaches to Identify Client Outcomes
• 2 Methodology
• 2.2 Data Preprocessing
• 2.3 Modelling Techniques
• 3 Discussion of Results
• 3.1 Weight Loss Prediction
• 3.2 Dropout Prediction
• 4 Summary and Discussion
• Injury Risk Prediction in Rugby League Players with Training Volume Data and Machine Learning
• 2.2 Modelling Techniques
• 2.3 Model Interpretation
• 3.1 Data Preprocessing
• 3.2 Model Performance Results
• 3.3 Model Interpretation
• Predicting Modified Rankin Scale Scores of Ischemic Stroke Patients Using Radiomics Features and Machine Learning
• 3.1 Dataset and Tools
• 3.2 Problem Definition and Data Preparation
• 3.3 Feature Extraction and Selection
• 3.4 Machine Learning Classification and Performance Evaluation
• 4 Results and Discussion
• 4.1 Feature Extraction and Selection
• 4.2 Performance Evaluation of the Machine Learning Classifiers
• 5 Conclusions and Future Work
• Use of Kernel Density Estimation to Understand the Spatial Trends of Attacking Possessions in Rugby League
• 3 Data
• 4 Use of KDEs to Identify the Spatial Trends of Attacking Possessions in Rugby League
• 5 Results
• References.
• Review of Recent Advances in Gaussian Process Regression Methods
• 2 Gaussian Process Regression Revisited
• 3 Sparse Gaussian Process Approximations
• 3.1 Prior Approximation
• 3.2 Posterior Approximations
• 4 Structured Sparse Approximation
• 4.1 Kronecker and Toeplitz Structures
• 4.2 Structured Kernel Interpolation Method
• 5 Hierarchical Matrix-Based Approximation
• 5.1 Structure of the HODLR Matrix
• 5.2 Fast Solving Algorithm for Gaussian Process
• 6 Performance Comparison
• 7 Conclusions
• A Comparative Study of Novelty Detection Models for Zero Day Intrusion Detection in Industrial Internet of Things
• 3 System Model
• 3.1 Network Model
• 3.2 Attack Model
• 3.3 Machine Learning Techniques
• 4.1 Dataset
• 4.2 Data Preprocessing and Model Fitting
• 5 Results and Discussions
• An Artificial Neural Networks (ANN) Based System for Optimal Taxiing Navigation of a BOEING-747 Aircraft
• 2 The Boeing 747 Aircraft Model
• 3 Aircraft Navigation Optimization
• 4 The PID Controller
• 4.1 The Limitation(s) of the PID Controller
• 4.2 The Clustering Solution
• 5 Artificial Neural Networks
• 5.1 Artificial Neural Networks Architecture (Backpropagation Neural Networks)
• 5.2 Neural Networks Design Process
• 6 Results
• 6.1 Taxiing Run Along a Circle Path
• 6.2 Taxiing Run Along a Rectangular Path
• Using Natural Language Processing and Machine Learning to Detect Online Grooming Attacks
• 1.1 Problem Statement
• 1.2 Scope
• 2.1 Existing Solutions
• 2.2 OG Attack Definition
• 3.1 Design Philosophy
• 3.2 Design
• 4 Results
• 4.1 Algorithmic Phase Coding
• 4.2 IFW Coding
• Machine Learning for Real Estate Time Series Prediction.

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ISBN

3-031-55568-6

Doi

• 10.1007/978-3-031-55568-8

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