Artificial intelligence XXXIX : 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Cambridge, UK, December 13-15, 2022, proceedings / / edited by Max Bramer and Frédéric Stahl

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (373 pages)
• Disciplina: 016.403
• Soggetto topico: Artificial intelligence
• Persona (resp. second.): BramerMax; StahlFrederic
• Note generali: Includes index.
• Nota di contenuto: Intro -- Preface -- Organisation -- Contents -- Best Technical Paper -- Practical Limits to Transfer Learning of Neural Network Controllers from Earth to Space Environments -- 1 Introduction -- 2 Space-Based Kinematics and Dynamics -- 3 Predictive Feedforward Control -- 4 Methodology -- 4.1 Barrett WAM Configuration -- 4.2 Implementation of Multiple-Target Prediction Algorithms -- 5 Results -- 6 Conclusion and Future Work -- References -- Best Application Paper -- Job Assignment Problem and Traveling Salesman Problem: A Linked Optimisation Problem -- 1 Introduction -- 2 Problem Background -- 3 Problem Formulation -- 3.1 Linked Problem Perspective -- 3.2 Job Assignment Problem JAP -- 3.3 Travelling Salesman Problem TSP -- 3.4 JAPTSP -- 4 Proposed Approach -- 4.1 Genetic Components -- 4.2 Sequential Approach -- 4.3 NSGALP Approach -- 4.4 MCRGALP Approach -- 5 Experiments -- 5.1 Benchmark Problems -- 5.2 Exploratory Analysis of Problem Linkages -- 5.3 Performance Metric -- 5.4 Parameter Settings -- 5.5 Experimental Results and Analysis -- 6 Conclusion and Future Work -- References -- AI for Health and Medicine -- Twitter Flu Trend: A Hybrid Deep Neural Network for Tweet Analysis -- 1 Introduction -- 2 Related Work -- 2.1 ILI Surveillance Using Twitter -- 2.2 Semantic Enrichment with Knowledge Sources -- 2.3 Semantic Representation with Deep Learning -- 3 Our Method -- 3.1 Word Embedding -- 3.2 Convolutional Neural Network (CNN) -- 3.3 Bidirectional Long Short Term Memory (BiLSTM) -- 3.4 Hybrid Neural Network -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Baseline Methods -- 5 Results -- 5.1 Correlation Evaluation -- 6 Conclusion -- References -- Data Augmentation for Pathology Prioritisation: An Improved LSTM-Based Approach -- 1 Introduction -- 2 Previous Work -- 3 Application Domain -- 4 Pathology Data Augmentation and Prioritisation. ; 4.1 Jittering -- 4.2 SMOTE -- 4.3 DFM -- 4.4 Guided Warping -- 4.5 LSTM for Prioritisation -- 5 Evaluation -- 5.1 Evaluation Data Set -- 5.2 Best LSTM Settings -- 5.3 Comparison of the Overall Performance of Prioritisation -- 6 Conclusions -- References -- Scanned ECG Arrhythmia Classification Using a Pre-trained Convolutional Neural Network as a Feature Extractor -- 1 Introduction -- 2 Previous Work -- 3 Proposed Approach -- 3.1 ECG Image Pre-processing -- 3.2 Feature Extraction -- 3.3 Dimensionality Reduction -- 3.4 Data Augmentation -- 3.5 Feature Vector Generation -- 4 Evaluation -- 4.1 Data Set -- 4.2 Best Combination of Techniques -- 4.3 Analysis of the Effect of Adding Additional Data -- 4.4 Comparison of Approaches -- 5 Conclusion -- References -- AI for Scientific Discovery and Decision Making -- Bootstrapping Neural Electronics from Lunar Resources for In-Situ Artificial Intelligence Applications -- 1 Introduction -- 2 Modes of Computation -- 3 3D Printer-Based Turing Machine -- 4 Analogue Neural Network Learning Circuitry -- 5 Analogue Neural Network Circuit Simulations -- 6 Analogue Neural Network Circuit Hardware -- 7 Conclusions -- References -- Query Resolution of Literature Knowledge Graphs Using Hybrid Document Embeddings -- 1 Introduction -- 2 Literature Review -- 2.1 Non-contextualized Embedding Models - CBOW -- 2.2 Contextualized Embedding Models - BERT and SciBERT -- 2.3 Knowledge Graph Embedding Models -- 3 The Hybrid Query-Resolution Approach -- 4 Random Walk Knowledge Graph Embedding -- 5 Evaluation -- 6 Conclusion -- References -- On an Artificial Neural Network Approach for Predicting Photosynthetically Active Radiation in the Water Column -- 1 Introduction -- 2 Related Work -- 3 Vertical Radiometric Measurement of the Water Column -- 4 Data Interpretation -- 5 Architecture of the Developed ANN. ; 6 Evaluation of the Artificial Neural Network -- 7 Conclusion -- References -- Morality, Machines, and the Interpretation Problem: A Value-based, Wittgensteinian Approach to Building Moral Agents -- 1 Intelligence and the Interpretation Problem (IP) -- 2 Normative Systems as Spaces of Possibility -- 3 Tackling the Interpretation Problem -- 4 On Implementing a Value-Based Virtuous AI -- References -- CRC: Consolidated Rules Construction for Expressive Ensemble Classification -- 1 Introduction -- 2 Related Work -- 3 The ReG-Rules Ensemble Learner -- 4 The CRC Ensemble Learner -- 4.1 Stacking and Consolidation Stage -- 4.2 Prediction Stage -- 5 Empirical Evaluation of CRC Learning Model -- 5.1 Experimental Setup -- 5.2 Results and Interpretation -- 6 Conclusion -- References -- Competitive Learning with Spiking Nets and Spike Timing Dependent Plasticity -- 1 Introduction -- 2 Literature Review -- 2.1 Standard Models and Commonly Used Systems -- 2.2 Unsupervised Learning Using STDP -- 3 Methods -- 3.1 Student Performance Categorisation -- 3.2 Digit Categorisation -- 4 The Student Performance Categorisation System -- 4.1 Student Performance Spiking Net Model -- 4.2 Results -- 5 The Digit Categorisation System -- 5.1 Spiking Neuron Network Model -- 5.2 Results -- 6 Discussion -- 7 Conclusion -- References -- An Evolutionary Game Theory Model of the Decision to Confront -- 1 Evolution of Cooperation and Confrontation -- 1.1 A Competition for Resources -- 2 Modelling the Evolution of Confrontation -- 2.1 Introduction -- 2.2 The Organisms -- 2.3 The Survival Cycle -- 3 Results -- 3.1 Genes Determining Confrontational Propensity (CP) -- 3.2 Gene Sensitive to Gain (RS1) -- 3.3 Gene Sensitive to Loss (RS2) -- 3.4 Gene Responsive to Health (HS1) -- 3.5 Gene Responsive to Death (HS2) -- 4 Discussion -- References. ; Hidden Markov Models for Surprising Pattern Detection in Discrete Symbol Sequence Data -- 1 Introduction -- 2 Related Work -- 3 Hidden Markov Models -- 4 Methods -- 5 Data -- 5.1 Sepsis Data Set -- 5.2 Self-rated Health Data Set -- 6 Results -- 6.1 Sepsis Data Analysis -- 6.2 Self-reported Health (SRH) Data Analysis -- 7 Discussion -- 8 Conclusions -- References -- AI for Industrial Applications -- The ODeLIndA Dataset for Field-of-View Obstruction Detection Using Transfer Learning for Real-Time Industrial Applications -- 1 Introduction -- 2 Background -- 3 Related Work -- 3.1 View Obstructions for Moving Cameras -- 3.2 Transfer Learning and Machine Vision -- 3.3 Deep Learning Models for Computer Vision -- 4 The ODeLIndA Dataset for FOV Obstruction Detection -- 5 Transfer Learning for Efficient Obstruction Detection -- 6 Results -- 7 Conclusion and Future Work -- References -- Automated Quality Inspection of High Voltage Equipment Supported by Machine Learning and Computer Vision -- 1 Introduction -- 2 Problem Statement -- 2.1 Bi-mode Insulated Gate Transistor -- 2.2 BIGT Assembly Process Verification -- 3 Solution Overview and Development Process -- 3.1 Hardware Architecture -- 3.2 Machine Learning Layer -- 3.3 Software Architecture -- 3.4 Issues Related to Moving to Production -- 4 Summary -- References -- On Predicting the Work Load for Service Contractors -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Tuning Hyper Parameters -- 3.2 Validation -- 4 Experiment Setup and Analysis of Results -- 4.1 Data-Set -- 4.2 Experiment Setup -- 4.3 Evaluation and Results -- 5 Conclusion -- References -- OAK4XAI: Model Towards Out-of-Box eXplainable Artificial Intelligence for Digital Agriculture -- 1 Introduction -- 2 OAK - Ontology-Based Knowledge Map Model -- 2.1 Knowledge Definitions -- 2.2 Ontology: Role and Design. ; 3 OAK4XAI Model and Architecture -- 3.1 OAK4XAI Architecture -- 3.2 Using OAK4XAI for Modeling and Explaining -- 3.3 XAI Transparency with OAK4XAI -- 4 Validation -- 4.1 Implementation -- 4.2 Explanation -- 4.3 Statistics -- 5 Conclusion and Future Work -- References -- Feasibility Studies of Applied AI -- Deep Learning for Detecting Tilt Angle and Orientation of Photovoltaic Panels on Satellite Imagery -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Rooftop Detection -- 3.2 Rooftop Classification -- 3.3 Tilt Angle Estimation -- 3.4 Orientation Detection -- 4 Results -- 5 Conclusion and Future Work -- References -- Recurrent Neural Networks for Music Genre Classification -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset -- 3.2 Feature Extraction -- 3.3 Model Training -- 4 Evaluation Results -- 5 Conclusion and Future Work -- References -- Explainable Boosting Machines for Network Intrusion Detection with Features Reduction -- 1 Introduction -- 2 Explainable Artificial Intelligence (XAI) -- 3 Explainable Boosting Machine -- 4 Network Intrusion Detection -- 5 Experiments and Evaluations -- 5.1 Can EBMs Classifiers Outperform Other Classifiers? -- 5.2 Deciding on Features with Obfuscation Information -- 5.3 Reducing the Number of Features Without Degrading the Performance -- 6 Conclusion and Future Work -- References -- Short Papers -- Accelerating Cyber-Breach Investigations Through Novel Use of Artificial Immune System Algorithms -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Artificial Immune Systems -- 3.2 Deterministic Dendritic Cell Algorithm -- 3.3 Methodology -- 4 Experiment Results and Application -- 5 Conclusions and Further Work -- References -- Comparing ML Models for Food Production Forecasting -- 1 Introduction -- 2 Background -- 3 Methodology -- 4 Experimental Results. ; 4.1 Dataset and Model Setting.
• Titolo autorizzato: Artificial intelligence XXXIX
• ISBN: 3-031-21441-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996503471503316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science