Advances in Computational Intelligence

Name: Advances in Computational Intelligence | 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, November 13-18, 2023, Proceedings, Part I
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22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, November 13-18, 2023, Proceedings, Part I

Hiram Calvo Lourdes Martínez-Villaseñor Hiram Ponce(Herausgeber*in)

Springer (Verlag)

Erschienen am 8. November 2023

XIX, 349 Seiten

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978-3-031-47765-2 (ISBN)

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Inhalt

Intro
Preface
Organization
Contents - Part I
Contents - Part II
Machine Learning
Stock Market Performance Analytics Using XGBoost
1 Introduction
2 Related Work
3 Methodology and Development
3.1 Dataset Description
3.2 Data Preparation
3.3 Using XGBoost for Stock Trend and Prices Prediction
3.4 Moving Average
4 Results and Discussion
4.1 Correlation of Stock
4.2 XGBoost Regressor
4.3 MACD
5 Conclusion
References
1D Quantum Convolutional Neural Network for Time Series Forecasting and Classification
1 Introduction
2 Preliminaries
2.1 Quantum Computation
2.2 Quantum Circuits
2.3 Variational Quantum Circuits
3 1D Quantum Convolution
4 Results and Discussion
4.1 Time Series Forecasting
4.2 Time Series Classification Using PTB Dataset
5 Conclusions
References
Hand Gesture Recognition Applied to the Interaction with Video Games
1 Introduction
2 Methodology
2.1 Hand Gesture Recognition
2.2 Video Game Application
3 Results
4 Conclusions and Future Work
References
Multiresolution Controller Based on Window Function Networks for a Quanser Helicopter
1 Introduction
2 Application of the Control Scheme to the Helicopter Model
2.1 Dynamic Identification
2.2 Proportional Multi-resolution Controller
2.3 Autotune of the Gains
3 Results
3.1 Open-Loop Simulation Results: Identification Process
3.2 Closed-Loop Simulation Results
3.3 Comparative Between PMR and PID Controllers
4 Conclusions and Future Work
References
Semi-supervised Learning of Non-stationary Acoustic Signals Using Time-Frequency Energy Maps
1 Introduction
2 Methods
2.1 STFT Maps
2.2 Dimensionality Reduction Using PCA
2.3 Background Subtraction
3 Description of Proposed Method
4 Acoustic Non-stationary Signals
4.1 Signals Acquisition
5 Classifier Results
5.1 Training and Classification
6 Conclusion
References
Predict Email Success Based on Text Content
1 Introduction
2 Related Work
3 Methodology
3.1 Description of the Dataset
3.2 Building and Training the Model
3.3 Model Evaluation
4 Results and Discussion
5 Conclusions
References
Neural Drone Racer Mentored by Classical Controllers
1 Introduction
2 Related Work
3 Methodology
3.1 Neural Pilot
3.2 Proportional-Integral Controller
3.3 Model Predictive Controller
3.4 Active Disturbance Rejection Control
3.5 Training Process
4 Experimental Framework
5 Conclusions
References
Eye Control and Motion with Deep Reinforcement Learning: In Virtual and Physical Environments
1 Introduction
2 Proposed Solution
2.1 Deep Reinforcement Learning and the NN's Structure
2.2 Optimising the Learning Process
3 The Experiment
3.1 Optimizing Training with Image Detection
3.2 Testing with a Real Camera
3.3 Performance Metrics
4 Training Results and Discussion
4.1 Testing with a Real Camera
5 Conclusions
References
Fingerspelling Recognition in Mexican Sign Language (LSM) Using Machine Learning
1 Introduction
2 Related Work
3 Methods
3.1 Data Capturing
3.2 Keypoints Detection
3.3 Keypoints Processing
3.4 Classification
4 Results
5 Conclusions
References
Load Demand Forecasting Using a Long-Short Term Memory Neural Network
1 Introduction
1.1 Artificial Neural Networks
2 Methodology
2.1 Data Provided by CENACE
2.2 LSTM Neural Network
2.3 Processing Data
2.4 Double-Input Long Short-Term Memory Neural Network
2.5 Triple-Input Long Short-Term Memory Neural Network
2.6 4-input Long Short-Term Memory Neural Network
2.7 Forecasting Errors
3 Results
3.1 Double-Input LSTM
3.2 Triple-Input LSTM
3.3 Four-Input LSTM
4 Discussion and Conclusion
References
Computer Vision and Image Processing
Benchmark Analysis for Backbone Optimization in a Facial Reconstruction Model
1 Introduction
2 Related Work
2.1 Face Reconstruction Models
2.2 Lightweight Backbone Architectures for Facial Reconstruction
3 Methodology
4 Results and Discussion
5 Conclusion
References
T(G)V-NeRF: A Strong Baseline in Regularized Neural Radiance Fields with Few Training Views
1 Introduction
2 Related Work
2.1 Neural Radiance Fields
2.2 Training a NeRF with (very) Few Images
3 Proposed Regularization Framework
3.1 Depth Map Regularization
3.2 Second-Order Regularization
3.3 Occlusion Regularization
4 Experimental Results
4.1 Implementation Details
4.2 Ablation Results
4.3 Quantitative Results
4.4 Qualitative Results
5 Conclusions
References
Nonlinear DIP-DiracVTV Model for Color Image Restoration
1 Introduction
2 DIP-Reg Models for Color Image Restoration
2.1 DIP-Reg Models
2.2 Euclidean Models: DIP-TV and DIP-VTV
2.3 Riemannian Model: DIP-RiemannVTV
3 Dirac Vectorial Total Variations of a Color Image
3.1 The Euclidean Dirac Operator and Its Main Properties
3.2 Dirac Operators for Color Images
4 Experiments
4.1 Numerical Scheme
4.2 Application to Denoising
4.3 Application to Deblurring
5 Conclusion
References
An Efficient Facial Verification System for Surveillance that Automatically Selects a Lightweight CNN Method and Utilizes Super-Resolution Images
1 Introduction
2 Related Work
3 Super-Resolution Methods
4 Light Facial Verification Methods
5 Proposed Dynamic Scaling with Super-Resolution Methods
6 Proposed Lightweight Facial Verification System
7 Experiments
7.1 Implementation Details
7.2 Datasets
7.3 Selection of Super-Resolution Method
7.4 Evaluation with the Custom Dataset
7.5 Evaluation of the LFVS with the Datasets
8 Discussion
9 Conclusion
References
Nonlinear L2-DiracVTV Model for Color Image Restoration
1 Introduction
2 The L2-VTV Model for Color Image Restoration
2.1 Variational Models for Image Restoration
2.2 The L2-VTV Model and Its Solutions
2.3 Primal-Dual Algorithm
3 Non Linear L2-DiracVTV Model for Color Image Restoration
3.1 Dirac Operators for Color Images
3.2 Weighted Dirac Operators for Color Images
3.3 Linear Weighted Dirac Vectorial Total Variation and Its Dual Formulation
3.4 Variational Models for Color Image Restoration
4 Experiments
4.1 On the Choice of the Color Space
4.2 Denoising
4.3 Deblurring
5 Conclusion
References
An FPGA Smart Camera Implementation of Segmentation Models for Drone Wildfire Imagery-4pt
1 Introduction
2 State-of-the-Art
2.1 Segmentation Models for Wildfire Detection and Characterization
2.2 Smart Camera Implementations for Computer Vision
3 Proposed Method
3.1 General Overview of the Optimization Approach
3.2 Dataset: Corsican Fire Database
3.3 Segmentation Model Training
3.4 Optimization
3.5 Proposed FPGA-Based Smart Camera System
4 Results and Discussion
4.1 Comparison Metrics
4.2 Quantitative Results
4.3 Qualitative Results
5 Conclusions
References
Intelligent Systems
An Argumentation-Based Approach for Generating Explanations in Activity Reasoning
1 Introduction
2 Background
3 Proposal
3.1 Human Activity Framework and Local Selection
3.2 Global Selection
4 Generating Explanations
5 Theoretical Evaluation
6 Conclusions and Future Work
References
A Decision Tree Induction Algorithm for Efficient Rule Evaluation Using Shannon's Expansion
1 Introduction
2 Background
3 Proposal
4 Experimental Results
5 Conclusions
References
Reasoning in DL-LiteR Based Knowledge Base Under Category Semantics
1 Introduction
2 Set-Theoretical Semantics of DL-LiteR
3 Category-Theoretical Semantics of DL-LiteR
4 Category-Theoretical Satisfiability for DL-LiteR
5 Conclusion
References
Applying Genetic Algorithms to Validate a Conjecture in Graph Theory: The Minimum Dominating Set Problem
1 Introduction
2 Background
3 The Conjecture to Be Verified
3.1 Generalized Quadrangle
3.2 The Minimum Dominating Set Problem
3.3 The Conjecture
4 Rank Genetic Algorithm for Finding Minimal Dominating Set
4.1 Solution Representation
4.2 Fitness Function
4.3 The Rank GA Operators
5 Results
6 Conclusions
References
Random Number Generator Based on Hopfield Neural Network with Xorshift and Genetic Algorithms
1 Introduction
2 NIST Testing Module
3 Related Works
3.1 Approaches Based on Chaotic Systems
3.2 Hybrid Pseudo Random Number Generator (PRNG)
3.3 Genetic Algorithms
4 PRNG Based on Hopfield Neural Network and Xorshift
5 PRNG Based on Genetic Algorithms
5.1 Selection
5.2 Loop
6 Tests and Results: PRNG Based on Hopfield Neural Network and Xorshift
6.1 Results of the NIST Module
6.2 Diehard Test Suite Results
7 Tests and Results: PRNG Based on Genetic Algorithms
7.1 Models to Compare with Ours
7.2 DieHarder Suite of Random Number Generator Tests
8 Discussion
9 Conclusion
References
Using Compiler Errors Messages to Feedback High School Students Through Machine Learning Methods
1 Introduction
2 Methodology
3 Classifier Development
3.1 Corpus Generation
3.2 Data Cleaning
3.3 Vectorization
3.4 Modelling the Classifier
3.5 Model Evaluation
3.6 Use of the Classifier
4 Giving Feedback
5 Conclusions and Future Work
References
Bayesian Network-Based Multi-objective Estimation of Distribution Algorithm for Feature Selection Tailored to Regression Problems
1 Introduction
2 Background
2.1 Multi-objective Evolutionary Optimization
2.2 Estimation of Distribution Algorithms (EDAs)
2.3 Bayesian Networks
3 Methodology
3.1 Representing and Evaluating Individuals
3.2 Selection of Individuals for Estimating the Parameters of the Bayesian Network
3.3 Creating Bayesian Network Graph
3.4 Creating Initial Population
3.5 Calculating Bayesian Network Parameters
3.6 Sampling from Bayesian Network
4 Experiments and Results
4.1 Datasets
4.2 Comparison of Our Proposal Against Other Techniques
5 Conclusion
References
Implementation of Parallel Evolutionary Convolutional Neural Network for Classification in Human Activity and Image Recognition
1 Introduction
2 State of the Art
3 Evolving the Coarse-Fine CNN by Means of the Genetic Algorithm
3.1 Codification
3.2 Fitness
4 Parallel GA Strategies
5 Experiments and Results
5.1 CFCNN Performance and Classification Results
5.2 State of the Art Comparison
6 Conclusions
References
Correction to: An FPGA Smart Camera Implementation of Segmentation Models for Drone Wildfire Imagery
Author Index

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