Intelligent Data Engineering and Automated Learning – IDEAL 2023

Main Track: Optimization of Image Acquisition for Earth Observation Satellites via Quantum Computing.-  Complexity-driven sampling for Bagging.- A pseudo-label guided hybrid approach for unsupervised domain adaptation .- Combining of Markov Random Field and Convolutional Neural Networks for Hyper/Multispectral Image Classification.- Plant Disease Detection and Classification using a Deep learning-based framework.- Evaluating Text Classification in the Legal Domain Using BERT Embeddings.- Rapid and Low-Cost Evaluation of Multi-Fidelity Scheduling Algorithms for Hyperparameter Optimization.- The Applicability of Federated Learning to Official Statistics.- Generating Wildfire Heat Maps with Twitter and BERT.- An urban simulator integrated with a genetic algorithm for efficient traffic light coordination.- GPU-Based Acceleration of the Rao Optimization Algorithms: Application to the Solution of Large Systems of Nonlinear Equations.- Direct determination of Operational Value-at-Risk using Descriptive Statistics.- Using Deep Learning models to Predict the Electrical Conductivity of the influent in a Wastewater Treatment Plant. -Unsupervised Defect Detection for Infrastructure Inspection.- Generating Adversarial Examples using LAD.- Emotion extraction from Likert-Scale questionnaires - an additional dimension to Psychology Instruments.- Recent applications of pre-aggregation functions.- A Probabilistic Approach: Querying Web Resources In The Presence Of Uncertainty.- Domain Adaptation in Transformer models: Question Answering of Dutch Government Policies.- Sustainable On-Street Parking Mapping with Deep Learning and Airborne Imagery.- Hebbian Learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks.- Hebbian Learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks.- Language Models for Automatic Distribution of Review Notes in Movie Production.- Extracting Knowledge from Incompletely Known Models.- Threshold-based Classification to Enhance Confidence in Open Set of Legal Texts.- Comparing ranking learning algorithms for information retrieval systems.- Analyzing the influence of market event correction for forecasting stock prices using Recurrent Neural Networks.- Measuring the relationship between the use of typical Manosphere discourse and the engagement of a user with the pick-up artist community .- Uniform Design of Experiments for Equality Constraints.- Globular Cluster Detection in M33 Using Multiple Views Representation Learning.- Segmentation of Brachial Plexus Ultrasound Images Based on Modified SegNet Model.- Unsupervised Online Event Ranking for IT Operations .- A Subgraph Embedded GIN with Attention for Graph Classification.- A Machine Learning Approach to Predict Cyclists' Functional Threshold Power.- Combining Regular Expressions and Supervised Algorithms for Clinical Text Classification.- MODELING THE INK TUNING PROCESS USING MACHINE LEARNING.- Depth and Width Adaption of DNN for Data Stream Classification with Concept Drifts*.- FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification.- Enhanced SVM-SMOTE with Cluster Consistency for Imbalanced Data Classification.- Preliminary Study on Unexploded Ordnance Classification in Underwater Environment Based on the Raw Magnetometry Data..- Efficient Model For Probabilistic Web resources under uncertainty.- Unlocking the Black Box: Towards Interactive Explainable Automated Machine Learning.- Machine Learning for Time Series Forecasting Using State Space Models.- Causal graph discovery for explainable insights on marine biotoxin shellfish contamination.- Special Session on Federated Learning and (pre) Aggregation in Machine Learning: Adaptative fuzzy measure for edge detection.- Special Session on Intelligent Techniques for Real-world Applications of Renewable Energy and Green Transport: Prediction and Uncertainty Estimation in Power Curves of Wind Turbines Using -SVR.- Glide Ratio Optimization for Wind Turbine Airfoils based on Genetic Algorithms.- Special Session on Data Selection in Machine Learning: Detecting Image Forgery Using Support Vector Machine and Texture Features.- Instance selection techniques for large volumes of data.