Progress in Artificial Intelligence 23rd EPIA Conference on Artificial Intelligence, EPIA 2024, Viana do Castelo, Portugal, September 3–6, 2024, Proceedings, Part II

The 3-volume set LNAI 14967, 14968, and 14969 constitutes the proceedings of the 23rd EPIA Conference on Artificial Intelligence, EPIA 2024, held in Viana do Castelo, Portugal, during September 3–6, 2024.

The 94 full papers presented in these proceedings were carefully reviewed and selected from 187 submissions. The papers are organized in the following topical sections:

Volume I:

AI and Creativity (AIC); Ambient Intelligence and Affective Environments (AmIA); Artificial Intelligence and IoT in Agriculture (AIoTA); Artificial Intelligence and Law (AIL); and Artificial Intelligence for Industry and Societies (AI4IS). 

Volume II:

Artificial Intelligence in Medicine (AIM); Artificial Intelligence in Power and Energy Systems (AIPES); Artificial Intelligence in Transportation Systems (AITS); Ethics and Responsibility in AI (ERAI); and General AI (GAI).

Volume III:

Generative AI – Foundations and Applications (GenAI); Intelligent Robotics (IROBOT); Knowledge Discovery and Business Intelligence (KDBI); Natural Language Processing, Text Mining and Applications (TeMA); and Data-Centric AI – Solutions and Emerging Technologies (DCenAI).

Artificial Intelligence in Medicine (AIM).- Synthetic Data for Robust Identification of Typical and Atypical Serotonergic Neurons using Convolutional Neural Networks.- Profiling Atopic Dermatitis Patients Using Decision Tree Classifiers to Anticipate Dupilumab Response.- Modeling Temporal Dynamics in Irregular ICU Data Using MWTA-LSTM.- Evaluating Asthma in Equines with Video Recordings.- Predicting Surgical Site Infections: a Time to Event Approach.- A Study on Automatic Analysis of Handwriting Alterations due to Parkinson’s Disease.- Cervical Cancer Detection in Pap Smear Images.- Automating the Clock Drawing Test with Deep Learning and Saliency Maps.- Acute Pancreatitis Mortality Prediction With Federated Learning.- Predictive Modeling for Medication Administration in Intensive Medicine: A Data-Driven Approach.- Artificial Intelligence in Power and Energy Systems (AIPES).- A Review of Intelligent Technologies in District Heating Systems.- Intelligent Data Mining on Power Systems: Examples from Case Studies.-Application of a Genetic Algorithm for Optimising the Location of Electric Vehicle Charging Stations.- Retrieval-augmented Generation based Assistant: A Smart Home Case Study.- Dynamic Online Parameter Configuration of Genetic Algorithms using Reinforcement Learning.- Assessing Advanced Computer Vision Techniques in Aerial Imagery: A Case Study on Transmission Tower Identification.- Generative Adversarial Networks for Synthetic Meteorological Data Generation.- Artificial Intelligence in Transportation Systems (AITS).- Fuel Efficiency Analysis of the Public Transportation System Based on the Gaussian Mixture Model Clustering.- Multi-Agent Based Simulation for Decentralized Electric Vehicle Charging Strategies and their Impacts.- Bi-LSTM Neural Networks for Traffic Flow Prediction: A Comprehensive Analysis.- Imbalance Management on Free-floating VSS: A Multi-agent Model Approach.- Ethics and Responsibility in AI (ERAI).- GASTeNv2: Generative Adversarial Stress Testing Networks with Gaussian Loss.- A Multidimensional Taxonomy for Recent Trends in Explainable Artificial Intelligence.- Explainability of fMRI Decoding Models can Unveil Insights into Neural Mechanisms Related to Emotions.- Dynamics of Fisheries in the Azores Islands: A Network Analysis Approach.- General AI (GAI).- Detection and Classification of Spam in Social Media Comments using Artificial Intelligence – a Case Study.- A Comparative Study of Continual Backprop.- Time Series Data Augmentation as an Imbalanced Learning Problem.- Domain Reductions after Preprocessing: Effects on Dynamic Variable Ordering in Constraint Satisfaction Search.- Efficient Image Search and Retrieval System in Cloud Platforms.- Unveiling Cetacean Voices: Entropy-Powered Spectrogram Denoising for Deep Learning Applications.