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The two-volume set LNCS 16008 & 16009 constitutes the refereed conference
proceedings of the 18th International Work-Conference on Advances in Computational Intelligence, IWANN 2025, held in A Coruña, Spain, during June 16 18, 2025.
The 103 revised full papers presented in these proceedings were carefully reviewed and selected from 144 submissions. The papers are organized in the following topical sections:
Part I: Advanced Topics in Computational Intelligence; AI:Bioinformatics and Biomedical Applications; ANN HW-Accelerators; Bio-Inspired Systems and Neuro-Engineering; Recent Advances in Deep Learning; Deep Learning Applied to Computer Vision, Healthcare and Robotics; and Emerging Methodologies in Time Series Forecasting.
Part II: Explainable and Interpretable Machine Learning (xAI) with a Focus on Applications; General Applications of AI; ITOMAD Intelligent Techniques for Optimization, Modeling, and Anomaly Detection; Machine Learning for 4.0 Industry Solutions; Machine Learning for Photovoltaic System Optimization and Control in Modern Energy Grids; New and future advances in BCI-based Spellers; and Social and Ethical aspects of AI.
Inhaltsverzeichnis
.- Explainable and Interpretable Machine Learning (xAI) with a Focus on Applications.
.- Understanding of Latent spaces in a battery aging prediction model through eXplainable AI.
.- Exploring brain lateralization using Tensor decomposition of EEG phase-amplitude coupling.
.- Ethical Considerations in Artificial Intelligence and Machine Learning.
.- Kolmogorov-Arnold Networks for the Development of Intrusion Detection Systems.
.- General Applications of AI.
.- Machine Learning based Screening for Psychological Distress using a Perceived Control Mobile App.
.- Tobacco and Weed Segmentation from Remote Images Using Artificial Intelligence.
.- A Hybrid ResNet50-LSTM Architecture for Video Sentiment Analysis.
.- Towards a Framework that facilitates the Construction of Image Segmentation Models.
.- TASER-Net: Transformer Based Speech Emotion Recognition.
.- Experimental Analysis and Modeling of Electrochemical Oxygen Pump Cell ECOpump.
.- Empowering Scalable Fraud Detection Using Graph Neural Networks and Incremental Learning.
.- Transfer Learning approach for prediction of maximum wave height in two locations of the Bay of Biscay: Bilbao and Cabo de Pe nas.
.- Classifier fusion for the detection of defects from active thermography.
.- Multimodal analysis of neuropsychological tests from EEG and fMRI data.
.- Solid-waste Classification Using Deep Learning Fusion Model.
.- Improving PV power prediction based on GRU and meteorological factors.
.- Poisson Hamiltonian Neural Networks: Structure-Preserving Learning of Dynamical Systems.
.- SEF-Net: A Hybrid Deep Learning Architecture for Multi-Step Forecasting in Sustainable Energy Markets.
.- A new approach to detecting occupational diseases using time series.
.- Comparative Analysis of Spiking Neurons Mathematical Models Training using Surrogate Gradients Techniques.
.- ITOMAD Intelligent Techniques for Optimization, Modeling, and Anomaly Detection.
.- Design and Capture of a 5G SA Traffic Dataset Under Jamming Conditions.
.- Predicting TiO2 and FeO Concentrations in Lunar Regolith Using Machine Learning Models: A Spectral Reflectance Approach.
.- Optimal malware mitigation in IoT networks: A comparative study of Neural ODEs and Pontryagin s maximum principle.
.- Study on the Impact of Low-Cost Sensor Alternatives for Photovoltaic Panel Modelling in Smart Grid Applications.
.- A Short Analysis of Hybrid Frameworks Based on Self-Organizing Maps to Improve Traditional Systems.
.- Comparative Performance of Convolutional Neural Networks and Vision Transformers for Quality Assurance of a Welding Process.
.- A Novel Indicator for Nitrogen Prediction in Wastewater Treatment Plants. Implementation of Intelligent Agent-Based.
.- Power Prediction System for Photovoltaic Panels Using Artificial Intelligence.
.- Towards safer hydrogen infrastructure: anomaly detection in synthetic hydrogen dispensing data.
.- Machine Learning for 4.0 Industry Solutions.
.- Physics Informed Machine Learning for Power Flow Analysis: Injecting Knowledge via Pre-, In-, and Post-Processing.
.- Dimensionality Reduction and Outlier Analysis for the NF-ToN-IoT Cybersecurity Dataset.
.- Data-Driven All-Optical Magnetometry: A Comparative Evaluation of Regression Models Using NV Center Fluorescence Lifetimes.
.- Smart Incident Prediction from NOC Alert Events in Digital TV Broadcasting Networks.
.- Machine Learning for Photovoltaic System Optimization and Control in Modern Energy Grids.
.- Symmetrical Current Flow Rec
