Advances in Computational Intelligence - 18th International Work-Conference on Artificial Neural Networks, IWANN 2025, A Coruña, Spain, June 16-18, 2025, Revised Selected Papers, Part I

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.

Sommario

.- Advanced Topics in Computational Intelligence.
.- Power Quality 24-hour Prediction Based on L-Transform Derivative Modular and Deep Learning Statistics Using Environmental Data in detached Smart Buildings.
.- Incremental Feature Learning of Shallow Feedforward Regression Neural Networks using Particle Swarm Optimisation.
.- Resilience Under Attack: Benchmarking Optimizers Against Poisoning in Federated Learning for Image Classification Using CNN.
.- VIDEM: VIDeo Effectiveness and Memorability Dataset.
.- Penetration Testing with AI: Case Studies on LLM and RL-Based Attack Agents.
.- A comparative study of deep learning approaches for classifying wild and cultivated fish.
.- Sparse Least Square SVM in Primal via Nesterov Accelerated Alternating Directions Method of Multipliers.
.- AI:Bioinformatics and Biomedical Applications.
.- A transformer-based model to predict micro RNA interactions.
.- Leveraging Large Language Models on Assay Descriptions to Improve the Prediction of Inhibitors for Mycobacterium tuberculosis.
.- Advancing Imminent Fracture Risk Prediction: Integrating Machine Learning with Enhanced Feature Engineering.
.- Self-organizing Maps for Missing Value Imputation in Transcriptomic Datasets.
.- ANN HW-Accelerators.
.- RECS: A Scalable Platform for Heterogeneous AI Acceleration in the Cloud-Edge Continuum.
.- Evaluating HBM to accelerate neural networks on FPGAs demonstrated using binary neural associative memories.
.- Resource-efficient Implementation of Convolutional Neural Networks on FPGAs with STANN.
.- High-Performance FPGA-based CNN Acceleration for Real-Time DC Arc Fault Detection.
.- Optimizing AI on the Edge: Partitioning Neural Networks Across Heterogeneous Accelerators.
.- Comparison of Hardware Component and Manycore Implementation for Anomaly Detection in Trustworthy System-on-Chips.
.- Bio-Inspired Systems and Neuro-Engineering.
.- An Emotional Classifier for Machine s Artificial Visual Aesthetic Appraisal.
.- Hardware and Software influence on EAs power consumption.
.- Properties of monoclinic gallium oxide film and its photomemristor application in nonlinear RMC circuit.
.- A perceptron-like neural network implementing a learning-capable K-nearest neighbor classifier.
.- From Biological Neurons to Artificial Neural Networks: A Bioinspired Training Alternative.
.- Recent Advances in Deep Learning.
.- Domain Adaptation of the Whisper ASR Model for Tourism Call Center Transcription in Polish.
.- Learning to Search with Subgoals.
.- Towards Speaker Independent Speech Emotion Recognition by means of Dataset Aggregation.
.- Learning Heuristics for k-NANN-A*: A Deep Learning Approach.
.- Evaluating Higher-Level and Symbolic Features in Deep Learning on Time Series: Towards Simpler Explainability.
.- Energy-Efficient Radio Resource Allocation in 5G Using Deep Q-Networks.
.- Multi-view Cross Contrastive Learning for Multimodal Knowledge Graph Recommendation.
.- MuleTrack: A Lightweight Temporal Learning Framework for Money Mule Detection in Digital Payments.
.- Modular Deep Neural Networks with residual connections for predicting the pathogenicity of genetic variants in non coding genomic regions.
.- Modeling Student Subject Interactions with GNNs for Grade Prediction.
.- Deploying Vision Foundation AI Models on the Edge. The SAM2 Experience.
.- Generative AI for Contextualizing Bronze Age Objects in Historical Scenes.
.- G-TED SAM: Node Classification via Graph Transformer to Simple Attention Model Distillation.
.- Expression Recognition in Faces Partially Occluded by Head-Mounted Displays.
.- Reinforcement Learning for Mapless Navigation: Enhancing Exploration with Image-Based Rewards.
.- Deep Learning Applied to Computer Vision, Healthcare and Robotics.
.- Human Activity Recognition in the Classroom using Low-cost Sensors.
.- Hybrid dropout for deep ordinal classification.
.- Enhanced video-based eye status detection in term infants.
.- Knee osteoarthritis severity grading using soft labelling and ordinal classification.
.- Self-attentive bidirectional LSTM networks for temporal decoding of EEG motor states.
.- Effects of Grouped Structural Global Pruning of Vision Transformers on Domain Generalisation.
.- MORENA: Empty images detection based on unsupervised reconstruction error analysis.
.- Methodological framework for the creation of digital twins for photovoltaic power plants.
.- Decoding Brain Lobe Contributions in EEG for automatic detection of obstructive sleep apnea.
.- Emerging Methodologies in Time Series Forecasting.
.- Forecasting Non-Stationary Time Series: A Comparison of Deep and Shallow Neural Network Architectures.
.- Deep Learning or Trees? A Trade-off Analysis for Multivariate Time Series Forecasting.
.- Hybrid AI Models for Structured Mobility Prediction in Metropolitan Areas.
.- XAI for univariate and multivariate time series forecasting. A case study on electricity consumption in Romania s National Electricity Network.
.- Assessing bias in the evaluation of blood glucose prediction models.

Dettagli sul prodotto

• Con la collaborazione di: Andreu Catala (Editore), Gonzalo Joya (Editore), Ignacio Rojas (Editore)
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 21.09.2025
• EAN: 9783032027245
• ISBN: 978-3-0-3202724-5
• Pagine: 657
• Illustrazioni: XIX, 657 p. 236 illus.
• Serie: Lecture Notes in Computer Science
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica; Artificial Intelligence, Computer Vision, clustering, distributed artificial intelligence, knowledge representation and reasoning, Cognitive Robotics, search methodologies, computing platforms, Machine learning/ Deep Learning