Rojas / Joya / Catala Advances in Computational Intelligence

18th International Work-Conference on Artificial Neural Networks, IWANN 2025, A Coruña, Spain, June 16–18, 2025, Proceedings, Part I
Erscheinungsjahr 2025
ISBN: 978-3-032-02725-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 669 Seiten

Reihe: Computer Science (R0)

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.

Zielgruppe

Research

Autoren/Hrsg.

Rojas, Ignacio

Joya, Gonzalo

Catala, Andreu

Inhaltsverzeichnis

.- 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.