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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
.- 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 Disti
