- ホーム
- > 電子洋書
Description
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.
Table of Contents
.- 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.
