Titlebook: Engineering Applications of Neural Networks; 25th International C Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne Conference proceedings

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An Autoencoder-Based Approach for?Anomaly Detection of?Machining Processes Using Acoustic Emission Stection of CNC machining processes is presented. To this end, acoustic emission signals of a real-world use case are considered. To prove the effectiveness of the proposed system, a comparison with an Isolation Forest algorithm, a well-known benchmark in this field, is made. The results show an impr

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Deep Echo State Networks for?Modelling of?Industrial Systemsel of each tank. We conducted numerical experiments to examine how the performance of the predictions is affected by the number of layers. Our findings indicate that increasing the number of recurrent layers leads to better predictions, and also highlight noteworthy differences in the dynamics of th

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Empirical Insights into?Deep Learning Models for?Misinformation Classification Within Constrained DaOur findings suggest that training language models on smaller datasets while considering key indicators of performance like model architecture and learned representation transfer is more beneficial than pre-training the models with past, related data.

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Enhancing Bandwidth Efficiency for?Video Motion Transfer Applications Using Deep Learning Based Keypion with VRNN based prediction for both video animation and reconstruction is demonstrated on three diverse datasets. For real-time applications, our results show the effectiveness of our proposed architecture by enabling up to 2. additional bandwidth reduction over existing keypoint based video mot

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HEADS: Hybrid Ensemble Anomaly Detection System for Internet-of-Things Networks improve the voting strategy for ensemble learning. The ensemble prediction is assisted by a Random Forest-based model obtained through the best F1 score for each label through dataset subset selection. The efficiency of HEADS is evaluated using the publicly available CICIoT2023 dataset. The evaluat

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1865-0929 5 submissions. They deal with reinforcement; natural language; biomedical applications; classificaiton; deep learning; convolutional neural networks.?.978-3-031-62494-0978-3-031-62495-7Series ISSN 1865-0929 Series E-ISSN 1865-0937

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1865-0929 24, held in Corfu, Greece, during June 27-30, 2024.?..The 41 full and 2 short papers included in this book were carefully reviewed and selected from 85 submissions. They deal with reinforcement; natural language; biomedical applications; classificaiton; deep learning; convolutional neural networks.?