Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

This book gathers the proceedings of the 21st Engineering Applications of Neural Networks Conference, which is supported by the International Neural Networks Society (INNS). Artificial Intelligence (AI) has been following a unique course, characterized by alternating growth spurts and AI winters. Today, AI is an essential component of the fourth industrial revolution and enjoying its heyday. Further, in specific areas, AI is catching up with or even outperforming human beings. This book offers a comprehensive guide to AI in a variety of areas, concentrating on new or hybrid AI algorithmic approaches with robust applications in diverse sectors.
One of the advantages of this book is that it includes robust algorithmic approaches and applications in a broad spectrum of scientific fields, namely the use of convolutional neural networks (CNNs), deep learning and LSTM in robotics/machine vision/engineering/image processing/medical systems/the environment; machine learning and meta learning applied to neurobiological modeling/optimization; state-of-the-art hybrid systems; and the algorithmic foundations of artificial neural networks.

Is dedicated to advancing the state of the art in AI algorithms and their applications Serves as a source of inspiration for colleagues from various scientific domains Presents new algorithms and new hybrid approaches, offering significant guidance for all AI researchers Offers extensive information on both theoretical aspects and application areas Covers areas such as convolutional neural networks, deep learning, and LSTM in robotics/machine vision/engineering/image processing/medical systems/the environment Describes state-of-the-art hybrid systems, the algorithmic foundations of artificial neural networks, and machine learning / meta learning as applied to neurobiological modeling/optimization

Inhalt
A compact sequence encoding scheme for online human activity recognition in HRI applications.- Classification of Coseismic Landslides using Fuzzy and Machine Learning Techniques.- Evaluating the Transferability of Personalised Exercise Recognition Models.- Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities.- Visual Movement Prediction for Stable Grasp Point Detection.- Accomplished level of reliability for seismic structural damage prediction using artificial neural networks.- Efficient Implementation of a Self-Sufficient Solar-Powered Real-Time Deep Learning-Based System.- Leveraging Radar Features to Improve Point Clouds Segmentation with Neural Networks.- LSTM Neural Network for Fine-Granularity Estimation on Baseline Load of Fast Demand Response.- Predicting Permeability Based On Core Analysis.