Engineering Application of Neural Networks (to be renamed to EAAAI-Engineering Applications and Advances of Artificial Intelligence) 22nd International Conference EANN 2021 Greece, 25 – 27 June 2021 Lazaros Iliadis Democritus University of Thrace, School of Engineering, Department of Civil Engineering Lab of Mathematics and Informatics (iSCE) Greece Email:
[email protected] John Macintyre University of Sunderland School of Computing and Technology United Kingdom Email:
[email protected] Chrisina Jayne School of Computing, Engineering & Digital Technologies Teesside University United Kingdom Email:
[email protected] Elias Pimenidis Department of Computer Science and Creative Technologies Faculty of Environment and Technology University of the West England United Kingdom Email:
[email protected] nd 22 EANN / (will change to EAAAI) 2021 Artificial Neural Networks (ANN) are a typical case of Machine Learning, which mimics the physical learning process of the human brain. More than 60 years have passed from the introduction of the first Perceptron. Since then, numerous types of NN architectures (e.g., Deep Learning-DL) have been developed. DL has found a variety of applications in numerous timely domains, like Self Driving cars, Fraud-Fake News Detection, Virtual Assistants, Image Analysis, Natural Language. Convolutional Neural Networks (a case of Deep Neural Network) are used in various domains, mainly in image recognition, document analysis, Biomedical systems). The technology of NN is progressing rapidly and we understand that there is no limit in their applications. However, we have not managed to fully simulate the human brain so far. Research both on theory and practice is advancing and the international scientific community is seen novel achievements all the time.