International Journal of Neural SystemsVol. 32, No. 12, 2202002 (2022) No AccessIntroductionLazaros IliadisLazaros IliadisLab of Mathematics and Informatics (ISCE), School of Engineering, Head of the Department of Civil Engineering, Sector of Mathematics, Programming and G.C., Democritus University of Thrace, University Campus, Kimmeria, Xanthi, PC 67100, Greecehttps://doi.org/10.1142/S0129065722020026Cited by:0 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles in neural networks today! FiguresReferencesRelatedDetails Recommended Vol. 32, No. 12 Metrics History Published: 26 October 2022 PDF download

Machine learning (ML) and more specifically deep learning (DL) algorithms are considered among the most paramount technologies of both artificial intelligence (AI) and 4 th industrial revolution.As such, they undoubtedly have significant influence in our daily and professional lives.Artificial intelligence is no longer a futuristic concept.Its algorithms are widely employed to mimic human reasoning.They are utilizing both structured and unstructured data, in order to achieve extremely complex tasks, with high efficiency and accuracy.The industrial and research applications of AI are related but not limited to Self-Driving cars, Natural Language, Visual and Speech Recognition, Fraud Detection, Cyber Security, Healthcare and several other real-life domains.The potential applications are endless, and they have a positive impact to our quality of life.This is the Editorial of the ''Deep Learning Modeling in Real Life: Anomaly Detection, Biomedical, Concept Analysis, Finance, Image analysis, Recommendation'' Issue of the Neural Computing and Applications (NCA) Journal.It presents timely cases of emerging deep learning advances, and applications in a wide range of scientific and engineering areas.Overall, 36 original research papers were submitted to be considered for publication in this Special Issue of the NCA Journal.Only sixteen (16) of them (44.4%) have been carefully selected for publication, after passing successfully through a peer review process by independent academic referees.All these high-quality papers are presenting innovative research, falling in the scientific domain that was specified by the journal's call.The first paper is entitled ''Intelligent fault diagnosis of rolling bearings based on LSTM with Large Margin Nearest Neighbor algorithm'' and it is authored by Anas H.

Modern critical infrastructures are characterized by a high degree of complexity, in terms of vulnerabilities, threats, and interdependencies that characterize them. The possible causes of a digital assault or occurrence of a digital attack are not simple to identify, as they may be due to a chain of seemingly insignificant incidents, the combination of which provokes the occurrence of scalar effects on multiple levels. Similarly, the digital explosion of technologies related to the critical infrastructure and the technical characteristics of their subsystems entails the continuous production of a huge amount of data from heterogeneous sources, requiring the adoption of intelligent techniques for critical analysis and optimal decision making. In many applications (e.g. network traffic monitoring) data is received at a high frequency over time. Thus, it is not possible to store all historical samples, which implies that they should be processed in real time and that it may not be possible to re-review old samples (one-pass constraint). We should consider the importance of protecting critical infrastructure, combined with the fact that many of these systems are cyber-attack targets, but they cannot easily be disconnected from their layout as this could lead to generalized operational problems. This research paper proposes a Multi-Task Learning model for Real-Time & Large-Scale Data Analytics, towards the Cyber protection of Critical Infrastructure. More specifically, it suggests the Multi Overlap LEarning STReaming Analytics (MOLESTRA) which is a standardization of the "Kappa" architecture. The aim is the analysis of large data sets where the tasks are executed in an overlapping manner. This is done to ensure the utilization of the cognitive or learning relationships among the data flows. The proposed architecture uses the k-NN Classifier with Self Adjusting Memory (k-NN SAM). MOLESTRA, provides a clear and effective way to separate the short-term from the long-term memory. In this way the temporal intervals between the transfer of knowledge from one memory to the other and vice versa are differentiated.

Nearly 1.3 million people die in road crashes annually in a global scale, on average 3,287 deaths per day [1]. Moreover, 20-50 million are injured or disabled [1]. Road traffic crashes rank as the 9th leading cause of deaths (2.2% of deaths globally) [1]. This research paper presents the contribution of soft computing (fuzzy logic) towards modeling of this huge problem. More specifically, the municipalities of Greece are classified according to their respective road accidents occurrence, by means of a special measure of similarity and consideration of the generating fuzzy transitive closure (FTC).

Prolonged and sustained warming of the sea, acidification of surface water and rising of sea levels, creates significant habitat losses, resulting in the proliferation and spread of invasive species which immigrate to foreign regions seeking colder climate conditions. This is happening either because their natural habitat does not satisfy the temperature range in which they can survive, or because they are just following their food. This has negative consequences not only for the environment and biodiversity but for the socioeconomic status of the areas and for the human health. This research aims in the development of an advanced Machine Hearing system towards the automated recognition of invasive fish species based on their sounds. The proposed system uses the Spiking Convolutional Neural Network algorithm which cooperates with Geo Location Based Services. It is capable to correctly classify the typical local fish inhabitants from the invasive ones.

This study presents an intelligent fuzzy ANN evaluation system that uses fuzzy set transformation by applying fuzzy dilution and fuzzy intensification processes. In this way it applies approximation hedges that offer proper linguistics describing the convergence of the network in various levels.