Please use this identifier to cite or link to this item: http://ds.knu.edu.ua/jspui/handle/123456789/5486
Title: Editorial for JEC Volume 3 Issue 1
Authors: Semerikov, Serhiy O.
Keywords: edge computing
doors 2024
IoT security
cyber attacks
LSTM
respiratory disease diagnosis
machine learning
IoT
open APIs
geolocation
telemetry
distributed systems
dynamic analysis
Issue Date: 21-тра-2024
Citation: Semerikov S. O. Editorial for JEC Volume 3 Issue 1 / Serhiy O. Semerikov // Journal of Edge Computing. – 2024. – Vol. 3. – Iss. 1. – P. 1–2. – DOI : https://doi.org/10.55056/jec.737
Abstract: This editorial introduces the first issue of the third volume of the Journal of Edge Computing (JEC). It provides an overview of the five articles featured in this issue, which cover diverse applications of edge computing technologies in domains such as cybersecurity, healthcare, and distributed systems. The first article summarizes the 4th Edge Computing Workshop (doors 2024), highlighting research advances in edge computing. The second article proposes an LSTM-based model for detecting cyber attacks in IoT systems using the CIC-IoT2023 dataset. The third article presents a machine-learning model for classifying respiratory system sounds to aid in the early diagnosis of respiratory diseases. The fourth article describes an IoT system that analyzes environmental data using geolocation to generate alerts about potential health risks. The fifth article explores the use of telemetry for dynamic analysis of distributed systems to identify architectural smells and anomalies. The editorial highlights the potential of edge computing technologies in addressing various challenges and expresses gratitude to the authors, reviewers, and editorial team for their contributions.
Description: Jony, A.I. and Arnob, A.K.B., 2024. A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset. Journal of Edge Computing, 3(1), pp.28–42. Available from: https://doi.org/10.55056/jec.648. DOI: https://doi.org/10.55056/jec.648 Klochko, O.V. and Fedorets, V.M., 2024. An IoT system based on open APIs and geolocation for human health data analysis. Journal of Edge Computing, 3(1), pp.65–86. Available from: https://doi.org/10.55056/jec.698. DOI: https://doi.org/10.55056/jec.698 Melek, N., 2024. Responding to challenge call for machine learning model development in diagnosing respiratory disease sounds. Journal of Edge Computing, 3(1), pp.43–64. Available from: https://doi.org/10.55056/jec.679. DOI: https://doi.org/10.55056/jec.679 Talaver, O.V. and Vakaliuk, T.A., 2024. Telemetry to solve dynamic analysis of a distributed system. Journal of Edge Computing, 3(1), pp.87–109. Available from: https://doi.org/10.55056/jec.728. DOI: https://doi.org/10.55056/jec.728 Vakaliuk, T.A. and Semerikov, S.O., 2024. Empowering the Edge: Research advances from doors 2024. Journal of Edge Computing, 3(1), pp.3–27. Available from: https://doi.org/10.55056/jec.747. DOI: https://doi.org/10.55056/jec.747
URI: https://acnsci.org/journal/index.php/jec/article/view/737
http://ds.knu.edu.ua/jspui/handle/123456789/5486
ISSN: 2837-181X
Appears in Collections:Кафедра професійної та соціально-гуманітарної освіти

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