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Research ArticleOpen accessEvery word in the titleA Novel Time-Based Switch Migration Method (TSSM) for Load Balancing in Distributed Software-Defined NetworkingThangaraj E, Dinesh K, Prabhakaran Paulraj et al.Switch migration is predominantly used for workload balancing in distributed Software Defined Network (SDN) controllers. The Time-Sharing Switch Migration (TSSM) technique allowed the switch's responsibility to be divided based on the time duration condition of overload, which resulted in fewer overload events, fewer ping-pong controller issues, and more efficient controllers. However, it has higher controller resource utilization during TSSM working … …Journal of Computer Science, Vol. 22, Issue 3 · 6 Mar 2026 · 1,591 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleReal Time Anomaly Detection in Massive Data Streams with ELK StackJakup Fondaj, Zirije HasaniReal time anomaly detection is very popular topic nowadays this because the number of data generated every day is larger and larger. Facing with the phenomena of Big Data is not an easy task. The main aim of this research is to fine appropriate architecture for real-time big data analytic and its main task is to detect anomalies in this … …Journal of Computer Science, Vol. 15, Issue 6 · 17 Jun 2019 · 7,799 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing DataSawsan Morkos GharghoryClassification of multivariate time series has got massive attention in the last decade. The traditional modeling classifiers are complicated patterns and are incompetent to capture the dependencies of multivariate time series data. To include both of the effective features and the embedded relationships in the multivariate time series, a new hybrid model which incorporates both Convolutional Neural Network (CNN) and … …Journal of Computer Science, Vol. 17, Issue 9 · 29 Sep 2021 · 6,693 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAdaptive Cybersecurity: Dynamically Retrainable Firewalls for Real-Time Network ProtectionSina AhmadiThe growing complexity of cyberattacks has necessitated the evolution of firewall technologies from static models to adaptive, machine learning-driven systems. This research introduces “Dynamically Retrainable Firewalls”, which respond to emerging threats in real-time. Unlike traditional firewalls that rely on static rules to inspect traffic, these advanced systems leverage machine learning algorithms to analyze network traffic patterns dynamically and identify threats. … …Journal of Computer Science, Vol. 21, Issue 9 · 7 Nov 2025 · 2,562 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleTrends in the Prevalence of Clostridioides difficile Using Real Time PCR in South Korea (2020-2024)Yonghee Lee, Yoo Na Chung, Jae Kyung KimClostridioides difficile is a major cause of infection in healthcare settings, and frequent antibiotic use exacerbates its occurrence. C. difficile infection (CDI) is a major public-health challenge owing to its complications and high mortality rate. We aimed to analyze current trends in C. difficile prevalence. This study was a retrospective, single center analysis of 7,371 samples from April 2020 to … …American Journal of Biochemistry and Biotechnology, Vol. 21, Issue 2 · 3 Oct 2025 · 2,363 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleOne Time Password (OTP) Solution for Two Factor Authentication: A Practical Case StudyOscar Efrén Acosta Mayorga, Sang Guun YooCurrently, static passwords are no longer secure, they expose accounts and data, more advanced and sophisticated approaches are required to guarantee user authentication and operations in digital systems. In this context, this study investigates how in a practical application, addressing security challenges in user authentication using One Time Passwords (OTP) can strengthen two-Factor Authentication (2FA), the use of a sec … …Journal of Computer Science, Vol. 21, Issue 5 · 26 Apr 2025 · 8,469 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleApplication of Time Series Modeling to Study River Water QualityMaryam Ghashghaie, Kaveh Ostad-Ali-Askari, Saeid Eslamian, Vijay P. SinghWater deficit problem originates from two factors: population increase and water pollution. However, studying and forecasting the quality of water are necessary to avoid serious problems in future through managerial works. In present study, using time series modeling, the quality of Madian Rood River is studied at Baraftab station using time series analysis. Nine parameters of water quality are studied … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 2 · 25 Apr 2018 · 8,938 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time SeriesSaugat Aryal, Dheynoshan Nadarajah, Prabath Lakmal Rupasinghe et al.Financial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has … …Journal of Computer Science, Vol. 16, Issue 10 · 27 Oct 2020 · 6,978 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEvolution of Time Concept in Physics and in PhilosophyC. Sivaram, Kenath Arun, O. V. KirenWhat is ’time’? This is a question that has perplexed many a physicist and philosophers alike. The perception of the passage of time appears ingrained in all biological systems where growth, evolution and decay of objects, etc. are measurable indicators of the passage of time. Here we look at various physical models of time, from Newtonian to relativistic. We also … …Physics International, Vol. 6, Issue 2 · 9 Jul 2015 · 8,964 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleFREQUENT CORRELATED PERIODIC PATTERN MINING FOR LARGE VOLUME SET USING TIME SERIES DATAG. M. Karthik, S. KarthikFrequent pattern mining has been a widely used in the area of discovering association and correlations among real data sets. However, discovering interesting correlation relationship among huge number of co-occurrence patterns are complicated, a majority of them are superfluous or uninformative. Mining correlations among large pile of useless information is extraordinarily useful in real-time applications. In this study, we propose … …Journal of Computer Science, Vol. 10, Issue 10 · 30 Jun 2014 · 6,611 accessesView PDFCite