Emerging technologies and supporting tools for earthquake disaster management: A perspective, challenges, and future directions

Progress in Disaster Science, Journal Year: 2024, Volume and Issue: 23, P. 100347 - 100347

Published: July 3, 2024

Seismology is among the ancient sciences that concentrate on earthquake disaster management (EQDM), which directly impact human life and infrastructure resilience. Such a pivot has made use of contemporary technologies. Nevertheless, there need for more reliable insightful solutions to tackle daily challenges intricacies natural stakeholders must confront. To consolidate substantial endeavors in this field, we undertake comprehensive survey interconnected More particularly, analyze data communication networks (DCNs) Internet Things (IoT), are main infrastructures seismic networks. In accordance, present conventional innovative signal-processing techniques seismology. Then, shed light evolution EQ sensors including acoustic based optical fibers. Furthermore, address role remote sensing (RS), robots, drones EQDM. Afterward, highlight social media contribution. Subsequently, elucidation diverse optimization employed seismology prolonging presented. Besides, paper analyzes important functions artificial intelligence (AI) can fulfill several areas Lastly, guide how prevent disasters preserve lives.

Language: Английский

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Software defined networking based network traffic classification using machine learning techniques

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 29, 2024

The classification of network traffic has become increasingly crucial due to the rapid growth in number internet users. Conventional approaches, such as identifying based on port numbers and payload inspection are becoming ineffective dynamic encrypted nature modern traffic. A researchers have implemented Software Defined Networking (SDN) using Machine Learning (ML) Deep (DL) models. However, studies had various limitations detection, inspection, poor detection accuracy, challenges with testing models both offline real-time modes. ML together SDN adopted nowadays enhance performance. In this paper, supervised (Logistic Regression, Decision Tree, Random Forest, AdaBoost, Support Vector Machine) unsupervised (K-means clustering) were used classify Domain Name System (DNS), Telnet, Ping, Voice flows simulated Distributed Internet Traffic Generator (D-ITG) tool. use tool effectively manages classifies types their application. study discussed dataset used, model selection, implementation model, techniques (such pre-processing, feature extraction, algorithm, evaluation metrics). proposed was Mininet for designing architecture generating Anaconda Python environment utilized techniques. Among tested, Tree learning achieved highest accuracy 99.81%, outperforming other algorithms. These results indicate that integration provides an efficient method accurately classifying traffic, enhanced quality service (QoS), packets, deep packet management.

Language: Английский

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Performance enhancement of artificial intelligence: A survey

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: unknown, P. 104034 - 104034

Published: Sept. 1, 2024

Language: Английский

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The Effect of Generating Synthetic Data in Smart City Network Systems

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(2)

Published: Feb. 15, 2025

Language: Английский

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Software Defined Network Traffic Classification for QoS Optimization Using Machine Learning

Journal of Network and Systems Management, Journal Year: 2025, Volume and Issue: 33(2)

Published: Feb. 26, 2025

Language: Английский

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Enhanced Hybrid Approach for Multi-Class DDoS Attack Detection and Classification in Software-Defined Networks Using Remote Sensing and Data Analytics

Remote Sensing in Earth Systems Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

Language: Английский

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Network traffic classification to improve quality of service (QoS)

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3282, P. 020007 - 020007

Published: Jan. 1, 2025

Language: Английский

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Optimized efficient predefined time adaptive neural network for stream traffic classification in software defined network

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 128086 - 128086

Published: May 1, 2025

Language: Английский

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Traffic Classification in Software-Defined Networking Using Genetic Programming Tools

Future Internet, Journal Year: 2024, Volume and Issue: 16(9), P. 338 - 338

Published: Sept. 19, 2024

The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, monitoring, engineering, dynamic resource allocation planning, and applying Quality Service (QoS) policies. programmability nature SDN, the holistic view through SDN controllers, capability adjustable reconfigurable controllersare fertile ground development new techniques classification. Although there are enough research works that have studied methods in environments, they several shortcomings gaps need to be further investigated. In this study, we investigated using publicly available trace datasets. We apply a series classifiers, such as MLP (BFGS), FC2 (RBF), (MLP), Decision Tree, SVM, GENCLASS, evaluate their performance terms accuracy, detection rate, precision. Of used, GenClass appears more accurate separating categories problem than rest, reflected both precision recall. key element method it can generate rules programmatically detect hidden associations exist between features desired classes. However, Genetic Programming-based require significantly higher execution time compared other machine learning techniques. This most evident feature construction where at each generation genetic algorithm, set models required trained generated artificial features.

Language: Английский

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Traffic data classification in SDN network based on machine learning algorithms

Wasit Journal of Pure sciences, Journal Year: 2024, Volume and Issue: 3(2), P. 161 - 171

Published: June 30, 2024

Traffic classification plays a crucial role in various domains of network management, including service measurement, architectural design, security monitoring, and advertising. Software-defined networks (SDN) is new technology that has the potential to solve typical problems by simplifying introduction programmability, provision global perspective network. In recent years, SDN brought opportunities classify traffic. techniques have been investigated, proposed, developed. This survey delves into traffic under SDN, which vital component for improving services, administration, security. We give an in-depth assessment categorization algorithms adapted emphasizing fresh they present. cover many metrics assessing effectiveness these algorithms, such as accuracy, precision, recall, F1 score, we examine numerous datasets serve performance benchmarks. The study also synthesizes findings existing research, revealing trends efficacy context SDN-enabled settings. document serves resource scholars practitioners seeking optimize strategies providing complete review approaches

Language: Английский

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