scholarly journals Multi-Level P2P Traffic Classification Using Heuristic and Statistical-Based Techniques: A Hybrid Approach

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2117
Author(s):  
Max Bhatia ◽  
Vikrant Sharma ◽  
Parminder Singh ◽  
Mehedi Masud
Keyword(s):  
Hybrid Approach ◽  
High Accuracy ◽  
Traffic Classification ◽  
Network Bandwidth ◽  
Classification Technique ◽  
C4.5 Decision Tree ◽  
Multi Level ◽  
P2p Applications ◽  
Network Administrators

Peer-to-peer (P2P) applications have been popular among users for more than a decade. They consume a lot of network bandwidth, due to the fact that network administrators face several issues such as congestion, security, managing resources, etc. Hence, its accurate classification will allow them to maintain a Quality of Service for various applications. Conventional classification techniques, i.e., port-based and payload-based techniques alone, have proved ineffective in accurately classifying P2P traffic as they possess significant limitations. As new P2P applications keep emerging and existing applications change their communication patterns, a single classification approach may not be sufficient to classify P2P traffic with high accuracy. Therefore, a multi-level P2P traffic classification technique is proposed in this paper, which utilizes the benefits of both heuristic and statistical-based techniques. By analyzing the behavior of various P2P applications, some heuristic rules have been proposed to classify P2P traffic. The traffic which remains unclassified as P2P undergoes further analysis, where statistical-features of traffic are used with the C4.5 decision tree for P2P classification. The proposed technique classifies P2P traffic with high accuracy (i.e., 98.30%), works with both TCP and UDP traffic, and is not affected even if the traffic is encrypted.

scholarly journals Quality Assessment of Ground Water in Pre and Post-Monsoon Using Various Classification Technique

2019 ◽  
Vol 8 (2) ◽  
pp. 5996-6003
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Ground Water ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Chemical Parameters ◽  
Accuracy Rate ◽  
Post Monsoon ◽  
Classification Technique

Quality assessment of water is one of the basic points which have pulled in a lot of thought in the progressing years. Diverse kinds of classification system are most convenient for the examination in this field of study. The present examination investigates the quality of ground water in Agastheeswaram which is located in Tamilnadu. Totally 138 water samples was accumulated in the midst of pre-monsoon (PRM) and post-monsoon (PSM) from the year of 2011 to 2012.The water quality (WQ) evaluation was carried out by assessing chemical parameters for both the seasons. This paper explores various classifier models such as DT, KNN and SVM to achieve prediction of groundwater quality. The classification is done based on the WQI of each sample. A near investigation of characterization systems was done dependent on the confusion matrix, accuracy, f1 score, precision and recall. The outcomes propose that SVM is a better method having high accuracy rate than other models

scholarly journals A Semi-Supervised QoS-Aware Classification for Wide Area Networks with Limited Resources

2019 ◽  
Vol 8 (11) ◽  
pp. 970-981
Keyword(s):  
Limited Resource ◽  
Resource Depletion ◽  
Error Rates ◽  
Wide Area ◽  
Area Network ◽  
Traffic Classification ◽  
Wide Area Network ◽  
Network Resource ◽  
Network Administrators

The task of network administrators to identify and determine the type of traffic traversing through the network is very critical to the rapid growth of new traffic each day. As the requirements of networks change over time, the situation of the network not able to meet some requirements is likely to occur. In a wide area network with a limited resource such as the low speed of links, frequent fragmentation of packets leading to extreme packet loss and costs is prominent resulting in the poor quality of service. As a result quantified amount of traffic flows can be classified at a time with limited features lowering the effectiveness of traffic classification. To improve upon the classification in such scenarios, we propose a hybrid semi-supervised clustering that is able to classify packet flows with restricted features and a small amount of packets while maintaining high accuracy in classification. We implement the above scenario in simulation and classify the limited flows obtained with our proposed algorithm. Evaluation results show that our proposed algorithm implemented into a classifier has good accuracy and precision values, with low processing time and error rates. The proposed strategy will enable network administrators during times of network resource depletion or upgrades provide and ensure the best quality of services and identify unwanted or malicious traffic.

TINGKAT KEAKURATAN DAN KEBERTERIMAAN TERJEMAHAN KALIMAT YANG MENGAKOMODASI TINDAK TUTUR KOMISIF PADA NOVEL INSURGENT (THE ACCURACY AND ACCEPTABILITY OF TRANSLATION ON THE SENTENCES WHICH ACCOMODATE COMMISSIVE SPEECH ACT IN THE INSURGENT NOVEL)

Widyaparwa ◽  
2017 ◽  
Vol 45 (2) ◽  
pp. 151-164
Author(s):  
Novita Sumarlin Putri
Keyword(s):  
Focus Group ◽  
Focus Group Discussion ◽  
Speech Act ◽  
Group Discussion ◽  
Document Analysis ◽  
High Accuracy ◽  
Pragmatic Approach ◽  
Componential Analysis ◽  
Pragmatic Aspects

Tindak tutur komisif merupakan salah satu aspek pragmatik yang harus diperhatikan oleh penerjemah ketika menerjemahkan teks. Hal itu dilakukan agar menghasilkan terjemahan yang berkualitas dari aspek keakuratan dan keberterimaan. Berdasarkan alasan tersebut, penelitian ini bertujuan mendiskripsikan tingkat keakuratan dan keberterimaan terjemahan kalimat yang mengakomodasi tindak tutur komisif dengan pendekatan pragmatik. Data yang digunakan ialah tuturan komisif dan hasil penilaian kualitas terjemahan. Data bersumber dari novel Insurgent karya Veronica Roth dan informan. Data dikumpulkan dengan cara analisis dokumen, kuesioner dan Focus Group Discussion. Selanjutnya, data dianalisis dengan cara analisis domain, taksonomi, komponensial, dan tema budaya. Hasil penelitian ini menunjukkan bahwa terjemahan dalam novel Insurgent mempunyai nilai keakuratan dan keberterimaan yang cukup tinggi. Berdasarkan penelitian ini, dapat disimpulkan bahwa tingkat keakuratan dan keberterimaan pada setiap jenis tindak tutur komisif memiliki dampak terhadap kualitas keseluruhan terjemahan kalimat yang mengandung tindak tutur komisif.Commissive speech act is one of the pragmatic aspects to regard by the translator in translating the text. It aims to produce a qualified translation in regarding accuracy and acceptability aspects. According to the aspects, this research aims to describe accuracy and acceptability of translation in sentences which accommodate commissive speech act using pragmatic approach. The data used is commissive speech and qualitative translation value result. The sources of the data are an Insurgent novel by Veronica Roth and informants. The data were collected through document analysis, questionnaire, and Focus Group Discussion then analyzed the domain, taxonomic, componential analysis, and cultural theme. The result shows that translation in the Insurgent novel has high accuracy and acceptability values. This research concludes that the accuracy and acceptability level in each commissive speech act has an impact on quality of whole translated sentences which contain commissive speech act.

scholarly journals Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge

2021 ◽  
Vol 18 (2) ◽  
pp. 156-164 ◽  
Author(s):  
Catherine L. Lawson ◽  
Andriy Kryshtafovych ◽  
Paul D. Adams ◽  
Pavel V. Afonine ◽  
Matthew L. Baker ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Structure Data ◽  
Model Evaluation ◽  
Data Bank ◽  
High Accuracy ◽  
Atomic Resolution ◽  
Software Developers ◽  
Data Archives ◽  
Modeling Software

AbstractThis paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.

Diagnostic performance and clinical implications for enhancing a hybrid quantitative flow ratio and fractional flow reserve revascularization decision making strategy

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J Peper ◽  
R.W Van Hamersvelt ◽  
B.J.M.W Rensing ◽  
J.P Van Kuijk ◽  
M Voskuil ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Hybrid Approach ◽  
High Accuracy ◽  
Intermediate Zone ◽  
Flow Ratio ◽  
Fractional Flow ◽  
Hybrid Strategy ◽  
Flow Reserve ◽  
Quantitative Flow Ratio ◽  
Quantitative Flow

Abstract Background Fractional flow reserve (FFR) adoption persists low mainly due to procedural and operator related factors as well as costs. An alternative for FFR, quantitative flow ratio (QFR) achieves a high accuracy mainly outside the intermediate zone without the need for hyperemia and wire-use. Currently, no outcome trials assess the role of QFR in the guidance of revascularization. Therefore, we evaluate a QFR-FFR hybrid strategy in which FFR is measured inside of the intermediate zone. Methods This retrospective multi-center study included consecutive patients who underwent both invasive coronary angiography and FFR in the participating centers. QFR was calculated for all vessels in which FFR was measured. Diagnostic performance of QFR was assessed using an FFR cut-off of 0.80 as reference standard. The QFR-FFR hybrid approach was modeled using the intermediate zone of 0.77 to 0.87 assuming that lesions within the intermediate zone follow the FFR binary cutoff. Results In total, 381 vessels in 289 patients were analyzed. The sensitivity, specificity and accuracy on a per vessel-based analysis were 84.6%, 86.3% and 85.6% for QFR and 91.1%, 95.3% and 93.4% for the QFR-FFR hybrid approach. The diagnostic accuracy of QFR-FFR hybrid strategy with invasive FFR measurement is 93.4% and results in a FFR reduction of 56.7%. Conclusion QFR has a good correlation and agreement with invasive FFR and a high diagnostic accuracy. A hybrid QFR-FFR approach could extend the use of QFR and reduces the proportion of invasive FFR-measurements needed while maintaining a high accuracy. Hybrid QFR-FFR strategy Funding Acknowledgement Type of funding source: None

scholarly journals Research on QoS Classification of Network Encrypted Traffic Behavior Based on Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1376
Author(s):  
Yung-Fa Huang ◽  
Chuan-Bi Lin ◽  
Chien-Min Chung ◽  
Ching-Mu Chen
Keyword(s):  
Machine Learning ◽  
Majority Voting ◽  
Deep Packet Inspection ◽  
Traffic Classification ◽  
K 13 ◽  
Packet Inspection ◽  
Majority Voting Method ◽  
Encrypted Traffic ◽  
Traffic Behavior

In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark.

Constraint-Based Reverse Engineering From Ultrasound Cross-Sections

1997 ◽  
Author(s):  
Jai Menon ◽  
Ranjit Desai ◽  
Jay Buckey
Keyword(s):  
Reverse Engineering ◽  
Cross Sections ◽  
Biomedical Applications ◽  
Engineering Application ◽  
Volume Data ◽  
Cross Sectional ◽  
Reverse Engineer ◽  
Network Bandwidth ◽  
Complex Topology

Abstract This paper extends the “cross-sectional” approach for reverse engineering, used abundantly in biomedical applications, to the mechanical domain. We propose a combination of “projective” and cross-sectional algorithms for handling physical artifacts with complex topology and geometry. In addition, the paper introduces the concept of constraint-based reverse engineering, where the constraint parameters could include one or more of the following: time, storage (memory, disk-space), network bandwidth, Quality of Service (output-resolution), and so forth. We describe a specific reverse-engineering application which uses ultrasound (tilt-echo) imaging to reverse engineer spatial enumeration (volume) representations from cross-sectional data. The constraint here is time, and we summarize how our implementation can satisfy real-time reconstruction for distribution of the volume data on the internet. We present results that show volume representations computed from static objects. Since the algorithms are tuned to satisfy time constraints, this method is extendable to reverse engineer temporally-varying (elastic) objects. The current reverse engineering processing time is constrained by the data-acquisition (tilt-echo imaging) process, and the entire reverse engineering pipeline has been optimized to compute incremental volume representations in the order of 3 seconds on a network of four processors.

scholarly journals A Hybrid Approach to Call Admission Control in 5G Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammed Al-Maitah ◽  
Olena O. Semenova ◽  
Andriy O. Semenov ◽  
Pavel I. Kulakov ◽  
Volodymyr Yu. Kucheruk
Keyword(s):  
Artificial Intelligence ◽  
Quality Of Service ◽  
Admission Control ◽  
Mobile Communications ◽  
Communication Systems ◽  
Call Admission Control ◽  
Hybrid Approach ◽  
5G Networks ◽  
Call Admission

Artificial intelligence is employed for solving complex scientific, technical, and practical problems. Such artificial intelligence techniques as neural networks, fuzzy systems, and genetic and evolutionary algorithms are widely used for communication systems management, optimization, and prediction. Artificial intelligence approach provides optimized results in a challenging task of call admission control, handover, routing, and traffic prediction in cellular networks. 5G mobile communications are designed as heterogeneous networks, whose important requirement is accommodating great numbers of users and the quality of service satisfaction. Call admission control plays a significant role in providing the desired quality of service. An effective call admission control algorithm is needed for optimizing the cellular network system. Many call admission control schemes have been proposed. The paper proposes a methodology for developing a genetic neurofuzzy controller for call admission in 5G networks. Performance of the proposed admission control is evaluated through computer simulation.

Solving Multi-Objective Multicast Routing Problem Using a New Hybrid Approach

2018 ◽  
Vol 9 (4) ◽  
pp. 22-36
Author(s):  
Mohammed Mahseur ◽  
Abdelmadjid Boukra ◽  
Yassine Meraihi
Keyword(s):  
Multicast Routing ◽  
Hybrid Approach ◽  
Bat Algorithm ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Quality Of Service Parameters

Multicast routing is the problem of finding the spanning tree of a set of destinations whose roots are the source node and its leaves are the set of destination nodes by optimizing a set of quality of service parameters and satisfying a set of transmission constraints. This article proposes a new hybrid multicast algorithm called Hybrid Multi-objective Multicast Algorithm (HMMA) based on the Strength Pareto Evolutionary Algorithm (SPEA) to evaluate and classify the population in dominated solutions and non-dominated solutions. Dominated solutions are evolved by the Bat Algorithm, and non-dominated solutions are evolved by the Firefly Algorithm. Old and weak solutions are replaced by new random solutions by a process of mutation. The simulation results demonstrate that the proposed algorithm is able to find good Pareto optimal solutions compared to other algorithms.

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