Classification of application reviews into software maintenance tasks using data mining techniques

2020 ◽  
Author(s):  
Assem Al-Hawari ◽  
Hassan Najadat ◽  
Raed Shatnawi
Keyword(s):  
Data Mining ◽  
Software Maintenance ◽  
Data Mining Techniques ◽  
Using Data

Classification of Intrusion Detection Using Data Mining Techniques

2018 ◽  
pp. 753-764 ◽  
Author(s):  
Roma Sahani ◽  
Shatabdinalini ◽  
Chinmayee Rout ◽  
J. Chandrakanta Badajena ◽  
Ajay Kumar Jena ◽  
...  
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Data Mining Techniques ◽  
Using Data

Classification of Product Rating Using Data Mining Techniques

2017 ◽  
pp. 27-36 ◽  
Author(s):  
Pinku Deb Nath ◽  
Sowvik Kanti Das ◽  
Fabiha Nazmi Islam ◽  
Kifayat Tahmid ◽  
Raufir Ahmed Shanto ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Using Data ◽  
Product Rating

scholarly journals Application of Data Mining Techniques for Medical Data Classification: A Review

2018 ◽  
Vol 150 ◽  
pp. 06003 ◽  
Author(s):  
Saima Anwar Lashari ◽  
Rosziati Ibrahim ◽  
Norhalina Senan ◽  
N. S. A. M. Taujuddin
Keyword(s):  
Data Mining ◽  
Data Classification ◽  
Past Research ◽  
Medical Data ◽  
Extensive Literature ◽  
Literature Analysis ◽  
Data Mining Techniques ◽  
Medical Data Classification ◽  
Using Data

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

scholarly journals Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2972
Author(s):  
Marek Amanowicz ◽  
Damian Jankowski
Keyword(s):  
Data Mining ◽  
Network Control ◽  
Flow Rule ◽  
Distributed Data ◽  
Software Defined Networks ◽  
Data Mining Techniques ◽  
Wireless Applications ◽  
Using Data ◽  
Data Centres

The increasing availability of mobile devices and applications, the progress in virtualisation technologies, and advances in the development of cloud-based distributed data centres have significantly stimulated the growing interest in the use of software-defined networks (SDNs) for both wired and wireless applications. Standards-based software abstraction between the network control plane and the underlying data forwarding plane, including both physical and virtual devices, provides an opportunity to significantly increase network security. In this paper, to secure SDNs against intruders’ actions, we propose a comprehensive system that exploits the advantages of SDNs’ native features and implements data mining to detect and classify malicious flows in the SDN data plane. The architecture of the system and its mechanisms are described, with an emphasis on flow rule generation and flow classification. The concept was verified in the SDN testbed environment that reflects typical SDN flows. The experiments confirmed that the system can be successfully implemented in SDNs to mitigate threats caused by different malicious activities of intruders. The results show that our combination of data mining techniques provides better detection and classification of malicious flows than other solutions.

Sign in / Sign up

Export Citation Format

Share Document