Method of Anomaly Detection of Temperature Data in Vacuum Thermal Test Based on Data Mining

2018 ◽  
Author(s):  
Jihui Xie ◽  
Dongliang Wu ◽  
Tao Liao
Keyword(s):  
Data Mining ◽  
Anomaly Detection ◽  
Temperature Data ◽  
Thermal Test

scholarly journals A Survey of Anomaly Detection Using Data Mining Methods for Hypertext Transfer Protocol Web Services

2015 ◽  
Vol 11 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Mohsen Kakavand ◽  
Norwati Mustapha ◽  
Aida Mustapha ◽  
Mohd Taufik Abdullah ◽  
Hamed Riahi
Keyword(s):  
Data Mining ◽  
Web Services ◽  
Anomaly Detection ◽  
Hypertext Transfer Protocol ◽  
Mining Methods ◽  
Using Data ◽  
Transfer Protocol

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

scholarly journals Deploying data mining techniques to gain deeper insight into Nigerian customers' financial activities

2020 ◽  
Vol 39 (2) ◽  
pp. 553-561
Author(s):  
A.M. Nwohiri ◽  
F.T. Sonubi
Keyword(s):  
Data Mining ◽  
Information Processing ◽  
Statistical Analysis ◽  
Anomaly Detection ◽  
Report Generation ◽  
Processing Methods ◽  
Data Mining Techniques ◽  
Digital World ◽  
Data Tables ◽  
Insight Into

Presently, Nigerian banks issue account statements in a tabular flat form. These statements mainly show basic logs of credit and debit transactions. They do not offer a deeper insight into the pure nature of transactions. Moreover, they lack rich mine-able data, and rather contain basic data tables that do not provide enough insights into customers' monthly/weekly/yearly expenses and earnings. In today’s fast-paced digital world, where information processing methods are rapidly changing, customers need not just a basic table of transactions but deeper analysis and detail report of their finances. This paper aims at identifying and addressing these problems by deploying data mining techniques and practices in building an application that helps customers gain a deeper insight and understanding of their spending and earnings over a particular period. Some of the techniques used are classification, statistical analysis, visualization, report generation and summarization. Keywords: Data mining, API, Anomaly Detection, GTBank, CBN, Bank statements, Nigeria

scholarly journals Cascaded Neural Network Based Data Mining Strategy for Cloud Intrusion Detection

2018 ◽  
Vol 11 (3) ◽  
pp. 1094
Author(s):  
P Purniemaa ◽  
R Jagadeesh Kannan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Knowledge Discovery ◽  
Detection System ◽  
Data Sets ◽  
And Performance ◽  
Performance Results ◽  
Data Mining Strategy

In recent years data mining has acquired huge popularity in the field of knowledge discovery. Thus, this approach has inspired several researches for anomaly detection, fraud detection and intrusion detection with higher accuracy, all round generalization of the problem and its sub cases; all giving higher performance in conditions subjected to continuous alteration. Though there remain quite a few challenging problems in design and implementation of a data mining based cloud intrusion detection system, as deception tactics and modeling of behavior remains a daunting problem to compute for anomaly owing to massive size of data to process in reasonable time. In this study we present a cascaded neural network based data mining strategy for cloud intrusion detection systems (IDSs) and presents the comparison and performance results tested on DARPA Intrusion Detection (ID) Data Sets, Knowledge Discovery and Data Mining Cup, NSL-KDD dataset. The study exhibits numerous advantages offered by the presented method and give reliable results of anomaly detection in real time scenario.

Sign in / Sign up

Export Citation Format

Share Document