Predicting paper making defects on-line using data mining

1998 ◽  
Vol 11 (5-6) ◽  
pp. 331-338 ◽  
Author(s):  
Robert Milne ◽  
Mike Drummond ◽  
Patrick Renoux
Keyword(s):  
Data Mining ◽  
Paper Making ◽  
On Line ◽  
Using Data

scholarly journals Detect Frauds in Credit Card using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 4891-4895
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Hidden Markov ◽  
K Nearest Neighbor ◽  
Detection Systems ◽  
Important Concern ◽  
On Line ◽  
Using Data ◽  
The Individual

In today era credit card are extensively used for day to day business as well as other transactions. Ascent within the variety of transactions through master card has junction rectifier to rise in the dishonest activities. In trendy day's fraud is one in every of the most important concern within the monetary loses not solely to the merchants however additionally to the individual purchasers. Data processing had competed a commanding role within the detection of credit card in on-line group action. Our aim is to first of all establish the categories of the fraud secondly, the techniques like K-nearest neighbor, Hidden Markov model, SVM, logistic regression, decision tree and neural network. So fraud detection systems became essential for the banks to attenuate their loses. In this paper we have research about the various detecting techniques to identify and detect the fraud through varied techniques of data mining

AN INTELLIGENT NETWORK INTRUSION DETECTION USING DATA MINING TECHNIQUES

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Mohd Afizi Mohd Shukran ◽  
Kamaruzaman Maskat
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Human Detection ◽  
Network Intrusion Detection ◽  
Intelligent Network ◽  
Data Mining Technique ◽  
Network Attacks ◽  
Network Intrusion ◽  
On Line ◽  
Using Data

Network Intrusion Detection is to detect malicious attacks to the networks for different uses from military to enterprise. Currently available approaches either rely on the known network attacks or have high proportion of normal network traffics that were erroneously reported as anomalous traffics. The aim of this paper is to develop an efficient algorithm for intrusion detection without prior knowledge of network attacks. Uniquely, our approach will integrate a newly developed data mining technique for data feature classification with techniques commonly used for human detection. The key idea is to achieve on-line and automated learning of new attacks for precise and real-time intrusion detection.

scholarly journals A Psychoanalysis of Data Privacy Maintenance Issues in Social Network using Data mining

2019 ◽  
Vol 9 (1) ◽  
pp. 7018-7022
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Elliptic Curve Cryptography ◽  
Data Privacy ◽  
On Line ◽  
The Common ◽  
Using Data ◽  
Present Subject ◽  
Existing Data

The existing data sharing systems relates with the on-line social networks (OSNs) suggest encoding of information before sharing, the multiparty get to the executives of scrambled information has turned into a troublesome issue. A safe information sharing subject proposed in OSNs upheld figure content approach trait based and Elliptic Curve Cryptography algorithmic principle re-encryption and mystery sharing. The work relates the gatekeeper clients' delicate information grants clients to redo get to approaches of their information thus source scrambled information to the OSNs administration provider. The proposed technique displays a multiparty get to the executive’s model that enables the communicator to refresh the entrance strategy of figure content. The characteristics fulfill the common access strategy. The work needs a fractional mystery composing development inside which the calculation overhead of client is essentially diminished by strengthening the vast majority of the mystery composing activities to the OSNs administration provider. Moreover, the check capacity on the outcomes originated from the OSNs administration provider to guarantee the rightness of fractional decoded figure content. The present subject partner affordable properties disavowal philosophy that accomplishes each forward and in reverse mystery. The insurance and execution examination results demonstrate that the arranged subject is secure and efficient in OSNs.

scholarly journals Study of Indoor Radon Using Data Mining Models Based on OLAP Cubes

2020 ◽  
pp. 53-61
Author(s):  
Javier García-Tobar
Keyword(s):  
Data Mining ◽  
Indoor Radon ◽  
Residential Building ◽  
Sql Server ◽  
Radon Measurement ◽  
Measurement Campaign ◽  
On Line ◽  
Analytical Processing ◽  
Using Data ◽  
Radon Measurements

This research has focused on a radon measurement campaign that was carried out in two dwellings in a residential building located in Madrid. A new methodology has been used in this field, such as the use of cubes based on On-Line Analytical Processing in SQL Server Analysis Services. The application of this methodology can be of particular interest in analysing thousands of radon measurements and complementary variables, which are easily obtained in any radon measurement campaign.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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