Fault Diagnosis of Automobile ECUs with Data Mining Technologies

2010 ◽  
Vol 40-41 ◽  
pp. 156-161 ◽  
Author(s):  
Yang Li ◽  
Yan Qiang Li ◽  
Zhi Xue Wang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Data Stream ◽  
Electronic Control Unit ◽  
Rapid Development ◽  
Reliability And Validity ◽  
Control Unit ◽  
Use Of Data ◽  
The Cost

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4891-4904
Author(s):  
Selahattin Bardak ◽  
Timucin Bardak ◽  
Hüseyin Peker ◽  
Eser Sözen ◽  
Yildiz Çabuk
Keyword(s):  
Data Mining ◽  
Goodness Of Fit ◽  
Bending Strength ◽  
Wood Preservation ◽  
Ambient Conditions ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
The Cost ◽  
Mining Algorithms ◽  
The Relationship

Wood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.

scholarly journals An Intelligent Data Mining System to Detect Healthcare Fraud

2011 ◽  
pp. 149-168 ◽  
Author(s):  
Guisseppi A. Forgionne ◽  
Aryya Gangopadhyay ◽  
Monica Adya
Keyword(s):  
Data Mining ◽  
Fraud Detection ◽  
Financial Impact ◽  
Classification Rules ◽  
Mining System ◽  
Quality Healthcare ◽  
Proactive Approach ◽  
Care Industry ◽  
Use Of Data ◽  
The Cost

There are various forms of fraud in the health care industry. This fraud has a substantial financial impact on the cost of providing healthcare. Money wasted on fraud will be unavailable for the diagnosis and treatment of legitimate illnesses. The rising costs of and the potential adverse affects on quality healthcare have encouraged organizations to institute measures for detecting fraud and intercepting erroneous payments. Current fraud detection approaches are largely reactive in nature. Fraud occurs, and various schemes are used to detect this fraud afterwards. Corrective action then is instituted to alleviate the consequences. This chapter presents a proactive approach to detection based on artificial intelligence methodology. In particular, we propose the use of data mining and classification rules to determine the existence or non-existence of fraud patterns in the available data. The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice.

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 40-54
Author(s):  
Ikong Ifongki
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Rules ◽  
Large Data ◽  
Added Value ◽  
Data Set ◽  
Use Of Data ◽  
Decision Tree Classification ◽  
C4.5 Algorithm

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.

scholarly journals Discovering Hidden Patterns in Loan Reimbursement

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Central Bank ◽  
Interest Rates ◽  
Wage Rates ◽  
Banking Regulations ◽  
Practical Model ◽  
Hidden Patterns ◽  
The Cost ◽  
Mining Tools

Loans are the major resources at banks. However, in some cases the cost that they incur to banks soar and finally makes them detrimental, as a result of irregular or delaying reimbursement or not paying at all. Due to the low wage rates in Iranian banks and the Central Bank of Iran (CBI) regulations in determining interest rates for deposits and loans, banks are becoming more and more dependent to the loans and their related profits. Therefore, banks have to look for customers with low risk for punctual payment. According to defect loan reimbursement in past years, banks have to specify severe prerequisites and limited contracts in granting loans to their customers. Contravening banking regulations and lack of consistent customers' accreditation banks are getting into heavy losses. Evaluating situations of the granted loans in EN Bank of Iran during a six-month period, based upon the profiles and loans history and the trend of payments useful patterns are discovered; designing a practical model of loan payment in Iran, the future default or failure to regain the granted loans is predicted and sensible methods of granting loans in Iran are developed. In order to extract hidden patterns in data statistical methods and data mining tools with focus on decision tree techniques are applied.

scholarly journals Data Mining and Machine Learning Techniques for Cyber Security Intrusion Detection

2020 ◽  
Vol 9 (3) ◽  
pp. 4084-4090
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Cyber Security ◽  
Data Stream ◽  
Machine Learning Techniques ◽  
Complex Nature ◽  
Use Of Data ◽  
Learning Techniques ◽  
Examination Paper ◽  
Discovery Rates

An interference discovery framework is customizing that screens a singular or an arrangement of PCs for toxic activities that are away for taking or blue-penciling information or spoiling framework shows. The most methodology used as a piece of the present interference recognition framework is not prepared to deal with the dynamic and complex nature of computerized attacks on PC frameworks. In spite of the way that compelling adaptable methodologies like various frameworks of AI can realize higher discovery rates, cut down bogus alert rates and reasonable estimation and correspondence cost. The use of data mining can realize ceaseless model mining, request, gathering and littler than ordinary data stream. This examination paper portrays a connected with composing audit of AI and data delving procedures for advanced examination in the assistance of interference discovery. In perspective on the number of references or the congruity of a rising methodology, papers addressing each procedure were recognized, examined, and compacted. Since data is so fundamental in AI and data mining draws near, some striking advanced educational records used as a piece of AI and data burrowing are depicted for computerized security is shown, and a couple of recommendations on when to use a given system are given.

scholarly journals Penerapan Metode Algoritma C4.5 dalam Menganalisa Pegajuan Kredit pada Koperasi Jasa Keuangan Syariah Kelurahan Limau Manis Selatan

2018 ◽  
Vol 7 (1) ◽  
pp. 28-42
Author(s):  
Febri Hadi
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Financial Services ◽  
Rapid Development ◽  
Simple Representation ◽  
Classification Technique ◽  
C4.5 Algorithm ◽  
Processing Techniques ◽  
Credit Analysis

The development of data processing techniques at this time has experienced rapid development. The Decision tree is a simple representation of a classification technique that is the process of teaching a function of purpose that maps each set of first attributes of a class defined previously. The decision tree can determine the hidden relationship between a number of potential target variables. In lending to customers, credit analysis is required for lending. The analysis of the kerdit can be done by utilizing data mining in the form of C4.5 algorithm. The C4.5 algorithm is used to provide credit decisions in order for the Sharia financial services cooperative to quickly analyze the credit application by members of a cooperative. The purpose of this research is to apply C4.5 algorithm method in analyzing credit application at Koperasi Jasa Keuangan Syariah Kelurahan Limau Manis Selatan.

scholarly journals PENGGUNAAN DATA MINING DALAM HIT RATE IMPORTASI JALUR MERAH DENGAN MODEL DECISION TREE

2021 ◽  
Vol 5 (2) ◽  
pp. 187-202
Author(s):  
Alfin Yudistira ◽  
Muh Nurkhamid
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Type A ◽  
Tree Model ◽  
Root Node ◽  
Hit Rate ◽  
Use Of Data ◽  
New Perspective ◽  
Risk Engine ◽  
Red Line

ABSTRACT:  Customs and Excise faces a big challenge to be able to increase the hit rate of red line imports by 40% in accordance with the Blueprint for the 2014-2025 Ministry of Finance Institutional Transformation Program and international benchmarks. Through a qualitative study, this study aims to determine the use of data mining that is applied to the risk engine based on import data, people's experiences, and research results of customs institutions of other countries. The data mining method used is CRISP-DM, classification method, and decision tree model, using data imported from the red line KPU BC Type A Tanjung Priok for the period September – December 2019 and January 2020. The results show that the use of data mining can increase the hit rate of red line importation. The most relevant attribute in classifying data is the sending country which is categorized as a root node, while the import duty tariff attribute does not provide information on data classification. This research is expected to provide a new perspective for the KPU BC Type A Tanjung Priok in an effort to improve the risk engine targeting and risk engine routing of Customs and Excise. Keywords: CRISP-DM, data mining, decision tree, hit rate, the red line import.   ABSTRAK: Bea dan Cukai menghadapi tantangan besar untuk dapat meningkatkan capaian hit rate importasi jalur merah sebesar 40% sesuai dengan Cetak Biru Program Transformasi Kelembagaan Kementerian Keuangan Tahun 2014 – 2025 dan benchmark internasional. Melalui studi kualitatif, penelitian ini bertujuan untuk mengetahui penggunaan data mining yang diterapkan dalam risk engine berdasarkan data importasi, pengalaman orang, dan data hasil penelitian institusi kepabeanan negara lain. Metode data mining yang digunakan adalah CRISP-DM, metode klasifikasi, dan model decision tree, dengan menggunakan data importasi jalur merah Kantor Pelayanan Utama (KPU) Bea dan Cukai (BC) Tipe A Tanjung Priok periode September – Desember 2019 dan Januari 2020. Hasil penelitian menunjukkan bahwa penggunaan data mining dapat meningkatkan capaian hit rate importasi jalur merah. Atribut yang paling relevan dalam mengklasifikasikan data adalah negara pengirim yang dikategorikan sebagai root node (akar), sedangkan atribut tarif bea masuk tidak memberikan informasi dalam klasifikasi data. Penelitian ini diharapkan dapat memberikan pandangan baru bagi KPU BC Tipe A Tanjung Priok dalam upaya perbaikan risk engine targeting dan risk engine penjaluran Bea dan Cukai. Kata Kunci: CRISP-DM, data mining, decision tree, hit rate, importasi jalur merah.  

Predicting Students Performance Based on Their Academic Profile

2020 ◽  
Vol 8 (2) ◽  
pp. 23-39
Author(s):  
Hadi Khalilia ◽  
Thaer Sammar ◽  
Yazeed Sleet
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
High Speed ◽  
Information Gain ◽  
Educational Data Mining ◽  
Computer Engineering ◽  
Use Of Data ◽  
Important Field ◽  
Index Measure ◽  
The University

Data mining is an important field; it has been widely used in different domains. One of the fields that make use of data mining is Educational Data Mining. In this study, we apply machine learning models on data obtained from Palestine Technical University-Kadoorie (PTUK) in Tulkarm for students in the department of computer engineering and applied computing. Students in both fields study the same major courses; C++ and Java. Therefore, we focused on these courses to predict student’s performance. The goal of our study is predicting students’ performance measured by (GPA) in the major. There are many techniques that are used in the educational data mining field. We applied three models on the obtained data which have been commonly used in the educational data mining field; the decision tree with information gain measure, the decision tree with Gini index measure, and the naive Bayes model. We used these models in our work because they are efficient and they have a high speed in data classification, and prediction. The results suggest that the decision tree with information gain measure outperforms other models with 0.66 accuracy. We had a deeper look on key features that we train our models; precisely, their branch of study at school, field of study in the University, and whether or not the students have a scholarship. These features have an influence on the prediction. For example, the accuracy of the decision tree with information gain measure increases to 0.71 when applied on the subset of students who studied in the scientific branch at high school. This study is important for both the students and the higher management of PTUK. The university will be able to do some predictions on the performance of the students. In the carried experiments, the prediction of the model was inline with the actual expectation.

The Study of Privacy Preserving Data Mining Technology for Information Security

2014 ◽  
Vol 556-562 ◽  
pp. 3532-3535
Author(s):  
Heng Li ◽  
Xue Fang Wu
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Network Database ◽  
Use Of Data

With the rapid development of computer technology and the popularity of the network, database scale, scope and depth of the constantly expanding, which has accumulated vast amounts of different forms of stored data. The use of data mining technology can access valuable information from a lot of data. Privacy preserving has been one of the greater concerns in data mining. Privacy preserving data mining has a rapid development in a short year. But it still faces many challenges in the future. A number of methods and techniques have been developed for privacy preserving data mining. This paper analyzed the representative techniques for privacy preservation. Finally the present problems and directions for future research are discussed.

The Application of Classification and Regression Trees Algorithm in the Production Data of Mounter

2011 ◽  
Vol 383-390 ◽  
pp. 4312-4317
Author(s):  
Lu Zhang ◽  
Yan Ling Shang
Keyword(s):  
Data Mining ◽  
Cost Efficiency ◽  
Rapid Development ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Production Data ◽  
Useful Knowledge ◽  
Database Technology ◽  
Classification And Regression ◽  
The Cost

With the rapid development of database technology and the explosive data growth, we urgently need a new smart technology to help us translate data into useful knowledge and information; so data mining generate. In this paper, We use the classification and regression trees algorithm to mine the data supplied by a factory and acquire some knowledge which can promote the efficiency of the production and reduce the cost. It proved that this method can improve production and reduce the cost efficiency.

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