Using data mining in the manufacturing systems for CAD model analysis and classification

2008 ◽  
Vol 3 (1/2) ◽  
pp. 147 ◽  
Author(s):  
Hisham Al Mubaid ◽  
Emad S. Abouel Nasr ◽  
Ali K. Kamrani
Keyword(s):  
Data Mining ◽  
Manufacturing Systems ◽  
Model Analysis ◽  
Cad Model ◽  
Using Data

scholarly journals Data Mining Application in Process Control of Smart Material Manufacturing

2020 ◽  
Vol 8 (5) ◽  
pp. 4999-5005
Keyword(s):  
Data Mining ◽  
Process Control ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Control Strategies ◽  
Smart Material ◽  
Knowledge Gain ◽  
System Control ◽  
Current Production ◽  
Using Data

According to Industry 4.0 concept, huge amounts of data is generated in Smart Material Manufacturing and this data needs to be collated, stored, organised and analysed in order to develop a more efficient manufacturing system. This study focuses on the prediction of smart material manufacturing process, based on the current production data. It presents a route of knowledge gain about predicting future manufacturing systems, using data mining. The model proposed for the actual manufacturing process is made to acquire the necessary data for process control. Implementing particular methods of data mining, and by altering the input parameters, we can predict the behaviour of the manufacturing processes. This prediction is then verified by the use of a simulation model. After analysing various methods, the method using neural networks is chosen for deployment of the latest data in the concluding phases. This research aims at designing and verifying the tools for mining data for supporting system control in manufacturing. It aims at improvement of the process of decision making. The practical control strategies can be accurately modified, depending on the predictions made and the targeted results of production. These strategies can then be used in real time manufacturing, without a chance of failure.

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%.

Forecasting Automobile Retail Sales Using Data Mining The Case of Ranchi, Jharkhand, India

2018 ◽  
Vol 6 (9) ◽  
pp. 572-574
Author(s):  
Gyaneshwar Mahto ◽  
Umesh Prasad ◽  
Rajiv Kumar Dwivedi
Keyword(s):  
Data Mining ◽  
Retail Sales ◽  
Using Data

Diabetes Prediction using Data Mining

2019 ◽  
Vol 7 (3) ◽  
pp. 749-753
Author(s):  
Suhasini Vijaykumar ◽  
Manjiri Moghe
Keyword(s):  
Data Mining ◽  
Diabetes Prediction ◽  
Using Data
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