An Intelligent System for Wheelchair Users Using Data Mining and Sensor Networking Technologies

2011 ◽  
Author(s):  
Ying-Hong Wu ◽  
Cheng-Chi Wang ◽  
Tzung-Shi Chen ◽  
Chih-Yu Li
Keyword(s):  
Data Mining ◽  
Intelligent System ◽  
Wheelchair Users ◽  
Sensor Networking ◽  
Networking Technologies ◽  
Using Data

scholarly journals Heart Disease Risk Predictor

2019 ◽  
Vol 8 (10) ◽  
pp. 701-705
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Prediction Models ◽  
Intelligent System ◽  
Disease Risk ◽  
Data Set ◽  
Using Data ◽  
Risk Predictor ◽  
Heart Disease Risk

Cardiovascular disease is one of the focused areas is medical area because its origins sickness and death amongst the population of the entire world. Data mining techniques play an important role to convert the large amount of raw data into meaningful information which will help in prediction and decision of Cardiovascular disease. The prediction models were technologically advanced using diverse amalgamation structures and sorting techniques such as k-NN, Naive Bayes, LR, SVM, Neural Network, Decision Tree. It is very necessary for the recital of the prediction models to choose the exact amalgamation of momentous features. The main Aim of the propose System is to develop an develop an Intelligent System using data mining modeling technique. The proposed system retrieves the data set and compare the data set with the predefined trained data set. The existing decision support system cannot predict the complex question for diagnosing the heart disease but the proposed system predicts the complex queries which will help and assist the healthcare practitioners to take appropriate decisions. This proposed system aims to provide a web platform to predict the occurrences of disease on the basis of various symptoms. The user can select various symptoms and can find the diseases with their probabilistic figures.

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
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