A comparative analysis of heterogeneity in road accident data using data mining techniques

2016 ◽  
Vol 8 (2) ◽  
pp. 147-155 ◽  
Author(s):  
Sachin Kumar ◽  
Durga Toshniwal ◽  
Manoranjan Parida
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Road Accident ◽  
Data Mining Techniques ◽  
Accident Data ◽  
Using Data

scholarly journals Comparative analysis on job prediction of students based on resume using data mining techniques

2018 ◽  
Vol 7 (2.7) ◽  
pp. 1100 ◽  
Author(s):  
T Ravi Kumar ◽  
P Yasaswini ◽  
G Rafi ◽  
Dhulipalla Vijay Krishna
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
First Person ◽  
Data Mining Techniques ◽  
Third Person ◽  
The Subject ◽  
Using Data

The manuscript should contain an abstract. The abstract should be self-contained and citation-free and should not exceed 200 words. The abstract should state the purpose, approach, results and conclusions of the work. The author should assume that the reader has some knowledge of the subject but has not read the paper. Thus, the abstract should be intelligible and complete in it-self (no numerical refer-ences); it should not cite figures, tables, or sections of the paper. The abstract should be written using third person instead of first person.  

Development of Data Mining Driven Software Tool to Forecast the Customer Requirement for Quality Function Deployment

2018 ◽  
pp. 625-658
Author(s):  
Shivani K. Purohit ◽  
Ashish K. Sharma
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Comparative Analysis ◽  
Time Series Analysis ◽  
Product Development ◽  
Quality Function Deployment ◽  
Software Tool ◽  
Quality Function ◽  
Data Mining Techniques ◽  
Using Data

Quality Function Deployment (QFD) is widely used customer driven process for product development. Thus, Customer Requirements (CRs) play a key role in QFD process. However, the diversification in marketplace makes these CRs more dynamic and changing, giving rise the need to forecast CRs to improve competitiveness and increase customer satisfaction. The purpose can be served by using Data Mining techniques of forecasting. With the pool of forecasting techniques available, it is important to evaluate a suitable one for more effective results. To this end, the paper presents a novel software tool to efficiently forecast CRs in QFD. The tool allows for forecasting using various data mining based time series analysis techniques that strongly assists in doing comparative analysis and evaluating out the most apt technique for forecasting of CRs. The tool is developed using VB.Net and MS-Access. Finally, an example is presented to demonstrate the practicability of proposed software tool.

An Approach to Explore Historical Construction Accident Data Using Data Mining Techniques

2020 ◽  
pp. 121-128
Author(s):  
María Martínez Rojas ◽  
Antonio Trillo Cabello ◽  
Mª del Carmen Pardo Ferreira ◽  
Juan Carlos Rubio Romero
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Accident Data ◽  
Using Data

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. 

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