Disease Predictive Models for Healthcare by using Data Mining Techniques: State of the Art

2020 ◽  
Vol 68 (10) ◽  
pp. 52-57
Author(s):  
Am an ◽  
Rajender Singh Chhillar
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
State Of The Art ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Development of predictive models using Data Mining techniques to detect borer infestation (Diatraea saccharalis) in sugarcane culture

2017 ◽  
Author(s):  
Nádia Vieira Ribeiro ◽  
Luiz Henrique Antunes Rodrigues ◽  
Monique Pires Gravina de Oliveira ◽  
FELIPE FERREIRA BOCCA
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Data Mining Techniques ◽  
Diatraea Saccharalis ◽  
Using Data

Predictive Models in Cybercrime Investigation

2012 ◽  
pp. 166-177
Author(s):  
A. S. N. Murthy ◽  
Vishnuprasad Nagadevara ◽  
Rahul De’
Keyword(s):  
Data Mining ◽  
Law Enforcement ◽  
Success Rate ◽  
Predictive Models ◽  
Law Enforcement Agencies ◽  
Data Mining Techniques ◽  
Investigation Methods ◽  
The World ◽  
Policy Measures ◽  
Using Data

With increased access to computers across the world, cybercrime is becoming a major challenge to law enforcement agencies. Cybercrime investigation in India is in its infancy and there has been limited success in prosecuting the offenders; therefore, a need to understand and strengthen the existing investigation methods and systems for controlling cybercrimes is greatly needed. This study identifies important factors that will enable law enforcement agencies to reach the first step in effective prosecution, namely charge-sheeting of the cybercrime cases. Data on 300 cybercrime cases covering a number of demographic, technical and other variables related to cybercrime was analyzed using data mining techniques to identify and prioritize various factors leading to filing of the charge-sheet. These factors and the respective priority rankings are used to suggest various policy measures for improving the success rate of prosecution of cybercrimes.

scholarly journals Building predictive models for MERS-CoV infections using data mining techniques

2016 ◽  
Vol 9 (6) ◽  
pp. 744-748 ◽  
Author(s):  
Isra Al-Turaiki ◽  
Mona Alshahrani ◽  
Tahani Almutairi
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Data Mining Techniques ◽  
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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