Notice Board :

Call for Paper
Vol. 5 Issue 9

Submission Start Date:
September 01, 2019

Acceptence Notification Start:
September 10, 2019

Submission End:
September 15, 2019

Final MenuScript Due:
September 25, 2019

Publication Date:
September 30, 2019


                         Notice Board: Call for PaperVol. 5 Issue 9      Submission Start Date: September 01, 2019      Acceptence Notification Start: September 10, 2019      Submission End: September 15, 2019      Final MenuScript Due: September 25, 2019      Publication Date: September 30, 2019




Volume III Issue V

Author Name
Shikha Sharma, Chetan Chauhan
Year Of Publication
2017
Volume and Issue
Volume 3 Issue 5
Abstract
Abstract:- Data Classification may be a very fashionable and computationally overpriced task. Most of those information classification techniques square measure supported the conception of call trees. several researchers have worked on the malady prediction systems exploitation the information mining techniques. a number of the systems square measure for predicting one malady and a few for the predicting the multiple diseases. Still there's scope to enhance the potency of the malady prediction. during this paper, we tend to square measure presenting associate degree updated ID3 rule. a brand new attribute choice rule has projected during this paper. The accuracy of the projected methodology is best than the present rule.
PaperID
2017/IJRRETAS/5/2017/23610

Author Name
Rahul Sharma, Prof. O.P. Sharma
Year Of Publication
2017
Volume and Issue
Volume 3 Issue 5
Abstract
In cloud computing, data is moved to a remotely located cloud server. Cloud server faithfully stores the data and return back to the owner whenever needed. Data and computation integrity and security are major concerns for users of cloud computing facilities. Today's clouds typically place centralized, universal trust in all the cloud's nodes.Hadoop is founded on MapReduce, which is among the most popular programming items for huge knowledge analysis in a parallel computing environment. In this paper, we reward a particular efficiency analysis, characterization, and evaluation of Hadoop MapReduce WordCount utility.
PaperID
2017/IJRRETAS/6/2017/24610