Notice Board: Call for Paper Vol. 8 Issue 10      Submission Start Date: September 30, 2021      Acceptence Notification Start: October 12, 2021      Submission End: October 18, 2021      Final MenuScript Due: October 22, 2021      Publication Date: October 31, 2021






Volume 5 Issue 4

Author Name
Naveen Haldkar, Sapna Choudhary
Year Of Publication
2018
Volume and Issue
Volume 5 Issue 4
Abstract
Millions of Indians buy various products online these days and give their feedback which benefits other consumers. Collaborative analysis of customer feedbacks is the process where we collect the feedbacks of customers on certain product and among the different type of hundreds of feedbacks; we conclude the review of product as the product got good feedback or bad feedback or a mixed feedback. Today in the time of E-business, millions of individuals make acquiring through e-commerce sites and these e-trade sites ask customers to compose the feedback from item they obtained. Since we have hundreds or thousands of criticisms for a product from numerous customers, it is hard to conclude whether customers are satisfied with the services and products or not. One approach is to conduct a survey which leads the overview on item that contains some number of inquiries with checkbox of yes and no; but again, it won't give the flexibility of expression to the customer as they are bound to some n
PaperID
2018/04/IJMERT/5/328

Author Name
Mamata Mishra, Amit Thakur
Year Of Publication
2018
Volume and Issue
Volume 5 Issue 4
Abstract
The continuity of stream data is major challenge for the process of data classification and categorization. The continuity of data creates some problem such as infinite length, feature evaluation, data drift. The data drift raised the problem of mapping of class. in this paper present the review of stream data classification based on feature optimization. The process of feature optimization reduces the problem of data drift and improve the classification ratio of classifier. In this paper present the review of stream data classification using different feature optimization technique
PaperID
2018/04/IJMERT/5/321

Author Name
Mamata Mishra, Amit Thakur
Year Of Publication
2018
Volume and Issue
Volume 5 Issue 4
Abstract
In current decade the stream data classification is major issue. The property of stream data creates the problem of data categorization such as infinite length, data drift and feature evaluation process. The classification process faced a problem of new feature evaluation. The concept of new feature evaluation cannot understand by classification algorithm. the classification algorithms generate error during find the new features set. For the selection of new features set used feature optimization technique. the feature optimization technique minimized the process of features for the mapping of class. in this paper proposed SOM based stream data classification algorithm. for the optimization of features of stream data used glow worm optimization algorithm. the glow worm optimization algorithm works on the principle of lubrification. The proposed algorithm implemented in MATLAB software. For the validation of algorithm used UCI data set glass, forest and croups
PaperID
2018/04/IJMERT/5/322



Notice Board :

Call for Paper
Vol. 8 Issue 10

Submission Start Date:
September 30, 2021

Acceptence Notification Start:
October 12, 2021

Submission End:
October 18, 2021

Final MenuScript Due:
October 22, 2021

Publication Date:
October 31, 2021