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 V Issue I

Author Name
Rashmi Patidar, Abhilasha Vyas
Year Of Publication
2019
Volume and Issue
Volume 5 Issue 1
Abstract
It has been found more significant to study and comprehend the environment of data before proceeding into mining. The big data classification process is essential, through the increasing amount of data and requirement for accuracy. Another stimulating research in building intricate big data classification models through semi-supervise learning. It has the capability to effect complex mix data sets tasks complete semantic necessities In this research work to reviewed precise discriminative semi-supervised learning algorithms aimed at classification that are expending big data feature extraction algorithm available, and discussed selected of the latest advances in creating those algorithms scalable We have reviewed numerous dissimilar algorithmic techniques for encoding such assumptions into learning. Completely of these can someway be seen as whichever explicitly or implicitly accumulation a regularize that encourages that the selected function reveals arrangement in the unlabeled data.
PaperID
2019/IJRRETAS/1/2019/37678

Author Name
Sachin Rathor, Kapil Sahu
Year Of Publication
2019
Volume and Issue
Volume 5 Issue 1
Abstract
Orthogonal Frequency Division Multiplexing is a multi carrier system which owing to its spectral efficiency has evolved as the primary solution to high speed data networks. The fundamental problem still lies in the fact that wireless channels exhibit frequency selectivity thereby rendering high bit error rate (BER) to the system. The present work presents a technique used for deep learning based on the Bayesian Regularized Deep Neural Network (BRDNN) for channel estimation of an OFDM based network. The performance is evaluated based on the mean square error found in channel estimation. Moreover the number of epochs has also been considered as an evaluation parameter for judging the performance of the system. It is found that the proposed system attains a mean squared error of 0.25% and a BER of 10-4. It has been observed that the variation in the number of pilots results in a variation in BER of the system.
PaperID
2019/IJRRETAS/1/2019/38677

Author Name
Ankita Patidar, Mohit Jain
Year Of Publication
2019
Volume and Issue
Volume 5 Issue 1
Abstract
Machine Learning is a field of artificial intelligence that can use past information for the future purpose. Machine learning is similar to data mining in the way that both are looking for the pattern. Machine learning can detect a pattern in data and adjust the action. Machine learning is a field that is used in every system. Machine learning is used in the educational system, pattern recognition, Games, Industries, Social media services, online customer support, Product Recommendation Etc. In the education system, its importance becomes more because of the future of the students.
PaperID
2019/IJRRETAS/1/2019/38676