Initially linear dataset is formed for capable recovery of data from a massive record. Before undergoing the process of data sighting feature reduction route is implemented. This reduces the dimensionality and increases the space of data cargo space. Hence the map reduce is processed for the next step in knowledge discovering process to remove unwanted and irrelevant data from the database. The Support Vector Machine is one of the classifications technique is used. This overcome the problem of k Means disadvantage, it does not support effectively for both linear and nonlinear format of data. Map Reduce method to add privacy to a huge database can be obtained by adding dual authentication technique which ensures the privacy of the user without over heading the process. This overcomes the overlapping issue caused by the k means algorithm and it also reduces the issue of finding the distance between the record and cluster.

1. International Conference on Advances & Challenges in Interdisciplinary Engineering and Management 42
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International Conference on Advances & Challenges in Interdisciplinary Engineering and
Management 2017 [ICACIEM 2017]
ISBN 978-81-933235-1-9 VOL 01
Website icaciem.org eMail icaciem@asdf.res.in
Received 10 – January – 2017 Accepted 28 - January – 2017
Article ID ICACIEM083 eAID ICACIEM.2017.083
Shared Cluster Decision Assembly method using sky
Traffic-Flow Organization
K Meiyalakan1
, G Padmapriya2
, P J Shriidhar3
1
Associate Professor, 2
Professors, 3
Assistant Professor, Department of CSE,
Vidyaa Vikas College of Engineering and Technology, Tiruchengode, India
Abstract: Initially linear dataset is formed for capable recovery of data from a massive record. Before undergoing the process of data sighting feature
reduction route is implemented. This reduces the dimensionality and increases the space of data cargo space. Hence the map reduce is processed for the
next step in knowledge discovering process to remove unwanted and irrelevant data from the database. The Support Vector Machine is one of the
classifications technique is used. This overcome the problem of k Means disadvantage, it does not support effectively for both linear and nonlinear
format of data. Map Reduce method to add privacy to a huge database can be obtained by adding dual authentication technique which ensures the
privacy of the user without over heading the process. This overcomes the overlapping issue caused by the k means algorithm and it also reduces the issue
of finding the distance between the record and cluster.
ISBN 978-81-933235-1-9 VOL 01
Website icaciem.org eMail icaciem@asdf.res.in
Received 10 – January – 2017 Accepted 28 - January – 2017
Article ID ICACIEM084 eAID ICACIEM.2017.084
Analysis of Micro-Channel Heat Exchangers
K Dinesh Kumar1
, G Gunasekaran2
, P Jaya Kumar3
, P Jeeva4
, K Karthick5
1,2,3,4,5
UG scholar, Department of Mechanical Engineering, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, India
Abstract: Due to the high performance of electronics components, Heat dissipation becomes a significant issue for stable operation of components.
Micro channels provide very high heat transfer coefficients because of their small diameters. In this study, two dimensional fluid flow and heat transfer
in a rectangular micro channel heat sink are analysed using FLUENT as solver with water as cooling fluid. With strong literature study it is found that
down to 50 μm of hydraulic diameter, macro scale model can be applicable. Three channels of height 50 μm, 100 μm and 150 μm are considered.
The study is mainly focused on Nussle Number and height effects on micro channel thermal performance. The highest temperature is encountered at the
heated surface of the heat sink immediately above the channel outlet.
This paper is prepared exclusively for International Conference on Advances & Challenges in Interdisciplinary Engineering and Management 2017 [ICACIEM
2017] which is published by ASDF International, Registered in London, United Kingdom under the directions of the Editor-in-Chief Dr K Samidurai and Editors
Dr. Daniel James, Dr. Kokula Krishna Hari Kunasekaran and Dr. Saikishore Elangovan. Permission to make digital or hard copies of part or all of this work for
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