M.Tech in Information Technology in Hyderabad Central University
- Full time
- Part time
- One time
Doing M.Tech. Project @
Institute for Development & Research in Banking Technology (IDRBT)
From July 2014
Doing one year Project related to "Spatial analytics" and "GameTheory"
University of Hyderabad
From July 2013
M.Tech in Information Technology
Optimal Search Engine
This system deals with the files in our personal computer as well as the content in the websites. It is a search engine which searches the text that we give as an input in our personal computer and in the websites as the user requires.
To optimize the search and to give better search results I have developed a web crawl algorithm and to give fast results it maintains the previously searched results by the concept of indexing using the help of apache LUCENE package
Performance Analysis of Open vSwitch under Congestion
we describe the results of our experiment
to determine the performance of Open vSwitch under
conditions of congestion. We created four compute nodes on
Mininet with each compute node having four VMs each. The
VMs within a compute node and the four compute nodes are
all connected using Open vSwitch. We generated a lot of traffic
between VMs using iperf and determined at what level of traffic
Open vSwitch saturates leading to a drastic drop in throughput.
We found that the Open vSwitch will saturate if the traffic
generated at 1.7Gbps.
Credit Scoring System
Credit scoring can be defined as a technique that helps credit providers decide Whether to grant credit to consumers or customers. Its increasing importance can be seen from the growing popularity and application of credit scoring in consumer credit. There are advantages not only to construct effective credit scoring models to help improve the bottom-line of credit providers, but also to combine models to yield a better performing combined model. In this paper we described 1) The use of data mining techniques to construct credit scoring models. 2)The combination of credit scoring models to give a superior final model.
Association Rule Mining using Apriori algorithm
Market-Basket Analysis is a process to analyse the habits of buyers to find the relationship between different items in their market basket. The discovery of these relationships can help the merchant to develop a sales strategy by considering the items frequently purchased together by customers. In this research, the data mining with market basket analysis method is implemented, where it can analyse the buying habit of the customers. The testing is conducted in Minimarket X. Searching for frequent item sets performed by Apriori algorithm to get the items that often appear in the database and the pair of items in one transaction. Pair of items that exceed the minimum support will be included into the frequent item sets are selected. Frequent item sets that exceed the minimum support will generate association rules after decoding. One frequent item sets can generate association rules and find the confidence. The test results show, the application can generate the information what kind of products are frequently bought in the same time by the customers according to Hybrid-dimension Association Rules criteria. Results from the mining process show a correlation between the data (association rules) including the support and confidence that can be analysed. This information will give additional consideration for owners of Minimarket X to make the further decision.
Qualifications & Certifications
B.Tech Computer Science
Aditya Institute of Technology and Management
srisatysai jr collage
University of Hyderabad(HCU)
VIVEKANANDA MODEL HIGH SCHOOL, PALAKONDA
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