Dr. K. Yogeswara Rao

Mobile: 9963265836

Email:yogiindusisu@gmail.com

Key Highlights:

MCA, M. Tech-IT, Ph.D.(CSE) with 18+ Years of IT & Academic exposure

Professional strengths: ➢ Possess good communication and team spirit.

➢ Extensive Knowledge of Data Mining and graph theory, DBMS, OOP, Artificial

Intelligence and Machine Learning, IoT, WSN(Wireless Sensor Networks). ➢ Strong urges to learn new technologies. ➢ Ability to complete assigned course within limited time period.

Summary of Skills & Experience (18+ years)

Work Experience:

Organization : AITAM Tekkali,

Designation : Associate Professor.

Duration* : June 2017 to tilldate.

Organization : GIET, Rajahmundry.

Designation : Assistant Professor.

Duration* : June 2015 to July 2016.

Organization : ANITS, VISAKHAPATNAM.

Designation : Assistant Professor.

Duration* : May 2008 to April 2013.

Organization : RAGHU Engineering College, Visakhapatnam

Designation : Assistant Professor.

Duration* : May 2007 to April 2008.

Organization : Sri Gowri Degree & P.G. College, Visakhapatnam

Designation : HOD.

Duration* : June 1999 to April 2007

Qualifications: Ph.D. : Awarded (CSE) from GITAM University, Visakhapatnam in January 2017. PG : M. Tech (IT) from AU, Visakhapatnam in 2010. UG : MCA from Madras University in 1997 BOI : M.P.C from Board of Intermediate Education, Hyderabad in 1991. SSC : A.P secondary board of Education, Hyderabad in 1989.

International Conferences/Grants/Publications:

International Conferences:

Metric-Driven Load Balancing System in WMS on Distributed computing

platform, IEEE 3rd International Conference on Machine Learning and

Computing(ICMLC-2011) organized at Singapore in the month of Feb-2011 ISBN

No: 978-1-4244-9253-4

Paper Publications:

Published a paper in IJAER 2016 with title is “Topic Ontology Assisted Multi-

Document Summary Generation” International Journal of Applied Engineering

Research ISSN 0973-4562 Volume 10, Number 23 (2015) pp 43501-43508(Scopus

Indexed).

Published a paper in IJCIR 2016 with title in “Ontology and Query-Focused Multi-

Document Summarization System” International Journal of Computational

Intelligence Research ISSN 0973-1873 Volume 12, Number 1 (2016), pp. 1-15 ©

Research India Publications http://www.ripublication.com

Published a paper in IJCST 2012 with title is “Classification Based Image

Segmentation Approach” IJCST Vol. 3, Iss ue 1, Jan. - March 2012 .

Published a paper in IJCST 2012 with title is “Classification Based Image

Segmentation Approach” IJCST Vol. 3, Iss ue 1, Jan. - March 2012 .

Published a paper in IJCST 2012 with title is “ Combinatorial Morphological

Algorithm to smooth images “, ( 0976-8491) , IJCST Volume 3 issue-4, oct- dec

2012.

Published a paper in IJCST 2011 with title is “A Fuzzy Grid-Clustering lgorithm”

IJCST Vol. 2, Issue 3, September 2011, ISSN: 2229-4333(Print) | ISSN : 0976-

8491(Online).

Published a paper in IJIRCCE 2016 with title is “User Plan Recommendation

using Mobile Call Log Analysis” Vol. 4, Issue 9, September 2016, ISSN(Online):

2320-9801, ISSN (Print): 2320-9798.

Published a paper in IJIRCCE 2016 with title is “A Study on Tools of Big Data Analytics”Volume 4, Issue 10, October 2016, ISSN (Online): 2320 – 9801, ISSN (Print): 2320– 9798.

Published a paper in IJIRCCE 2016 with title is “Product Recommendation System

from Users Reviews using Sentiment Analysis” International Journal of Computer

Applications (0975 – 8887) Volume 169 – No.1, July 2017

Published a paper in IICTT2017 with title is” An Automated Traffic Surveillance

Control system by using STC algorithm”Volume 2, Issue 4, December 2017, ISSN (Online): 2231-2803

Professional Activities:

Memberships Attained

➢ Member in Computer Society of India(CSI).

Workshops/ Seminars Participated ➢ I attended for Faculty Development Program which is conducted by

NITTTER CHENNAI, ANITS.

➢ I attended for Staff development program which is organized by CSE

department of ANITS and sponsored by AICTE.

➢ I am attended the workshop at ISI Kolkata in “TEXT MINING”.

Academic Activities:

➢ B.Tech Question Paper Setter for AUTONMOUS Institutions.

➢ External Examiner for Project Viva Voce in AUTONMOUS Institutions.

➢ Attendance Committee Member in ANITS Engineering College. ➢ Uploaded AICTE Research Promotion Scheme Proposal for the Academic year

2017-18 at AITAM Teekkali. ➢ Completed NPTEL Courses on DATA ANALYTICS , OPERATING SYSYEMS

and DATA MINING. Teaching Skills:

Subjects Taught (UG-B. Tech): Operating Systems, DBMS, DWDM, ML, Computer Architecture, Data Structures, OOAD, WT, OSS.

Subjects Taught (PG-M. Tech): Data Mining, IRS.

Labs Handled (UG-B. Tech): IT Workshop, C Language, C++, Java, VB 6.0, Web Technologies, OOAD.

Labs Handled (PG-M. Tech, MCA): OOAD, JAVA, C++.

Technical Skills

Languages : C, java, j2ee (web technologies), LISP etc.

Servers & Frame Works: Tomcat-web server, Struts1.x & JSF.

Designing Tool : Rational Rose Enterprise Edition 8.0, UML-let.

Packages : Ms-office suit, Oracle.

Operating System : Ms-dos, Windows suit, Linux.

Projects/Roles:

Worked as a project guide for 60 batches in B.Tech & M.tech.

BRIEF PROFILE

Date of Birth

Gender

Nationality

: 24-07-1974. : Male.

: Indian. Languages Known

Marital Status

Location

: English, Telugu & Hindi. : Married.

: Visakhapatnam.

The above submitted information is true and accurate to the best of my knowledge.

(Dr.K.Yogeswara Rao)

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International Journal of Applied Engineering Research ISSN 0973 -4562 Volume 10, Number 23 (2015) pp 43501-43508 © Research India Publications. http://www.ripublication.com

Topic Ontology Assisted Multi-document Summary Generation

K. Yogeswara Rao

Department of Computer Since and Engineering, GITAM University, Visakhapatnam, Andhrapradesh. yogiindusisu@gmail. com

P. V. Nageswara Rao

Department of Computer Since and Engineering, GITAM University, Visakhapatnam, Andhrapradesh. nagesh@gitam. in

Abstract

An effective Multiple Document Summarization (MDS) system is a sound method to provide concise and comprehensive information in a short-form. The conventional summarization techniques exploit the machine learning techniques based sentence extraction and sentence position hypothesis to summarize the huge document collection under the same topic. However, these techniques may lead the redundant and less informative sentences in the summary due to the lack of semantic analysis. To tackle this constraint, this paper attempts to exploit the ontology and word

position hypothesis. This paper proposes multi-document SummarY generatioN with the tOPic ontology asSIStance (SYNOPSIS) approach. The core aim of this approach is to balance the objective function that refers to improve the coherence, and salience of the summary and diminish the redundancy of the sentences. To achieve this aim, the SYNOPSIS model investigates, the two phases, namely optimal sentence ranking and sentence selection in MDS system. Initially, the SYNOPSIS uses Yago ontology to identify the context of the keyword semantically and employs the word position hypothesis to discover the importance of the sentence by ranking the document sentences. To

further reduce the document content, the SYNOPSIS approach focuses on shortening the sentence length by applying the structure analysis of the original sentences. Finally, it selects the key sentences based on the most relevant information on sentence rank and constructs the summary based on the satisfaction of the objective function. The experimental results demonstrate that the SYNOPSIS approach achieves better performance than the conventional summarization method.

Keywords: Ontology, summary, word position, document summarization, coherence, salience, and redundancy

1. Introduction

Due to emerging usability of online information, the attention of effective result provisioning according to the user queries is an important process in Information Retrieval (IR) system. As exponential growth of a number of textual documents on the web, discovering the hidden information is often an arduous task in real-world applications. With the aim of addressing this constraint, Multiple Document Summarization (MDS) model creates a greater impact on the web searching field that facilitates the users to obtain the core information within a short time [1, 2]. MDS focuses on providing the succinct and informative summaries from the larger document collections.

This task is achieved by reducing the irrelevant sentences from the collection of documents to generate essential text summary in IR system. It is the extraction based MDS method that generates the summary by only omitting the sentences from the original text sentences not constructing the novel sentences. MDS is a non-trivial process in real time applications instead of summarizing a single document. For instance, News aggregation application in IR process: when multiple news feeds are submitted about a particular news topic, an IR system is forced to provide a short and comprehensive summary to users for improving the user convenience. It has the responsibility to create an understandable summary. Hence, an automatic multi-document summarization is necessary for IR system to summarize the multiple documents into an extractive summary.

To provide the accurate summarization, Ontology is the most valuable source that captures the hidden semantic information and comprises the abundant concepts and domain-related information [3]. It deals with either questions or input texts to identify the entities and its similarity based hierarchical structure. Most of the conventional researchers exploit the ontology to measure the semantic similarity [4] and to improve the document

clustering [5] in text mining. Comparatively, some of the researchers are focused on the ontology-assisted document summarization. However, the extraction based MDS approaches to meet the redundancy problem since the top-ranked sentences of the multiple documents convey the similar information. Some of the existing methods resolve the redundancy issue while summarizing the multiple documents. The main contribution of multi-document SummarY generatioN with tOPic ontology asSIStance (SYNOPSIS) approach includes two phases such as identification of sentence importance in the document set and optimal summarization of the

document set. • The SYNOPSIS approach summarizes the

multiple documents under the same topic using Yago ontology and word position hypothesis, beneficial in effective IR process.

• The SYNOPSIS approach recognizes the entity of each keyword in the document sentences using Yago ontology after performing the preprocessing steps. Applying entity score, and word position, frequency and distance factors on the sentences enables the SYNOPSIS to assign the score to each sentence semantically.

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International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 12, Number 1 (2016), pp. 1-15 © Research India Publications http://www.ripublication.com

Ontology and Query-Focused Multi-Document

Summarization System

Mr. K. Yogeswara Rao and Dr. P. V. Nageswara Rao

Department of Computer Science and Engineering,

GITAM University, Visakhapatnam, Andhra Pradesh, India

yogiindusisu@gmail.com, nagesh@gitam.in

Abstract

Due to the increasing growth of online information on the specific topic, Multiple Document Summarization (MDS) has become a non-trivial task. The MDS facilitates the

user to understand the large volume of information in a short time by creating a concise and comprehensive summary. In addition, user’s query based MDS system provides a

consistent summary, including the core of the information. The conventional summarization techniques focus on the dynamic query based summary generation.

However, it lacks in providing the entire user’s information in a single convenient

summary according to the particular topic. As a result, it leads to the complexity of the numerous summary generation process to each query. Hence, ensuring the effective query

relevant information in extractive summary is a crucial task in MDS system. To address this constraint, this paper introduces oNtology and query focused muLti documEnt

sUmmarization System (NUCLEUS). It incorporates the two essential steps such as query based summary type detection and summary generation. In the first step, NUCLEUS

analyzes the document set as well as queries using ontology and Web Search Query Log (WSQL) to determine the summary type. To identify the proper context of the summary,

it categorizes the document sentences based on the entities of the words in a sentence. In

the second step, the NUCLEUS generates the score to each relevant query sentence using the Vector Space Model (VSM) and then, the sentences are compressed by linguistic

structure analysis. Eventually, it measures the edge weight between the sentences to order coherently the sentences which having high salience and information diversity in the final

summary. The evaluation results show that the NUCLEUS system can obtain significant improvement over the conventional summarization method.

Keywords: Multi-document summarization, Query-focused, sentence score, WSQL, and ontology

ISSN(Online): 2320-9801

ISSN (Print) : 2320-9798

International Journal of Innovative Research in Computer

and Communication Engineering (An ISO 3297: 2007 Certified Organization)

Vol. 4, Issue 10, October 2016 Copyright to IJIRCCE DOI: 10.15680/IJIRCCE.2016. 0410149 18815

A Study on Tools of Big Data Analytics K.Yogeswara Rao 1, S.Adinarayana 2

Assistant Professor, Dept. of CSE, Gitam University, Andhra Pradesh, India1 Associate

Professor, Dept. of IT, Sri Vishnu Engineering College for Women, Bhimavaram, Andhra

Pradesh, India2

ABSTRACT: In today’s competitive world of internet era, users of different applications

including social networking sites , mobile apps, blogs, movie reviews, e –commerce websites

and travel websites generates huge volume of data(called as big data) which is mostly

unstructured in nature. Handling and processing such a huge volume of data is notan easy

task with existing traditional tools. With shorter product life-cycles, evolving client behavior

associated aneconomy that travels at the speed of sunshine and knowledge has gone on to be

a lot of concerning analytics andbusiness connexion. Therefore, there is a need and demand

for high end tools to handle Big data to process in to useful information for business

intelligence. In this paper we have discussed and explored the tools that are a unit the most

effective within the market to harness, analyze and build the foremost sense out of Big Data.

KEYWORDS: Large data, Hadoop, Thor cluster, Node, Grid grain, Pentaho, MongoDB

I. INTRODUCTION

In earlier days, web is employed for less than the aim of causation e-mails messages,

browsing queries so it can be handled effectively with native RDBMS tools. However, in this

internet era web generates large amount of knowledge every second and it will increase day

by day hour by hour. Thus such form of information is troublesome to method as a result of it

contains the billons records of million individuals. info that features the net sale, social

networks, on-line transactions, e-mails, audios, videos, images, device information, science

analysis, health records, search queries, click streams, posts, cloud computing, mobile phones

and their applications. It cannot be deals with ancient information like RDBMS, DBMS, and

ORDBMS.In the last twenty years, the information is increasing day by day across the planet.

There are quite a pair of billion web users within the world these days and 4.61 billion mobile

phones in 2016[1]. Curiously, twitter tweets statistics as of today (now) are 544,748. Some

facts regarding the data are, there are quite 350,000 tweets each minute [2], quite a pair of

million searchings on Google each minute, quite seventy hours of videos are uploaded to

YouTube, there are quite one hundred million emails are sent, quite four hundred GB of data

is process in face book and quite 570 websites are created each minute on web. Face book

has quite one billion folks active accounts from that 751 million mistreatment face book from

a mobile. Throughout 2012, 2.5 large integer bytes of data were created each day. Huge

knowledge and its analysis are the middle of recent science and business areas. Large amount

of information is generated from the numerous sources either in structured or unstructured

kind. Such style of data keep in databases then it became tough to extract, transform and load.

So this leads to the development of tools for extracting knowledge from big data.

ISSN(Online): 2320-9801

ISSN (Print): 2320-9798

International Journal of Innovative Research in Computer

and Communication Engineering (An ISO 3297: 2007 Certified Organization)

Vol. 4, Issue 9, September 2016 Copyright to IJIRCCE DOI: 10.15680/IJIRCCE.2016. 0409006 15736

User Plan Recommendation using Mobile call

Log Analysis Salina Adinarayana, K. Yogeswara Rao

Associate Professor, Dept. of I.T, Shri Vishnu Engineering College for Women, Bhimavaram, Andhra Pradesh, India

Assistant Professor, Dept. of CSE, Gitam University, Visakhapatnam, Andhra Pradesh, India

ABSTRACT: In recent times, we can observe that there is a tremendous increase in mobile users with the

usage of smart phones, i-pads and tablet PCs for efficient communication with respect to both individual and official

communication strata. Studies on mobile user’s statistics showing that there is also an emerging research area

of analyzing this new type of user data. In this paper, we perform call log analysis on mobile user data, peak

time load of the operator, offers proving by the operator and recommended a suitable plan for the customer.

We also investigate how customers are using proposed plan and rating the operators.

KEYWORDS: Call log statistics; Ratings; Peak load Analysis; Recommendation;

I. INTRODUCTION As the demand for mobile phone grows, Mobile operators are providing new plans for the customers to generate

revenue for expanding their network capacity. With the drastic explosion in the quantity of calls made/received and

data usage, there is a need for analyses of patterns in these call logs to assist the user a best plan to select. Based on

the feedback from customers on different issues on mobile usage statistics like call and data usage, mobile data

explosion, operators are searching for the ways to improve their architectures and radio access technologies to handle

this growth without any problem. Switching to Long Term Evaluation (LTE), adding more radio towers or small-scale

base stations such as femtocells or Pico cells and using Wi-Fi access points for offloading the burden on their network

[2], [3], [4], [5] are some of the solutions operators are currently working on. According to Hindu Business Line [13]

dated August 12 Customers are floating to Reliance stores has been increased by at least three times during the past

20-25 days, with customers mostly seeking LYF handsets and 4G SIM cards because of Reliance Jio 4G services and

most of these customer visits are also translating into sales.