Raj Mohan Singh

Work place: Department of Computer Science & Engineering, Dr.BR Ambedkar National Institute of Technology, Jalandhar

E-mail: rm_singh14@yahoo.com


Research Interests: Data Structures and Algorithms, Distributed Computing, Systems Architecture, Autonomic Computing, Computer systems and computational processes


Raj Mohan Singh received his M Tech degree in Computer Science and Engineering from Dr. B R Ambedkar National Institute of Technology, Jalandhar. His research interests include Cloud Computing,Operating Systems Distributed Computing, and Data Analytics.

Author Articles
Analysis and Prediction of Individual Stock Prices of Financial Sector Companies in NIFTY50

By Vikalp Ravi Jain Manisha Gupta Raj Mohan Singh

DOI: https://doi.org/10.5815/ijieeb.2018.02.05, Pub. Date: 8 Mar. 2018

Prediction of the stock market is currently a big business opportunity for the data analytic solution providers. As the vast range of factors influencing the stock market index are available, it is essential to find the relation between those macroeconomic variables with company share prices and predict the accurate results. Our research is analyzing different relation between the prediction and individual stock prices of financial sector companies in National Stock Exchange 50(NIFTY 50). To make a strong portfolio the selection of different companies is one of the vital decisions we should attempt for a good investment. Trending researches regarding financial forecast are based on the accuracy of the models that how well National Stock Exchange (NSE) index values can be predicted. There is significant literature survey available on the prediction of the stock market as well as its pricing. NIFTY 50 is one of the well-known indexes in India for the investors seeking a good investment. In our research, we attempt, to forecast the stock values of different organizations of Banking and Financial sectors in NIFTY 50. Before including the factors to forecast share market index we are trying to find the relation between different factors and indices of those companies. The study empirically proves that the proposed model is precise to be used in real time stock prediction which can benefit the sellers, investors and stakeholders in their real time savings, investment, and speculation.

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Improved Architecture of Focused Crawler on the basis of Content and Link Analysis

By Bhupinderjit Singh Deepak Kumar Gupta Raj Mohan Singh

DOI: https://doi.org/10.5815/ijmecs.2017.11.04, Pub. Date: 8 Nov. 2017

World Wide Web is a vast, dynamic and continuously growing collection of web documents. Due to its huge size, it is very difficult for the users to search for the relevant information about a particular topic of interest. In this paper, an improved architecture of focused crawler is proposed, which is a hybrid of various techniques used earlier. The main goal of a focused crawler is to fetch the web documents which are related to a pre-defined set of topics/domains and to ignore the irrelevant web pages. To check the relevancy of a web page, Page Score is computed on the basis of content similarity of the web page with reference to the topic keywords. URLs Priority Queue is implemented by calculating the Link Score of extracted URLs based on URLs attributes. URLs queue is also optimized by removing the duplicate contents. Topic Keywords Weight Table is expanded by extracting more keywords from the relevant pages database and recalculating the keywords weight. The experimental result shows that our proposed crawler has better efficiency than the earlier crawlers.

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Sentiment Analysis of Twitter User Data on Punjab Legislative Assembly Election, 2017

By Akhilesh Kumar Singh Deepak Kumar Gupta Raj Mohan Singh

DOI: https://doi.org/10.5815/ijmecs.2017.09.07, Pub. Date: 8 Sep. 2017

Sentiment Analysis is the way of gathering and inspecting data based on the personal emotions, reviews, and contemplations. The sentimental analysis is also recognized as opinion mining since it mines the major feature from people opinions. There are various social networking platforms, out of which Twitter is praised by lawmakers, academicians, and journalists for its potential political values. In literature, numerous studies have been performed on the data ecstatic to elections on Twitter. The greater part of them has been on the U.S Presidential Elections where there are two main applicants who fight it out. Since individuals discuss so many political parties and candidates and their prospects too in rendered messages, the issues of distinguishing their political feeling become extensive and fascinating. Consideration of all these aspects along with a sheer volume of data propelled us to look into the data and find interesting inferences in it.
To select the 117 members of the Punjab Legislative Assembly, Legislative Assembly election was held in Punjab, the State of India on 4 February 2017. As per the Election Commission, a total of 1.9 crore voters is eligible for voting in August 2016 in Punjab. The data set that is collected with the help of Twython was analyzed to find out trivial things and interesting patterns in the data. The central idea of this research paper is to carry out the sentiment analysis on Legislative Assembly election 2017 that was held in the Punjab, a state of India for the Political Parties like BJP, INC, and AAP. We have analyzed and fetch significant implications from the tweets gathered over the whole period of elections.

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