Qualification: 
MS, MSc
m_vijaykrishna@cb.amrita.edu

Vijay Krishna Menon is currently an Assistant Professor at Amrita Center for Computational Engineering and Networking (CEN), headed by Prof. K. P. Soman. He has worked on several project pertaining to text mining, text plagiarism detection, bioinformatics and comparative proteomics and genomics, machine translation and has good experience solving several machine learning problems. His work with formalisms and text mining is extensive; he has developed a translation system for English to Indian Languages. He has also worked with CDAC in the same domain of text mining and formalism based language modelling. Furthermore he has a great deal of experience in setting up Hadoop and Apache Spark clusters and maintaining huge data collection in them. His expertise also includes Functional Programming and Scala language and is a developer for Apache Spark and is an expert in Traditional Parallel Programming with MPI on Linux clusters. He is currently pursuing PhD studies under Prof. K. P. Soman and his area of research is Evolutionary and other Parsing techniques for Tree Adjoining Grammars.

EDUCATION

  • 2008: M. S. By Research (Computational Informatics)
    Amrita Vishwa Vidyapeetham
  • 2006: M.Sc. (Applied Science) Software Engineering
    Bharithiyar University
     

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

A. N., Vijay Krishna Menon, and Dr. (Col.) Kumar P. N., “Cluster Computing Paradigms – A Comparative study of Evolving Frameworks”, IJCTA, (International Conference Soft Computing Systems, ICSCS-2016), vol. 8, pp. 1911-1916, 2016.[Abstract]


Cluster computing is an approach for storing and processing huge amount of data that is being generated. Hadoop and Spark are the two cluster computing platforms which are prominent today. Hadoop incorporates the MapReduce concept and is scalable as well as fault-tolerant. But the limitations of Hadoop paved way for another cluster computing framework named Spark. It is faster and can also manage multiple workloads due to its inmemory processing. In this paper, we discuss the underlying concepts of Hadoop and mention the limitations that led to the development of Spark. Further we give a detailed description about Spark framework and its advantages. We demonstrate a wordcount problem in both Hadoop and Spark and do a comparative study.

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2015

Journal Article

Vijay Krishna Menon, Rajendran, S., and Soman, K. P., “Training Tree Adjoining Grammars with Huge Text Corpus using Spark Map Reduce”, ICTACT Journal on Soft Computing, special Issue on Soft computing techniques for Big Data, vol. 5, pp. 1021-1026, 2015.[Abstract]


Tree adjoining grammars (TAGs) are mildly context sensitive formalisms used mainly in modelling natural languages. Usage and research on these psycho linguistic formalisms have been erratic in the past decade, due to its demanding construction and difficulty to parse. However, they represent promising future for formalism based NLP in multilingual scenarios. In this paper we demonstrate basic synchronous Tree adjoining grammar for English-Tamil language pair that can be used readily for machine translation. We have also developed a multithreaded chart parser that gives ambiguous deep structures and a par dependency structure known as TAG derivation. Furthermore we then focus on a model for training this TAG for each language using a large corpus of text through a map reduce frequency count model in spark and estimation of various probabilistic parameters for the grammar trees thereafter; these parameters can be used to perform statistical parsing on the trained grammar.

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2015

Journal Article

A. S. Lakshmi, Saranya, S., Sourab, S., Vaishali, V., Vignesh, T., and Vijay Krishna Menon, “Sentiment Analysis of Static and Dynamic Data Sets”, International Journal of Applied Engineering Research, ICAAET'15 , vol. 10, pp. 1725-1727, 2015.

Publication Type: Conference Paper

Year of Publication Publication Type Title

2016

Conference Paper

Y. C. Nair, PV, N., Dr. Soman K. P., and Vijay Krishna Menon, “Real Time Vehicular Data Analysis utilising Big Data Platforms on Cost Effective ECU Networks”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.

2016

Conference Paper

Vijay Krishna Menon and Dr. Soman K. P., “A New Evolutionary Parsing Algorithm for LTAG”, in International Conference on Advanced Computing, Networking, and Informatics (ICACNI'16), , Rourkela, Odisha, 2016.

2016

Conference Paper

Vijay Krishna Menon, Vasireddy, N. Chakravart, Jam, S. Aswin, Pedamallu, V. Teja Navee, Sureshkumar, V., and Dr. Soman K. P., “Bulk Price Forecasting using Spark over NSE Data Set”, in International Conference on Data Mining and Big Data, Bali, Indonesia, 2016.[Abstract]


Financial forecasting is a widely applied area, making use of statistical prediction using ARMA, ARIMA, ARCH and GARCH models on stock prices. Such data have unpredictable trends and non-stationary property which makes even the best long term predictions grossly inaccurate. The problem is countered by keeping the prediction shorter. These methods are based on time series models like auto regressions and moving averages, which require computationally costly recurring parameter estimations. When the data size becomes considerable, we need Big Data tools and techniques, which do not work well with time series computations. In this paper we discuss such a finance domain problem on the Indian National Stock Exchange (NSE) data for a period of one year. Our main objective is to device a light weight prediction for the bulk of companies with fair accuracy, useful enough for algorithmic trading. We present a minimal discussion on these classical models followed by our Spark RDD based implementation of the proposed fast forecast model and some results we have obtained.

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2016

Conference Paper

B. Ashwini, Vijay Krishna Menon, and Soman, K. P., “Prediction of Malicious Domains Using Smith Waterman Algorithm”, in International Symposium on Security in Computing and Communication, Jaipur, Rajasthan, 2016.[Abstract]


IT security is an issue in today world. This is due to many reasons such as, malicious domains. Predicting the malicious domain in a set of domains is important. Here we have proposed a method for analysing such domains. In this method Wireshark is used for capturing the network packets. These packets are further given to client server machine and store in server database which makes an interface between the wireshark and machine. The data from the server database are then compared with the dictionary to predict the malicious websites. It is identified in such a way that if a word in a domain matches with any one of the dictionary word then it is considered as non-malicious websites others are malicious websites.

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2016

Conference Paper

A. P, Kumar, V. Suresh, Dr. P. Balasubramanian, and Vijay Krishna Menon, “Measuring stock price and trading volume causality among Nifty 50 stocks:The Toda Yamamoto Method”, in International Conference on Advances in Computing, Communications and Informatics, ICACCI, Jaipur, Rajasthan, 2016.[Abstract]


This paper analyzes the existence of a Granger causality relationship between stock prices and trading volume using minute by minute data (transformed from tick by tick data) of Nifty 50 companies traded at the National Stock Exchange, India for the period of one year from July 2014 to June 2015. Since the time series data taken is not integrated in of the same order, the Toda-Yamamoto methodology was applied to test for causality. The results show that 29 companies out 50 companies have two-way (bi-directional) causality between price and volume and 15 companies have one way (unidirectional) causal relationship where price causes volume and volume does not cause price and 6 other companies have no causal relationship in either way. The study suggests that the Efficient Markets Hypothesis does not hold true for these 29 companies during the period of this study.

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2015

Conference Paper

Vijay Krishna Menon, Rajendran, S., and Dr. Soman K. P., “A synchronised tree adjoining Grammar for English to Tamil Machine Translation”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015 (Fourth International Symposium on Natural Language Processing (NLP'15)), SCMS Group of Institutions, Corporate Office CampusPrathap Nagar , Muttom, Aluva, Kochi, Kerala; India, 2015, pp. 1497-1501.[Abstract]


Tree adjoining Grammar (TAG) is a rich formalism for capturing syntax and some limited semantics of Natural languages. The XTAG project has contributed a very comprehensive TAG for English Language. Although TAGs have been proposed nearly 40 years ago by Joshi et al, 1975, their usage and application in the Indian Languages have been very rare, predominantly due to their complexity and lack of resources. In this paper we discuss a new TAG system and methodology of development for Tamil Language that can be extended for other Indian languages. The trees are developed synchronously with a minimalistic grammar obtained by careful pruning of XTAG English Grammar. We also apply Chomskian minimalism on these TAG trees, so as to make them simple and easily parsable. Furthermore we have also developed a parser that can parse simple sentences using the above mentioned grammar, and generating a TAG derivation that can be used for dependency resolution. Due to the synchronous nature of these TAG pairs they can be readily adapted for Formalism based Machine Translation (MT) from English to Tamil and vice versa. © 2015 IEEE. More »»

2015

Conference Paper

Vijay Krishna Menon, Rajendran, S., Anandkumar, M., and Soman, K. P., “Dependency resolution and semantic mining using Tree Adjoining Grammars for Tamil Language”, in International Conference on Tamil language computing and Tamil Internet, Singapore, 2015.

2014

Conference Paper

Vijay Krishna Menon, Rajendran, S., M. Kumar, A., and Soman, K. P., “A new TAG Formalism for Tamil and Parser Analytics”, in iDravidian, International Symposium for Dravidian Languages, Co-located with ICON2014, Goa, 2014.[Abstract]


Tree adjoining grammar (TAG) is specifically suited for morph rich and agglutinated languages like Tamil due to its psycho linguistic features and parse time dependency and morph resolution. Though TAG and LTAG formalisms have been known for about 3 decades, efforts on designing TAG Syntax for Tamil have not been entirely successful due to the complexity of its specification and the rich morphology of Tamil language. In this paper we present a minimalistic TAG for Tamil without much morphological considerations and also introduce a parser implementation with some obvious variations from the XTAG system

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Publication Type: Thesis

Year of Publication Publication Type Title

2008

Thesis

Vijay Krishna Menon, “English to Indian Languages Machine Translation using LTAG”, Amrita Vishwa Vidyapeetham, Coimbatore, 2008.

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