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  • 1. Dr.G.Manoj Someswar, B. Satheesh, G.Vivekanand / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.717-723 Finance Mining – Analysis Of Stock Market Exchange For Foreign Using Classification Techniques. Dr.G.Manoj Someswar, B. Satheesh, G.VivekanandAbstract – Data Mining is a Business intelligence technique, which the finance model is considered to hardestway to make easy money. Mining certainly motivated by the prospect of discovering a financial issue such asbuying/selling for stock/contract analysis. However the survey designs a successful model poses manychallenges includes securing and cleaning data acquiring a sufficient amount of financial domain knowledgebounding the complexity. This work describes the modern finance finding efficient way to summarize andvisualize the stock market data to give individuals useful information about the behavior for investmentmarket decision and the data mining classification for stock markets. Our proposed analysis revealsprogressive applications in addition to existing analysis trading technique using data mining or geneticalgorithms for exchange of foreign rates.Keywords – Finance, Data Mining, Classification, Stock Market, Trading.INTRODUCTIONII Knowledge mining is increasing synonymousto wealth creation and as a strategy plan for competing Dynamic markets, and rapidly decreasingin the market place can be no better than the [1] cycles of technological innovation provide importantinformation on which it is based, the importance of challenges for the banking and finance industry.information in today‟s business can never be seen asan exogenous factor to the business. Organizations and Worldwide just-in-time availability ofindividuals having access to the right information at information allows enterprises to improve theirthe right moment, have greater chances of being flexibility. In financial institutions considerablesuccessful in the epoch of globalization and cut-throat developments in information technology have led tocompetition. Currently, huge electronic data huge demand for continuous analysis of resulting data.repositories are being maintained by banks and other Data mining can contribute to [15] solving businessfinancial institutions across the globe. Valuable bits of problems in banking and finance by finding patterns,information are embedded in these data repositories. causalities, and correlations in business informationThe huge size of these data sources make it impossible and market prices that are not immediately apparent tofor a human analyst to come up with interesting managers because the volume data is too large or isinformation that will help in the decision making generated too quickly to screen by experts.process. A number of commercial enterprises have Almost every computational method has beenbeen quick to recognize the value of this concept, as a explored and used for financial modeling. We willconsequence of which the software [14] market itself name just a few recent studies. Monte-Carlofor data mining is expected to be in excess of 10 simulation of option pricing, finite-differencebillion USD. Business Intelligence focuses on approach to interest rate derivatives and fast Fourierdiscovering knowledge from various electronic data transform for derivative pricing New developmentsrepositories, both internal and external, to support augment traditional technical analysis of stock marketbetter decision making. Data mining techniques curves (Murphy, 1999) that has been used extensivelybecome important for this knowledge discovery from by financial institutions. Such stock charting helps todatabases. In recent years, business intelligence identify buy/sell signals (timing "flags") usingsystems have played pivotal roles in helping graphical patterns. Data mining as a process oforganizations to fine tune business goals such as discovering useful patterns, correlations has its ownimproving customer retention, market penetration, niche in financial modeling. Similarly to otherprofitability and efficiency. In most cases, these computational methods almost every data mininginsights are driven by analyses of historical data. method and technique has been used in financial modeling. An incomplete list includes a variety of linear and non-linear models, multi-layer neural 717 | P a g e
  • 2. Dr.G.Manoj Someswar, B. Satheesh, G.Vivekanand / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.717-723networks, k-means and hierarchical clustering; k- leads to much more complex models than traditionalnearest neighbors, decision tree analysis, regression data mining models designed for. This is one of the(logistic regression; general multiple regression), major reasons that such interactions are modeled usingARIMA, principal component analysis, and Bayesian ideas from statistical physics rather than fromlearning. Less traditional methods used include rough statistical data mining.sets, relational data mining methods (deterministic 2.2. Application of Finance data mining: Bankinductive logic programming and newer probabilistic industry data mining is heavily used to model andmethods, support vector machine, independent predict credit fraud to evaluate risk to perform trendcomponent analysis, Markov models and hidden analysis profitability. In the financial markets neuralMarkov models. networks have been used in stock price forecasting in option trading inbound rating in portfolio management SECTION II in commodity price prediction mergers and2.1. Finance with Data Mining acquisitions as well as in forecasting financial The current efficient market theory applications.hypothesis attempt to discover long-term trading rules 2.2.1 Forecasting the Stock: Several softwareregularities with significant profit, Greenstone and applications on the market use data mining methodsOyer (2000) examine the month by month measures of for stock analysis such an application used for stockreturn for the computer software and computer stock indexes to determine whether theseindexes‟ price movements reflect genuine deviationsfrom random chance using the standard t-test. Theyconcluded that although Wall Street analystsrecommended to use the “summer swoon” rule (sellcomputer stocks in May and buy them at the end ofsummer) this rule is not statistically significant.However they are able to confirm several previouslyknown „calendar effects” such as “January effect”noting meanwhile that they are not the first to warn ofthe dangers of easy data mining and unjustified claimsof market inefficiency. The market efficiency theorydoes not exclude that hidden short-term local.Conditional regularities may exist. These regularitiescan not work “forever,” they should be correctedfrequently. It has been shown that the financial dataare not random and that the efficient markethypothesis is merely a subset of a larger chaoticmarket hypothesis (Drake and Kim, 1997). Thishypothesis does not exclude successful short termforecasting models for prediction of chaotic time Screen 1 Shows the Stock Market.series (Casdagli and Eubank, 1992). Data mining doesnot try to accept or reject the efficient market theory. NETPROPHET by neural networks Data mining creates tools which can be useful corporation is a stock prediction application thatfor discovering subtle short-term conditional patterns makes use of neural networks, the most widespreadand trends in wide range of financial data. This means use of data mining in banking is in the area of fraudthat retraining should be a permanent part of data detection. HNC is the credit fraud detection to monitormining in finance and any claim that a silver bullet 160 million payment card accounts in a year. Theytrading has been found should be treated similarly to also claim a healthy return on investment. While fraudclaims that a perpetuum mobile has been discovered. is decreasing applications for payment card accountsThe impact of market players on market regularities are rising as much as 50 % a year. Widespread use ofstimulated a surge of attempts to use ideas of statistical data mining in banking has not been unnoticed. Inphysics in finance (Bouchaud and Potters, 2000). If an 1996 bank[3] systems & technology commented thatobserver is a large marketplace player then such most important data mining application is financialobserver can potentially change regularities of the services. Finding banking companies who use datamarketplace dynamically. Attempts to forecast in such mining is not easy, given their proclivity for silence.dynamic environment with thousands active agents The following list of financial companies that use data 718 | P a g e
  • 3. Dr.G.Manoj Someswar, B. Satheesh, G.Vivekanand / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.717-723mining required some digging into SEC reports from income. Rather than add more agents and usedata mining vendors that are made available to the questionable handover techniques, RightPoint offerspublic. The list includes: Bank of America, First USA us the potential to convert inbound service calls intoBank, Headlands Mortgage Company, FCC National profitable sales. If organizations don‟t tackle theBank, Federal Home Loan Mortgage Corporation, revenue-generation aspect of their call-centerWells Fargo Bank, Nations- Banc Services, Mellon activities, they will not be able to [5] afford the currentBank N.A., Advanta Mortgage Corporation, Chemical unbridled growth in service traffic.Bank, Chevy Chase Bank, U.S. Bancorp, and USAAFederal Savings Bank. Again it is reasonable toassume that most large banks are performing some sort SECTION IIIof data mining. 3. Problem Definition: Data mining is a business and2.2.2. Finance Halifax Bank in Real Time: Halifax knowledge sharing application, which we can applyPLC is second largest bank in London has chosen its for shopping, stock market, sales, compares the loss &right point real marketing suite as the foundation for a gain results using operations and transactional data.customer relationship initiative right point will enable Financial data produce huge data sets that build aHalifax customer service representatives to mobilize foundation for approaching these enormously complexvital information about a customer and determine and dynamic problems with data mining tools.which campaigns product or services to offer at the Potential significant benefits of solving these problemspoint of customer. motivated extensive research for years. Our paper Halifax direct customer service center analyzes the overview on how to apply data mining onreceives more than 20 million customer calls per year finance, its benefits more efficient.and employs 800 customer service representatives.With the call center increasingly becoming thecustomer interaction center a customer decision to dobusiness with a company is often on whether acompany is aware of that customer‟s preferences andacts upon them accordingly. Using RightPoint, Halifaxrepresentatives will have a valuable tool for reliablypredicting and delivering on the requirements of theircustomers in real time Figure 3 depicts the proposed analysis for Foreign Exchange using Data Mining Techniques. 3.1. Mining Stock Market Techniques: Forecasting Call with problem Agent Solution stock market bank bankruptcies understanding and Call with problem Agent Solution managing financial risk trading futures credit ratingFigure 1 describes before real time marketing call loan management bank customer profiling and moneywith 20M +Calls is equal to 20M+Missed laundering analyses are core financial tasks for dataOpportunities. mining. Stock market forecasting includes uncoveringFigure 2 describes after real time marketing call with market trends planning investment strategies20M+calls annually is equal to significant increase identifying the best time to purchase the stocks andin customer wallet share. what stocks to purchase. Direct channel is playing an ever-increasingrole in delivering customer contact, with call volumes 3.1.1. Decision Tree on Stock Market: In a stockpredicted to grow to more than 50 million calls per market, how to find right stocks and right timing toyear over the next three years,” says Dick [4] buy has been of great interest to investors. To achieveSpelman, director of distribution at Halifax. “We need this objective, Muh-Cherng et al. (2006) present ato ensure that we can harness customer data at the stock trading method by combining the filter rule andpoint of contact so that a customized service is the decision tree technique. The filter rule, havingdelivered in real-time. This is what the RightPoint been widely used by investors, is used to generatesolution will deliver to our agents. The other parallel candidate trading points. These points arechallenge that call centers face is generating sales 719 | P a g e
  • 4. Dr.G.Manoj Someswar, B. Satheesh, G.Vivekanand / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.717-723subsequently clustered and screened by the application Enke and Suraphan Thawornwong introduced anof a decision tree algorithm. Chi-Lin Lu and Ta-Cheng information gain technique used in machine learningChen have employed decision tree-based mining for data mining to evaluate the predictive relationshipstechniques to explore the classification rules of of numerous financial and economic variables. Neuralinformation transparency levels of the listed firms in network models for level estimation and classificationTaiwan‟s stock market. The main purpose of their are then examined for their ability to provide anstudy is to explore the hidden knowledge of effective forecast of future values. A cross validationinformation disclosure status among the listed technique is also employed to improve thecompanies in Taiwan‟s stock market. Moreover, the generalization ability of several models. The resultsmultilearner model constructed with decision tree show that the trading strategies guided by thealgorithm has been applied. The numerical results classification models generate higher risk-adjustedhave shown that the classification accuracy has been profits than the buy-and-hold strategy, as well as thoseimproved by using multi-leaner model in terms of less guided by the level-estimation based forecasts of theType I and Type II errors. In particular, the extracted neural network and linear regression models (Davidrules from the data mining approach can be developed and Suraphan, 2005). Defu et al. (2007) dealt with theas a computer model for the prediction or application of a well-known neural network technique,classification of good/poor information disclosure multilayer back propagation (BP) neural network, inpotential. By using the decision tree-based rule mining financial data mining. A modified neural networkapproach, the significant factors with the forecasting model is presented, and an intelligentcorresponding equality/ inequality and threshold mining system is developed. The system can forecastvalues were decided simultaneously, so as to generate the buying and selling signs according to thethe decision rules. Unlike many mining approaches prediction of future trends to stock market, andapplying neural networks related approaches in the provide decision-making for stock investors. Theliterature; the decision tree approach is able to provide simulation result of seven years to Shanghai compositethe explicit Classification rules. Moreover, a multi- index shows that the return achieved by this mininglearner model constructed by boosting ensemble system is about three times as large as that achieved byapproach with decision tree algorithm has been used to the buy-and-hold strategy, so it is advantageous toenhance the accuracy rate in this work. Based on the apply neural networks to forecast financial time series,extracted rules, a prediction model has then been built so that the different investors could benefit from itto discriminate good information disclosure data from (Defu et al., 2004). Accurate volatility forecasting isthe poor information disclosure data with great the core task in the risk management in which variousprecision. Moreover, the results of the experiment portfolios pricing, hedging, and option strategies arehave shown that the classification model obtained by exercised. Tae (2007) proposes hybrid models withthe multi-learner method has higher accuracy than neural network and time series models for forecastingthose by a single decision tree model. Also, the multi- the volatility of stock price index in two viewpoints:learner model has less Type I and Type II errors. It deviation and direction. It demonstrates the utility ofindicates that the multilearner model is appropriate to the hybrid model for volatility forecasting.elicit and represent experts‟ decision rules, and thus ithas provided effective decision supports for judging 3.1.3. Clustering on Stock Market: Basaltoathe information disclosure problems in Taiwan‟s stock et al. (2005) apply a pair wise clustering approach tomarket. By using the rule based decision models, the analysis of the Dow Jones index companies, ininvestors and the public can accurately evaluate the order to identify similar temporal behavior of thecorporate governance status in time to earn more traded stock prices. The objective of this attention is toprofits from their investment. It has a great meaning to understand the underlying dynamics which rules thethe investors, because only prompt information can companies‟ stock prices. In particular, it would behelp investors in correct investment decisions (Jie and useful to find, inside a given stock market index,Hui, 2008). groups of companies sharing a similar temporal behavior. To this purpose, a clustering approach to the3.1.2. Neural Networks on Stock Market: problem may represent a good strategy. To this end,Advantage of neural networks is that they can the chaotic map clustering algorithm is used, where aapproximate any nonlinear function to an arbitrary map is associated to each company and the correlationdegree of accuracy with a suitable number of hidden coefficients of the financial time series to the couplingunits (Hornik et al., 1989). The development of strengths between maps. The simulation of a chaoticpowerful communication and trading facilities has map dynamics gives rise to a natural partition of theenlarged the scope of selection for investors. David data, as companies belonging to the same industrial 720 | P a g e
  • 5. Dr.G.Manoj Someswar, B. Satheesh, G.Vivekanand / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.717-723branch are often grouped together. The identification category association and possible stock categoryof clusters of companies of a given stock market index investment collections. Then the K-means algorithm iscan be[7][8] exploited in the portfolio optimization a methodology of cluster analysis implemented tostrategies (Basaltoa et al., 2005). Graph representation explore the stock cluster in order to mine stockof the stock market data and interpretation of the category clusters for investment information. By doingproperties of this graph gives a new insight into the so, they propose several possible Taiwan stock marketinternal structure of the stock market. Vladimar et al. portfolio alternatives under different circumstances(2006) study different characteristics of the market (Shu-Hsien et al., 2008).graph and their evolution over time and came toseveral interesting conclusions based on their analysis. 3.2. How to avoid Risk by Applying Data mining inIt turns out that the power-law structure of the market Finance: Managing of risk is at the core of everygraph is quite stable over the considered time financial organization major challenge in the bankingintervals; therefore one can say that the concept of and insurance world is therefore the implementation ofself-organized networks, which was mentioned above, risk systems in order to identify measure and controlis applicable in finance, and in this sense the stock business exposure. Integrated measurement ofmarket can be considered as a “self-organized” different kinds of risk is moving into focus. These allsystem. Another important result is the fact that the are based on models representing single financialedge density of the market graph, as well as the instruments or risk factors their behavior and theirmaximum clique size, steadily increases during the last interaction.several years, which supports the well-known idea 3.2.1. Financial Risk: For single financialabout the globalization of economy instruments that is stock indices interest rates orwhich has been widely discussed recently. They also currencies market risk measurements is based onindicate the natural way of dividing the set of financial models [10] depending on a set of underlying riskinstruments into groups of similar objects (clustering) factor such as interest rates stock indices or economicby computing a clique partition of the market graph. development. Today different market risk measurement approaches exist. All of them rely on3.1.4. Association Rule on Stock Market: Agrawal models representing single instrument their behavioret al. (1993) discovering association rules is an and interaction with overall market.important data mining problem, and there has beenconsiderable research on using association rules in the SECTION IVfield of data mining problems. The associations‟ rules 4.1. Technical Application of Data Mining inalgorithm is used mainly to determine the relationships Finance: Several ways for deciding when to buy/sell abetween items or features that [9] occur synchronously stock/contract, the process of financial data miningin the database. For instance, if people who buy item synthesizes technical analysis with machine learningX also buy item Y, there is a relationship between item for constructing successful models. A model is definedX and item Y, and this information is useful for as a series of trades, which identifies a buy and selldecision makers. Therefore, the main purpose of date and time, the stock/contract process upon enteringimplementing the association rules algorithm is to find a trade and a number of shares. Technical analysissynchronous relationships by analyzing the random involves constructing one or more mathematicaldata and to use these relationships as a reference models based upon the stock/contract movement orduring decision making (Agrawal et al., 1993). One of change in Volume. One of the keys to effective datathe most important problems in modern finance is mining is acquisition of specific domain knowledge.finding efficient ways to summarize and visualize the Possessing domain knowledge speeds up the datastock market data to give individuals or institutions mining process because it allows the modeler touseful Hajizadeh et al. 115 information about the discriminate between multitudes of strategies that maymarket behavior for investment decisions. The be available. Furthermore, possessing domainenormous amount of valuable data generated by the knowledge helps in recognizing a “good” model. Instock market has attracted researchers to explore this this case, the technical indicators serve as the domain-problem domain using different methodologies. Shu- specific knowledge. A technical indicator is anHsien et al. (2008) investigated stock market algorithm constructed using price or volumeinvestment issues on Taiwan stock market using a two parameters. There are more than 100 technicalstage data mining approach. The first stage apriori indicators available. Common examples includealgorithm is a methodology of association rules, which moving averages, relative strength index, oris implemented to mine knowledge and illustrate commodity channel index. One of the challenges inknowledge patterns and rules in order to propose stock the model formulation process is to focus on only a 721 | P a g e
  • 6. Dr.G.Manoj Someswar, B. Satheesh, G.Vivekanand / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.717-723relatively few technical indicators which appear to be can be repeated daily for several months collectingmost promising. Models may be constructed solely quality estimates. This paper proposes a data mining inwith technical indicators. However, students are finance stock market and specific requirements forencouraged to synthesize technical indicators with data mining methods including in makingvarious machine learners. interpretations, incorporating relations and4.2. Financial Trading: Trading techniques are going probabilistic learning for exchange.long means buying a stock contract at a particularprice with the intent that the stock contract willincrease in price. When short a stock contract isinitially sold with the intent that the stock contract will Referencedecrease in price. Example a stock sold for $10 then [1] Agrawal R, Imilienski T, Swami A (1993).later bought at $8 would result in a $2 profit thus it is Mining association rules between sets of itemspossible to make money in either market direction. in large databases, In Proceedings of the ACMWhen an investor initially buys sells a stock contract SIGMOD international conference onthe price may move in the opposite direction resulting management of a losing position. An investor must decide how [2] Basaltoa N, Bellottib R, De Carlob F, Facchiblarge a loss he or she is willing to tolerate or whether P, Pascazio S (2005).to risk the situation will turn in their favor amount of [3] Clustering stock market companies via chaotictolerance for loss is called drawdown for this map synchronization, Physica A. Berry MJA,percentage may be 10% for futures it may increase or Linoff GS (2000). Mastering data mining, Newfixed points. York: Wiley. The profit for a stock trade is equal to the [4] Boris K, Evgenii V (2005). Data Mining forpurchase price P minus the selling price S times the Financial Applications, the Data Mining andnumber of shares N purchased. Knowledge Discovery Handbook. Profit (Long Trade) = (P-S)*N [5] Chi-Lin L, Ta-Cheng C (2009). A study ofFutures contracts operates as follows for a long applying data mining approach to theposition an investor may buy one EMini S &P goes up information disclosure for Taiwan‟s stockthe price of the contract for $2,000 for each point the S market investors, Expert Systems with& P goes up the price of the contract increases by 50 Applications,. Cowan A (2002). Book review:dollars. If a contract is bought at $ 950 sold at $ 960 Data Mining in Finance, Int. J. forecasting.then the investor nets 10 points times $ 50 for a profit [6] David E, Suraphan T (2005). The use of dataof 500 dollars on an initial investment of $ 2,000 or mining and neural networks for forecasting25% increase in just one transaction. stock market returns, Expert Systems with CONCLUSION V Applications.Increase of economic globalization information [7] Defu Z, Qingshan J, Xin L (2004). Applicationtechnology financial data are being generated and of Neural Networks in Financial Data Mining,accumulated at an unprecedented pace. A critical need Proceedings of world academy of science, Eng.for automated approaches to effective and efficient Technol.utilization of massive amount of financial data to [8] Hornik K (1989). Stinchcombe M. and Whitesupport organizations in strategic investment H., “Multilayer feed forward networks aredecisions. Data mining techniques have been used to universal approximators”, Neural Networks.uncover hidden patterns and predict future trends and [9] Hsiao-Fan W, Ching-Yi K (2004). Factorbehaviors in financial markets. The competitive Analysis in Data Mining, Computers andadvantages achieved by data mining include increased Mathematics with Applications.revenue, reduced cost, and much improved responsiveness and awareness. There has a large body of research and practice focusing on les/associationrules_intr o.htm.exploring data mining techniques to solve financial [10] Huarng K, Yu HK (2005). A type 2 fuzzy timeproblems. To be successful, a data mining project series model for stock index forecasting,should be driven by the application needs and results Physica.should be tested quickly. Financial applications [11] Jar-Long W, Shu-Hui C (2006). Stock marketprovide a unique environment where efficiency of the trading rule discovery using two-layer biasmethods can be tested instantly, not only by using decision tree, Expert Systems withtraditional training and testing data but making real Applications.stock forecast and testing it the same day. This process 722 | P a g e
  • 7. Dr.G.Manoj Someswar, B. Satheesh, G.Vivekanand / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.717-723[12] Jie S, Hui L (2008). Data mining method for listed companies‟ financial distress prediction, Knowledge-Based Systems.[13] Teaching Financial Data Mining using Stocks and Futures Contracts Gary D. BOETTICHER Department of Computer Science, University of B. Satheesh Ph.D M.Tech Houston – Clear Lake Houston, Texas 77058, Current working USA Jyothismathi Inst of Tech[14] Petra Hunziker, Andreas Maier, Alex Nippe, & Scie Karimnagar as an Markus Tresch, Douglas Weers, and Peter Asst Prof in the Dept of Zemp, Data Mining at a major bank: Lessons CSE. His areas of Web from a large marketing application retrieved 5 th Technologies Computer January, 2006 from Networks, Attended a work shop on TESTING-TOOLS fo/pkdd98.pdf conducted by SREE Dattha Institutions, HYD in the[15] Michal Meltzer, Using Data Mining on the year 2008, work shop on DESIGN PATTERNS road to be successful part III, published in conducted by SVAROG SOFTWARE SOL., HYD October 2004, retrieved 2nd January, 2006 from in the year 2011and Conferences : Attended- SUNTECH-DAYS FEB-2009 on JAVA-FX, JSF, article.cfm?nl=bireport&articleId=1011392&iss HITEX, HYDERABAD ue=20082[16] Fuchs, Gabriel and Zwahlen, Martin, What’s so special about insurance anyway?, published in G.Vivekanand pursuing DM Review Magazine, August 2003 issue, Ph.D M.Tech Current retrieved 5th January, 2006 from working Jyothismathi Inst of Tech & Scie cleId=7157 Karimnagar as an Asst Prof in the Dept of CSE. His areas of Web Technologies, Data Dr.G.Manoj Someswar, Mining, Attended a B.Tech., M.S.(USA), work shop on M.C.A., Ph.D. is having TESTING-TOOLS 20+ years of relevant conducted by SREE work experience in Dattha Institutions, HYD in the year 2008, work shop Academics, Teaching, on DESIGN PATTERNS conducted by Industry, Research and SVAROG SOFTWARE SOL., HYD in the year Software Development. 2011and Conferences: Attended-SUNTECH-DAYS At present, he is working FEB-2009 on JAVA-FX, JSF, HITEX, as an Associate HYDERABAD Professor, Dept. of IT,MVSR Engineering College, Nadergul, Hyderabad,A.P. and utilizing his teaching skills, knowledge,experience and talent to achieve the goals andobjectives of the Engineering College in the fullestperspective. He has attended more than 100 nationaland international conferences, seminars andworkshops. He has more than 10 publications to hiscredit both in national and international journals. He isalso having to his credit more than 50 research articlesand paper presentations which are accepted in nationaland international conference proceedings both in Indiaand Abroad. 723 | P a g e