Volume-4 , Issue-3 , Jun 2016, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  A Comparative Study of Financial Performance of SBI and ICICI Banks in India

  A. Jaiswal, C. Jain

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.1-6, Jun-2016

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  The banking sector is very important for the economic development of a country. Traditionally the banks worked as finance depositor and finance provider only but presently as the scenario have changed and many policies and other technical changes have become the part of economies therefore now banks also play many roles in the development of economy. The study is an attempt to analyze the financial performance of SBI and ICICI banks. The State Bank of India, popularly known as SBI is one of the leading bank of public sector in India. SBI has 14 Local Head Offices and 57 Zonal Offices located at important cities throughout the country. ICICI bank is the second largest, leading bank of private sector in India The Bank has 2,533 branches and 6,800 ATMs in India. The study is descriptive and analytical in nature. The collected data was secondary in nature and collected from various reports issued by these banks through internet. The comparison of financial performance of these two banks was made on the basis of ratio analysis. The results indicated that the SBI is performing well and financially sound than ICICI Bank. Also the market position of SBI is better than ICICI in terms to earning per share, price ratio per share and dividend payout ratio, but on the other hand ICICI bank is performing well in terms of NPA and provision for NPA in comparison of SBI bank.

  Key-Words / Index Term
  Dividend Payout Ratio, EPS,, ICICI, NPA, Price Ratio per Share, SBI

  References
  [1] M. Kumbirai, R. Webb, “A Financial Ratio Analysis of Commercial Bank Performance in South Africa”, African Review of Economics and Finance, Vol.2, Issue.1, pp.30-53, 2010.
  [2] Mohi-ud-Din Sangmin, T. Nazir, “Analyzing Financial Performance of Commercial Banks in India: Application of CAMEL Model”, Pakistan Journal of Commerce & Social Sciences, Vol.4, Issue.1, pp.40-55, 2010.
  [3] R. Mitra, R. Shankar, “Measuring performance of Indian banks: an application Data Envelopment Analysis”, International Journal of Business Performance Management , Vol. 10, Issue.1, pp.57-85, 2008.
  [4] BA. Singh, P. Tandon, “A Study of Financial Performance: A Comparative Analysis of SBI and ICICI Bank”, International Journal of Marketing, Financial Services & Management Research, Vol. 1, Issue.11, pp.56-71, 2012.
  [5] M.Y. Khan, “Financial Management”, Tata McGraw Hill Publication. India, pp.1-665, 2007.
  [6] S. Padmalatha, J. Paul, “Management of Banking & Financial Services”, Pearson Education, India, pp.1-620, 2010.
  [7] J. Saluja, R. Kaur, “Profitability Performance of Public Sector Bank in India”, Indian Journal of Finance, Vol.4, No.4, pp.17-25, 2010.
  

  Citation
  A. Jaiswal, C. Jain, "A Comparative Study of Financial Performance of SBI and ICICI Banks in India," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.1-6, 2016

• Open Access   Article

  A Predictive Voice-Input and Output Alternative Communication System using MFCC & DTW

  S. Pal, VP. Singh

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.7-10, Jun-2016

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  Modern world have lots of applications that are related to speech recognition, and so a lots of researches have been carried out in this field which empower the world more and more automated. In this paper another kind of application has been designed for peoples suffering difficulty in speaking the words properly. The system records the impaired speech and takes it as input for further processing. As per input of the speech system display predictive suggestions and then finally complete sentence will be spoken by the system. Thus it is a voice input voice output predictive system.

  Key-Words / Index Term
  ASR; Mel Frequency; Cepstrum Coefficient; Feature & Vector Space; Distance Measurement

  References
  [1] S. Furui, “50 years of Progress in speech and Speaker Recognition Research”, ECTI Transactions on Computer and Information Technology,Vol.1. No.2 pp.64-74, 2005.
  [2] K.H. Davis, R. Biddulph, S. Balashek, “Automatic recognition of spoken Digits”, Journal of Acoust. Soc.Am., Vol.24, Issue.6, pp.637-642,1952.
  [3] H.F. Olson, H. Belar, “Phonetic Typewriter”, Journal of Acoust Soc Am., Vol.28, Issue.6, pp.1072-1081,1956.
  [4] D.B. Fry, “Theoritical Aspects of Mechanical speech Recognition , and P.Denes”, Journal of the British Institution of Radio Engineers, Vol.19, Issue.4, pp.211-8, 1959.
  [5] J.W. Forgie, C.D. Forgie, “Results obtained from a vowel recognition computer program”, The Journal of the Acoustical Society of America, Vol.31, Issue.11, pp.1480-1489.1959.
  [6] J. Suzuki, K. Nakata, “Recognition of Japanese Vowels Preliminary to the Recognition of Speech”, J ournal of Radio Research Lab, Vol.37, Issue.8, pp.193-212,1961.
  [7] T. Sakai, S. Doshita, “The phonetic typewriter”, IFIP Congress, Vo.445, pp.449-458, 1962.
  [8] K. Nagata, Y. Kato, S. Chiba, “Spoken Digit Recognizer for Japanese Language”, NEC Research Development, Vol.8, No.6, pp.347-352, 1963.
  [9] T.B. Martin, A.L. Nelson, H.J. Zadell, “Speech Recognition b Feature Abstraction Techniques, Air Force Avionics Lab, US, pp.1-136, 1964.
  [10] T.K. Vintsyuk, “Speech Discrimination by Dynamic Programming”, Kibernetika, Vol.4, Issue.2, pp.81-88, 1968.
  [11] H. Sakoe, S. Chiba, “Dynamic programming algorithmoptimization for spoken word recognition”, IEEE Transaction Acoustics, Speech, Signal Procssing, Vol.26, Issue.1, pp.43-49,1978.
  [12] D.R. Reddy, “An Approach to Computer Speech Recognition by Direct Analysis of the Speech Wave”, Technical Report No.C549 (Stanford University), USA, pp.1-173, 1966.
  [13] V.M. Velichko, N.G. Zagoruyko , “Automatic Recognition of 200 words”, Internatioanl Journal of Man-Machine Studies, Vo. 2, pp. 223-234, 1970.
  [14] H. Sakoe, S. Chiba, “Dynamic Programming AlgorithmOptimization for Spoken Word Recognition”, IEEE Transaction Acoustics, Speech, Signal Procssing, Vol.26, Issue.1, pp.43-49,1978.
  [15] F. Itakura, “Minimum Prediction Residula Applied toSpeech Recognition”, IEEE Transaction Acoustics, Speech, Signal Procssing, Vol.23, Issue.1, pp.67-72, 1975.
  [16] C.C. Tappert, N.R. Dixon, A.S. Rabinowitz, W.D. Chapman, “Automatic Recognition of Continuous Speech Utilizing Dynamic Segmentation, DualClassification, Sequential Decoding and Error Recover”, Rome Air Dev Cen, Rome, NY, pp.71-146,1971.
  [17] F. Jelinek, L.R. Bahl, R.L. Mercer, “Design of a Lingusistic Statistical Decoder for the Recognition ofContinuous Speech”, IEEE Transaction Information Theory, Vol.21, pp.250-256, 1975.
  [18] F. Jelinek, “The Development of an ExperimentalDiscrete Dictation Recognizer”, Proceeding of IEEE, Vol.73, Issue.11, pp.1616- 624, 1985.
  [19] L.R. Rabiner, S.E. Levinson, A.E. Rosenberg, J.G. Wilpon, “Speaker Independent Recognition ofIsolated Words Using Clustering Techniques, IEEE Transaction Acoustics, Speech, Signal Procssing, Vol.27, pp.336-349, 1979.
  

  Citation
  S. Pal, VP. Singh, "A Predictive Voice-Input and Output Alternative Communication System using MFCC & DTW," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.7-10, 2016

• Open Access   Article

  Green Computing - A New Trend in It

  L. Lakhani

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.11-13, Jun-2016

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  Abstract
  In past decades, computing has become the real thrust of the world. It has reduced the time consumption and is known for speedy calculations, faster analysis and mare complex problems. But no one has ever known that these computers are also responsible for the environmental degradation. It is responsible for generating tons of e-waste, carbon emissions and carbon foot prints. It is the very serious problem that the world is facing in present scenario. This is where we use the concept of green computing i.e. using the computers and related resources in environment friendly manner. This technology is one of the major concerns that needed to be implemented as early as possible.This study focuses on the production and the operation phases of the product life cycle and demonstrates what actions will result in overall carbon footprint reduction for personal and business computing under various operational conditions and environments.

  Key-Words / Index Term
  Green computing, Eco-friendly technology, Carbon emissions, Carbon foot print, E- waste, Degradation

  References
  [1] S. Mittal, "A survey of techniques for improving energy efficiency in embedded computing systems", International Journal of Computer Application and Engineering Technology, Vol.6, Issue.4, pp.440–459, 2014.
  [2] S. Mittal, "Power Management Techniques for Data Centres: A Survey", 2014.
  [3] San Murugesan, “Harnessing Green IT: Principles and Practices”, IEEE IT Professional, Vol.10, Issue.1, pp.24-33, 2008.
  [4] B.E. Smith, “Green Computing: Tools and Techniques for Saving Energy Money and Resources”, CRC Press (Taylor & Francis), US, pp.1-262, 2013
  [5] J. Bonello, "Intel and Google Join with Dell, EDS, EPA, HP, IBM, Lenovo, Microsoft, PG&E, World Wildlife Fund and Others to Launch Climate Savers Computing Initiative", Google Inc and Intel Corporation, US, pp.1-5, 2007.
  [6] Wikipedia, "What exactly is the Climate Savers Computing Initiative?". Climate Savers Computing Initiative, 2007.
  [7] "President Bush Requires Federal Agencies to Buy EPEAT Registered Green Electronic Products", from Green Electronics Council, 2007.
  [8] "Executive Order: Strengthening Federal Environmental, Energy, and Transportation Management", The White House: Office of the Press Secretary, 2007.
  [9] M. Poess, R. Nambiar, K. Vaid, J.M. Stephens, K. Huppler, E. Haines, "Energy benchmarks: a detailed analysis", ACM International Conference on Energy-Efficient Computing and Networking, Germany, pp. 131-140, 2010.
  [10] "SPEC Power and Performance Committee", SPEC, 2013.
  [11] O. Tickoo, R. Iyer, R. Illikkal, D. Newell, “Modeling virtual machine performance: challenges and approaches”, ACM SIGMETRICS Performance Evaluation Review, Vol.37, Issue.3, pp.55-60, 2010.
  [12] S. Murugesan, “Harnessing Green IT: Principles and Practices”, IEEE Computer Society, Vol. 10, Issue.1, pp.24-33, 2008.
  [13] P. Rani, Rekha, D. Singha, "Challenges To The Sustainable Green Computing", International Journal Of Advance Research In Science And Engineering, Vol.2, No.2, pp.1-5, 2013.
  

  Citation
  L. Lakhani, "Green Computing - A New Trend in It," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.11-13, 2016

• Open Access   Article

  Information Communication Technology Integration in Education

  LK. Ojha, LK. Tiwary, R.i Sharma

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.14-15, Jun-2016

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  ICT can be used as a tool to provide high quality educational content for the schools in remote areas. Education of socially disadvantage group is always a challenge for the developing country. The importance of such kind of technology is a need of present scenario. ICT integration in education can provide better environment for those who are not in the main stream of society and geographically distributed students. ICT also allows the academic institutions to reach disadvantaged groups and new international educational markets. ICT increases the flexibility of delivery of education so that learners can access knowledge anytime and from anywhere. Thus, ICT- supported learning can act as a catalyst of education. It also leads to the democratization of education. Various ICT products such as, teleconferencing, video calling, audio cassettes and CD ROMs can be used in education for different purpose. ICT provide a unique platform to all type of learner and it has a great impact in the era of modern educational development.

  Key-Words / Index Term
  ICT, Socially Disadvantage Group, Learner and ICT Products

  References
  [1]. J. Archer, “The link to higher scores”, In The Jossey-Bass Reader on Technology and Learning, CA, pp. 112–125, 2000.
  [2]. Sanyal, B. C. (2001), “New functions of higher education and ICT to achieve education for all”, International Institute for Educational Planning, Paris, pp.18-32, 2001.
  [3]. T. Mooij, “Design of educational and ICT conditions to integrate differences in learning: Contextual learning theory and a first transformation step in early education`, Computers in Human Behavior, Vol.23, Issue.3, pp.1499-1530, 2007.
  [4]. I. Bhattacharya,K. Sharma, “India in the knowledge economy – an electronic paradigm”, International Journal of Educational Management, Vol. 21 No. 6, pp. 543-568, 2007.
  [5]. S. Chandra, V. Patkar, “ICTS: A catalyst for enriching the learning process and library services in India”, The International Information & Library Review, Vol.39, Issue.1, pp.1-11, 2007.
  [6]. M. Cross, F. Adam, “ICT Policies and Strategies in Higher Education in South Africa: National and Institutional Pathways”, Higher Education Policy, Vol.20, Issue.11, pp.73-95, 2007.
  [7]. UNESCO, “Open And Distance Learning Trends, Policy And Strategy Considerations”, Unesco, Paris, pp.1-12, 2002.
  

  Citation
  LK. Ojha, LK. Tiwary, R.i Sharma, "Information Communication Technology Integration in Education," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.14-15, 2016

• Open Access   Article

  Accurate and Efficient Query Processing at Location Based Services Using online Route APIs

  RK. Savarapu, Lavanya

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.16-19, Jun-2016

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  Location-based services (LBS) allow mobile users to query the points-of-interest (e.g., restaurants, cafes) on various features (e.g., price, quality, and variety). Additionally users require accurate query results with up-to-date travel times. Lacking the monitoring infrastructure for road traffic, the LBS may obtain live travel times of routes from online route APIs in order to provide accurate results. Our aim is to reduce the number of requests issued by the LBS significantly while preserving accurate query results. Initially, we suggest exploiting recent routes requested from route APIs to answer queries accurately. Then, we design successful lower/upper bounding techniques and ordering techniques to process queries conveniently. Also, we study parallel route requests to further reduce the query response time. Our experimental evaluation shows that our solution is three times more successful than a competitor, and still achieves high result accuracy (above 98 percent).

  Key-Words / Index Term
  Time Dependent Service Graph; Spatial Data Mining; LBS, Elapsed Time

  References
  [1]. Yu Li, ML. Yiu, “Route Saver:Leveraging Route APIs for accurate and efficient Query Processing at Location Based Services”, in Spatial data mining, Vol. 27, No. 1, pp.56-68, 2001.
  [2]. U. Demiryurek, FB. Kashani, C Shahabi, A Ranganathan, “Online computation of fastest path in time-dependent spatialnetworks”, International Symposium on Spatial and Temporal Databases (Springer), Berlin pp.92–111, 2011.
  [3]. EP. Chan, Y. Yang,“Shortest path tree computation in dynamic graphs”, IEEE Transaction on Computer, Vol.58, No. 4, pp. 541–557, 2009.
  [4]. H Hu, DL Lee, VCS. Lee, “Distance indexing on road networks,” Proceedings of the 32nd international conference on Very large data bases, Korea, pp.894-905, 2006.
  [5]. Hai Yang, "Traffic restraint, road pricing and network equilibrium", Transportation Research Part B: Methodological Vol.31, Issue.4, pp.303-314, 1997.
  [6]. M. Safar, “Group k-nearest neighbors queries in spatial network databases”, Journal of geographical systems, Vol.10, Issue.4, pp.407-416, 2008.
  [7]. T. Seidl, HP. Kriegel, “Optimal multi-step k-nearest neighbor search”, ACM SIGMOD Record, Vol.27, No.2, pp.154-165, 1998.
  [8]. HP. Kriegel, P Kr•oger, M Renz, T Schmidt, “Hierarchical Graph Embedding for Efficient Query Processing in Very Large Traffic Networks", Proceedings of the 20th international conference on Scientific and Statistical Database Management, China, pp.150-167, 2008.
  [9]. J. Rishede,T. Man, L. Yiu, “Effective Caching of Shortest Paths for Location-Based Services”, In SIGMOD, Vol.12, pp.313-324, 2012.
  [10]. D. Zhang , CY. Chow, Q. Li, X. Zhang, Y. Xu, “Efficient Evaluation of k-NN Queries Using Spatial Mashups”, Advances in Spatial and Temporal Databases (Lecture Notes in Computer Science), Vol. 6849, pp.348-366, 2011
  [11]. D. Zhang, CY. Chow, Q Li, X Zhang, Y Xu, “SMashQ: Spatial mashup framework for k-NN queries in timedependent road networks”, Distributed. Parallel Databases, Vol. 31, pp. 259–287, 2012.
  

  Citation
  RK. Savarapu, Lavanya, "Accurate and Efficient Query Processing at Location Based Services Using online Route APIs," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.16-19, 2016

• Open Access   Article

  An Attribute-Assisted Reranking Model for Web Image Search

  ST. Tangudubilli, AS. Kumar

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.20-25, Jun-2016

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  Image search reranking is a successful approach to refine the text-based image search result. Most existing reranking ways are based on low level visual features. This paper proposes to make use of semantic attributes for image search reranking. Depend on the classifiers for all the predefined attributes, each image is represented by an attribute feature containing the responses from these classifiers. A hypergraph is then used to model the relationship between images by combining low level visual features and attribute features. Hypergraph ranking is then performed to order the images. The basic principle is that visually similar images should have similar ranking scores. In this paper, we propose a visual attribute joint hypergraph learning approach at the same time to explore two information sources. A hypergraph is created to model the relationship of all images. We conduct experiments on more than 1,000 queries in MSRA-MMV2.0 data set. The experimental results indicate the productiveness of our approach.

  Key-Words / Index Term
  Text base query; Attribute-assisted; Image retrieval; Query image; hyper graph learning; Image reranking

  References
  [1]. J. Cai, ZJ Zha, M. Wang, S. Zhang, Q. Tian, “An Attribute-Assisted Reranking Model for Web Image Search”, IEEE Transactions On Image Processing, Vol. 24, No. 1, pp.261-272, 2015.
  [2]. L. Yang, A. Hanjalic, “Prototype-Based Image Search Reranking”, IEEE Transactions On Multimed, Vol.14, No.3,pp. 871-88, 2012.
  [3]. S. Changpinyo, E. Sudderth, “Learning image attributes using the Indian Buffet Process”, Technical report ofBrown University, USA, pp.1-146, 2012.
  [4]. B. Siddiquie, R.S. Feris, L.S. Davis, “Image ranking and retrieval based on multi-attribute queries”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, pp.801-808, 2011 .
  [5]. J. Feng, S. Jegelka, S. Yan and T. Darrell, "Learning Scalable Discriminative Dictionary with Sample Relatedness", 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp.1645-1652, 2014.
  [6]. N. Kumar, A. C. Berg, P. N. Belhumeur and S. K. Nayar, "Attribute and simile classifiers for face verification," 2009 IEEE 12th International Conference on Computer Vision, Kyoto, pp.365-372, 2009.
  [7]. M. Wang, L. Yang, X.-S.Hua, “MSRA-MM: Bridging research and industrial societies for multimedia,” Tech. Rep. MSR-TR-2009-30, 2009.
  [8]. K. Järvelin, J. Kekäläinen, “IR evaluation methods for retrieving highly relevant documents”, 23rd annual international ACM SIGIR conference on Research and development in information retrieval, Greece , pp.41–48, 2000.
  [9]. W. H. Hsu, L. S. Kennedy, S.-F.Chang, “Video search reranking via information bottleneck principle”, Proceedings of the 14th ACM international conference on Multimedia, Santa Barbara, pp.35–44, 2006.
  [10]. Y. Huang, Q. Liu, S. Zhang, D.N. Metaxas, “Image retrieval via probabilistic hypergraph ranking”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, pp.3376–3383, 2010.
  [11]. C. H. Lampert, H. Nickisch, S. Harmeling, “Learning to detect unseen object classes by betweenclass attribute transfer”, IEEE Conference on Computer Vision and Pattern Recognition, Miami, pp. 951–958, 2009.
  [12]. R. Yan, A. Hauptmann, and R. Jin, “Multimedia search with pseudo- relevance feedback”, Proceedings of the 2nd international conference on Image and video retrieval, Urbana-Champaign, pp. 238–247, 2003.
  

  Citation
  ST. Tangudubilli, AS. Kumar, "An Attribute-Assisted Reranking Model for Web Image Search," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.20-25, 2016

• Open Access   Article

  An Efficient Approach for Secure Data Aggregation Method in Wireless Sensor Networks with the impact of Collusion Attacks

  U. Korupolu, S. Kartik, GK. Chakravarthi

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.26-29, Jun-2016

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  Abstract
  The basic component in wireless sensor networks (WSNs) is represented by the nodes. The sensor node consumes energy during sensing, processing and transmission. The aggregation of data from multiple sensor nodes which is done at the aggregating node is to be performed by simple method such as averaging. In Wireless sensor network power and energy resources are limited. In this paper the monitoring system and READA technique will be used. The number of sensor nodes can detect simultaneously a single target of interest. Redundant and correlated data are collected. If every node sends data to the base station, energy will be wasted and due to that the network energy will be consume quickly. Redundancy Elimination for Accurate Data Aggregation (READA) uses a grouping and compression mechanism to remove duplicate data in the aggregated set of data to be sent to the base station without losing large accuracy of the final aggregated data. In wireless sensor network, security and energy efficiency issues are found.

  Key-Words / Index Term
  Wireless Sensor Networks; Network Lifetime; Collision attack; Data Aggregation techniques

  References
  [1]. P. saini, M. Sharma, "Impact of Multimedia Traffic on Routing Protocols in MANET", IJSR in Network Security and Communication, Vol.3, Issue.3, pp.1-5, 2015,
  [2]. L. Pal, P. Sharma, N. Kaurav, S.L. Mewada, "Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc –Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.5, pp.1-4,2013.
  [3]. R. Nathiya, S.G. Santhi, "Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)", International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.36-43, 2014.
  [4]. H. Singh, "Randomly Generated Algorithms and Dynamic Connections", International Journal of Scientific Research in Network Security and Communication, Vol.2, Issue.1, pp.1-4, 2014.
  [5]. O. Hiteshreddy, P. Singh, S. Chahuan, "A Review on Cluster Based Data Aggregation Protocols in Wireless Sensor Network", International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.37-45, 2015.
  [6]. D. Tamrakar, S. Bhattacharya, S. Jain, "A Scheme to Eliminate Redundant Rebroadcast and Reduce Transmission Delay Using Binary Exponential Algorithm in Ad-Hoc Wireless Networks", International Journal of Scientific Research in Network Security and Communication, Vol.2, Issue.2, 1-5, 2014.
  [7]. Md.A. Mushtaque, "Comparative Analysis on Different parameters of Encryption Algorithms for Information Security", International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.76-82, 2014.
  [8]. Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Translation Journal on Magnetics in Japan, Vol. 2, Issue.8, pp.740–741, 1987.
  [9]. R. Sharma, Heena Das, S.N. Das, "WSN for Computerized Irrigation System in Tea Gardens", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.2, pp.26-30, 2016.
  

  Citation
  U. Korupolu, S. Kartik, GK. Chakravarthi, "An Efficient Approach for Secure Data Aggregation Method in Wireless Sensor Networks with the impact of Collusion Attacks," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.26-29, 2016

• Open Access   Article

  An Optimal Route Search Using Spatial Keyword Query using Keyword Nearest Neighbor Expansion

  HKN. Peddinti, JA. Lavanya, GK. Chakravarthi

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.29-32, Jun-2016

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  Abstract
  In our daily life the need to find optimal routes between the two points is critical, for instance finding the shortest distance to the nearest hospital. The internet based maps are now generally used for this purpose. Route search and optimal route queries are two significant classes of queries based on the road network concept. Route search queries find the route based on the given constraints. The optimal route queries find the optimum route from a set of specifications by a user. In the road map queries, users would have to give the specification of the starting point and the ending point of their travelling with or without the constraints. A few spatial features about the categories and the different locations should be specified along with this. If the travelling constraints are given then it should be unique. These constraints may be either a total order or a partial order. In this specification order there should be information about both the starting point and the destination point of the travelling. The optimal route queries optimize the possible routes and give the optimal route that meets all the constraints. This paper describes the survey on optimal route query processing, two categories namely optimal route query processing and spatial search with categorical information have been considered, an analysis on technique for optimal route query with constraints and without constraint is also included. The total order needs a specification of list of points and in the same order that they should be visited but that is not required for partial order constraints. Lastly this paper concludes with pros and cons of different techniques under optimal route queries.

  Key-Words / Index Term
  Spatial keyword query , spatial objects, spatial database, best keyword cover query

  References
  [1]. C. Zeng, JF. Naughton, JY. Cai, “On differentially private frequent itemset mining”, VLDB Endowment, Vol.6, Issue.1, pp.25-36, 2012.
  [2]. H. Chen, WS. Ku, MT. Sun, R. Zimmermann, “The partial sequenced route query with traveling rules in road networks”, GeoInformatica, Vol.15, Issue.3, pp.541-69, 2011.
  [3]. J. Sander, M. Ester, HP. Kriegel, X. Xu, “Density-based clustering in spatial databases: The algorithm gdbscan and its applications”, Data mining and knowledge discovery, Vol.2, Issue.2, pp.169-94, 1998.
  [4]. J. Li, YD. Yang, N. Mamoulis, “Optimal Route Queries with Arbitrary Order Constraints”, IEEE Trans. Computers, vol. 25, no. 5, pp. 1097- 1110, 2013.
  [5]. A. Nanopoulos A, P. Bozanis, “Categorical Range Queries in Large Databases”, Springer-Verlag, Berlin-Heidelberg, pp. 122-139, 2003.
  [6]. M. Kolahdouzan, C. Shahabi, “Voronoi-based k nearest neighbor search for spatial network databases”, International conference on Very large data bases, Vol.30, pp.840-851, 2004.
  [7]. M. Sharifzadeh, MR. Kolahdouzan, C. Shahabi, “The Optimal Sequenced Route Query”, International Journal of Very Large Data Bases, Vol.17, No.4, pp.765-787, 2008.
  [8]. JB. Rocha-Junior, A. Vlachou, C. Doulkeridis, K. Nørvåg, “Efficient processing of top-k spatial preference queries”, VLDB Endowment, Vol.4, Issue.2, pp.93-104, 2010.
  

  Citation
  HKN. Peddinti, JA. Lavanya, GK. Chakravarthi, "An Optimal Route Search Using Spatial Keyword Query using Keyword Nearest Neighbor Expansion," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.29-32, 2016

• Open Access   Article

  Monitoring and Analysis of Real time detection of traffic from twitter stream analysis

  A. Jalaparthi, AS. Kumar

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.34-37, Jun-2016

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  Citation

  Abstract
  Social networks have been, at a recent time utilized as a source of information for event detection, with particular referral to road traffic congestion and for the car accidents. In this paper, we present a real-time monitoring system for traffic event detection from the twitter stream analysis. The system fetches tweets from the twitter according to several search criteria; processes tweets, by applying the text mining techniques; and finally performs the classification of tweets. The aim is to assign the suitable class label to each tweet, as relevant to a traffic event or not. The traffic detection system was exploited for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, generally before online traffic news web sites. We employed the support vector machine as a classification model, and we attain an accuracy value of 95.75% by solving a binary classification problem (traffic versus non-traffic tweets). We would also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification issue and obtaining accuracy value of 88.89%.

  Key-Words / Index Term
  Twitter; Traffic event detection; tweet classification; text mining, social sensing

  References
  [1]. F. Atefeh, W. Khreich, “A survey of techniques for event detection in Twitter”, Computer Intelligence, Vol.31, No.1, pp. 132–164, 2015
  [2]. P. Ruchi, K. Kamalakar, “ET: Events from tweets,” in Proc. 22nd Int. Conf. World Wide Web Computer, Brazil, pp. 613–620, 2013.
  [3]. A. Mislove, M. Marcon, K.P. Gummadi, P. Druschel, B. Bhattacharjee, “Measurement and analysis of online social networks”, in Proc. 7th ACM SIGCOMM Conf. Internet Meas., San Diego (USA), pp. 29–42, 2007.
  [4]. J. Kothari, T.i Shah, B. Nagaria, A. Choubey, S.D.i Pabba, "Automated Real Time In-Store Retail Marketing Using Beacon", International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.110-113, 2016.
  [5]. T. Sakaki, M. Okazaki, Y. Matsuo, “Tweet analysis for real-time event detection and earthquake reporting system development”, IEEE Transaction Knowledage Data Engineering, Vol.25, No. 4, pp. 919–931, 2013.
  [6]. M. Krstajic, C. Rohrdantz, M. Hund, A. Weiler, “Getting there first: Real-time detection of real-world incidents on Twitter”, 2nd IEEE Work Interactive Vis. Text Anal—Task-Driven Anal. Soc. Media IEEE VisWeek, Seattle (USA),pp.128-134, 2012.
  [7]. J. Yin, A. Lampert, M. Cameron, B. Robinson, R. Power, “Using social media to enhance emergency situation awareness”, IEEE Intell. Syst., Vol.27, No. 6, pp. 52–59, 2012.
  [8]. T. Sakaki, Y. Matsuo, T. Yanagihara, N. P. Chandrasiri, K. Nawa, “Real-time event extraction for driving information from social sensors”, IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Bangkok, pp. 221-226, 2012.
  [9]. H. Ning, H. Liu, “Cyber-physical-social based security architecture for future internet of things”, Advances in Internet of Things, Vol.2, Issue.1, pp.1-12, 2012.
  [10]. A. Schulz, P. Ristoski, H. Paulheim, “I see a car crash: Real-time detection of small scale incidents in microblogs”, The Semantic Web: ESWC 2013 Satellite Events, Vol.7955, pp. 22–33, 2013.
  [11]. P. Agarwal, R. Vaithiyanathan, S. Sharma, G. Shro, “Catching the long-tail: Extracting local news events from Twitter”, in Proc. Sixth International AAAI Conference on Weblogs and Social Media, Ireland, pp. 379–382, 2012.
  

  Citation
  A. Jalaparthi, AS. Kumar, "Monitoring and Analysis of Real time detection of traffic from twitter stream analysis," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.34-37, 2016

• Open Access   Article

  Opinion Targets and Opinion Words Co-Extraction from Online Reviews Based on the Novel Approach with Partially Supervised Alignment Model

  D. Charliena, GK. Chakravarthi

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.3 , pp.38-41, Jun-2016

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  One of the most significant tasks of opinion mining is mining opinion targets and opinion words from the online reviews. The major key component is to detect opinion relations between words. This paper proposes a novel approach based on the partially-supervised alignment model which identifies opinion relations as an alignment process. There after a graph-based co-ranking algorithm is made used, in order to estimate the confidence of each candidate. Lastly, candidates having higher confidence are extracted as the opinion targets or the opinion words. When compared with the other methods, this model is making the task of opinion relations for long-span relations. When compared to the syntax-based methods, our word alignment model effectively reduces the negative effects of the parsing errors when dealing with the informal online texts. So, when compared to the traditional unsupervised alignment model, the proposed model obtains better precision because of the usage of partial supervision. When estimating the candidate confidence, we got to know that higher-degree vertices in our graph-based co-ranking algorithm to decrease the probability of generation of error.

  Key-Words / Index Term
  Opinion mining; opinion targets extraction; opinion words extraction

  References
  [1]. K. Liu, L. Xu, J. Zhao, “Co-extracting opinion targets and opinion words from online reviews based on the word alignment model”, IEEE Transactions on Knowledge and Data engineering, Vol.27, No,3, pp. 636-650, 2015.
  [2]. Z. Hai, K. Chang, J.J. Kim, C.C. Yang, “Identifying features in opinion mining via intrinsic and extrinsic domain relevance,” IEEE Transaction on Knowledge Data Engineering,Vol.26, No,3, pp.623–634, 2014.
  [3]. L. Zhang, B. Liu, S. H. Lim, and E. O’Brien-Strain, “Extracting and ranking product features in documents”, 3rd International Conference on Computational Linguistics, Beijing, pp.1462–1470, 2010.
  [4]. R. Asritha , B.R. Reddy, "Opinion Target Extraction Using Word Based Alignment Model", International Journal of Innovative Research in Computer and Communication Engineering, Vol.4, Issue.1, pp.97-101,2016
  [5]. B. Pang, L. Lee, “Opinion mining and sentiment analysis”, Foundations and Trends in Information Retrieval, Vol.2, Issue1, pp.1-35, 2008.
  [6]. Q. Mei, X. Ling, M. Wondra, H. Su, C. Zhai, “Topic sentiment mixture: modeling facets and opinions in weblogs”, 6th International conference on World Wide Web, Alberta, pp. 171–180, 2007.
  [7]. F. Li, C. Han, M. Huang, X. Zhu, Y. Xia, S. Zhang, H. Yu, “Structure-aware review mining and summarization”, In Proceedings of the 23rd international conference on computational linguistics, China, pp. 653–661, 2010.
  

  Citation
  D. Charliena, GK. Chakravarthi, "Opinion Targets and Opinion Words Co-Extraction from Online Reviews Based on the Novel Approach with Partially Supervised Alignment Model," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.38-41, 2016