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In this article we present a survey on different text summarization techniques in natural language processing. Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of text. There are many techniques of doing text summarization i.e. some are extractive as well as abstractive techniques. But we need that technique which will give meaningful summary without showing any redundancy or any type of ambiguity whether the summary will contain original text or not.

Text Summarization, WordNet, Abstractive, Extractive

[1] E.PadmaLahari, D.V.N.Siva Kumar,S. Shiva Prasad, “Automatic Text Summarization with Statistical and Linguistic Features using Successive Thresholds”, IEEE International Conference on Advanced Communication Control and Computing Technologies, ISBN No. 978-1-4799-3914-5/14 ©2014.
[2] Alok Ranjan Pal, Diganta Saha, “An Approach to Automatic Text Summarization using WordNet”, IEEE International Conference on Advanced Communication Control and Computing Technologies, 978-1-4799-2572-8/14©2014.
[3] Ms.Pallavi D.Patil, Prof.N.J.Kulkarni, “Text Summarization Using Fuzzy Logic”, International Journal of Innovative Research in Advanced Engineering (IJIRAE) Volume 1 Issue 3, May 2014.
[4] D.Y. Sakhare, Dr. Raj Kumar, “Syntactic and Sentence Feature Based Hybrid Approach for Text Summarization”, I.J. Information Technology and Computer Science, 03,Page no- 38-46 , 2014
[5] A.R.Kulkarni, S.S.Apte, “An Automatic Text Summarization using lexical cohesion and correlation of sentences”, International Journal of Research in Engineering and Technology, Volume: 03 Issue: 06 , Jun-2014.
[6] Atif Khan, Naomie Salim, “A Review On Abstractive Summarization Methods”, Journal of Theoretical and Applied Information Technology. Vol. 59, No.1,10th January 2014
[7] Dipti Y. Sakhare, Dr.Rajkumar, “Neural Network Based Approach To Study The Effect Of Feature Selection On Document Summarization”, ISSN: 0975-4024 Vol. 5, No. 3, Jun-Jul 2013.
[8] Anjali R. Deshpande, Lobo L. M. R. J., “Text Summarization using Clustering Technique”, International Journal of Engineering Trends and Technology (IJETT) - Vol.4 ,Issue8- August 2013.
[9] Mohsen Pourvali and Mohammad Saniee Abadeh, “Automated Text Summarization Base on Lexical Chain and graph Using of WordNet and Wikipedia Knowledge Base”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, January 2012.
[10] Manisha Prabhakar, Nidhi Chandra, “ Automatic Text Summarization Based On Pragmatic Analysis”, International Journal of Scientific and Research Publications, Volume 2, Issue 5, May 2012.
[11] Ms.Meghana.N.Ingole, Mrs.M.S.Bewoor, Mr.S.H.Patil, “Text Summarization using Expectation Maximization Clustering Algorithm”, International Journal of Engineering
Research and Applications (IJERA) Vol. 2, Issue 4, July-August 2012, pp.168-171.
[12] G.PadmaPriya, K.Duraiswamy, “An Approach for Concept-based Automatic Multi-Document Summarization using Machine Learning”, International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 ,Volume3, No3., July 2012.

Kirtipreet kaur and Deepinderjeet Kaur, "A Review on Automatic Text Summarization Techniques in NLP," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.62-64, 2015.

The Back propagation Algorithm is a multilayered, feed forward neural network and is one of the most popular and efficient techniques used. This can be used for dataset classification with suitable combination of training, learning and transfer functions. However, there are some problems associated with this Algorithm like Step-size Problem and Local Minima. In this paper we will discuss about the working of the algorithm and efficient ways to perform learning by overcoming the problems in it. We use three common classification problems to illustrate the ways of efficient learning. All the methods and algorithms were implemented using the features of Java.

Back Propagation Algorithm, Neural Network, Programming Neural Networks

[1] Breast Cancer Wisconsin (Original) Dataset - https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29
[2] Iris Data Set:
https://archive.ics.uci.edu/ml/datasets/Iris
[3] J. T. Lalis, B. D. Gerardo and Y. Byun (2014). “An Adaptive Stopping Criterion for Backpropagation Learning in Feedforward Neural Network”. International Journal of Multimedia and Ubiquitous Engineering Vol.9, No. 8, pp. 149-156
[4] JeffHeaton (2005). “Programming Neural Networks in Java”. Heaton Research
[5] Saurabh Karsoliya (2012). “Approximating Number of Hidden layer neurons in MultipleHidden Layer BPNN Architecture”. International Journal of Engineering Trends and Technology-Volume3 Issue6
[6] Tom M. Mitchell (1997). “Machine Learning”. McGraw Hill
[7] Wouter F. Schmidt, Martin A. Kraaijveld and Robert P.W. Duin (1992). “Feed Forward Neural Networks With Random Weights”- Proceedings. 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems
[8] Wine Data Set - https://archive.ics.uci.edu/ml/datasets/Wine

N.V. Saiteja Reddy and T. Srikanth, "Class Label Prediction using Back Propagation Algorithm: A comparative study with and without Thresholds (Bias)," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.65-70, 2015.

We present a generic construction of a public key encryption scheme that is resilient to key leakage from any hash proof system. The construction does not rely on additional computational assumptions, and the resulting scheme is as efficient as the underlying hash proof system. Existing constructions of hash proof systems imply that our construction can be based on a variety of theoretic assumptions. We achieve leakage-resilience under the respective static assumptions of the original systems in the standard model, while also preserving the efficiency of the original schemes.

Public Key, Hash Proof, Encryption, Aggregate Key

[1]. M. Chase and S.S.M. Chow, “Improving Privacy and Security in Multi-Authority Attribute-Based Encryption,” Proc. ACM Conf. Computer and Comm. Security, pp. 121-130. 2009.
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[3]. R. Canetti and S. Hohenberger, “Chosen-Ciphertext Secure Proxy Re-Encryption,” Proc. 14th ACM Conf. Computer and Comm. Security (CCS ’07), pp. 185-194, 2007.
[4]. C.-K. Chu and W.-G. Tzeng, “Identity-Based Proxy Re-encryption without Random Oracles,” Proc. Information Security Conf. (ISC ’07), vol. 4779, pp. 189-202, 2007.
[5]. D. Boneh, C. Gentry, B. Lynn, and H. Shacham, “Aggregate and Verifiably Encrypted Signatures from Bilinear Maps,” Proc. 22nd Int’l Conf. Theory and Applications of Cryptographic Techniques (EUROCRYPT ’03), pp. 416-432, 2003.
[6]. M.J. Atallah, M. Blanton, N. Fazio, and K.B. Frikken, “Dynamic and Efficient Key Management for Access Hierarchies,” ACM Trans. Information and System Security, vol. 12, no. 3, pp. 18:1-18:43, 2009.
[7]. J. Benaloh, M. Chase, E. Horvitz, and K. Lauter, “Patient Controlled Encryption: Ensuring Privacy of Electronic Medical Records,” Proc. ACM Workshop Cloud Computing Security (CCSW ’09), pp. 103-114, 2009.
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[13]. R.S. Sandhu, “Cryptographic Implementation of a Tree Hierarchy for Access Control,” Information Processing Letters, vol. 27, no. 2, pp. 95-98, 1988.
[14]. Y. Sun and K.J.R. Liu, “Scalable Hierarchical Access Control in Secure Group Communications,” Proc. IEEE INFOCOM ’04, 2004.
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[18]. D. Boneh and M.K. Franklin, “Identity-Based Encryption from the Weil Pairing,” Proc. Advances in Cryptology (CRYPTO ’01), vol. 2139, pp. 213-229, 2001.
[19]. Ratheesh , Jogesh A Visual Cryptographic Scheme For Owner Authentication Using Embedded Shares ,Indian Journal of Computer Science and Engineering (IJCSE) ,ISSN : 0976-5166 Vol. 5 No.5 Oct-Nov 2014, pgno:190-195
[20]. S.S.M. Chow, Y. Dodis, Y. Rouselakis, and B. Waters, “Practical Leakage-Resilient Identity-Based Encryption from Simple As-sumptions,” Proc. ACM Conf. Computer and Comm. Security, pp. 152-161, 2010.
[21]. F. Guo, Y. Mu, and Z. Chen, “Identity-Based Encryption: How to Decrypt Multiple Ciphertexts Using a Single Decryption Key,” Proc. Pairing-Based Cryptography

M.sarika, J.Sasikiran, L.Sunitha , D.KoteswaraRao, "Leakage-Resilient Cryptosystem with Efficient and Flexible Key Delegation in Scalable Cloud Storage," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.71-74, 2015.

Traffic accidents contributing major death problems due to increase in number of vehicles. Globally, more than 1 million people die each year from traffic crashes and about 25–50 million are injured or permanently wounded. In India, thousands of people are dying due to road accidents. It is a necessary task to reduce accidents by performing analysis and taking pertinent countermeasures.So, identification of accident black spots is an important aspect in accident studies. Accident analysis aim at identification of high rate accidents spots, and safety areas. For proper road accident analysis, use of GIS is important.

Accident black spots, Geographic Information System (GIS), Optimized Route, Risk factors

[1] T Kiran Kumar, R Srinivasa Rao,”Traffic Analysis and Road Accidents: A Case Study of Hyderabad using GIS”, vol., no.2, sept 2 014, pp.1813-1823.
[2] Liang LY, Hua LT, Ma’some DM 2005. Traffic accident application using Geographic Information System. J Eastern Asia Soc Trans Studies 2005; 6: 3574 – 3589.
[3] Sarin S.M., 1998, “Road Traffic Safety in Indian-Issues and Challenges Ahead”, Indian Highway.
[4] Saxena A., Ganesh Babu, R. K. Bajpai and S.M. Saurian, 2000, “G IS as an aid identify accidents patterns”, http://www.gisdevelopment.net/application/Utility/
Transport /GIS as an aid to identity accidents patterns.htm.
[5] M. Bhagyaiah, B. Shrinagesh, “Traffic Analysis and Road, accidents.A Case Study of Hyderabad using GIS”, 7th IGRSM International Remote Sensing & GIS Conference 2014. Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp.3574 – 3589, 2005.
[6] D.L. Harkey, “Evaluation of Track Crashes using GIS-Based Crash Referencing and Analysis System. TRB 78th Annual Meeting”. Washington, D.C, 1999.
[7] LIANG, L. U., MA’SOME, D. M., HUA, L. T., “Traffic Accident Application Using Geographic Information System”, Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp.3574 – 3589, 2005.
[8] Deepthi Jayan.K, B.Ganeshkumar,”Identification of Accident Hot Spots: A GIS Based Implementation for Kannur District, Kerala”,International Journal Of Geomatics And Geosciences, Volume 1, No 1, 2010.
[9] Reshma E.K, Sheikh Umar Sharif,”Priortization of Accident Black Spots using GIS”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 9, September 2012.
[10] Srinivasan, N. S., Iyer, V. S., Chand, M., and Srinath, K., “Scientific identification and improvement of accident prone locations on national highways in Kerala, Journal of the Indian Road Congress, Vol.48 (3), pp.1-10, 1987
[11] Pillai, B. B. and Joseph, K., “Causes and Consequences of Road Accidents in Kerala”, International Journal of Research in IT & Management, Vol. 1, pp.83-95, 2011.
[12] Nagarajan, M., and Cefil, M., “Identification of Black Spots & Accident Analysis on NH-45 Using Remote Sensing & GIS”, International Journal of Civil Engineering Science, Vol. 1, pp.1-7, 2012.
[13] K. Geurts, G. Wets,” Black Spot Analysis Methods: Literature Review”, Feb2003.
[14] X. M. Hu, T. Liu, T. H. Zhang, Y. Huang, H. W. Xie and T. Yu, “Highway Black Spot Recognition and Improve- ment,” Journal of Traffic and Transportation Engineering, Vol. 4, No. 1, 2004, pp. 106-109
[15] Flahaut, B.,Mouchart,M., San Martin, E. and I. Thomas,”The local spatial autocorrelation and the kernel method for identifying black zones. A comparative approach. Accident Analysis and Prevention”, 2002.
[16] Geurts, K., Wets G., Brijs T. and K. Vanhoof ,”Profiling high frequency accident locations using association rules. In Proceedings of Transportation Research Board (CD-ROM), Washington, USA, 11-16 January, 2003.
[17] Apparao. G, P. Mallikarjunareddy Dr. SSSV Gopala Raju,” Identification Of Accident Black Spots For National Highway Using GIS”, International Journal Of Scientific & Technology Research, ISSN 2277-8616, Volume 2, Issue 2, Feb2013.
[18] Gopala Raju SSSV., 2011, ―Vehicular Growth and Its Management in Visakhapatnam City – Case Study‖, Indian Journal of Science and Technology, Vol. 4, No. 8 , pp 903 – 906.
[19] Jitendra Kumar, Sripati Mishra and Neeraj Tiwari; Identifi-cation of Hotspots and Safe ones of Crime in Uttar Pradesh, India: Geo-spatial Analysis Approach; IJRSA, 2012.
[20] Liu Lei, The GIS-based Research on Criminal Cases Hotspots .
2011

[21] Xiao Qin, Steven Parker, Yi Liu, Andrew J. Graettinger and Susie Forde; Intelligent geocoding system to locate traffic crashes, Accident Analysis and Prevention, Elsevier, 2013
[22] M. Vijaya Kumar and Dr. C. Charasekar; Spatial Statistical Analysis of burglary Crime in Chennai City Promoters Apartments: A Case Study, IJETT, 2011.
[23] http://www.gisresources.com/types-interpolation- methods_3/; accessed on 29-04-2014.
[24] Shahebaz M. Ansari, Dr. K. V. Kale, Methods for Crime Analysis Using GIS, IJSER, Volume 5, Issue 12, December-2014.
[25] Uday R. R. Manepalli, Ghulam H. Bham,” Evaluation of hotspots identification using kernel density estimation and Getis-ord (Gi*) on I-630”, Submitted to the 3rd International Conference on Road Safety and Simulation,September 14-16, 2011, Indianapolis, USA.

Umesh M.Raut, Rajesh K.Dhumal, Ajay D.Nagne ,K.V.Kale, "GIS Contribution for Identification of Accident Black Spots: A Review," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.75-80, 2015.

In the modern era of the computing world, the data producing and using it is becoming large and instant at various places. For availing the data at different locations for processing we need to store it in the global platform. Cloud environment provides a best and easy way for this. Cloud computing is becoming as the essential thing for high-quality data services. However there are some potential problems with respect to data security. Here encryption techniques can be used for providing security, but with restricted efficiency. In this paper we propose a new encryption mechanism for providing data security in cloud environment. We propose a two round searchable encryption which supports multi keyword retrieval. Here we adapted a vector space model for improving search accuracy; the elgamal encryption technique allows users to involve in the ranking, while the essential key part of encryption will be done at the source itself. The proposed improves the data security and reduces data leakage.

Cloud server, Data security, Structure strength, Resemblance matching, Vector model

[1] M. Arrington, “Gmail Disaster: Reports of Mass Email Deletions,”http://www.techrunch.com/2006/12/28/gmail-disasterreports-of-mass-email-deletions/, Dec.2006.
[2] Amazon.com, “Amazon s3 Availability Event: July 20, 2008,” http://status.aws.amazon.com/s3-20080720.html, 2008.
[3] M. Arumbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, “A View of Cloud Computing” Comm. ACM, vol 53, no.4,pp.50-58, 2010.
[4] C. Leslie, “ NSA Has Massive Database of Americans, Phonecalls,”http://usatoday30.usatoday.com/news/washington/ 2006-05-10/, 2013.
[5] D. Song, D. Wanger, and A. Perrig, “Practical Techniques for Searches on Encrypted Data,” Proc. IEEE Symp. Security and Privacy, 2000.
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[9] A .Swaminathan, Y. Mao, G.M. Su, H. Gou, A.L. Varna, S. He, M. Wu, and D.W. Oard, “Confidentiality Preserving Rank-Ordered Search,” Proc. Workshop Storage Security and Survivability, 2007.
[10] N. Cao, C. Wang, M. Li, K. Ren, and W.Lou, “Privacy-Preserving MultiKeyword Ranked Search over Encrypted Cloud Data,” Proc. IEEE INFOCOM, 2011.
[11] H. Hu, J. Xu, C. Ren, and B. Choi, “ Processing Private Queries over Untrusted Data Cloud through Privacy Homomorphism,” Proc. IEEE 27th INt’l Conf. Data Eng. (ICDE), 2011.
[12] D. Dubin, “The Most Influential Paper Gerard Salton Never Wrote,” Library Trends, vol. 52, no. 4, pp. 748-764, 2004.
[13] S. Gries, “ Useful Statistics for Corpus Linguistics,” A Mosaic of Corpus Linguistics: Selected Approaches, Aquilino Sanchez Moises Almela, eds., pp. 269-291, Peter Lang, 2010.
[14] R. Curtmol, J.A. Garay, S. Kamara, and R. Ostrovsky, “Searchable Symmetric Encryption: Improved Definitions and Efficient Constructions,” Proc. ACM 13th Conf. Computer and Comm. Security (CCS), 2006.

T. Kavitha and P. Nageswara Rao , "Loss Less and Privacy Preserved Data Retrieval in Cloud Environment Using TRSE," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.81-84, 2015.

Wireless sensor networks comprise of numerous sensor nodes which are randomly distributed in an observing region. These nodes are not only supervised the surrounding but also gathered the data from it. Various routing protocols have been aimed for wireless sensor networks to effective and efficient utilization of energy since SNs are powered with limited and non-rechargeable batteries. In this paper, an enhanced version of K-LEACH routing protocol is proposed i.e. Multihop K-LEACH to minimize the power depletion for WSNs. The proposed technique utilizes the k-medoid clustering method to uniform clustering of nodes and multihop routing approach to reduce the power depletion, which leads to prolonging the lifespan of network. The performance of the proposed technique is compared with K-LEACH using NS2. The simulation shows the better results of multihop K-LEACH over the previous technique.

Wireless sensor networks, K-LEACH, Routing techniques, Multihop K-LEACH

[1] Parul Bakaraniya, Sheetal Mehta, “Features of WSN and Various Routing Techniques for WSN: A Survey”, International Journal of Research in Engineering and Technology, ISSN: 2319 - 1163, Volume 1(Issue-3), 348 -354, NOV 2012.
[2] Jamal N. Al-Karaki, The Hashmite University Ahmed E. Kamal, Lowa state University, “Routing techniques in WSN: A survey”, IEEE Wireless communication, 2004.
[3] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless Sensor Networks: A Survey”, Computer Network Elsevier Journal, Vol. 38, no. 4, pp. 393-422, 2002.
[4] K. Kishan Chandl, P Vijaya Bharati and B. Seetha Ramanjaneyulu, Member, IEEE, “Optimized Energy Efficient Routing Protocol for Life-Time Improvement in Wireless Sensor Networks”, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012) March 30, 31, 2012.
[5] W. Heinzelman, A. Chandrakasan and H.Balakarishnan, “Energy- Efficient Communication Protocols for Wireless Microsensor Networks,” Proceedings of the Hawaiian International Conference on Systems Science, January 2000.
[6] Lee, S. H., Yoo, J. and Chung, T. C., “Distance-based energy efficient clustering for wireless sensor networks,” Proc. of the 29th Annual IEEE International Conference on Local Computer Networks (LCN’04), 2004.
[7] S. Lindsey and C.S. Raghavendra, “PEGASIS: Power-Efficient Gathering in Sensor Information Systems,” 2002.
[8] P. Bakaraniya and S. Mehta, “K-LEACH: An improved LEACH Protocol for Lifetime Improvement in WSN,” International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 5- May 2013.
[9] S. Tyagi and Neeraj Kumar, “A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor network,” Journal of Network and Computer Applications-Elsevier, pp: 623-645, 2013.

Charanpreet Kaur and Amit Chhabra, "An Energy Efficient Multihop Routing Protocol for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.86-91, 2015.

Data size is growing exponentially, it is coming in more and more connected form. Graph Database Management System (GDBMS) provides efficient solution to data storage in current scenarios. Nowadays, many companies rely on cloud services where you pay as per your need basis and most of the cloud platforms supports non-relational database to avoid scalability issues. Graph Databases have applications in many domains such as social network, organization management, banking, insurance, fraud detection, etc. Therefore there is need to migrate data from relational to non-relational database. Also many companies shifting from traditional relational database to NoSQL databases to avoid scalability issues. In this paper a web based ETL approach has been suggested to convert a Relational Database to Graph Database. Experimental results have been presented to show feasibility of the proposed methodology. Also query execution comparison is done on source and target databases.

GDBMS, NoSQL, TRDB

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[2] E.F. Codd,“A Relational Model of Data for Large Shared Data Banks, Communications of the ACM.
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[5] C. Strauch, “NoSQL Databases”, ACM.
[6] https://en.wikipedia.org/wiki/NoSQL.
[7] http://highscalability.com/neo4j-graph-database-kicks-Buttox.
[8] Neotechnology. (http://www.neotechnology.com/ebay-walmart-adopt-neo4j-graph-transforming retail/)

[9] Rob McColl, David Ediger, “A Brief Study of Open Source Graph Databases”.
[10] A Brief Study of Open Source Graph Databases.
[11] Mohammed Shafeeq Ahmed, “Data Warehousing Applications: An Analytical Tool for Decision Support System”, International Journal of Computer Science and Informatics.
[12] Relational-databases-sandbox-handout.
[13] https://en.wikipedia.org/wiki/NoSQL.
[14] Database (http://neo4j.com/developer/graph-database).
[15] Arora and R.R. Aggarwal, “An Algorithm for Transformation of Data from MySQL to NoSQL (MongoDB)”, International Journal of Advanced Studies in Computer Science and Engineering (IJASCSE).
[16] Roberto De Virgilio, Antonio Maccioni Riccardo Torlone, “Converting Relational to Graph Databases”, 2013 ACM.

Sonali D. Chaure, M. U. Kulkarni and Pankaj M. Jadhav, "Web based ETL Approach to Transform Relational Database to Graph Database," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.92-97, 2015.

Digital cameras captures image by using a single image sensor overlaid with a Colour Filter Array (CFA), so image demosaicing is applied to render these images into a viewable format. Image demosaicing is the technique of digital image processing that reconstructs missing color pixels using incomplete color samples. Today Image Demosaicing becomes a major area of research. Bayer Pattern CFA which can be expressed as square matrices is most familiar among the various CFA patterns. The methods of recovering full-color images from a CFA-based detector are commonly referred as colour interpolation or colour demosaicing algorithms. In this paper, we have critically analyzed various demosaicing algorithms and also covered its basic methods and various common demosaicing artifacts.

CFA, Image Demosaicing, Bayer Pattern, Colour interpolation

[1] Ling Shao, Amin Ur Rehman, “Image demosaicing using content and colour-correlation analysis”, Science Direct, Signal Processing 103, 2014, pp 84-91.
[2] Xiangdong Chen, Liwen He, Gwanggil Jeon, Jechang Jeong, “Local adaptive directional color filter array interpolation based on inter-channel correlation”, Science Direct, Optics Communication 324, 2014, pp 269-276.
[3] Xingyu ZHANG, Ming-ting SUN, Lu FANG, Oscar C.AU, “Joint Denoising and demosaicking of noisy CFA images based on inter-color correlation”, IEEE International Conference on Acoustic , Speech and Signal Processing (ICASSP), 2014, pp 5784-5788.
[4] Mohanbaabu, Vinothkumar, Ponvasanth, “A modified low power color filter array interpolation”, IEEE International Conference on Communication and Signal Processing, April 3-5, 2014, India, pp 011-015.
[5] Ryo Kuroiwa, Ryo Matsuoka, Seisuke Kyochi, Keiichiro Shirai, Masahiro Okuda, “Lossless/ Near-lossless color image coding by inverse demosaicing”, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2014, pp 2011-2014.
[6] Guorui Feng, Zifeng Zuo, Haiyan Zhang, “Demosaicking authentication codes using adaptive step via color difference estimation”, Science Direct, 2014.
[7] Satohiro Tajima, Ryohei Funatsu, Yukihiro Nishida, “Chromatic interpolation based on anisotropy-scale-mixture Statistics”, Science Direct, Signal Processing 97, 2014, pp 262-268.
[8] Xiangdong Chen, Liwen He, Gwanggil Jeon, Jechang Jeong, “Color filter array interpolation by successive refinement over color channels using gradient inverse-weighted filtering”, Science Direct, Optics Communication 318, 2014, pp 189-198.
[9] Yu Zhang, Guangyi Wang, Jiangtao Xu, Zaifeng Shi, DeXing Dong, “The modified gradient edge detection method for the color filter array image of the CMOS image sensor”, Science Direct, Optics and Laser Technology 62, 2014, 73-81.
[10] Ibrahim Pekkucuksen, Yucel Altunbasak, “Multiscale gradients-based color filter array interpolation”, IEEE Transactions on Image Processing, Vol. 22, No. 1, January 2013, pp 1057-7149.
[11] Yung-Hsiang Chiu, Kuo-Liang Chung, Wei-Ning Yang, Chien-Hsiung Lin, Yong-Huai Huang, “Universal intra coding for arbitrary RGB color filter arrays in HEVC”, Science Direct, J.Vis.Commun. Image.R.24, 2014, pp 867-884.
[12] Ting-Chun Wang, Yi-Nung Liu, Shao-Yi Chien, “Color filter array demosaicking using self-validation framework”, IEEE International Conference on Multimedia and Expo, 2012, pp 604-609.
[13] Jing-Ming Guo, Yun-Fu Liu, Bo-Syun Lai, Peng-Hua Wang, Jiann-Der Lee, “Classified-filter-based post-compensation interpolation for color filter array demosaicing”, IEEE ICASSP 2012, pp 921-924.
[14] Wei-Jen Yan, Kuo-Liang Chung, Hong-Yuan Mark Liao, “Quality-efficient demosaicing for digital time delay and integration images using edge-sensing scheme in color difference domain”, Science Direct, J.Vis.Commun, Image.R.23, 2012, pp 729-741.
[15] Jung, Miyoun, Xavier Bresson, Tony F. Chan, Luminita A. Vese, "Nonlocal Mumford-Shah regularizers for color image restoration", Image Processing, IEEE Transactions Vol. 20, No. 6, 2011, pp 1583-1598.
[16] Popescu, Alin C., Hany Farid, "Exposing digital forgeries in color filter array interpolated images", Signal Processing, IEEE Transactions Vol. 53, No. 10, 2005, pp 3948-3959.

Gurpreet Kaur and Jaskaranjit Kaur, "A Comparative Study of Image Demosaicing," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.98-102, 2015.

An important phase in the care and improvement of a patient’s health is performed by a caretaker. Thus the proposed work provides the prototype of an algorithm named Emergency Caller comprising of both hardware and software components both at patient and nurse ends. Here is a system implementing a clinical scenario where this electronic system will reduce the possibility of nurse ignorance and improve safety of the patient. The work is an early attempt to introduce effective care in clinical scenarios. Nurses are the main caring faculty in hospitals. This care is very important for the patient who cannot walk or stand for long time and mostly for the critical condition patient who can’t wait for a second. Care takers at hospitals are unable to take care of each and every patient simultaneously. To address this problem, given approach is used for patients. The patient will be provided with a remote having keys on it corresponding to particular tasks. This remote designed using AVR micro-controller and RF Module will transmit the instructions wirelessly to the PC of the ward incharge. The PC is connected with RF Receiver. Received information is streamed into a computer using software developed in MATLAB and the instructions get announced to the ward in charge automatically. The foremost objective of this project is to develop a system which reduces the cost of health care and ease the burden on the nurses thus providing better supervision of the patients as their call cannot be ignored thereafter. The prototype finds many applications in hospitals, nursing homes, rehabilitation centers and other health departments.

RF Module;MATLAB GUI;Speech Processing using MATLAB;AVR micro-controller

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Arshdeep Kaur and Baljit Kaur Gill, "Emergency Caller- An On-bed Patient Health Care in Hospitals," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.103-109, 2015.

Speech is a natural mode of communication for people. So people are so comfortable with speech recognition systems. The overall performance of any speech recognition system is highly depends on the feature extraction technique and classifier. In this paper, we presented Isolated Word Recognition System for Hindi Language using MFCC as feature extraction and KNN as pattern classification technique. The system is trained for 10 different Hindi words. The experimental result of our system is that it gives 89% accuracy rate.

Pattern Recognition, Automatic Speech Recognition (ASR), DCT, FFT

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[4] Abhishek Thakur, Naveen Kumar, “Automatic Speech Recognition System for Hindi Utterance with Regional Indian Accents: A Review”, International Journal of Electronics & Communication Technology, Vol. 4, April – June 2013.
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[11] Ankit Kumar, Mohit Dua, Tripti Choudhary, “Continuous Hindi Speech Recognition Using Monophone based Acoustic Modeling”, International Journal of Computer Applications 2014.
[12] S B Harisha , S Amarappa , Dr. S V Sathyanarayana, “Automatic Speech Recognition - A Literature Survey on Indian languages and Ground Work for Isolated Kannada Digit Recognition using MFCC and ANN”, International Journal of Electronics and Computer Science Engineering.
[13] Borde, Prashant, Amarsinh Varpe, Ramesh Manza, and Pravin Yannawar. "Recognition of isolated words using Zernike and MFCC features for audio visual speech recognition." International Journal of Speech Technology.2015.
[14] Anand Vardhan Bhalla, Shailesh Khaparkar “Performance Improvement of Speaker Recognition System”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol 2, March 2012.
[15] Munish Bhatia1, Navpreet Singh2, Amitpal Singh, “Speaker Accent Recognition by MFCC Using KNearest Neighbour Algorithm: A Different Approach”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 1, January 2015
[16] Mel Frequency Cepstral Coefficient (MFCC) tutorial, Accessed 24 June 2015.
[17] Rajesh Kumar Aggarwal, “Improving Hindi Speech Recognition Using Filter Bank Optimization and Acoustic Model Refinement”PHD Thesis, 2012.
[18] M. Kalamani, Dr. S. Valarmathy, S. Anitha , “Automatic Speech Recognition using ELM and KNN Classifiers”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 4, April 2015.
[19] Tsang-Long Pao, Wen-Yuan Liao and Yu-Te Chen, “A Weighted Discrete KNN Method for Mandarin Speech and Emotion Recognition”, International Journal of Innovative Computing, Information and Control ICIC International Volume 6, February 2010.
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[21] Tsang-Long Pao, Wen-Yuan Liao and Yu-Te Chen, “A Weighted Discrete KNN Method for Mandarin Speech and Emotion Recognition”, www.intechopen.com.

Suman K. Saksamudre, R. R. Deshmukh , "Isolated Word Recognition System for Hindi Language," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.110-114, 2015.