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• Open Access   Article

  Initialization of weights in deep belief neural network based on standard deviation of feature values in training data vectors

  N. Rezazadeh

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.1-8, Aug-2017

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  Nowadays, the feature engineering approach has become very popular in deep neural networks. The purpose of this approach is to extract higher-level and more efficient features compared to those of learning data and to improve the learning of machines. One of the common ways in feature engineering is the use of deep belief networks. In addition, one of the problems in deep neural networks` training is the training process. The problems of the training process will be further enhanced in the event of an increase in the dimensions of the features and the complexity of the relationship between the initial features and the higher-level features. In the present paper, we attempt to set the initial weights based on the standard deviation of the feature vector values. Hence, a part of the training process is initially conducted and a better starting point can be provided for the weight training process. However, the impact of this method, to a large extent, depends on the relationship between the training data itself and the degree of independence of the training data`s feature values. Experiments conducted in this field have achieved acceptable results.

  Key-Words / Index Term
  Neural Network; Restricted Boltzman Machine; Deep Belief Network

  References
  . Liu, S. Zhou, Q. Chen, “Discriminative deep belief networks for visual data classification”, Pattern Recognition, vol.44, Issue.10, pp. 2287–2296, 2011.
  [2] N. Rezazadeh, “A modification of the initial weights in the restricted Boltzman machine to reduce training error of the deep belief neural network”,International Journal of Computer Science and Information Security, vol.15, Issue.7, pp.1-6, 2017.
  [3] R. Salakhutdinov, G. Hinton, “Deep Boltzmann Machines”, International Conference on Artificial Intelligence and Statistics (AISTATS 2009), Canada, pp.448-455, 2009.
  [4] H. Lee, C. Ekanadham, and A. Ng, “Sparse deep belief net model for visual area V2” Advances in neural information processing systems, vol.20, pp.873–880, 2008.
  [5] G. E. Hinton, R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks”, Science, vol.313, Issue.578, pp.504–507, 2006.
  [6] V. Nair and G. Hinton, “3D object recognition with deep belief nets”, Advances in Neural Information Processing Systems, vol.22, pp.1339–1347, 2009.
  [7] R. Salakhutdinov, G. E. Hinton, “Deep boltzmann machines,” in Proceedings of the international conference on artificial intelligence and statistics, vol.5, pp.448–455, 2009.
  [8] R. Salakhutdinov, A. Mnih, G. Hinton , “Restricted Boltzman Machine for Collaborative Filtering”, Proceedings of the 24th international conference on Machine learning(2007), pp.791-798, 2007.
  [9] N. Le Roux, Y. Bengio , “Representation Power of Restricted Boltzman Machines and Deep Beliefe Networks”, Vol.20, Issue.6, pp.1631-1649, 2008.
  [10] Y. Bengio, “Learning Deep Architectures for AI”,Foundations and Trends in Machine Learning” Vol. 2, Issue.1, pp.1–127, 2009
  [11] A. Fischer ,C. Igel, “An Introduction to Restricted Boltzmann Machines”, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applicationspp(CIARP 2012), pp.14-36, 2012.
  [12] H. Larochelle, Y. Bengio, “Classification using discriminative restricted Boltzmann machines,” in Proceedings of the 25th international conference on Machine learning, New York, USA, pp. 536–543, 2008.
  [13] G. Hinton, “A practical guide to training restricted boltzmann machines”, Machine Learning Group, University of Toronto, Technical report, 2010.
  [14] X. Wang, Vincent Ly, Ruiguo, Chandra Kambhamettu, “2D-3D face recognition via Restricted Boltzmann Machines”, International Conference on Computer Vision Theory and Applications (VISAPP),Lisbon, 2015.
  [15] S. Iyanaga, Y. Kawada, “Distribution of Typical Random Variables” , Encyclopedic Dictionary of Mathematics. Cambridge MA(MIT Press), pp.1483-1486, 1980.
  [16] M. Abramowitz, Stegun, I. A. (Eds.). “Probability Functions” Ch. 26 in Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, Vol.9, pp.925-964, 1972.
  [17] [17]-Car Evaluation Dataset http://archive.ics.uci.edu/ml/datasets/Car+Evaluation [Online Available].
  [18] Heart Disease Dataset http://archive.ics.uci.edu/ml/datasets/Heart+Disease [Online Available].
  [19] G. Hinton, S. Osindero, and Y. W. Teh. “A fast learning algorithm for deep belief nets. Neural Computation”, Vol.18, Issue.7,1527–1554, 2006.
  

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  N. Rezazadeh , "Initialization of weights in deep belief neural network based on standard deviation of feature values in training data vectors," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.1-8, 2017

• Open Access   Article

  Community Structure Detection in Social Networking Data Using Text Mining Approach

  S.Arora, P. Shukla, N. Karankar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.9-15, Aug-2017

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  In data mining techniques some of the problems are resolved using the visualization techniques. Among them some of techniques are derived from the graph theory and transparent data modelling. The data structures such as decision trees and semantic graph representation is one of the key implementation of the graph based solution development. Among these technique one of the mathematical model termed as the community detection is a part of data mining solution discovery technique. Data mining techniques are used for finding the application centric patterns recovery from the raw set of data. Additionally the community detection technique is a visual technique for performing the unsupervised learning. During community detection the data objects are keeping connected to represent the bounding among them. Therefore in order to perform categorization task in automatic manner this technique can be employed in different nature of data. In this presented work the social media text is used for community detection. Communities are the group of objects that are highly similar in their properties. Therefore an algorithm is proposed in this work, that first refines the text content, then the text features are computed form raw text. In next the data is evaluated to find the number of possible communities in the data and finally the data is grouped in the communities and their visualization is performed. The proposed algorithm not only used to find the community structure from the data that also provides the relationship among two different communities. The experimental results in terms of precision, recall, f-measures demonstrate the proposed model is efficient and accurate as compared to traditional clustering algorithms namely the k-means clustering.

  Key-Words / Index Term
  Clustering, Community Detection, Complex Network, Tweeter, Data Mining

  References
  [1] A.Biswas, B.Biswas, “Investigating community structure in perspective of ego network”, Expert Systems with Applications, 42 (2015) 6913–6934, 2015 ,Elsevier Ltd
  [2] Arnaboldi, Passarella, Conti, Pezzoni “Analysis of ego network structure in online social network”,pp. 31-40, 2012
  [3] Y.Wang, &L.Gao“An edge-based clustering algorithm to detect social circles in ego networks”, Journal of Computers, Vol 8,2013
  [4] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. “Measurement and analysis of online social networks”. In IMC ’07, pp 29–42, 2007.
  [5] G. Palla, I. Der´enyi, I. Farkas, and T. Vicsek. “Uncovering the overlapping community structure of complex networks in nature and society”. Nature, 435(7043):pp814–818, 2005.
  [6] J. Leskovec, L. Adamic, and B. Huberman. “The dynamics of viral marketing”. TWeb, ACM, 1(1), 2007.
  [7] S. L. Feld. “The focused organization of social ties”. Am. J. of Sociology, 86(5):1015–1035, 1981.
  [8] G. Flake, S. Lawrence, and C. Giles.“ Efficient identification of web communities”. In KDD ’00, pages 150–160, 2000.
  [9] M. Girvan and M. Newman. “Community structure in social and biological networks”. PNAS, 99(12):7821–7826, 2002.
  [10] M. S. Granovetter. “The strength of weak ties”. Am. J. of Sociology, 78:1360–1380, 1973.
  [11] R. Solanki, “Principle of Data Mining”, McGraw-Hill Publication, India, pp. 108-128, 1998.
  [12] Liu, H., &juan Ban, X. .“Clustering by growing incremental self-organizingneural network”. Expert Systems with Applications, 42:4965–4981,2015,ElsevierLtd
  [13] R.R.Khorasgani., J.Chen, &O.R.Zaïane, O. R. “Top leaders community detectionapproach” in information networks. In International conference on knowledge discovery and data mining,KDD’10: ACM,Citeseer, (pp. 9),2010
  

  Citation
  S.Arora, P. Shukla, N. Karankar, "Community Structure Detection in Social Networking Data Using Text Mining Approach," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.9-15, 2017

• Open Access   Article

  New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval

  Kanakam Siva Ram Prasad

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.16-21, Aug-2017

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  In this paper we propose an automatic approach to annotating and retrieving images based on a training set of images. We assume that regions in an image can be described using a small vocabulary of blobs. Blobs are generated from image features using clustering. Given a training set of images with annotations, the annotation process implemented in our system is based on CMRM. Using a set of annotated images the system learns the joint distribution of the blobs and concepts in this paper show those probabilistic models which allow predicting the probability of generating a word given the blobs in an image. This may be used to automatically annotate and retrieve images given a word as a query. We show that relevance models. Allow us to derive these probabilities in a natural way. Experiments show that the annotation performance of this cross-media relevance model is almost six times as than a model based on word-blob co-occurrence model and twice as good as a state of the art model derived from machine translation.

  Key-Words / Index Term
  Automatic Image Annotation, Content Based Image Retrieval, Semantic Gap, Annotation Based Image Retrieval, relevance model

  References
  [1] Models J. Jeon, V. Lavrenko and R. Manmatha, Automatic Image Annotation and Retrieval using CrossMediaRelevance, Center for Intelligent Information Retrieval Computer Science department University of Massachusetts Amherst, MA 01003.
  [2] RitendraDattaJia Li James Z. Wang, Content-Based Image Retrieval - Approaches and Trends of the New Age, The Pennsylvania State University, University Park, PA 16802, USA datta@cse.psu.edu, jiali@stat.psu.edu, jwang@ist.psu.edu
  [3] .K. Barnard and D. Forsyth , Learning the semantics of words and pictures, In International Conference on Computer Vision, Vol.2, pages 408-415, 2001.
  [4]. D. Blei, Michael, and M. I. Jordan, Modeling annotated data. To appear in the Proceedings of the 26th annual international ACM SIGIR conference.
  [5] K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei, and M. I. Jordan., Matching words and pictures.Journal of Machine Learning Research, 3:1107{1135, 2003}.
  [6] HimaliChaudhari et al, A Survey on Automatic Annotation and Annotation Based Image Retrieval. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 1368-1371.
  [7] R.Ramya. M.E, An Efficient Query Mining Framework Using Spatial Hidden Markov Models for Automatic Annotation of Images. International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 1 – May 2014 ISSN: 2231-2803
  [8] A. Agarwal, S.S. Bhadouria “An Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIR” Dept. of Computer Science and Engineeing, NITM, Gwalior, India.International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.56-62, April (2017)
  

  Citation
  Kanakam Siva Ram Prasad, "New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.16-21, 2017

• Open Access   Article

  Secure SMS Communication Using Quasigroup Algorithm

  Abhishek Shetty, Haroon Antapur, Mit Chauhan, Nita Patil

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.22-24, Aug-2017

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  This paper is on Secure SMS Communication Using Quasigroups Algorithm. We have developed an intermediate application between the bank and the user that would encrypt the data in the SMS sent by the user. Using this SMS banking service, the user can check balance or even transfer funds to someone else’s account provided both accounts are of the same banks. This will be easier to use as the account holder without a smart phone can also get fruitful results out of this. This will enable the account holder to avail banking facilities via SMS. The mobile operator is the service provider to our user. Also, the confidential information will be encrypted such that the mobile operator will not have access to the contents of the SMS.

  Key-Words / Index Term
  cipher, encryption, decryption, quasigroup matrix, SMS banking

  References
  [1] Smile Markovski and Aleksandra Kuzmanovska : Secure SMS Communication Based on Quasigroup Transformations , 2009.
  [2] V.A. Shcherbacov : Quasigroups in Cryptology, 2009.
  [3] Matthew Battey, Abhishek Parakh : An Efficient Quasigroup Block Cipher, Springer Science+Business Media New York 2012.
  [4] Borujeni, S. (2000). Speech encryption based on fast fourier transform permutation. In The 7th IEEE international conference on electronics, circuits and systems, 2000 (ICECS 2000) (Vol. 1, pp. 290–293).
  [5] Mosa, E., Messiha, N., & Zahran, O. (2009). Chaotic encryption of speech signals in transform domains. In International conference on computer engineering systems, 2009 (ICCES 2009) (pp. 300–305).
  [6] Ian, G. S., Blake, F., & Smart, N. P. (2005). Advances in elliptic curve cryptography. Cambridge University Press.
  [7] Hoffstein, J., Pipher, J., & Silverman, J. H. (1998). Ntru: A ring-based public key cryptosystem. In Lecture Notes in Computer Science (pp. 267–288). Springer: Berlin.
  

  Citation
  Abhishek Shetty, Haroon Antapur, Mit Chauhan, Nita Patil, "Secure SMS Communication Using Quasigroup Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.22-24, 2017

• Open Access   Article

  Semantics Based Document Clustering

  Apurva Dube, Pradnya Gotmare

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.26-31, Aug-2017

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  Document clustering is a technique used to organize large datasets of documents into meaningful groups. The associated documents are described by the relevant words which serve as cluster labels. The traditional approach for document clustering uses bag-of-words representation. This representation often ignores the semantic relations between the words. Therefore ontology-based document clustering is proposed. One of the ways to deal with reusability and remix of learning objects in context of e-learning is via the use of appropriate ontologies. The more appropriate use of ontology the better will be the annotation of learning material. To couple document clustering with ontology will help in producing better clusters which will not ignore the semantic relation between the words. The proposed system uses “an ontology-based document clustering” approach based on two-step clustering algorithm. Since it is two step clustering, it uses both partitioning as well as hierarchical clustering algorithms. Ontology is introduced through defining a weighting scheme. This weighing scheme integrates traditional scheme of co-occurrences of words paired with weights of relations between words in ontology. The algorithm used from partition clustering technique is K-means whereas from hierarchical clustering technique is hierarchical agglomerative algorithm. Thus we can say that the clustering approach that uses the semantics of the documents for term weighting produces better results than the approach without semantics.

  Key-Words / Index Term
  Document Clustering, Ontology-based Clustering, eLearning, Ontology Generation, Semantic Relation, eLearning Concept

  References
  [1] Sara Alaee and Fattaneh Taghiyareh, “A semantic ontology based document organizer to cluster E-Learning documents”, 2016 Second international conference on web research(ICWR), 2016 IEEE.
  [2] Nadana Ravishankar. T and Shriram. R, “Ontology based clustering algorithm for information retrieval”, 4th ICCNT, July 2013, IEEE.
  [3] Hongwei Yang, “A document clustering algorithm for web search engine retrieval system”,2010 International conference on e-education, e-business, e-management and e-learning,2010 IEEE.
  [4] XiQuan Yang, DiNa Guo, XueYa Cao and JianYuan Zhou, “Research on Ontology-based Text Clustering”, 2008 Third International Workshop on Semantic Media Adaptation and Personalization, 2008 IEEE.
  [5] Enrico G. Caldarola and Antonio M. Rinaldi, “An Approach to Ontology Integration for Ontology Reuse”, IEEE 17th International Conference on Information Reuse and Integration, 2016.
  [6] Apra Mishra and Santosh Vishwakarma, “Analysis of TF-IDF Model and its Variant for Document Retrieval”, International Conference on Computational Intelligence and Communication Networks, 2015 IEEE.
  [7] Sanket S.Pawar,Abhijeet Manepatil,Aniket Kadam and Prajakta Jagtap, “Keyword Search in Information Retrieval and Relational Database System: Two Class View, International Conference on Electrical”, Electronics, and Optimization Techniques (ICEEOT) , 2016 IEEE.
  [8] Dorian Kokoshi and Betim Çiço, “Integration of Semantic WEB in an eLearning Environment”, Fourth Balkan Conference in Informatics, 2009 IEEE.
  [9] Jaskaranjit Kaur and Harpreet Singh “Performance Evaluation of a Novel Hybrid Clustering Algorithm using Birch and K-Means”, IEEE, 2015.
  [10] Li Jun Tao, Liu Yin Hong and Hao Yan “The Improvement and Application of a K-Means Clustering Algorithm”, International Conference on Cloud Computing and Big Data Analysis, IEEE, 2016.
  [11] 11.Yusuke TAMURA and Sadaaki MIYAMOTO, “A Method of Two Stage Clustering Using Agglomerative Hierarchical Algorithms with One-Pass 𝑘-Means++ or 𝑘-Median++”, IEEE International Conference on Granular Computing (GrC), 2014 IEEE.
  [12] I. Bedini and B. Nguyen, "Automatic Ontology Generation: State of the Art," PRiSM Laboratory Technical Report, University of Versailles, Versailles, 2007.
  [13] Elizabeth D. Liddy, “Document Retrieval Automatic”, Syracuse university, 2005.
  

  Citation
  Apurva Dube, Pradnya Gotmare, "Semantics Based Document Clustering," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.26-31, 2017

• Open Access   Article

  Resistance of Watermarked Image against Solidity Attacks

  S. Mudassar, M. Jamal, F. S. Mehmood, M. R. Rehman

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.31-35, Aug-2017

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  Many techniques are implemented to secure digital contents with the passage of time. Watermarking is the best technique to provide security for multimedia contents. It can also use for the stoutness and authentication of digital images against planned and unplanned attacks during the inserting and extracting procedure of watermarking into host/cover digital image. Singular Value Decomposition (SVD) and Discrete Wavelet Transformation (DWT) is non-blind watermarking procedure because this technique efficiently insert information into the original contents without loss the quality. My fine objective is to use hybrid DWT-SVD based colour image watermarking. According to this technique non authorized user cannot visit the hidden information. Using DWT-SVD method the colour channels of host and watermark images were divided into R, G and B sub-channels. For inserting watermark image, the R channel of both cover image and watermark image were integrated by secret alpha key. Image can also be extracted from the watermarked image by applying reverse procedure. Different parameters are implemented for the quality of watermarked and extracted watermark image in experimental result.

  Key-Words / Index Term
  PSNR, SSIM, DWT, SVD, NC

  References
  [1] A.Khalid , Al-Afandy1, EL-Sayed M. EL-Rabaie2, Fathi E. Abd El-Samie2, Osama S. Faragallah1, Ahmed ELmhalaway1, A. M. Shehata1 "Robust Color Image Watermarking Technique Using DWT and Homomorphic based SVD" Year 2016
  [2] Md. Atiqur Rahman & M. M. Fazle Rabbi Non-Blind DWT-SVD based Watermarking Technique for RGB Image Volume 15 Issue 4 Version 1.0 Year 2015.
  [3] M.Barni., E Bartolini, V. Cappellini, and A. Piva, “A DCT- domain system for robust image watermarking,” Signal Processing Vol. 66, No. 3, pp. 357-372, May 1998.
  [4] Suhad Hajjara, Moussa Abdallah, Amjad Hudaib, “Digital Image Watermarking Using Localized Biorthogonal Wavelets” European Journal of Scientific Research, ISSN 1450-216X Vol.26 No.4, pp.594-608, 2009.
  [5] Ahmed Salama, Randa Atta, Rawya Rizk and Fayez Wanes 2011, A Robust Digital Image Watermarking Technique Based on Wavelet Transform. ICSET 2011
  [6] Based on Hybrid Fractal-Wavelet 2011 International Conference on Computer Communication and Management Proc. of CSIT vol.5 (2011) IACSIT Press, Singapore
  [7] Sanaz Shahraeini and Mahdi Yaghoobi 2011. A Robust Digital Image Watermarking Approach against JPEG Compression Attack.
  [8] S.Poonkuntran, R.S.Rajesh 2011. A Messy Watermarking for Medical Image Authentication. 2011 IEEE.
  [9] J.Mishra , M. V. Patil, and J. S. Chitode, “An effec- tive audio watermarking using DWT-SVD,” Inter- national Journal of Computer Applications, vol. 70, no. 8, pp. 6–11, 2013.
  [10] A.Sleit., S. Abusharkh, R. Etoom, and Y. Khero, “An enhanced semi-blind DWT - SVD based water marking technique for digital images,” The Imaging Science Journal, vol. 60, no. 1, pp. 29–38, 2012.
  [11] B.Surekha and G. N. Swamy, “Sensitive digital im- age watermarking for copyright protection,” Inter- national Journal of Network Security, vol. 15, no. 2, pp. 113–121, 2013.
  [12] K.Navas, M. C. Ajay, M. Lekshmi, T. S. Archana, and M. Sasikumar, “DWT-DCT-SVD based wa- termarking,” in Third International Conference on Communication Systems Software and Middleware and Workshops, pp. 271–274, 2008.
  [13] B.Surekha and G. N. Swamy, “Sensitive digital im- age watermarking for copyright protection,” Inter- national Journal of Network Security, vol. 15, no. 2, pp. 113–121, 2013.
  [14] A.Rajani ,Dr.T.Ramashri,”IMAGE WATERMARKING ALGORITHM USING DCT, SVD AND EDGE DETECTION TECHNIQUE”, Interna-tional Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 Vol. 1, Issue 4, pp.1828-1834
  [15] Prabhishek Singh, R S Chadha “A Survey of Digital Watermarking Techniques, applications and Attacks” International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 9.
  

  Citation
  S. Mudassar, M. Jamal, F. S. Mehmood, M. R. Rehman, "Resistance of Watermarked Image against Solidity Attacks," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.31-35, 2017

• Open Access   Article

  LabVIEW based detection of Pulse Transit Time from Plethysmogram and ECG signals for estimation of Blood Pressure

  Alister Dsouza, M.S. Panse

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.36-40, Aug-2017

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  Abstract
  Blood Pressure is the pressure exerted by blood on the walls of arteries. Normal blood pressure is considered to be a systolic blood pressure of 120 millimetres of mercury and diastolic pressure of 80 millimetres of mercury (stated as "120 over 80"). If an individual were to have a consistent blood pressure reading of 140 over 90, he would be evaluated for having high blood pressure. If left untreated, high blood pressure can damage important organs, such as the brain and kidneys, as well as lead to a stroke. Thus it becomes important to measure blood pressure as it can lead to early diagnosis of diseases that may be linked to high or low blood pressure. PTT is the time taken by the arterial pulse propagating from the heart to a peripheral site. This can be calculated from ECG signals and PlethysmoGram signals. Since, PTT has been found to be correlated to Blood Pressure, it is imperative to calculate PTT accurately. In this paper, algorithms used to calculate the points of interest in the ECG signal and the signal along with the calculation of PTT from them is developed. The coding has been done in LabVIEW which is has a graphical programming syntax that makes it simple to visualize, create, and code engineering systems.

  Key-Words / Index Term
  Blood Pressure, ECG,PTT,PPG, LabVIEW

  References
  [1] Ding, X., Zhang, Y., Liu, J., Dai, W. and Tsang, H. (2016). Continuous Cuffless Blood Pressure Estimation Using Pulse Transit Time and Photoplethysmogram Intensity Ratio. IEEE Transactions on Biomedical Engineering, 63(5), pp.964-972.
  [2] Gesche, H., Grosskurth, D., Küchler, G. and Patzak, A. (2011). Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method. European Journal of Applied Physiology, 112(1), pp.309-315.
  [3] Kounalakis, S. and Geladas, N. (2009). The Role of Pulse Transit Time as an Index of Arterial Stiffness During Exercise. Cardiovascular Engineering, 9(3), pp.92-97.
  [4] HALLOCK, P. (1934). ARTERIAL ELASTICITY IN MAN IN RELATION TO AGE AS EVALUATED BY THE PULSE WAVE VELOCITY METHOD. Archives of Internal Medicine, 54(5), p.770.
  [5] Smith, R., Argod, J., Pepin, J. and Levy, P. (1999). Pulse transit time: an appraisal of potential clinical applications. Thorax, 54(5), pp.452-457.
  [6] OCHIAI R, TAKEDA J, HOSAKA H, SUGO Y, TANAKA R, SOMA T: The relationship between modified pulse wave transit time and cardiovascular changes in isoflurane anesthetized dogs. J ClinMonitComput 15: 493-501, 1999.
  [7] AHLSTROM C, JOHANSSON A, UHLIN F, LÄNNE T, ASK P: Noninvasive investigation of blood pressure changes using the pulse wave transit time: a novel approach in the monitoring of hemodialysis patients. J Artif Organs 8: 192-197, 2005.
  [8] S. Alty, N. Angarita-Jaimes, S. Millasseau and P. Chowienczyk, "Predicting Arterial Stiffness From the Digital Volume Pulse Waveform", IEEE Transactions on Biomedical Engineering, vol. 54, no. 12, pp. 2268-2275, 2007.
  [9] Fechir M, Schlereth T, Purat T, Kritzmann S, Geber C, Eberle T, Gamer M, Birklein F: patterns of sympathetic responses induced by different stress tasks. openneurol j 2: 25-31, 2008.
  [10] Schmalgemeier H, Bitter T, Bartsch S, Bullert K, Fischbach T, Eckert S, Horstkotte D, Oldenburg O: pulse transit time: validation of blood pressure measurement under positive airway pressure ventilation. sleep breath 16: 1105-1112, 2012.
  [11] Contal O, Carnevale C, Borel J-C, Sabil A, Tamisier R, Lévy P, Janssens J-P, Pépin J-L: pulse transit time as a measure of respiratory effort under non-invasive ventilation. eurrespir j 41: 346-353, 2013.
  [12] KortekaasMc, NiehofSp, Van VelzenMhn, Galvin Em, HuygenFjpm, StolkerRj: pulse transit time as a quick predictor of a successful axillary brachial plexus block. ActaAnaesthesiolScand 56: 1228-1233, 2012.
  [13] Ma, H. (2014). A Blood Pressure Monitoring Method for Stroke Management. BioMed Research International, 2014, pp.1-7.
  [14] Zhang, G., Liu, C., Ji, L., Yang, J. and Liu, C. (2016). Effect of a Percutaneous Coronary Intervention Procedure on Heart Rate Variability and Pulse Transit Time Variability: A Comparison Study Based on Fuzzy Measure Entropy. Entropy, 18(7), p.246.
  [15] Gómez García, M., Troncoso Acevedo, M., Rodriguez Guzmán, M., Alegre de Montaner, R., FernándezFernández, B., del Río Camacho, G. and González-Mangado, N. (2014). Can Pulse Transit Time Be Useful for Detecting Hypertension in Patients in a Sleep Unit?.Archivos de Bronconeumología (English Edition), 50(7), pp.278-284.
  [16] Ding, X., Zhang, Y. and Tsang, H. (2016). Impact of heart disease and calibration interval on accuracy of pulse transit time–based blood pressure estimation. Physiological Measurement, 37(2), pp.227-237.
  [17] T. Wibmer, et al., "Pulse transit time and blood pressure during cardiopulmonary exercise tests," Physiol. Res., vol. 63, pp. 287-296, 2014.
  

  Citation
  Alister Dsouza, M.S. Panse, "LabVIEW based detection of Pulse Transit Time from Plethysmogram and ECG signals for estimation of Blood Pressure," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.36-40, 2017

• Open Access   Article

  Voltage regulation and reactive power compensation by SSSC based on 48-pluse GTO (VSC)

  Ajinkya Pachghare, R.M.Sahare

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.41-44, Aug-2017

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  Abstract
  In this paper, the performance of power systems decreases with the size, the loading and the complexity of the system networks. This is related to problems with power oscillations, large power flow and voltage instability with inadequate control, so the power flow control plays a major role in power systems. In this paper SSSC series FACTS device are regulates system voltage by absorbing or generating reactive power and the control of active, reactive power flow and voltage well as damping power system oscillations in long transmission line. In this paper Simulation studies were carried out in the MATLAB simulation environment to observe the compensation achieved by the SSSC device. The system parameters such as voltage, current, active power and reactive power transmissions are observed when the SSSC is connected to the power system.

  Key-Words / Index Term
  Gate Turn-Off (GTO), Static Shunt Compensator (STATCOM), voltage source convertor (VSC)

  References
  [1] M. S. El-Moursi and A. M. Sharaf, “Novel Controllers for the 48-Pulse VSC STATCOM and SSSC for Voltage Regulation and Reactive Power Compensation” IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 20, NO. 4, NOVEMBER 2005
  [2] C. Schauder and H. Mehta ―Vector analysis and control
  of advanced static var compensator‖ in Proc. Inst. Elect. Eng. Int. Conf. AC DC Transmission 1991 pp. 299–306. Paper no. 345.
  [3] D.Soto and C. Green ―A comparison of high-power converter topologies for the implementation of facts controllers‖ IEEE Trans. Indust. Electron. vol. 49 no. 5 pp. 1072–1080 Oct. 2002.
  [4] K. K. Sen ―SSSC—static synchronous series compensator: Theory modeling and application‖ IEEE Trans. Power Del. vol. 13 no. 1 pp. 481–486 Jan. 1998.
  [5] R. M. Mathur and R. K. Varma Thyristor-Based FACTS Controllers for Electrical Transmission Systems. Piscataway NJ: IEEE Press 2002.
  [6] Sahoo A. K. Murugesan K. Thygarajan T. ―Modelling and Simulation of 48-pulse VSC based STATCOM using Simulink’s Power System Blockset‖ Proceedings of India International Conference on Power Electronicspp. 303-308 December 2006.
  [7] Huang S. P. Li Y. J. Jin G.B. Li L. ―Modelling and Dynamic Response Simulation of GTO based STATCOM‖ Proceedings of the International Conference on Electrical and Control Engineeringpp. 1293-1296 June2010
  [8] X.-P. Zhang, ―Advanced modeling of the multicontrol functional static synchronous series compensator (SSSC) in Newton power flow,‖ IEEE Trans. Power Syst., vol. 18, no. 4, pp. 1410–1416, Nov. 2003.
  [9] A. H. Norouzi and A. M. Sharaf, “An auxiliary regulator for the SSSCtransient enhancement,” in Proc. IEEE 35th North Amer. Power Symp.,Rolla, MO, Oct. 2003.
  [10] K. K. Sen, “SSSC-static synchronous series compensator: Theory, modeling, and applications,” IEEE Trans. Power Del., vol. 13, no. 1, pp. 241–246, Jan. 1998.
  [11] X.-P. Zhang, “Advanced modeling of the multicontrol functional static synchronous series compensator (SSSC) in Newton power flow,” IEEETrans. Power Syst., vol. 18, no. 4, pp. 1410–1416, Nov. 2003
  

  Citation
  Ajinkya Pachghare, R.M.Sahare, "Voltage regulation and reactive power compensation by SSSC based on 48-pluse GTO (VSC)," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.41-44, 2017

• Open Access   Article

  Secure Data Retrieval using Cipher Text Policy-Attribute Based Encryption in Hybrid Networks

  G.L. Pavani, Ch.Ramesh

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.45-48, Aug-2017

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  Abstract
  Encrypted information of broadcast is the scheme that a sender encrypts messages for a designated group of receivers, and sends the cipher texts by broadcast over the networks. Many research papers have done it using elliptic curve cryptography. In this paper, we propose the broadcast encryption scheme based on braid groups cryptography which is an alternative method in the public key cryptography and can reduce the computational cost. Here new ancient, a gathering of individuals arrange a typical open encryption key while every part holds an unscrambling key. A sender seeing people in general gathering encryption key can confine the unscrambling to a subset of individuals from his decision. We present a new BE scheme that is aggregately. The aggregatability property is shown to be useful to construct advanced protocols.

  Key-Words / Index Term
  Broadcast encryption, Braid groups, Asymmetric group key Agreement, Contributory Broadcast Encryption, and Provable Security

  References
  [1] Qianhong Wu, Member, IEEE, Bo Qin, Lei Zhang, Member, IEEE, Josep Domingo-Ferrer, Fellow, IEEE Oriol Farr`as, and Jes´us A. Manj´on, “Contributory Broadcast Encryption with Efficient Encryption and Short Ciphertexts”, IEEE Transactions On Computers, Vol. Xxx, No. Xxx, Xxx 2015.
  [2] A. Fiat and M. Naor, “Broadcast Encryption,” in Proc. Crypto 1993, 1993, vol. LNCS 773, Lecture Notes in Computer Science, pp. 480-491.
  [3] I. Ingemarsson, D.T. Tang and C.K. Wong, “A Conference Key Distribution System,” IEEE Transactions on Information Theory, vol. 28, no. 5, pp. 714-720, 1982.
  [4] Q. Wu, Y. Mu, W. Susilo, B. Qin and J. Domingo-Ferrer, “Asymmetric Group Key Agreement,” in Proc. Eurocrypt 2009, 2009, vol. LNCS 5479, Lecture Notes in Computer Science, pp. 153-170.
  [5] http://en.wikipedia.org/wiki/PRISM surveillance program, 2014.
  [6] Q. Wu, B. Qin, L. Zhang, J. Domingo-Ferrer and O. Farr`as, “Bridging Broadcast Encryption and Group Key Agreement,” in Proc. Asiacrypt 2011, 2011, vol. LNCS 7073, Lecture Notes in Computer Science, pp. 143-160.
  [7] D. H. Phan, D. Pointcheval and M. Strefler, “Decentralized Dynamic Broadcast Encryption,” in Proc. SCN 2012, 2011, vol. LNCS 7485, Lecture Notes in Computer Science, pp. 166-183.
  [8] M. Steiner, G. Tsudik and M. Waidner, “Key Agreement in Dynamic Peer Groups,” IEEE Transactions on Parallel and Distributed Systems, vol. 11, no. 8, pp. 769-780, 2000.
  [9] A. Sherman and D. McGrew, “Key Establishment in Large Dynamic Groups Using One-way Function Trees,” IEEE Transactions on Software Engineering, vol. 29, no. 5, pp. 444-458, 2003.
  [10] Y. Kim, A. Perrig and G. Tsudik, “Tree-Based Group Key Agreement,” ACM Transactions on Information System Security, vol. 7, no. 1, pp. 60-96, 2004.
  [11] Y. Mao, Y. Sun, M. Wu and K.J.R. Liu, “JET: Dynamic Join-Exit-Tree Amortization and Scheduling for Contributory Key Management,” IEEE/ACM Transactions on Networking, vol. 14, no. 5, pp. 1128-1140, 2006.
  [12] W.-G. Tzeng, “A Secure Fault-Tolerant Conference-Key Agreement Protocol,” IEEE
  Transactions on Computers, vol. 51, no.4, pp. 373- 379, 2002.
  [13] X. Yi, “Identity-Based Fault-Tolerant Conference Key Agreement,” IEEE Transactions Dependable Secure Computing vol. 1, no. 3, 170- 178, 2004.
  [14] M. Burmester and Y. Desmedt, “A Secure and Efficient Conference Key Distribution System,” in Proc. Eurocrypt 1994, 1994, vol. LNCS 950, Lecture Notes in Computer Science, pp. 275-286.
  [15] A. Joux, “A One Round Protocol for Tripartite Diffie-Hellman,” Journal of Cryptology, vol. 17, no. 4, pp. 263-276, 2004.
  [16] D. Boneh and A. Silverberg, “Applications of Multilinear Forms to Crytography,” Contemporary Mathematics, vol. 324, pp.71-90, 2003.
  

  Citation
  G.L. Pavani, Ch.Ramesh, "Secure Data Retrieval using Cipher Text Policy-Attribute Based Encryption in Hybrid Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.45-48, 2017

• Open Access   Article

  Earth Observation Satellites Series and its Potentialities

  S. Verma, S. K. Rathi, V. S. Rathore

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.4 , pp.49-55, Aug-2017

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  Abstract
  Science is one of the tools to explore earth Information. Space science leading its efforts in three main areas: (i) Development of Remote Sensing Satellites, (ii) Development of Communication and Metrological Satellites, (iii) Development of Different types of Launch Vehicles to put the satellites in proper orbits. Remote Sensing means acquiring information about a phenomenon object or surface while at a distance from it. Satellite is a secondary object that revolves in a closed orbit around a planet or the sun for scientific research. Remote Sensing Satellites has proved way for human to understand of weather forecasting, to observe the phenomena on the earth’s surface, under oceans and underneath the surface of the earth. This paper describes a review of Earth Observation Satellites which has launched by ISRO after 2013 and their scope in the field of Remote Sensing and future missions to be undertaken.

  Key-Words / Index Term
  Remote Sensor, Satellite, Satellite Orbit, Payload

  References
  [1] Rajat kumar, Ancy.S, “A Series of Earth Resource Satellites and its Potentialities”, International Journal of Advancements in Research & Technology, Vol. 3, Issue 4, April-2014, ISSN 2278-7763.
  [2] R. R. Navalgund, V. Jayaraman, A. S. Kiran Kumar, Tara Sharma, Kurien Mathews and K K Mohanty, “Remote Sensing Data Acuisition, Platforms and Sensor Requirements”, journal of the Indian Society of Remote Sensing, Vol 24, No. 4, 1996.
  [3] Ramapriyan, Hampapuram, Behnke, Jeanne, Sofinowski, Edwin, Lowe, “Evolution of the Earth Observing System(EOS) data and information system”, Lecture Notes in Geoinformation and Cartography, 63 – 92.
  
  B) Annual Report
  1) Annual Report 2016 – 2017 of ISRO, by Government of India department of Space
  2) Annual Remote Sensing, Vol. I and II. (ed. Colwell) American Society of Photogrammetry, Falls Church, Va. 1983
  3) Indian Remote Sensing Satellites – e-Learning 112.133.214.6/gis/gis2-3.aspx
  4) Current Science, special section IRS – 1C, 70(7), 2007
  5) Current Science, special issue, Vols 3 & 4, 1996
  
  C) Websites
  [C1] https://www.nrsc.gov.in/Earth_Observation_ Missions
  [C2] http://www.isro.gov.in
  [C3] http://www.isro.gov.in/Spacecraft/saral
  [C4] http://www.isro.gov.in/Spacecraft/cartosat-2-series-satellite-1
  [C5] http://space.skyrocket.de/doc_sdat/resourcesat-2.htm
  [C6] http://www.isac.gov.in/earth-observation/html/cartosat-2s3.jsp
  [C7] www.geol-amu.org/notes/mw4-2-6.htm
  [C8] http://satellite.imd.gov.in/dynamic/insat_3DR.htm
  [C9] http://space.skyrocket.de/doc_sdat/risat-1.htm
  [C10] http://space.skyrocket.de/doc_sdat/gisat-1.htm
  

  Citation
  S. Verma, S. K. Rathi, V. S. Rathore , "Earth Observation Satellites Series and its Potentialities," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.49-55, 2017