Volume-5 , Issue-3 , Jun 2017, ISSN 2321-3256 (Electronic Version) Go Back

• Open Access   Article

  Accelerate Image Reconstruction Using GPU

  S.M. Walunj, S.V. Gaikar, A.D. Potgantwar

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.68-75, Jun-2017

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  Abstract
  The Region-of-Interest approaches of image processing attempt to assign more bits to ROI’s and fewer bits to other regions. The perceptual quality of image degrades as the background image gets distorted. To address the issue, the system generate as hierarchical framework of 3 layers i.e. image search, patch matching and image synthesis(image reconstruction). Compared with exsisting techniques i.e. ROI, the proposed system achieve better results in terms of Time, Recall, Precision and Fmeasure(Accuracy). The proposed system enhances the speed of reconstruction of reconstruction of image by 5-6% and also increases the accuracy by 8% to 10% than existing approach.

  Key-Words / Index Term
  

  References
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  Citation
  S.M. Walunj, S.V. Gaikar, A.D. Potgantwar, "Accelerate Image Reconstruction Using GPU," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.68-75, 2017

• Open Access   Article

  Product Recommendation using Multiple Filtering Mechanisms on Apache Spark

  S.N. Patil, S.M. Deshpande, Amol D. Potgantwar

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.76-83, Jun-2017

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  Abstract
  Recommendation system is used by enormous users. Recommendations based on rating prediction provides useful recommendations for products, ratings are predicted using various methods. It is used commonly in recent years, and is used in variable areas in many popular applications which comprise of movies, songs, bulletin, files, research courses, online shopping, social networking sites, and product recommendation. Gradually, the amount of users, objects and facts has matured rapidly, the big data scrutiny problem for examination of recommender systems. Conventional recommender systems frequently suffer from scalability, efficiency and real time recommendation problems while processing or analyzing documents taking place at huge scale. To get rid from these problems, recommendation algorithms such as SVD, Trust SVD, and Incremental SVD are implemented on Apache Hadoop and Spark for evaluating most efficient recommendation. Analysis proves which prediction algorithm works more efficient than other algorithms. Proposed framework will provide real time and multiple recommendations to multiple users simultaneously.

  Key-Words / Index Term
  Bigdata, Collaborative, Hadoop, Incremental SVD, MapReduce, Product, Recommendations, Spark, SVD, Trust SVD

  References
  [1] Guibing Guo, Jie Zhang, and Neil Yorke-Smith, “A Novel Recommendation Model Regularized with User Trust and Item Ratings”, IEEE transactions on knowledge and data engineering, Vol.28, Issue.7, pp.1607-20, 2016.
  [2] Gejianxin and Liu jiaomin, “Software Test Cases Recommendation System Research based on Collaborative Filtering”, IEEESNPD, China, pp. 557-562, 2016.
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  [15] Suyun Wei, Ning Ye, Shuo Zhang, Xia Huang, Jian Zhu, “Item-based Collaborative Filtering Recommendation Algorithm Combining Item Category with Interestingness Measure”, International Conference on Computer Science and Service System, China, pp. 2038-2041, 2012.
  [16] Michael D. Ekstrand, John T. Riedl and Joseph A. Konstan, “Collaborative Filtering Recommender Systems”, Foundations and Trends R in Human Computer Interaction, Vol.4, No.2, pp. 291-324, 2010.
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  [18] Yanhong Guo,Xuefen Cheng, Dahai Dong, Chunyu Luo and Rishuang Wang, “An improved collaborative filtering algorithm based on trust in e-commerce recommendation system”, Cina, pp.1-4, 2010.
  [19] Jun Wang, Arjen P. de Vries, “Unifying User based and Item based Collaborative Filtering Approaches by Similarity Fusion”, SIGIR06, USA, pp. 501-508, 2006.
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  Citation
  S.N. Patil, S.M. Deshpande, Amol D. Potgantwar, "Product Recommendation using Multiple Filtering Mechanisms on Apache Spark," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.76-83, 2017

• Open Access   Article

  Real-time Packet Performances under Socket Application

  Prathap M, Antony Selvadoss Thanamani

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.84-89, Jun-2017

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  Abstract
  Real-time packet capture and analysis has become a challenging problem, since it plays a vital role not only in identifying the problem causing entity but also to manage the network efficiently. The administrator has to know the network bandwidth and other resources that are used for accounting and auditing. This creates an emphasis to monitor network traffic and conduct analysis to ensure smooth operations performed routinely. Time is an important factor, which contributes to the variations of packet flow. In this paper, initially, the packet movement in the network was monitored with only hypertext transfers using HTTP. The same movement was analyzed with respect to other file transfer applications. The number of file downloads between the HTTP server and clients are also varied to follow the behavior of data. We also designed and developed multithreaded socket applications using java which was again monitored and analyzed using the above methodology. We compared the reading and the experimental results shows that the developed application outperforms in terms of packet acceleration compared to the previous ones.

  Key-Words / Index Term
  bandwidth, delay, throughput, hypertext, HTTP, server, client, multithread and socket

  References
  [1] Kahlon KS, Kumar H., “Survey of scheduling algorithms in IEEE 802.16 PMP networks”, Egyptian Informatics Journal, Vol.15, Issue.1, pp.25-36, 2014.
  [2] Sol Lim, Kye Joo Lee, So Yeon Kim, Chang Seok Chae, Intae Hwang, Dae Jin Kim, “Implementation of IR-UWB MAC Development Tools Based on IEEE 802.15.4a” International Journal of Control and Automation, Vol.8, No. 4, pp.275-286, 2015.
  [3] Mahesh Kumar, Rakhi Yadav, “TCP & UDP PACKETS ANALYSIS USING WIRESHARK”, International Journal of Science, Engineering and Technology Research Vol.4, Issue.7, pp.1-7, 2015
  [4] Niu, Xiaoguang, Zhu, Ying, Cao, Qingqing, Zhang, Xining, Xie, Wei, Zheng, Kun, “An online-traffic prediction based route finding mechanism for smart city”, International Journal of Distributed Sensor Networks, Vol.2015, Issue.3, pp.19-31, 2015.
  [5] H. Bast, “Route planning in transportation networks”, Springer International Publishing, Barlin, pp. 19-80, 2016.
  [6] Allan M de Souza, “Traffic management systems: A classification, review, challenges, and future perspectives” International Journal of Distributed Sensor Networks, Vol.13, Issue.4, pp. 15501477-16683612 2017.
  [7] Vivek Raich, Pradeep Sharma, Shivlal Mewada , Makhan Kumbhkar, “Performance Improvement of Software as a Service and Platform as a Service in Cloud Computing Solution. International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.6, pp.13-16, 2013.
  

  Citation
  Prathap M, Antony Selvadoss Thanamani, "Real-time Packet Performances under Socket Application," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.84-89, 2017

• Open Access   Article

  LIOP based Feature Detection and Matching in SfM for 3D Object Reconstruction

  Amit Banda, Rajesh Patil

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.90-94, Jun-2017

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  Abstract
  3D object reconstruction is growing popular due to its various applications such as movie industry and research simulations. Multi-View Stereo (MVS) based reconstruction using camera without extra hardware helps in reducing the cost of the system. The process consists of solving correspondence problem between images cause by camera motion using feature detector and descriptor or the optical flow technique. We propose to use LIOP proposed recentlyas a feature descriptor algorithmin the SFM based method for 3D object reconstruction.Based on the feature descriptor, it is more robust to noise, rotation, translation and monotonic intensity changes. Use of such rich feature descriptors increases the accuracy of reconstruction.

  Key-Words / Index Term
  

  References
  [1] Amit Banda, Rajesh A Patil, “Review on Feature Detection and Matching Algorithms for 3D Object Reconstruction”, IJRTER, Vol. 3, Jan 2017, pp. 219-225
  [2] Seitz, Steven M., "A comparison and evaluation of multi-view stereo reconstruction algorithms" 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR06), USA, pp. 519-528 ,2006
  [3] Soo Mi Choi, "Volumetric object reconstruction using the 3D-MRF model-based segmentation”, IEEE Transactions on Medical Imaging, Vol.16, Issue.6, pp.887-892, 1997.
  [4] K. Kutulakos, S. Seitz., “A theory of shape by space carving”, IJCV, Vol.38, Issue.3, Issue.3, pp.199-218, 2000
  [5] R. Szeliski, “A multi-view approach to motion and stereo”, In CVPR, Vol.1, Issue.4, pp.157-163, 1999.
  [6] O. Faugeras, E. Bras-Mehlman, J.-D. Boissonnat, “Representingstereo data with the Delaunay triangulation”, Artificial Intelligence, Vol.44, Issue.1-2, pp.41-87, 1990.
  [7] A. Manessis, A. Hilton, P. Palmer, P. McLauchlan, X. Shen, “Reconstruction of scene models from sparse 3D structure”, In CVPR, vol.1, Issue.3, pp.666-673, 2000.
  [8] D. Morris, T. Kanade , “Image-consistent surface triangulation”, In CVPR, Vol.1, Issue.3, pp.332-338, 2000.
  [9] C. J. Taylor, “Surface reconstruction from feature based stereo”, In ICCV, USA, pp. 184-190, 2003.
  [10] Marius Muja, David G. Lowe, “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration”, VISAPP, Vol.1, Issue.2, pp.331-340, 2009.
  [11] DL Baggio, “Mastering OpenCV with practical computer vision projects”, Packt Publishing Ltd, UK, pp.1-134, 2012.
  [12] RI. Hartley, “Triangulation", Computer vision and image understanding, Vol.68, Issue.2, pp.146-157, 1997.
  [13] R. Hartley, Andrew Zisserman, “Multiple view geometry in computer vision”, Cambridge university press, UK, pp.34-57, 2003.
  [14] E. Hildreth, "Recovering three-dimensional structure from motion with surface reconstruction", Vision research, Vol.35, Issue.1, pp.117-137, 1995.
  [15] JL. Barron, “Performance of optical flow techniques", International journal of computer vision, Vol.12, Issue.1, pp.43-77, 1994.
  [16] Berthold KP, Brian G. Schunck, "Determining optical flow", Artificial intelligence, Vol.17, Issue.1-3, pp.185-203, 1981.
  [17] Kapila Sharma, "An Effective Approach of Thinning for Morphological Features An Effective Approach of Thinning for Morphological Features", International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.58-60, 2015.
  [18] Zach Christopher, "Robust bundle adjustment revisited", European Conference on Computer Vision. Springer International Publishing, Europe, pp.12-19, 2014.
  [19] Lindeberg Tony, "Scale invariant feature transform", Scholarpedia, Vol.7, Issue.5, pp.10491-10498, 2012.
  [20] K. Mikolajczyk, C. Schmid, “A performance evaluation of local descriptors”, IEEE T-PAMI, Vol.27, Issue.10,pp.12-16, 2005
  [21] Miksik Ondrej, Krystian Mikolajczyk, "Evaluation of local detectors and descriptors for fast feature matching", International Conference on Pattern Recognition (ICPR), Japan, pp.11-15, 2012.
  [22] Wang Zhenhua, Bin Fan, Fuchao Wu, "Local intensity order pattern for feature description", 2011 IEEE International Conference on Computer Vision (ICCV), Spain, pp.603-610, 2011.
  [1] Amit Banda, Rajesh A Patil, “Review on Feature Detection and Matching Algorithms for 3D Object Reconstruction”, IJRTER, Vol. 3, Jan 2017, pp. 219-225
  [2] Seitz, Steven M., "A comparison and evaluation of multi-view stereo reconstruction algorithms" 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR06), USA, pp. 519-528 ,2006
  [3] Soo Mi Choi, "Volumetric object reconstruction using the 3D-MRF model-based segmentation”, IEEE Transactions on Medical Imaging, Vol.16, Issue.6, pp.887-892, 1997.
  [4] K. Kutulakos, S. Seitz., “A theory of shape by space carving”, IJCV, Vol.38, Issue.3, Issue.3, pp.199-218, 2000
  [5] R. Szeliski, “A multi-view approach to motion and stereo”, In CVPR, Vol.1, Issue.4, pp.157-163, 1999.
  [6] O. Faugeras, E. Bras-Mehlman, J.-D. Boissonnat, “Representingstereo data with the Delaunay triangulation”, Artificial Intelligence, Vol.44, Issue.1-2, pp.41-87, 1990.
  [7] A. Manessis, A. Hilton, P. Palmer, P. McLauchlan, X. Shen, “Reconstruction of scene models from sparse 3D structure”, In CVPR, vol.1, Issue.3, pp.666-673, 2000.
  [8] D. Morris, T. Kanade , “Image-consistent surface triangulation”, In CVPR, Vol.1, Issue.3, pp.332-338, 2000.
  [9] C. J. Taylor, “Surface reconstruction from feature based stereo”, In ICCV, USA, pp. 184-190, 2003.
  [10] Marius Muja, David G. Lowe, “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration”, VISAPP, Vol.1, Issue.2, pp.331-340, 2009.
  [11] DL Baggio, “Mastering OpenCV with practical computer vision projects”, Packt Publishing Ltd, UK, pp.1-134, 2012.
  [12] RI. Hartley, “Triangulation", Computer vision and image understanding, Vol.68, Issue.2, pp.146-157, 1997.
  [13] R. Hartley, Andrew Zisserman, “Multiple view geometry in computer vision”, Cambridge university press, UK, pp.34-57, 2003.
  [14] E. Hildreth, "Recovering three-dimensional structure from motion with surface reconstruction", Vision research, Vol.35, Issue.1, pp.117-137, 1995.
  [15] JL. Barron, “Performance of optical flow techniques", International journal of computer vision, Vol.12, Issue.1, pp.43-77, 1994.
  [16] Berthold KP, Brian G. Schunck, "Determining optical flow", Artificial intelligence, Vol.17, Issue.1-3, pp.185-203, 1981.
  [17] Kapila Sharma, "An Effective Approach of Thinning for Morphological Features An Effective Approach of Thinning for Morphological Features", International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.58-60, 2015.
  [18] Zach Christopher, "Robust bundle adjustment revisited", European Conference on Computer Vision. Springer International Publishing, Europe, pp.12-19, 2014.
  [19] Lindeberg Tony, "Scale invariant feature transform", Scholarpedia, Vol.7, Issue.5, pp.10491-10498, 2012.
  [20] K. Mikolajczyk, C. Schmid, “A performance evaluation of local descriptors”, IEEE T-PAMI, Vol.27, Issue.10,pp.12-16, 2005
  [21] Miksik Ondrej, Krystian Mikolajczyk, "Evaluation of local detectors and descriptors for fast feature matching", International Conference on Pattern Recognition (ICPR), Japan, pp.11-15, 2012.
  [22] Wang Zhenhua, Bin Fan, Fuchao Wu, "Local intensity order pattern for feature description", 2011 IEEE International Conference on Computer Vision (ICCV), Spain, pp.603-610, 2011.
  

  Citation
  Amit Banda, Rajesh Patil, "LIOP based Feature Detection and Matching in SfM for 3D Object Reconstruction," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.90-94, 2017

• Open Access   Article

  Characterization of Enhancement AlInN/GaN Hemts using Partial P-Type GaN Gate

  Dilip Jaiswal, Nandkishor Chavan, Hemant Pardeshi, Swati Sharma

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.95-98, Jun-2017

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  Abstract
  This work attempts to characterize the Enhancement mode (E-mode) AlInN/GaN HEMT devices implemented using p-GaN gate for getting positive threshold voltage (Vt). The device channel consists of a lattice-matched wideband Al0.83In0.17N and narrowband GaN layers, along with p-GaN layer below the E-mode device. The 2D Sentaurus TCAD simulation is done using the hydrodynamic model. The simulation model is calibrated with the initially published experimental result. A comprehensive, quantitative investigation of transfer characteristics, transconductance, gate capacitance, gate leakage and RF gain for E-mode devices is done. The E-mode device exhibit a Vt of + 1.0 V. This new device exhibit almost similar transconductance characteristics. The E-mode device shows lower off-state leakage current, higher ION/IOFF ratio and lower SS. These results demonstrate the feasibility for fabricating an E-mode AlInN/GaN HEMT device which is extremely desirable for high speed and high-frequency applications.

  Key-Words / Index Term
  Enhancement mode (E-mode), HEMT, p-GaN, AlInN/GaN

  References
  [1]. J J. Kuzmik, “Power Electronics on InAlN/(In)GaN: Prospect for a Record Performance”, IEEE Electron Device Letters, Vol.22, Issue.1, pp.510-512, 2001.
  [2]. L. R. Khoshroo, C. Mauder, W. Zhang, “ Optimisation of AlInN/GaN HEMT structures”, Physica Status Solidi-C, Vol.5, Issue.1, pp.2041-243, 2008.
  [3]. F. Medjdoub, J. F. Carlin, M. Gonschorek, “Can InAlN/GaN be an alternative to high Power/high temperature AlGaN/GaN devices?”, International Electron Devices Meeting IEDM-06, CA, pp.1-4, 2006.
  [4]. F. Medjdoub, M. Alomari, J.-F. Carlin, “Barrier-Layer Scaling of InAlN/GaN HEMTs”, IEEE Electron Device Letters, Vol.29, Issue.5, pp.422-425, 2008.
  [5]. M. Alomari, F. Medjdoub, J.F. Carlinet, “InAlN/GaN MOSHEMT With Self-Aligned Thermally Generated Oxide Recess”, IEEE Electron Device Letters, Vol.30, Issue.11, pp.1131-1133, 2009.
  [6]. Z. H. Feng, Y. G. Zhou, S. J. Cai, Kei. May. Lau, “Enhanced thermal stability of the twodimensional electron gas in GaN∕AlGaN∕GaN heterostructures by Si3N4 surface-passivation induced strain solidification”, Applied Physics Letters, Vol.85, Issue.22, pp.5248-5250, 2004.
  [7]. Hiroki, H. Yokoyama, N. Watanabe, T. Kobayashi, “High-quality InAlN/GaN heterostructures grown by metal – organic vapor phase epitaxy”, Superlattice and Microstructures, Vol.40, Issue.4, pp.214-218, 2006.
  [8]. A. Dadgar, F. Schulze, A. Diez, “High-sheet-charge – carrier-density AlInN∕GaN field effect transistors on Si (111)”, Applied Physics Letters, Vol.85, Issue.22, pp.5400-5402, 2004.
  [9]. M. Kanamura, "Enhancement-Mode GaN MIS-HEMTs With n-GaN/i-AlN/n-GaN Triple Cap Layer and High- k Gate Dielectrics", in IEEE Electron Device Letters, Vol. 31, No.3, pp.189-191, 2010.
  [10]. W. B. Lanford, T. Tanaka ,Y. Otoki , I. Adesida, “High-performance InP-based enhancement-mode HEMTs using non-alloyed ohmic contacts and Pt- based buried-gate technologies”, Electronics Letters, Vol.41, Issue.7, pp.449-450, 2005,
  [11]. K. J. Chen, T. Enoki, K. Maezawa, K. Arai, “Enhancement-Mode AlGaN/AlN/GaN High Electron Mobility Transistor with Low On- State Resistance and High Breakdown Voltage”, IEEE Transactions on Electron Devices, Vol.43, Issue.2, pp.252-257, 1996.
  [12]. M. Alomari, "InAlN/GaN MOSHEMT With Self-Aligned Thermally Generated Oxide Recess", IEEE Electron Device Letters, Vol.30, Issue.11, pp.1131-1133, 2009.
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  Citation
  Dilip Jaiswal, Nandkishor Chavan, Hemant Pardeshi, Swati Sharma, "Characterization of Enhancement AlInN/GaN Hemts using Partial P-Type GaN Gate," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.95-98, 2017

• Open Access   Article

  Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark

  Amit Palve, Rohini D.Sonawane, Amol D. Potgantwar

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.99-103, Jun-2017

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  Abstract
  Twitter is free social networking micro blogging service. In that micro-blogging allows to registered members to broadcasting the short posts also called tweets. It can broadcast the tweets by using multiple platforms and devices. Twitter member replies to tweets. Existing system focuses on document level sentiment analysis that means they used Hadoop Framework for concerning moving or product reviews. In that system web pages or blocks on which posts are published therefore in that system complexity of document level opinion mining many efforts have been made towards the sentence level sentiment analysis. The existing systems classify the accuracy only one word. This process is time consuming due to documentation. In the system which we are devolve in that we used spark framework instead of Hadoop framework. Due to the use of Spark Framework, garbage or unclean data are removing. So that user gets better efficiency and less time required for processing, than earlier system.

  Key-Words / Index Term
  Big data, classification, Map-Reduce, Spark, sentiment analysis, text mining, Twitter

  References
  [1]. A. I. Baqapuri, S. Saleh, M. U. Ilyas, M. M. Khan, A. M. Qamar, "Sentiment classification of tweets using hierarchical classification", 2016 IEEE International Conference on Communications (ICC), Malaysia, pp.1-7, 2016.
  [2]. Tumasjan A, Sprenger TO, Sandner PG, Welpe IM, “Predicting elections with twitter: What 140 characters reveal about political sentiment”, Icwsm Vol.10, Issue.1, pp.178-85, 2010.
  [3]. R.V. Patil, S.S. Sannakki, V.S. Rajpurohit, "A Survey on Classification of Liver Diseases using Image Processing and Data Mining Techniques", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.29-34, 2017.
  [4]. Madnani N, “Getting started on natural language processing with Python”, Crossroads, Vol.13, Issue.4, pp.5-9, 2007.
  [5]. B. Pang, L. Lee, S. Vanity Nathan, “Sentiment classification using machine learning techniques”, In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, Vol.5, Issue.4, pp.79-86, 2002.
  [6]. Y. Yamamoto, T. Kumamoto, A. Nadamoto, “Role of emotions for multidimensional sentiment analysis of twitter”, In Proceedings of the 16th International Conference on Information Integration and Web-based Applications, USA,pp.107-115, 2014.
  [7]. V. N. Khuc, C. Shivved, R. Namath, and J. Ramayana., ”Towards building large-scale distributed systems for twitter sentiment analysis”, .In Pro-ceedings of the 27th Annual ACM Symposium on Applied Computing, pages 459-464, 2012.
  [8]. Dean J, Ghemawat S. “MapReduce: simplified data processing on large clusters”, Communications of the ACM, Vol.51, Issue.1, pp.107-113, 2008.
  [9]. L. Zhuang, F. Jing, X.-Y. Zhu, “Movie review mining and summa-rization”, In Proceedings of the 15th ACM International Conference on Information and Knowledge Management, USA, pp.43-50, 2006.
  [10]. W. Zhang, C. Yu, W. Men, “Opinion retrieval from blogs”, In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, USA, pp.831-840, 2007.
  [11]. T. Wilson, J. Wienie, P. Ho Mann, “Recognizing contextual polarity in phraselevel sentiment analysis”, In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, USA, pp.347-354, 2005.
  [12]. T. Wilson, J. Wienie, P. Ho Mann, “Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis”, Com put. Linguist, USA, pp.399-433, 2009.
  [13]. Neetu Anand, Tapas Kumar , "Text and Emotion Analysis of Twitter Data", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.181-285, 2017.
  [14]. H. Yuh, V. Huitzilopitchli, “Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences”, In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, USA, pp.129-136, 2003.
  

  Citation
  Amit Palve, Rohini D.Sonawane, Amol D. Potgantwar, "Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.99-103, 2017

• Open Access   Article

  An Efficient Low-Profile Microstrip Antenna for Aqueous Glucose Measurement

  Ashwini S Jagtap , S.D. Sawarkar

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.104-108, Jun-2017

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  Abstract
  Millions of people are suffering from Diabetes worldwide. According to the World Health Organization (WHO), number of diabetes patients is likely to rise to 101 million in India by 2030 and will become the world’s 7th largest killer. Hence we have to utilize technology to eradicate this disease. Measuring glucose level in a diabetic patient on a regular basis and administering insulin are the keys to keep patient safe and device which measures blood glucose level is called Glucometer. Presently glucose can be measured using two techniques, invasive and non-invasive, also known as in-vivo and in-vitro respectively. microwave resonator antenna is used in this project for non-invasive technique as a new approach towards blood glucose measurement. In this work, low profile microstrip antennas for glucometer application are proposed. As the dielectric constant of the material used as a substrate placed above antenna changes, resonant characteristic of antenna varies and shift in the frequency is observed. The proposed microstrip antenna resonators are designed using Advanced Design System 2011.05 for an operating frequency as low as 1 GHz. The operating frequency of antenna should be low as the low frequency microwave signal penetrate deep into tissues and have potential for practical application because of low physical area, low cost and better frequency resolution. Further, a high frequency resolution i.e. for a small change in glucose concentration leads to considerable shift in operating frequency, is achieved. The proposed Microstrip antennas are designed and tested with aqueous glucose substrate. This is advancement towards developing microstrip sensor for non-invasive glucometer application.

  Key-Words / Index Term
  Diabetes, Aqueous Glucose, Dielectric constant, Microwave Sensors, Microwave Resonator

  References
  [1]. A. Muley and R. B. Ghongade, “Design and Simulate an Antenna for Aqueous Glucose Measurement”, Annual IEEE India Conference (INDICON), India, pp.1-6, 2014.
  [2]. S. Seewattanapon, T. Wattakeekamthorn, T. Somwong, P. Akkaraekthalin, “A MicrostripFolded Resonator Sensor for Measurement of Dielectric Constant”, 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Thailand, 2008, pp. 245-248.
  [3]. X. Xiao and Q. Li, "A Noninvasive Measurement of Blood Glucose Concentration by UWB Microwave Spectrum", IEEE Antennas and Wireless Propagation Letters, Vol.16, No.10 , pp.1040-1043, 2017.
  [4]. Mehak, Shashi B. Rana and Yogesh Bhomia, "Design and Analysis of Modified Sierpinski Gasket Fractal Antenna", International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.150-155, 2016.
  [5]. Maximilian Hofmann, Thomas Fersch, Robert Weigel, Georg Fischer, Dietmar Kissinger, “A Novel Approach to Non-Invasive Blood Glucose Measurement based on RF Transmission”, IEEE International Symposium on Medical Measurements and Applications, Itly, pp. 39-42, 2011.
  [6]. Heungjae Choi, Steve Luzio, Adrian Porch, “Dielectric Properties of Aqueous Glucose Solutions Using Microwave Cavity and Coaxial Probe”, 2016 IEEE 2nd Australian Microwave Symposium (AMS), Australia, pp.7-8, 2016.
  

  Citation
  Ashwini S Jagtap , S.D. Sawarkar, "An Efficient Low-Profile Microstrip Antenna for Aqueous Glucose Measurement," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.104-108, 2017

• Open Access   Article

  Extraction of FECG from Non-Invasive AECG signal for Fetal Heart Rate Calculation

  Apurva Gaikwad, M.S. Panse

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.109-112, Jun-2017

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  Abstract
  A Non-Invasive Fetal Electrocardiogram (FECG) is an effective diagnostic tool for determining health status of the fetus. Abdominal Electrocardiogram (AECG) is a composite ECG signal that consist of both fetal as well as maternal ECG (MECG). This paper presents an efficient technique to extract the FECG signal from AECG by means of different Time – Frequency localized transforms and algorithms. A combination of Band Pass Filter , Hilbert Transform and Adaptive Thresholding algorithm has been used for detecting FECG QRS complexes. Further an enhancement of FECG signal is done using Wavelet De-noising and Fetal Heart Rate (FHR) is calculated. This algorithm is performed using MATLAB simulation software and tested on 5 non-invasively recorded abdominal and direct FECG signals taken from MIT Physionet database.

  Key-Words / Index Term
  ECG, Non-Invasive monitoring, Maternal ECG, Pre-processing, Hilbert Transform, Wavelet De-noising, QRS detection, FECG extraction, FHR

  References
  [1] M. Manikandan, Jayasubha RY, S. Krishna Kumar,“ Fetal Heart Monitoring From Maternal ECG”, International Journal Of Scientific and Technology Research Vol.5, Issue.3, pp.164-166, 2016.
  [2] Shimrit Yaniv Salem,Gershon Holcberg, Amir B. Geva,Sergey Vaisman, “Passive fetal monitoring by adaptive wavelet denoising method”, Computers in biology and medicine, Vol.42, Issue.2, pp.171–179, 2012.
  [3] Mbachu, C.B, Arinze, A.W, “A Finite Impulse Response (Fir) Adaptive Filtering Technique for the Monitoring Of Foetal Health and Condition”, American Journal of Engineering Research (AJER), Volume-3, Issue-10, pp-68-74, 2014.
  [4] Sreedharamurthy S K , H.R.Sudarshana Reddy, "Feature Subset Selection Using Genetic Algorithms for Handwritten Kannada Alphabets Recognition", International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.94-99, 2015.
  [5] Manuel Blanco-Velasco , Binwei Weng , Kenneth E. Barner, “ECG signal denoising and baseline wander correction based on the empirical mode decomposition”, Computers in Biology and Medicine, Vol.38, Issue.1, pp.1-13, 2008.
  [6] Pallas R.A., “Interference-Rejection Characteristics of Biopotential Amplifiers: A Comparative Analysis”, IEEE Transaction on Biomedical Engineering, Vol.35, Issue.11, pp.953-9, 1988.
  [7] Spinelli E., Mayosky M., Pallas R.A., “A practical approach to electrode skin impedance unbalance measurement”, IEEE Transaction on Biomedical Engineering, Vol.53, Issue.7, pp.1451-1453, 2006.
  [8] Manpreet Kaur, Birmohan Singh, “Comparisons of Different Approaches for Removal of Baseline Wander from ECG Signal”, 2nd International Conference and workshop on Emerging Trends in Technology (ICWET), India, pp.1290-1294, 2011.
  [9] R. Panda, “Removal of Artifacts from Electrocardiogram using Digital Filter”, Electrical, Electronics and Computer Science (SCEECS), IEEE Students Conference, India, pp.1-4, 2012.
  [10] R. Limacher, “Removal of Power Line Interference from the ECG Signal by an Adaptive Digital Filter”, in Proc. of European Tel. Conf., Garmisch-Part, USA, pp.300-309, 1996.
  [11] Gizeaddis Lamesgin, Yonas Kassaw, and Dawit Assefa, “Extraction of Fetal ECG from Abdominal ECG and Heart Rate Variability Analysis”, Springer International Publishing, Switzerland, pp.65-76, 2015.
  

  Citation
  Apurva Gaikwad, M.S. Panse, "Extraction of FECG from Non-Invasive AECG signal for Fetal Heart Rate Calculation," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.109-112, 2017

• Open Access   Article

  A Novel RDH Scheme for Real Time Applications

  Uma Shivalli, B.K.Sujatha

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.113-116, Jun-2017

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  Abstract
  Significance of Multimedia security is ever increasing due to threat of duplication, modification and manipulation of the multimedia data like photograph and documents. In the recent days reversible data hiding(RDH) in encrypted images is gaining widespread application in the digital world. Use of RDH for real time application helps in losslessly recovering the original image after the embedded data is extracted and image content’s confidentiality is also protected. In this paper we propose a novel method of RDH for real time multimedia data by reserving room. Performance analysis is done for different embedding rates. Experimental results show that, this novel method ensures better security of the multimedia data and protects the original image from manipulation by the data hider.

  Key-Words / Index Term
  Image encryption, Reversible data hiding, Histogram, data extraction, PSNR

  References
  [1] Xiaolong Li, Weiming Zhang, Xinlu Gui, Bin Yang, “A Novel Reversible Data Hiding Scheme Based on Two-Dimensional Difference-Histogram Modification”, IEEE Transactions on Information forensics and security, Vol. 8, No. 7, pp.1-7, 2013.
  [2] Shobha Elizabeth Rajan, Sreedevi P, "Enhancing Visual Cryptography Using Digital Watermarking", International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.152-156, 2015.
  [3] Kede Ma, Weiming Zhang, Xianfeng Zhao, Nenghai Yu, Fenghua Li, “Reversible Data hiding in Encrypted Images by Reserving Room before Encryption”, IEEE Trans. Inf. Forensics Security, Vol. 8, No. 3, pp. 826-832, 2013.
  [4] W .Hong, T. Chen, H. Wu, “An improved reversible data hiding in encrypted images using side match”, IEEE Signal Process. Lett., Vol.19, No. 4, pp. 199-202, 2012.
  [5] X. Zhang, “Separable reversible data hiding in encrypted image”, IEEE Trans. Inf. Forensics Security, Vol. 7, No. 2, pp. 826-832, 2012.
  [6] X.L.Li, B.Yang, and T.Y.Zeng, “Efficient reversible watermarking based on adaptive prediction-error expansion and pixel selection”, IEEE Trans. Image Process., Vol.20, No.12, pp.3524-3533, 2011.
  [7] Zhi-Hui Wang, Chin-Chen Chang, Pei-Yu Tsai, “Hiding Secret Data in an Image Using Codeword Imitation”, Journal of Information Processing Systems, Vol.6, No.4, pp.435-452, 2010.
  [8] Akhila Sreenivas K. and Pretty Babu , "An Approach for Data Hiding Technique Based on Reversible Texture Synthesis", International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.81-85, 2016.
  [9] Nitin Shelake and S. R. Durugkar , "Extracting Hidden Data from Encrypted Images Using IWT", International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.219-222, 2015.
  [10] J. Fridrich and M. Goljan, “Lossless data embedding for all image for-mats”, in Proc. SPIE Proc. Photonics West, Electronic Imaging, Secu-rity and Watermarking of Multimedia Contents, San Jose, CA, USA, pp. 572-583, 2002.
  

  Citation
  Uma Shivalli, B.K.Sujatha, "A Novel RDH Scheme for Real Time Applications," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.113-116, 2017

• Open Access   Article

  Detection of Faulty Sensor Node within Wireless Sensor Network for improving Network Performance

  Tejashree Phatak, S.D. Sawarkar

  Research Paper | Journal (IJSRNSC)

  Vol.5 , Issue.3 , pp.117-122, Jun-2017

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  Abstract
  In most of application such as Habitat and Ecosystem- tem Monitoring, Seismic Monitoring, Civil Structural Health Monitoring, Monitoring Groundwater Contamination, Rapid Emergency Response, Industrial Process Monitoring, Perimeter Security and Surveillance Automated Building and Climate Control it is essential to observe or monitor the behavior of surrounding environment at a fine resolution over large spatial- temporal scales for this reason wireless Sensor Network (WSN) is used. WSN have high ability to connect physical world to the virtual world and sensor devices are tiny device and low battery devices due this use of wireless sensor network for monitoring surrounding behavior is increased time to time. Accuracy of WSN is enhanced by increasing the no of sensor within the network. As no of sensor increase this will increase the probability of failure of sensor within the network because WSN may be operated in unattended and hostile environment .If any of the sensor node is fails, this will lead to the change the network topology, also create network partitioning , increases distance between node pair and reduce the number of alternative available routes. In short it will degrade Network Performance of WSN, to improve network performance of WSN we have to find out faulty sensor node within the network. This paper will give one easy method for finding faulty sensor node within the network. This method measure Round Trip Time (RTT) of Round Trip Path (RTP) and compare the calculated RTT with threshold value(threshold value for RTT) if premeditated RTT is greater than threshold value or infinity then particular node from that respective path is declared as dead node or malicious node.

  Key-Words / Index Term
  Faulty sensor node, Network Performance, Round Trip Time, Round Trip Path

  References
  [1] RN. Duche, Nisha P. Sarwade, ”Sensor Node Failure Detection Based on Round Trip Delay and Paths in WSNs” IEEE SENSORS JOURNAL, VOL. 14, NO. 2, pp. 455-464, 2014.
  [2] P. Jiang, ”A new method for node fault detection in wireless sensor networks” Sensors, vol. 9, no. 2, pp. 1282-1294, 2009.
  [3] Enakshmi Nandi and Debabrata Sarddar, "A Modified MapReduce-K-Means Clustering Based Load Distribution Method for Wireless Sensor Network in Mobile Cloud computing", International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.107-110, 2016.
  [4] X.-L. Zhang, F. Zhang, J. Yuan, J. lanWeng, W. hua Zhang, “Sensor fault diagnosis and location for small and medium-scale wireless sensor networks”, in Sixth International Conference on Natural Computation, China, pp.3628 3632, 2010.
  [5] S. Marti, T. J. Giuli, K. Lai, M. Baker, “Mitigating routing mis- behavior in mobile ad hoc networks”, in Proc. 6th annual international conference on Mobile computing and networking, USA, pp. 255265.
  [6] A mahapatro, P.M. Khillar , “fault Diagnosis in wireless Sensor Networks: A survey”, in IEEE communication surveys and Tutori- als, Vol.15, No 4, pp.1-4, 2013
  [8] H. Redwan, G. Akele, K.H. Kim, "Cluster-based failure detection and recovery scheme in wireless sensor network", 2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN), China, pp. 407-412, 2014.
  [9] R. N. Duche and N. P. Sarwade, “Sensor node failure or malfunctioning detection in wireless sensor network” ACEEE Int. J. Commun., vol. 3, no. 1, pp. 57-61, 2012.
  [10] S. Sharma, D. Kumar, K. Kishore, "Wireless Sensor Networks- A Review on Topologies and Node Architecture", International Journal of Computer Sciences and Engineering, Vol.1, Issue.2, pp.19-25, 2013.
  [11] S.Anusuya and S.Meenakshi, "An Improved Dynamic Source Routing Protocol for Detection and Removal of Black Hole Attack in Mobile Ad-Hoc Network", International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.112-117, 2015.
  

  Citation
  Tejashree Phatak, S.D. Sawarkar, "Detection of Faulty Sensor Node within Wireless Sensor Network for improving Network Performance," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.117-122, 2017