Volume-6 , Issue-1 , Feb 2018, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Selective Small Reconstruction Error Based LDRC Multimodal Biometric Authentication

  Savitha G., Vibha L., Venugopal K. R.

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.1-10, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.110

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Biometric authentication has attracted great interest due to its importance in numerous real-world applications. In this paper, the accuracy issue addressed through multimodal biometric combination. The proposed multimodal biometric combination scheme delivers face, finger print and signature as biometric characteristics, as an input for security purpose. The proposed methodology incorporates Wiener filter for preprocessing the acquired images and Discrete Wavelet Transform (DWT) was used for achieving feature subsets. Then, Linear Discriminant Regression Classification (LDRC) was designed with the combination of Selective Small Reconstruction Error (SSRE), which helps to select the appropriate classes. In experimental analysis, the proposed approach improves the authentication rate by means of False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER). The experimental outcome shows that the proposed methodology improved accuracy in biometric authentication rate up to 5-10% compared to the existing method: Linear Regression Classification (LRC).

  Key-Words / Index Term
  Biometric Authentication; Discrete Wavelet Transform; Selective Small Reconstruction Error; Wiener Filter

  References
  [1] A.K. Jain, A. Ross, S. Prabhakar, “An introduction to biometric recognition”, IEEE Transactions on Circuits and Systems for Video Technology, Vol.14, No.1, pp.4-20, 2004.
  [2] A.K. Jain, A. Ross, “Multibiometric systems”, Communications of the ACM, Vol.47, No.1, pp.34-40, 2004.
  [3] R. Raghavendra, B. Dorizzi, A. Rao, G.H. Kumar, “Designing efficient fusion schemes for multimodal biometric systems using face and palmprint”, Pattern Recognition, Vol.44, No.5, pp.1076-1088, 2011.
  [4] N. Poh, J. Kittler, “A unified framework for biometric expert fusion incorporating quality measures”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.34, no.1, pp.3-18, 2012.
  [5] M. Hofmann, S.M. Schmidt, A.N. Rajagopalan, G. Rigoll, “Combined face and gait recognition using alpha matte preprocessing”, In the Proceedings of 5th IAPR International Conference on Biometrics (ICB), pp.390-395, 2012.
  [6] U. Uludag, S. Pankanti, S. Prabhakar, A.K. Jain, “Biometric cryptosystems: issues and challenges”, Proceedings of the IEEE, Vol.92, No.6, pp.948-960, 2004.
  [7] W.J. Scheirer, T.E. Boult, “Bio-cryptographic protocols with bipartite biotokens,” In the Proceedings of Biometrics Symposium, pp.9-16, 2008.
  [8] A. Mishra, “Multimodal biometrics it is: need for future systems”, International journal of computer applications, Vol.3, No.4, pp.28-33, 2010.
  [9] A. Ross, A.K. Jain, “Multimodal biometrics: An overview”, In the Proceedings of 12th European Conference In Signal Processing, pp.1221-1224, 2004.
  [10] S. Sheena, S. Mathu, “A Study of Multimodal Biometric System”, IJRET: International Journal of Research in Engineering and Technology, Vol.3, No.14, pp.93-97, 2014.
  [11] K. Sasidhar, V.L. Kakulapati, K. Ramakrishna, K. KailasaRao, “Multimodal biometric systems-Study to improve accuracy and performance”, International Journal of Computer Science & Engineering Survey (IJCSES), Vol.1, No.2, 2010.
  [12] M. Soltane, M. Bakhti, “Multi-modal biometric authentications: concept issues and applications strategies”, International Journal of Advanced Science and Technology, Vol.48, 2012.
  [13] M. Pathak, N. Srinivasu, “Analysis of Multimodal Biometric System Based on Level of Fusion”, International Journal of Inventive Engineering and Sciences (IJIES), Vol.3, no.8, 2015.
  [14] Y. Li, M. Shi, E. Zhu, J. Yin, J. Zhao, “A Multimodal Fusion Algorithm Based on FRR and FAR Using SVM”, International Journal of Security and Its Applications, Vol.7, No.3, pp.65-74, 2013.
  [15] G. Savitha, L. Vibha, K.R. Venugopal, “Multimodal Biometric Authentication System Using Ldr Based on Selective Small Reconstruction Error”, Journal of Theoretical and Applied Information Technology, Vol.92, No.1, pp.171, 2016.
  [16] A. Muthukumar, C. Kasthuri, S. Kannan, “Multimodal Biometric Authentication using Particle Swarm Optimization Algorithm with Fingerprint And Iris”, ICTACT Journal on Image and Video Processing, Vol.2, No.3, 2012.
  [17] Y. Lu, X. Fang, B. Xie, “Kernel linear regression for face recognition”, Neural Computing and Applications, Vol.24, No.7-8, pp.1843-1849, 2014.
  [18] S. M. Huang, J. F. Yang, “Linear Discriminant Regression Classification for Face Recognition”, IEEE Transactions On Signal Processing Letters, Vol.20, No.1, 2013.
  [19] B. Ma, Y. Wang, C. Li, Z. Zhang, D. Huang, “Secure multimodal biometric authentication with wavelet quantization based fingerprint watermarking”, Multimedia tools and applications, Vol.72, No.1, pp.637-666, 2014.
  [20] H. Saevanee, N. Clarke, S. Furnell, V. Biscione, “Continuous user authentication using multi-modal biometrics”, Computers & Security, Vol.53, pp.234-246, 2015.
  [21] A. Gupta, E. Sharma, N. Sachan, N. Tiwari, “Door Lock System through Face Recognition Using MATLAB”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, No.3, pp.51-55, 2013
  

  Citation
  Savitha G., Vibha L., Venugopal K. R., "Selective Small Reconstruction Error Based LDRC Multimodal Biometric Authentication," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.1-10, 2018

• Open Access   Article

  Calculation of Texture Features for Polluted Leaves

  S.K. Badugu, R.K. Kontham, V.K. Vakulabharanam, B. Prajna

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.11-21, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.1121

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  This paper discuss about polluted leaves texture features. Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation , bio-medical image processing and remote sensing. Now a day’s pollution is increasing day to day. classification of polluted leaves essential for some applications. To get the leaf based features image processing techniques are applied on the image of leaf. So texture features like mean, median ,skewness, kurtosis,GLCM and RMS of polluted leaves are published in this Article .Not only that Histogram of polluted leaves were published. Histograms of polluted leaves compared with data distribution measures like skewness and kurtosis.Manual polluted leafs identification task is time consuming process. Automatic detection of pollutant leaves is an active research topic now a days. Automatic polluted leaf identification will save time.In this paper histograms of nearly 30 polluted leafs of seven statistical measures were published.

  Key-Words / Index Term
  Texture Features, Classification, Pollution, Histogram

  References
  [1] Sumitra Giri ,”Effect of air pollution on chlorophyll content of leaves”,Current Agriculture Journal Vol,1(2),93-98(2013)
  http://dx.doi.org/10.12944/CARJ.1.2.04
  [2] B.V Ramana Reddy A.Suresh Classification of Textures Based on Features Extracted from Preprocessing Images on Random Windows International Journal of Advanced Science and Technology Volume 9, August, 2009
  [3] A New Approach for Texture Segmentation Using Gray Level Textons International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 6, No. 3, June, 2013.
  [4] Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features Agric Eng Int: CIGR Journal Open access at http://www.cigrjournal.org Vol.15 No.1 211
  [5] Alireza Pourkhabbaz.Nayerah Rastin, “Influence of Environmental Pollution on leaf properties of urban plane trees,Platanus orientalis L.”, Bull Environ Contam Toxicol(2010)85:251-255,DOI 10.1007/s00128-010-0047-4 Busgenweg 1a,37077 Gottingen,Germany ,Springer 12 December 2009/Accepted:11 june 2010/Published online:25 june 2010.
  [6] University “Al.I.Cuza”Iasi , “Air Pollution Effects on the Leaf Structure of some Fabaceae Spacies”, Print ISSN 0255-965X;Electronic ISSN 1842-4309,Not.Bot.Hort.Agrobot.Cluj 37(2) 2009,57-63,Available online at www.notulaebotanicae.ro
  [7] Anett C.Hansen and Harald K.Selte,”Air Pollution and Sick-leaves –is there a connection? A Case study using Air Pollution Data from Oslo”,Discussion Papers No-197 Statistics Norway.july- 1997.
  [8] V.Venkata Krishna, “Classification of metals using texture features”, International Journal of Computer Science and Technology and Vol.4, Issue.spl-4, ISSN:0976-8491(online)/ISSN:2229-4333, 2013.
  [9] Revathi, P., and M. Hemalatha. "Cotton Leaf Spot Diseases Detection Utilizing Feature Selection with Skew Divergence Method." International Journal of Scientific Engineering and Technology (ISSN: 2277-1581) 3 (2014): 22-30.
  [10] Rastogi, A.; Arora, R.; Sharma, S., "Leaf disease detection and grading using computer vision technology & fuzzy logic," in Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on , vol., no., pp.500-505, 19-20 Feb. 2015.
  [11] Classification of metals using texture features ,suneel kumar badugu ,venkata Krishna vakulabharanam et all IJCST IJCST Vol. 4, Iss ue Spl - 4, Oct - Dec 2013 , ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print).
  [12] Comparative study of various classification algorithms combined with K means algorithm for Leaf Identification International Journal Of Engineering And Computer Science ISSN: 2319-7242 Volume 5 Issues 6 June 2016, Page No. 17022-17025.
  [13] Abdul Kadir et all Leaf classification using shape,color,and texture features, International Journal of Computer Trends and Technology- July to Aug Issue 2011, ISSN: 2231-2803.
  [14] T. Acharya, & A.K. Ray, “Image Processingg Principles and Applications”, New Jersey: John Wiley & Sons, Inc, 2005 .
  [15] Y.A.O. Min, Y.I. Win-Sheng,S. Bin, &D.A.I. Hong-Hua, “An Image Retrieval System Based on Fractal Dimension”, Journal Zheijang University Science, vol. 4(4), p. 421-425, 2003. M. Petrou, & P.G. Sevilla, “Image Processing Dealing with Texture”, Chichester: John Wiley & Sons, Ltd., 2006.
  [16] M. Petrou, & P.G. Sevilla, “Image Processing Dealing with Texture”, Chichester: John Wiley & Sons, Ltd., 2006.
  [17] A. Gebejes, & Huertas, R. (2013). Texture Characterization Based on Grey-Level Co-Occurrence Matrix.
  [18] Studying the Kidney Textural Using Statistical Features and Local Binary Pattern Vol.20 (4), December, 2017, pp.64-76 Journal of Al-Nahrain University.
  

  Citation
  S.K. Badugu, R.K. Kontham, V.K. Vakulabharanam, B. Prajna, "Calculation of Texture Features for Polluted Leaves," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.11-21, 2018

• Open Access   Article

  Optical Fiber Cables Networks Defects Detection using Thermal Images Enhancement Techniques

  Hussam Elbehiery

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.22-29, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.2229

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Image enhancement is a process to output an image which is more suitable and useful than original image for specific application. Thermal image enhancement includes many techniques used in Quality Control, Problem Diagnostics, and Insurance Risk Assessment. Various enhancement schemes are used for enhancing an image which includes gray scale manipulation, filtering and Histogram Equalization (HE), Fast Fourier Transform which results in Highlighting interesting detail in images, removing noise from images, making images more visually appealing, edge enhancement and increase the contrast of the image. This research article explains how could the various stated techniques and operations will be useful in the detection of the defects for the optical fiber cables and their connectors and most of optical devices to be more effective in Optical fiber based communication systems.

  Key-Words / Index Term
  Histogram Equalization, Linear Filtering, Adaptive Filtering, Fast Fourier Transform, 3D Shaded surface plot

  References
  [1] Komal Vij, et al., “Enhancement of Images Using Histogram Processing Techniques,” Vol 2, pp309-313, 2009.
  [2] Kevin Loquin, et al., “Convolution Filtering and Mathematical Morphology on an Image: A Unified View,” pp 1-4, 2010.
  [3] M. Kowalczyk, et al., “Application of mathematical morphology operations for simplification and improvement of correlation of images in close-range photogrammetry,” pp 153-158, 2008.
  [4] J. Zimmerman, S. Pizer, E. Staab, E. Perry, W. McCartney, and B. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Transactions on Medical Imaging, pp. 304-312, 1988.
  [5] M. Abdullah-Al-Wadud, Md. Hasanul Kabir, M. Ali Akber Dewan, and Oksam Chae., “A dynamic histogram equalization for image contrast enhancement,” IEEE Transactions. Consumer Electron., vol. 53, no. 2, pp. 593- 600, 2007.
  [6] Rafael C. Gonzalez, and Richard E. Woods, “Digital Image Processing. 2nd edition, Prentice Hall, 2002.
  [7] A. K. Jain, “Fundamentals of Digital Image Processing,” Englewood Cliffs, NJ: Prentice-Hall, 1991.
  [8] J. Alex Stark., “Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equalization,” IEEE Transactions on Image Processing, Vol. 9, No. 5, 2000.
  [9] Dakin, Pratt, “Distributed Optical Fiber Raman Temperature Sensor Using A Semiconductor Light Source And Detector,” Electronics Letters 20th, Vol. 21 No. 13, 1985.
  [10] M. Niklès, B. Vogel, F. Briffod, S. Grosswig, F. Sauser, S. Luebbecke, A. Bals, and T. Pfeiffer, “Leakage detection using fiber optics distributed temperature monitoring,” Smart Struct. and Mat.: Smart Sensor Techn. and Meas, 2004.
  [11] Najman, L. and Schmitt, M., “Geodesic saliency of watershed contours and hierarchical segmentation, Pattern Analysis and Machine Intelligence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.18, No. 12, Pp.1163-1173, 1996.
  [12] Abedin, K. S. and T. Morioka, “Remote Detection of Fiber Fuse Propagating in Optical Fibers,” Proceedings of Conference on Optical Fiber Communication, collocated National Fiber Optic Engineers Conference Vol. 1-5, pp. OThD5, San Diego, CA, USA, 2009.
  [13] Percival, R. M., E. S. R. Sikora and R. Wyatt, “Catastrophic damage and accelerated ageing in bent fibers caused by high optical powers,” Electronics Letters, Vol. 36, No. 5, pp. 414-416, ISSN: 0013-5194, 2000.
  [14] P. Soile, “Morphological image analysis, principles and applications,” Springer, Berlin, 2003.
  [15] W. Wolberg, W. N. Street, and O. L. Mangasarian, “Machine learning to diagnose breast cancer from image-processed features,” Rep. of Uni. Wisconsin, USA, 1994.
  [16] Matlab user manual – Image processing toolbox, MathWorks, Natick, 1999.
  [17] Holst, and Gerald C. “Electro-optical Imaging System Performance,” JCD Publishing Winter Park, Florida USA, ISBN: 0-8194-6179-2, 2006.
  [18] Ulrich Kienitz, “Thermal imaging as a modern form of pyrometery. Journal of Sensors and Sensor Systems,” 265–271, 2014.
  [19] C.T. Lin, and M. Thomous, “Study and Overview of Venation of leaf using Image Processing,” International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), Vol.4, Issue.5, pp.25-30, India, 2016.
  

  Citation
  Hussam Elbehiery, "Optical Fiber Cables Networks Defects Detection using Thermal Images Enhancement Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.22-29, 2018

• Open Access   Article

  Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

  Nilamadhab Mishra

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.30-36, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.3036

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The latest internet of everything (IoE) advancements in data elicitation and digital storage technology leads to a large heterogeneous data depository, in which the IoE data are stored in a column oriented relational framework. The main purposes of the research are to design and explore data models, frameworks, architectures, and algorithms on network-centric data, mainly IoE data to accomplish the data science and knowledge analytic tasks for Intellectual domain applications. Some storage incompatibilities are there in the relational structure of multi-objective IoE data base that creates threats to data integrity and consistency. In a large scale IoE database, huge numbers of rows are there along with limited number of columns. So, column oriented relational framework greatly improve the performance of IoE data base in terms of data depository and access management. Knowledge analytic is the major part of data science; Analytic is a never ending process because of progressive technological change requirements as well as the business change requirements. The beauty of Analytics is that two data scientist with same problem may come up with two different new solutions. So, in this work, I discuss the overall data science and knowledge analytic streams for an effective IoE database management and knowledge discovery.

  Key-Words / Index Term
  column oriented database, IoE database, knowledge analytic, data depository, data science

  References
  [1] Mishra, Nilamadhab, Chung-Chih Lin, and Hsien-Tsung Chang. "A Cognitive Oriented Framework for IoT Big-data Management Prospective."Communication Problem-Solving (ICCP), 2014 IEEE International Conference on. IEEE, 2014.
  [2] S.J. Nasti, M. Asgar, M.A. Butt , "Analysis of Customer Behaviour using Modern Data Mining Techniques", International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.64-66, 2017.
  [3] Zarko, Ivana Podnar, et al. "IoT data management methods and optimisation algorithms for mobile publish/subscribe services in cloud Environment. “Networks and Communications (EuCNC), 2014 European Conference on. IEEE, 2014.
  [4] Gubbi, Jayavardhana, et al. "Internet of Things (IoT): A vision, architectural elements, and future directions." Future Generation Computer Systems 29.7 (2013): 1645-1660.
  [5] Anantharam, Pramod, Payam Barnaghi, and Amit Sheth. "Data Processing and Semantics for Advanced Internet of Things (IoT) Applications: modeling, annotation, integration, and perception." Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics. ACM, 2013.
  [6] Mishra, Nilamadhab, Hsien-Tsung Chang, and Chung-Chih Lin. "An IoT Knowledge Re-engineering Framework for Semantic Knowledge Analytics for BI-services." Mathematical problems in engineering, vol. 2015, Article id-759425, 12 pages (2015).
  [7] Dagnino, Aldo, and David Cox. "Industrial Analytics to Discover Knowledge from Instrumented Networked Machines." Proceedings of the 26th International Conference on Software Engineering and Knowledge Engineering (SEKE’14), Vancouver, Canada. 2014.
  [8] Chang, H. T., Mishra, N., & Lin, C. C., IoT big-data centred knowledge granule analytic and cluster framework for BI applications: a case base analysis. PloS one, 10(11), 2015.
  [9] Mishra, N., Chang, H. T., & Lin, C. C. Sensor data distribution and knowledge inference framework for a cognitive-based distributed storage sink environment. International Journal of Sensor Networks, 26(1), 26-42,2018.
  [10] Mishra N,. "In-network Distributed Analytics on Data-centric IoT Network for BI-service Applications", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 2, Issue 5, pp.547-552, September-October.2017.
  [11] Patnaik, B. C., & Mishra, N. “A Review on Enhancing the Journaling File System”, Imperial Journal of Interdisciplinary Research, 2, no. 11 (2016)
  [12] Chang, H. T., Yu-Wen Li., & Mishra, N. “mCAF: A Multi-dimensional Clustering Algorithm for Friends of Social Network Services”, Springer Plus, 2016.
  [13] Chang, H. T., Liu, S. W., & Mishra, N. “A tracking and summarization system for online Chinese news topics”, Aslib Journal of Information Management, 67(6), 687-699,2015.
  [14] Mishra, N., Lin, C. C., & Chang, H. T. “A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective”, International Journal of Distributed Sensor Networks, 2015.
  [15] Mishra, N., Lin, C. C., & Chang, H. T. “Cognitive inference device for activity supervision in the elderly”, The Scientific World Journal, 2014.
  [16] Mishra, N., Chang, H. T., & Lin, C. C. “Data-centric Knowledge Discovery Strategy for a Safety-critical Sensor Application”, International Journal of Antennas and Propagation, Article ID 172186, 11 pages, 2014. doi:10.1155/2014/172186.
  [17] Cheng, Hsu-Chen, and Wen-Wei Liao. "Establishing a lifelong learning environment using IOT and learning analytics." Advanced Communication Technology (ICACT), 2012 14th International Conference on. IEEE, 2012.
  [18] http://sites.parstream.com/parstream-iot-survey-whitepaper(survey march 2015)
  [19] Chawla, S., Hartline, J., & Nekipelov, D. , Mechanism design for data science. In Proceedings of the fifteenth ACM conference on Economics and computation (pp. 711-712). ACM, 2014-june.
  [20] https://en.wikipedia.org/wiki/Data_science
  [21] Provost, F., & Fawcett, T., Data Science for Business: What you need to know about data mining and data-analytic thinking. "O`Reilly Media, Inc." 2013.
  [22] http://www.businessdictionary.com/definition/analytics.htm.
  [23] http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/
  [24] Tukey, J. W. , We need both exploratory and confirmatory. The American Statistician, 34(1), 23-25, 1980.
  [25] Hayashi, C., Yajima, K., Bock, H. H., Ohsumi, N., Tanaka, Y., & Baba, Y. (Eds.). (2013). Data Science, Classification, and Related Methods: Proceedings of the Fifth Conference of the International Federation of Classification Societies (IFCS-96), Kobe, Japan, March 27–30, 1996. Springer Science & Business Media.
  [26] Davenport, T. H., Harris, J. G., & Cantrell, S., Enterprise systems and ongoing process change. Business Process Management Journal, 10(1), 16-26, 2004.
  [27] Shron, M. (2014). Thinking with Data: How to Turn Information Into Insights. " O`Reilly Media, Inc.".
  [28] https://en.wikipedia.org/wiki/Internet_of_Things.
  [29] http://www.scaledb.com/internet-things-database.php
  [30] Cheng, Hsu-Chen, and Wen-Wei Liao. "Establishing a lifelong learning environment using IOT and learning analytics." Advanced Communication Technology (ICACT), 2012 14th International Conference on. IEEE, 2012.
  [31] Zarko, Ivana Podnar, et al. "IoT data management methods and optimization algorithms for mobile publish/subscribe services in cloud environments. “Networks and Communications (EuCNC), 2014 European Conference on. IEEE, 2014.
  [32] Gubbi, Jayavardhana, et al. "Internet of Things (IoT): A vision, architectural elements, and future directions." Future Generation Computer Systems 29.7 (2013): 1645-1660.
  [33] Anantharam, Pramod, Payam Barnaghi, and Amit Sheth. "Data Processing and Semantics for Advanced Internet of Things (IoT) Applications: modeling, annotation, integration, and perception." Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics. ACM, 2013.
  [34] Dagnino, Aldo, and David Cox. "Industrial Analytics to Discover Knowledge from Instrumented Networked Machines." Proceedings of the 26th International Conference on Software Engineering and Knowledge Engineering (SEKE’14), Vancouver, Canada. 2014.
  [35] Dou, Dejing, Hao Wang, and Haishan Liu. "Semantic data mining: A survey of ontology-based approaches." Semantic Computing (ICSC), 2015 IEEE International Conference on. IEEE, 2015.
  [36] Philip Chen, C. L., & Zhang, C. Y., Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences 2014, 275, 314-347.
  [37] Mehta, A., Big Data: Powering the Next Industrial Revolution. Tableau Software White Paper 2011.
  [38] Comas, D. S., Pastore, J. I., Bouchet, A., Ballarin, V. L., & Meschino, G. J., Type-2 Fuzzy Logic in Decision Support Systems. In Soft Computing for Business Intelligence (pp. 267-280). Springer Berlin Heidelberg 2014.
  [39] Lin, Y. Y., Chang, J. Y., & Lin, C. T. , A TSK-type-based self-evolving compensatory interval type-2 fuzzy neural network (TSCIT2FNN) and its applications. Industrial Electronics, IEEE Transactions on, 61(1), 447-459 2014.
  [40] Alelyani, S., Tang, J., & Liu, H., Feature Selection for Clustering: A Review. Data Clustering: Algorithms and Applications, 2013, 29.
  [41] Sheta, A. F., Braik, M., Öznergiz, E., Ayesh, A., & Masud, M., Design and Automation for Manufacturing Processes: An Intelligent Business Modeling Using Adaptive Neuro-Fuzzy Inference Systems. In Business Intelligence and Performance Management 2013 (pp. 191-208). Springer London.
  [42] Makridou, G., Atsalakis, G. S., Zopounidis, C., & Andriosopoulos, K., Gold price forecasting with a neuro–fuzzy–based inference system. International Journal of Financial Engineering and Risk Management, 2013, 1(1), 35-54.
  [43] Lemos, A., Caminhas, W., & Gomide, F., Multivariable gaussian evolving fuzzy modeling system. IEEE Transactions on Fuzzy Systems, 2011, 19(1), 91-104.
  [44] F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. , Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European Business Review, 2014, 26(2), 106-121.
  [45] Chen, Y., & Chiu, Y., Enhancing business intelligence for supply chain operations through effective classification of supplier management. Uncertain Supply Chain Management, 2014, 2(4), 229-236.
  [46] Al-Shayea, Q. K., & Al-Shayea, T. K., Customer Behavior on RFMT Model Using Neural Networks. In Proceedings of the World Congress on Engineering 2014 (Vol. 1).
  [47] Al-Shayea, Q. K., & El-Refae, G. A., Evaluation of banks insolvency using artificial neural networks. In Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED`12), Cambridge, United Kingdom, 2012 February (pp. 22-24).
  [48] Wu, D. (2012). On the fundamental differences between interval type-2 and type-1 fuzzy logic controllers. Fuzzy Systems, IEEE Transactions on, 20(5), 832-848.
  [49] Adderley, R., Seidler, P., Badii, A., Tiemann, M., Neri, F., & Raffaelli, M. (2014, July). Semantic Mining and Analysis of Heterogeneous Data for Novel Intelligence Insights. In IMMM 2014, The Fourth International Conference on Advances in Information Mining and Management (pp. 36-40).
  [50] Bhanudas Suresh Panchabhai, Anand Jayantilal Maheshwari, Sunil DhonduMone, "The Road Map of Cloud Computing to Internet of Things", Notice: Undefined index: jour_sub_name in /home/isrosa2l/public_html/journal/IJSRCSE/special_issue.php on line 283, Vol.06, Issue.01, pp.37-42, 2018.
  

  Citation
  Nilamadhab Mishra , "Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.30-36, 2018

• Open Access   Article

  Security Challenges of Virtualization in Cloud Environment

  Anitha H M, P. Jayarekha

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.37-43, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.3743

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Cloud computing is the latest technology used by different category of users. Cloud computing services such as Software as service, Platform as service and Infrastructure as service are offered to the users using virtualization technology. Virtualization enables sharing resources of one system such as CPU, Memory and Storage to many users as per the requirement. Mainly Infrastructure as service is offered to the users through the virtual machines. Although there are advantages of using the virtual machines, security is one of the aspect which is taken care to a lesser extent. Virtual machines are exposed to different attacks such as malwares, malicious users. There are threats like denial of service, cross virtual machine attacks, insecure virtual machine migration, attacks on virtual machine image and hypervisor etc., hence virtual machine security has to be looked with high priority. Mitigation of the risks at virtualization level are surveyed.In this paper, several security vulnerabilities are identified and present various algorithms and implemented approaches to provide security to virtualization layer.

  Key-Words / Index Term
  Virtual Machine, Hypervisor, Virtualization, Cloud provider,Denial of Service, Cross Virtual Machine attacks

  References
  [1] Avram, Maricela-Georgiana. "Advantages and challenges of adopting cloud computing from an enterprise perspective." Procedia Technology 12 ,pp.529-534,2014.
  [2] Shoaib, Yasir, and Olivia Das. "Pouring Cloud Virtualization Security Inside Out." arXiv preprint arXiv:pp.1411.3771,2014.
  [3] Traditional systems and virtualized systems, https://www.vmware.com/pdf/virtualization.pdf. Accessed on January 25,2018.
  [4] S Malhotra, Lakshay, Devyani Agarwal, and Arunima Jaiswal. "Virtualization in cloud computing." J. Inform. Tech. Softw. Eng4.2 ,2014.
  [5] Daniel A. Menasce, "Virtualization: Concepts, applications, and performance modeling." In Int. CMG Conference, pp. 407-414. 2005.
  [6] Jenni Susan Reuben, “A Survey on Virtual Machine Security”, Seminar of Network Security, Helsinki University of Technology, 2007.
  [7] Farzad Sabahi, "Virtualization-level security in cloud computing." Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on. IEEE, pp. 250-254, 2011.
  [8] Hashizume, K., Rosado, D. G., Fernández-Medina, E., & Fernandez, E. B.. "An analysis of security issues for cloud computing." Journal of Internet Services and Applications 4.1 (2013): 5,2013.
  [9] Christopher Jarabek, "A Review of Cloud Computing Security: Virtualization, Side-Channel Attacks, and Management ", from University of Calgary: http://people.ucalgary.ca/~cjjarabe/papers /jarabek_cloud_security.pdf, 2011.
  [10] Kazim, Muhammad, Rahat Masood, Muhammad Awais Shibli, and Abdul Ghafoor Abbasi. "Security aspects of virtualization in cloud computing." In Computer Information Systems and Industrial Management, pp. 229-240. Springer, Berlin, Heidelberg, 2013.
  [11] Covert channels, https://www.sans.org/security-resources/ idfaq/what-is-covert-channel/ -and-what-are-some-examples/2/17.Accessed on 3 November 2017.
  [12] Leginamate users and Attackers,https://blog.radware.com/security/2013/06/distinguish-between-legitimate-users-and-/attackers-the-secret-/-sauce-of-ddos-protection/.Accessed on 3 November 2017.
  [13] Virtual Machine Access, https://www.pcisecuritystandards.org/documents/Virtualization_InfoSupp_v2.pdf.Accessed on 25 january,2018.
  [14] Li, Min, Yulong Zhang, Kun Bai, Wanyu Zang, Meng Yu, and Xubin He. "Improving Cloud Survivability through Dependency based Virtual Machine Placement." In SECRYPT, pp. 321-326. 2012.
  [15] Wahid, Khan Ferdous, Nicolai Kuntze, and Carsten Rudolph. "Trusted Virtual Machine Management for Virtualization in Critical Environments." In ICS-CSR. 2013.
  [16] Wu, Hanqian, Yi Ding, Chuck Winer, and Li Yao. "Network security for virtual machine in cloud computing." In Computer Sciences and Convergence Information Technology (ICCIT), 2010 5th International Conference on, pp. 18-21. IEEE, 2010.
  [17] Schwarzkopf, Roland, Matthias Schmidt, Christian Strack, Simon Martin, and Bernd Freisleben. "Increasing virtual machine security in cloud environments." Journal of Cloud Computing: Advances, Systems and Applications 1, no. 1 (2012): 12,2012.
  [18] Almodawar, Abdalrhman, Mahmoud Al-Ayyoub, and S. Mohammad. "Security-aware placement and migration algorithm in iaas interclouds." The fourth international conference on information and communication systems (ICICS 2013). 2013.
  [19] Su, Kui, Lei Xu, Cong Chen, Wenzhi Chen, and Zonghui Wang. "Affinity and conflict-aware placement of virtual machines in heterogeneous data centers." In Autonomous Decentralized Systems (ISADS), 2015 IEEE Twelfth International Symposium on, pp. 289-294. IEEE, 2015.
  [20] Wang, Gary, Zachary John Estrada, Cuong Manh Pham, Zbigniew T. Kalbarczyk, and Ravishankar K. Iyer. "Hypervisor Introspection: A Technique for Evading Passive Virtual Machine Monitoring." In WOOT. 2015.
  [21] Lombardi, Flavio, and Roberto Di Pietro. "Secure virtualization for cloud computing." Journal of Network and Computer Applications 34.4 (2011): 1113-1122,2011.
  [22] Ammons, Glenn, Vasanth Bala, Todd Mummert, Darrell Reimer, and Xiaolan Zhang. "Virtual machine images as structured data: the mirage image library." Proceedings of the USENIX HotCloud , 2011.
  [23] Wei, Jinpeng, Xiaolan Zhang, Glenn Ammons, Vasanth Bala, and Peng Ning. "Managing security of virtual machine images in a cloud environment." In Proceedings of the 2009 ACM workshop on Cloud computing security, pp. 91-96. ACM, 2009.
  [24] Sabahi, Farzad. "Secure virtualization for cloud environment using hypervisor-based technology." International Journal of Machine Learning and Computing 2.1 (2012): 39,2012.
  [25] Aiash, Mahdi, Glenford Mapp, and Orhan Gemikonakli. "Secure live virtual machines migration: issues and solutions." Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on. IEEE, 2014.
  [26] Anala, M. R., Jyoti Shetty, and G. Shobha. "A framework for secure live migration of virtual machines." Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on. IEEE, 2013.
  [27] Nagesh, O. Sri, Tapas Kumar, and Venkateswara Rao Vedula. "A Survey on Security Aspects of Server Virtualization in Cloud Computing." International Journal of Electrical and Computer Engineering (IJECE) 7.3 (2017): pp.1326-1336,2017.
  [28] Anitya Kumar Gupta, Srishti Gupta, "Security Issues in Big Data with Cloud Computing", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.27-32, 2017
  

  Citation
  Anitha H M, P. Jayarekha , "Security Challenges of Virtualization in Cloud Environment," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.37-43, 2018

• Open Access   Article

  GSM devices Property proof and Tracking

  Hussam Elbehiery

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.44-49, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.4449

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  All GSM network devices such as smart phones become a target of choice for thieves because they are small, valuable, and there is a market for them. A thief can sell a stolen electronic device to an unsuspecting used computer store or pawnshop, and easily receive up to half its value in cash. The penalties for a property crime are less severe than those of a crime against a person is another reason of wide spreading of this crimes. Cost of this crimes is not just its replacement cost, but also the cost of configuring and loading replacement software, and the cost of lost time for the owner while the device is being replaced. An even greater cost is the potential exposure and liability that results from lost confidential corporate and client information. Designed web page will be used to access these devices to take always a copy of the important data and will be saved on the cloud area related to the web site which make it easier to retrieve the stored information in theft or lost case. The introduced research explains how you can keep strangers from accessing your personal information, safely back up and retrieve the stored data in the device. The introduced design will help in trying of minimization of the theft cases also minimizing extortion by thieves or some of exploiters depending on that all GSM device especially cellular phones could be tracked using IMEI (International Mobile Station Equipment Identity) or MEID (Mobile Equipment Identifier).

  Key-Words / Index Term
  Cellphone tracking, Property Proof, and Data Retrieval

  References
  [1] Josh Lockhart, "Modern PHP: New Features and Good Practices Paperback, " laxmi publications, USA, 2015.
  [2] W. Stallings, “Data and Computer Communications,” ©2007 Pearson Education, Inc. Pearson Prentice Hall Pearson Education, Inc. Upper Saddle River, NJ 07458, 8th Edition, ISBN: 0-13-243310-9, USA, 2007.
  [3] Michael Hogan, Fang Liu, Annie Sokol, and Jin Tong, “NIST-SP 500-291, NIST Cloud Computing Standards Roadmap,” National Institute of Standards and Technology Special Publication 500-291 V2, Spec. Publ. 500-291, 108 pages, NIST Cloud Computing Program Information Technology Laboratory, USA, 2013.
  [4] Sikha Bagui and Richard Earp, "Database Design Using Entity Relationship Diagrams,"
  AUERBACH PUBLICATIONS, A CRC Press Company Library of Congress Cataloging-in-Publication Data ISBN:0849315484, UK, 2011.
  [5] U. S. Pandey and Rahul Srivastava, “E- Commerce and Mobile Commerce Technologies,” S. Chand Publisher, Vedams eBooks (P) Ltd., ISBN 10: 8121928419 / ISBN 13: 9788121928410, New- Delhi, India, 2007.
  
  [6] Mathew Johnson, “A New Approach to Internet banking,” Technical Report, UCAM-CL-TR-731, University of Cambridge Computer Laboratory, ISSN 1476-2986, UK, 2008.
  [7] N. H. MohdAlwi and I.S. Fan, “Information security threats analysis fore-learning,” Proc. 1st Inter. Conf. of TECH-EDUCATION, CCIS, vol. 73, pp. 285-291, Athens, Greece, 2010.
  [8] A. Kapil and A. Garg, “Secure web access model for sensitive data, ”International Journal of Computer Science & Communication (IJCSC),vol. 1, no. 1, pp. 13-16, India, 2010.
  [9] Michael E. Whitman, and Herbert J. Mattord, “Principles of Information Security,” 4th Edition, CENAGE Learning, ISBN-13: 978-1-111-13821-9, ISBN-10: 1-111-13821-4, Nelson Education Ltd., USA, 2012.
  [10] Melchor, C.A., and Gaborit, “A fast private information retrieval protocol,” In ISIT 2008. pp. 1848 – 1852. IEEE, 2008.
  [11] Ostrovsky, R., Skeith, and W.E., “A Survey of Single-Database Private Information Retrieval: Techniques and Applications,” In Okamoto, T., Wang, X. (eds.) PKC 2007. LNCS, vol. 4450, pp.393–411. Springer, 2007.
  [12] Drew Springall, Travis Finkenauer, Zakir Durumeric, Jason Kitcat,
  Harri Hursti, Margaret MacAlpine, and J. Alex Halderman, "Security Analysis of the Estonian Internet Voting System,” CCS `14 Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security Pages 703-715, ISBN: 978-1-4503-2957-6, Scottsdale, Arizona, USA, 2014.
  [13] V. Ciriani, S. D. C. di Vimercati, S. Foresti, S. Jajodia, S. Paraboschi, and P. Samarati, “Combining fragmentation and encryption to protect privacy in data storage,” ACM Transactions on Information and System Security(TISSEC), vol. 13, no. 3, New York, USA, 2010.
  [14] Baek, J., Susilo, W., and Zhou, J., “New constructions of fuzzy identity-based encryption,” In ASIACCS 2007, pp. 368–370. ACM, Singapore, 2007.
  [15] Zhou, Jianying, Young, Moti, Bao, and Feng (Eds.), “Applied Cryptography and Network Security,” 4th International Conference, ACNS 2006, Singapore, June 6-9, 2006, Proceedings, ISBN 978-3-540-34704-0, Springer Publishing, USA, 2006.
  [16] Vitaly Dubravin, "Is Data Masking better than Encryption,” Droid Cafe; Life in Technology, 2011.
  
  [17] Jeffrey Hoffstein, Jill Pipher and Joseph H. Silverman, "An Introduction to Mathematical Cryptography," Springer Science and Business Media, LLC, ISBN 978-1-4939-1711-2, 2nd edition, XVII, 538 p. 32 illus., USA, 2014.
  [18] Siddharth Agarwal, AbhinavRungta, R.Padmavathy, Mayank Shankar and NipunRajan, "An Improved Fast and Secure Hash Algorithm," Journal of Information Processing Systems, Volume 8, No.1, ISSN: 2092-805X, Korea, 2012.
  [19] Nabil H. Shaker, Hanady H. Issa, Khaled A. Shehata and Somaia N. Hashem, "Design of F8 Encryption Algorithm Based on Customized Kasumi Block Cipher," International Journal of Computer and Communication Engineering, Volume 2, No. 4, ISSN: 2010-3743, International Academy Publishing (IAP), USA, 2013.
  [20] Jon Duckett, “HTML and CSS: Design and Build Websites,” John Wiley & Sons, Inc., Indianapolis, 1st Edition, ISBN: 978-1-118-00818-8, Indiana, USA, 2011.
  [21] Chen, T.H., Hsiang, H.C. and Shih, W.K., “Security improvement on a remote user authentication scheme using smart cards,” 4thInternational Conference of Information Security and Assurance, Communications in Computer and Information Science, Vol. 76,pp.9–16, Japan, 2010.
  [22] Debiao, H., Jianhua, C. and Jin, H.,“Weaknesses of a dynamic ID-based remote user authentication scheme,” International Journal of Electronic Security and Digital Forensics, Vol. 3, No. 4, pp.355–362, USA, 2010.
  [23] Khan, M.K., Kim, S.K. and Alghathbar, K., “Cryptanalysis and security enhancement of a ‘more efficient & secure dynamic ID-based remote user authentication scheme’,” Journal Computer Communications, Vol. 34, No. 3, pp.305–309, ISSN: 0140-3664, Elsevier Science Publishers B. V. Amsterdam, The Netherlands, 2011.
  [24] Wang, Y.Y., Kiu, J.Y., Xiao, F.X. and Dan, J.,“A more efficient and secure dynamic ID-based remote user authentication scheme,” Computer Communications, Vol. 32, No. 4, pp.583–585, ISSN:0140-3664, Elsevier Science Publishers B. V. Amsterdam, The Netherlands, 2009.
  [25] B.G. Nagaraja, Ravi Rayappa, M. Mahesh, Chandrasekhar M. Patil, and Dr. T.C. Manjunath, “Design & Development of a GSM Based Vehicle Theft Control System,” 978-0-76953516-6/08©2008IEEE, DOI10.1109 / International Conference on Advanced Computer Control ICACC 2009, pp.148-152., Singapore, 2009.
  [26] CCMTA, “Best Practice Models for Combating Auto Theft,” Version 6.1, Anti Auto-Theft Project Group, Canada, 2006.
  [27] EdigaLingappa, Geetavani.B, and JambulaHareesha, “Online Signature Verification using Dynamic Properties,” International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), Vol.5, Issue.6, pp.33-38, India, 2017.
  

  Citation
  Hussam Elbehiery, "GSM devices Property proof and Tracking," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.44-49, 2018

• Open Access   Article

  Keyword based Marathi Interface to the Database using Natural Language Processing

  A.B. Patil, S.C. Pawar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.50-55, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.5055

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Now a day’s a computer plays a vital role in almost all the sectors/application. The available information is stored and retrieved in/from the database. Database Management system (such as SQL, Oracle) allows to handle the database by creation of the database, querying the database, updating database, and administration of databases. So there is need of skilled person (database experts) in order to deal with database management system, but this is not always true. The common people with lack of expertise are there to handle the database, who doesn’t know about the syntax/format of queries to be fire on the database. Also they are more comfortable in their native languages like “Marathi”, “Hindi”, “Arabic”. It is essential to have the database interface in native language (e.g Marathi) so that the non-expert person can also interact with database for storing and extracting the data. In this paper we are proposing the Marathi language interface using Natural Language Processing (NLP) to handle the database. This interface can handle the queries in native language which may be syntactically and semantically incorrect and convert it into correct form.(e.g SQL query). Rest of the paper is organized as follows, Section I contains the introduction of natural language processing , Section II contain the related work of natural language interface to the database system, Section III contain the problem definition and proposed system, Section IV contain the architecture and algorithm of proposed system, section V explain the working of proposed system with example, Section VI give conclusion with future direction.

  Key-Words / Index Term
  Natural Language Processing, SQL, Keyword based interface, Natural language interface

  References
  [1] W. Woods, R. Kaplan,“The lunar sciences natural language information system: final report” Published in 1978.
  [2] B.Sujatha, Dr.S.Viswanadha Raju and H. Shaziya , “A Survey of Natural Language Interface to Database Management System” International Journal of Science and Advanced Technology (ISSN 2221-8386) Volume 2 No 6 June 2012.
  [3] E. D. Sacerdoti , “Language Access to Distributed Data with Error Recovery”
  [4] H. D. David Warren and Fernando C. N. Pereira, “An Efficient Easily Adaptable System for Interpreting Natural Language Queries” American Journal of Computational Linguistics, volume 8, Number 3-4, July-December 1982
  [5] A.Shah, Dr. J. Pareek, H. Patel and N. Panchal , “NLKBIDB - Natural Language and Keyword Based Interface to Database” , 978-1-4673-6217-7/13 ,2013 IEEE
  [6] F.Siasar, M.Norouzifard1, S.H.Davarpanah2, M.H. “Using Natural Language Processing in Order to Create SQL Queries” Proceedings of the International Conference on Computer and Communication Engineering at Kuala Lumpur, Malaysia,
  [7] M. Dua, S. Kumar, Z. Singh Virk, “Hindi Language Graphical User Interface to Database Management System” , 12th International conference on Machine Learning and Application, 2013.
  [8] Prof. A. Shingala and Dr. P. Virparia, “Enhancing the Relevance of Information Retrieval by Querying the Database in Natural form” , International Conference on Intelligent Systems and Signal Processing (ISSP),2013
  

  Citation
  A.B. Patil, S.C. Pawar, "Keyword based Marathi Interface to the Database using Natural Language Processing," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.50-55, 2018

• Open Access   Article

  Segmentation Technique to Detect the AD in Hippocampus Shape using Region Growing in Support Vector Machine

  R. Viswanathan, K. Perumal

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.56-59, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.5659

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The main cause for AD is low brain activity and blood flow. The important component of human brain is hippocampus; here the segmentation technique is used for medical images on real set of Alzheimer’s disease Patients. The normal behavior is depends on functionality of hippocampus. There are various techniques available for image processing in segmentation, in which region growing is used for segmenting the hippocampus region. The brain images are converted into binary form in two approaches. The first approach is based upon block mean and mask approach. The second approach is on top hat and mask. In this some part of images contains hole in which it interrupts the segmentation process. To overcome this problem image hole filtering techniques are implemented. The shape analysis structure of hippocampus will result in classifying the Alzheimer’s disease in human brain on Support Vector Machines.

  Key-Words / Index Term
  Hippocampus; Segmentation; Alzheimer’s disease; SVM classifier

  References
  [1]. Martha E.Shenton, Guido Gerig, Robert W.McCarley, Gabor Szekely, Ron Kikinis “Amygdala – hippocampal shape differences in schizophrenia: The application of 3D shape models to volumetric MR data” Elsevier 5 April 2002.
  [2]. E.Geuze, E.Vermetten and JD Bremner: “MR – based in vivo hippocampal volumetrics: Findings in neuropsychiatric disorders, Molecular Psychiatry 10, 2005.
  [3]. Chupin M., Hasboun D., Poupon F., Baillet S., Garnero L. “Segmentation of the amygdalo – hippocampal complex by competitive region growing [MRI analysis], IEEE International Symposium, 2002.
  [4]. Andreasen, N.C., Flashman, L., Flaum, M., Arndt, S., Swayze II, V., O’Leary, D.S., Ehrhardt, J.C., Yuh, W.T.C., 1994. “Regional brain abnormalities in schizophrenia measured with magnetic resonance imaging”. JAMA 272, 1763 – 1769.
  [5]. Angenent, S., Haker, S., Tannenbaum, A., Kikinis, R., 1999. “On the Laplace – Beltrami operator and brain surface flattening”. IEEE Transactions on Medical Imaging 18, 700 – 711.
  [6]. Attali, D., Montanvert, A., 1994. “Semicontinuous skeletons of 2D and 3D shapes”, proceedings from the Second International Workshop on Visual Form pp 32 – 41.
  [7]. Bartley, A.J., Jones, D.W., Weinberger, D.R., 1997. “Genetic variability of human brain size and cortical gyral patterns” Brain 120, 257 – 269.
  [8]. Blum, H., 1973. “Biological shape and visual science”. Journal of Theoretical Biology 38, 205 – 287.
  [9]. Sethian, J.A., “A fast Marching Level Set Method for Monotonically Advancing Fronts” Proc. Nat. Acad. Sci., 93, 4, pp, 1591 -1595, 1996.
  

  Citation
  R. Viswanathan, K. Perumal, "Segmentation Technique to Detect the AD in Hippocampus Shape using Region Growing in Support Vector Machine," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.56-59, 2018

• Open Access   Article

  Digital Health Record and Prescription

  A.P. Mohod, A. Pankar, A Mohite, A. Dondalkar, A. Kadhe, Manish Naik

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.60-62, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.6062

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  A majority of patients face problems due to negligence of officials. And to maintain all the records in registers or papers for Person, Doctor or even Government is a very complicated task. The hand written prescriptions can be sometimes difficult for shopkeeper or Chemist to understand and finally results in giving the wrong medicines that causes more harm than help. And so there is need to maintain the health record digitally to reduce the doctor’s task and help the patients to understand their health record. In this project, we are developing a cloud based application which works as SaaS that will store every registered person’s lifetime health record that can be retrievable anytime, anywhere. This application will be used by the doctors, where they will be able to access patient’s medical history by their ID and diagnose the current sickness accordingly. Doctors can read, write and edit Patient’s reports and prescriptions will be automatically generated which will reduce the chances of misunderstanding with the chemists.

  Key-Words / Index Term
  Cloud Storage, Patient Health Record, SaaS, Doctors, Patients

  References
  [1]. E. Coiera, The Guide to Health Informatics (2nd Edition). Arnold, London, October 2003. [Chapter 10: The electronic medical record, p111-123] [See also: Chapter 9: Information management systems p101-110].
  [2]. Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA 19122, USA.
  [3]. Sebastien Haneuse, Harvard University, A general framework for selection bias due to missing data in electronic health records-based research
  [4]. https://www.nap.edu/read/24654/chapter/5#37
  [5]. Isaac S. Kohane Using electronic health records to drive discovery in disease genomics http://www.nature.com/nrg/journal/v12/n6/full/nrg2999.html?foxtrotcallback=true
  

  Citation
  A.P. Mohod, A. Pankar, A Mohite, A. Dondalkar, A. Kadhe, Manish Naik, "Digital Health Record and Prescription," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.60-62, 2018

• Open Access   Article

  Survey on Search Engine Localization and Optimization

  P. Meshram, T. Katole, N. Parate, A. Allewar, S. Dafre, S. Patil, S. Naveed

  Survey Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.1 , pp.63-66, Feb-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.6366

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  User get bulk of data whenever he/she search for a particular topic on internet which is not useful for user due to this lots of time is wasted of user. This application which will provide an optimized result for search query according to the user behavior and his previous history. Search engine optimization is a strategical technique to take a web document in top search results of a search engine. Also, localization is a part which will provide notifications of the nearby places, along with the range filter, as per the requirement of the customer. Google is most user friendly Search Engine proved for the Indian users which gives user oriented results. In addition, most of other search engines uses Google search pattern so we have concentrated on it. So, if a page is optimized in Google it is optimized for most of the search engines. This research is based on reviewing various practices that are used to make websites search engine friendly.

  Key-Words / Index Term
  Search Engine Localization; Search engine Optimization; White Hat SEO; Black Hat SEO; Location-based Services

  References
  [1] Neha Sahu, Richa Chhabra,” Review on Search Engine Optimization”, Volume 6, Issue 6, June (2016).
  [2] Joeran Beel, Bela Gipp, and Erik Wilde,”Academic Search Engine Optimization (ASEO): Optimizing Scholarly Literature for Google Scholar” 41 (2): 176–190, January 2010.
  [3] Vinit Kumar Gunjan1 , Pooja2 , Monika Kumari3 ,Dr Amit Kumar4 ,Dr (col.) Allam appa rao5,” Search engine optimization with Google ” IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, January 2012.
  
  [4] J. Bao, Y. Zheng and M. F. Mokbel, “Location-Based and Preference-Aware Recommendation” Proceedings of the 20th Inter- national Conference on Advances in Geographic Information Systems SIGSPATIAL, Redondo Beach, 6-9 November 2012, pp. 199-208.
  

  Citation
  P. Meshram, T. Katole, N. Parate, A. Allewar, S. Dafre, S. Patil, S. Naveed, "Survey on Search Engine Localization and Optimization," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.63-66, 2018