Volume-6 , Issue-2 , Apr 2018, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  An Approach to Network Level Support for Inter-Cloud Live Migration

  Supreet Kaur

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.64-71, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.6471

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  Abstract
  Inter-Cloud live migration of virtual machines is beneficial to a number of scenarios to keep communications between and to virtual machines uninterrupted, inter-cloud live migration must rely on support from the network layer to handle the dynamic relocation of virtual machines between different networks. We developed a prototype implementation of these mechanisms for the ViNe virtual network developed at University of Florida. ViNe provided the methods that simplify resource management and deal with connectivity constraints and support legacy applications for distributed systems by enabling global address ability of VN-connected machines through either a common layer 2 Ethernet or a NAT-free layered 3 IP networks.

  Key-Words / Index Term
  ViNe, IPOP, TAP device, ViNe Overlay, HWDT

  References
  [1] Mauricio Tsugawa, Pierre Riteau, Andrea Matsunaga, Jose Fortes, “User-level Virtual Networking Mechanisms to Support Virtual Machine Migration Over Multiple Clouds”, IEEE International Workshop on Management of Emerging Networks and Services, pp. 568-572, 2010
  [2] Bo An, Zhicheng Cui, Ying Zhang, “Towards a Model-Defined Cloud-of-Clouds”, Services Transactions of Cloud Computing, Volume. 4, Issue. 2, pp. 1-14, 2016
  [3] Pierre Riteau, “Building Dynamic Computing Infrastructures over Distributed Clouds”, International Parallel Distributed Processing Symposium, pp. 1-5, 2011
  [4] David Isaac Wolinsky, Yonggang Liu, “On the Design of Scalable, Self-Configuring Virtual Networks”, ACM, Volume. 9, No. 11, pp. 1-12, 2009
  [5] A. Ganguly, R. Figueiredo, “IP over P2P: Enabling self-configuring virtual IP networks for grid computing”, International Parallel and Distributed Processing Symposium, Volume. 4, Issue. 2, pp. 1-10, 2016
  

  Citation
  Supreet Kaur, "An Approach to Network Level Support for Inter-Cloud Live Migration," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.64-71, 2018

• Open Access   Article

  Facial Expression Recognition Using Static Facial Images

  G.Sowmiya, V. Kumutha

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.72-75, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.7275

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  Abstract
  Abstract Delicate concerns about the treatment of individuals during interviews and interrogations have stimulated efforts to develop "non-intrusive" technologies for rapidly assessing the credibility of statements by individuals in a variety of sensitive environments. Methods or processes that have the potential to exactly focus investigative resources will advance operational excellence and improve investigative capabilities. Facial expressions have the capacity to communicate emotion and regulate interpersonal behavior. Facial Expression Recognition -FER has been dramatically developed in recent years, especially machine learning, Image processing and human cognition. For this reason, the bang and possible usage of automatic facial expression recognition system have been mounting in a broad range of applications, including human-computer interaction, robot control and driver state observation. This paper proposes an automatic facial expression recognition using static facial images, capable of distinctive the four universal emotions: neutral, happiness, sadness and surprise. It is designed to be person independent and tailored only for static images.

  Key-Words / Index Term
  Keywords Facial Expression recognition, eye and lip detection, Bezier curve, emotion, RGB color, binary image pixel.

  References
  [1] Noor A. Ibraheem, Rafiqul Z. Khan, Mokhtar M. Hasan. Comparative Study of Skin Color based Segmentation Techniques. August 2013 –IJAIS.
  [2] Saket S Kulkarni, Narender P Reddy, and SI Hariharan. Facial expression (mood) recognition from facial images using committee neural networks. August 2009-Biomed central.
  [3] Andreea Pascu, Prof. Ross King. Facial expression recognition system, April 2015.
  [4] Ce Zhan, Wanqing Li, Philip Ogunbona, and Farzad Safaei. A Real-Time Facial Expression Recognition System for Online Games. Hindawi-2008.
  [5] Claude C. Chibelushi, Fabrice Bourel. Facial Expression Recognition: A Brief Tutorial Overview.
  [6] V.Sathya, T.chakravarthy. Facial Expression Recognition System Performance Using Various Database. Sep.- Oct. 2017- IOSR-JCE.
  [7] C.Karuna Sharma, T.Aswini, A.Vinodhini, V.Selvi. Accurate Emotion Detection of Digital Images Using Bezier Curves. IJARCET-3, March 2015.
  [8] Ratri Dwi Atmaja, Muhammad Ary Murti, Junartho Halomoan, Fiky Yosef Suratman. An Image Processing Method to Convert RGB Image into Binary. IJEECS, August 2016, pp. 377 - 382.
  [9] Manish Dixit and Sanjay Silakari. Face Recognition Using Approximated Bezier Curve and Supervised Learning Approach. IJMUE-2015, pp.311-24.
  [10] P. Rai and M. Dixit, “Smile detection Via Bezier Curve Of Mouth Interest Points”, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, (2013) July, pp. 802-806.
  [11] Ms. K.T.Chalekar, Prof T. Yengantiwar. REVIEW PAPER ON IMAGE CONTRAST ENHANCEMENT TECHNIQUES. (IJARCET) Volume 3 Issue 3, March 2014.
  [12] S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat, and J. Jatakia. Human Skin Detection Using RGB, HSV and YCbCr Color Models.
  [13] Prof. Rahul, Mangesh, Viplove Wahane, Akshay Murkhe. Analysis of Emotion Recognition using Facial Expressions, using Bezier curve. IJLTET.
  

  Citation
  G.Sowmiya, V. Kumutha, "Facial Expression Recognition Using Static Facial Images," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.72-75, 2018

• Open Access   Article

  Comparative Study between FA, ACO, and PSO Algorithms for Optimizing Quadratic Assignment Problem

  Vanita Jain, Aarushi Jain, Achin Jain, Arun Kumar Dubey

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.76-81, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.7681

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  Abstract
  Many advancements have been made in the development of optimization algorithms which are based on the evolutionary concept of behavior of biotic creatures like fish, ants, birds etc. This paper compares three such nature inspired algorithms; firefly, particle swarm optimization and ant colony optimization algorithm for optimizing Quadratic Assignment Problem. These algorithms are compared on the grounds of their time of computation of result, the no. of iterations required to solve the problem, and the accuracy .

  Key-Words / Index Term
  Quadratic Assignment Problem; Facility Location Problems; Firefly Algorithm; Ant Colony Optimization Algorithm; Particle Swarm Optimization

  References
  [1] Guha, S., & Khuller, S, “Greedy Strikes Back: Improved Facility Location Algorithms” Journal of Algorithms, 31(1), 228–248. (1999)
  [2] Wolsey, Laurence A,.et. al. “The Uncapacitated Facility Location Problem”, CM university, Pittsburgh, management science research rep., (1983).
  [3] Arya, V., Arya, V., Garg, N., Garg, N., Khandekar, R., Khandekar, R., Pandit, V., “Local search heuristic for k-median and facility location problems”, Proceedings of the Thirty-Third Annual {ACM} Symposium on Theory of Computing, 33(3), 21–29, (2001)
  [4] Vygen, Jens, “Approximation algorithms facility location problems” Forschungsinstitut für Diskrete Mathematik, Rheinische Friedrich-Wilhelms-Universität, 1–59, 2005
  [5] Verter, Vedat. "Uncapacitated and capacitated facility location problems" Foundations of Location Analysis. Springer US, (2011), 25-37.
  [6] Choi, Darwin., "Quadratic Assignment Problems (QAP) and its Size Reduction Method" Math Journal 4(1), (2003).
  [7] P. Pardalos, F. Rendl, and H. Wolkowicz, “The quadratic assignment problem: A survey and recent developments” DIMACS Series in Discrete Mathematics and Theoretical Computer Science 16, (1994), 1–42
  [8] Eliane Maria,.et..al., “A survey for the quadratic assignment problem,” European Journal of Operational Research, vol. 176, (2007), pp. 657–690
  [9] R.E. Burkard, “Quadratic Assignment Problems”, EJOR 15, (1984), 283-289.
  [10] Taillard, Éric. "Robust taboo search for the quadratic assignment problem", Parallel computing 17(4), (1991), pp.443-455.
  [11] Azarbonyad, Hosein, and Reza Babazadeh. "A Genetic Algorithm for solving Quadratic Assignment Problem (QAP)” ,arXiv preprint arXiv:1405.5050, (2014).
  [12] Heragu S., “Facilities Design”, Boston, PWS Publishing company, (1997).
  [13] Maniezzo, Vittorio, and Alberto Colorni. "The ant system applied to the quadratic assignment problem" Knowledge and Data Engineering, IEEE Transactions on 11(5), (1999): 769-778.
  [14] Gambardella, L. M., ÉD Taillard, and M. Dorigo. “Ant Colonies for the Quadratic Assignment Problem”, The Journal of the Operational Research Society 50(2), (1999): 167–176.
  [15] Burkard, Rainer E., Stefan E. Karisch, and Franz Rendl. "QAPLIB–a quadratic assignment problem library." ,Journal of Global optimization 10.4 (1997): 391-403.
  [16] Yang, Xin-She, and Xingshi He., "Firefly algorithm: recent advances and applications", International Journal of Swarm Intelligence 1(1), (2013): 36-50.
  [17] Yang, Xin-She. "Firefly algorithm, Levy flights and global optimization”, Research and development in intelligent systems XXVI. Springer London, (2010), 209-218.
  [18] Dorigo, M., & Stu¨tzle, T., “The ant colony optimization metaheuristic: Algorithms, applications and advances”, In F. Glover & G. Kochenberger (Eds.), Handbook of Metaheuristics, vol. 57 (2002).
  [19] Venter, Gerhard, and Jaroslaw Sobieszczanski-Sobieski. "Particle swarm optimization" American Institute of Aeronautics and Astronautics Journal ,41(8), (2003): 1583-1589.
  [20] Bharat Bhushan and Sarath S. Pillai, "Particle Swarm Optimization and Firefly Algorithm: Performance Analysis", IEEE International Advance Computing Conference, (2013).
  [21] X.-S. Yang, “Analysis of Algorithms,” in Nature-Inspired Optimization Algorithms, 1sted. London, Elsevier, (2014), pp.15-16
  [22] S. Agarwal, “Edge Detection in blurred images using Ant Colony Optimization Techniques”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.2, pp.21-24, 2013
  [23] J. E. Bell and P. R. McMullen, “Ant colony optimization techniques for the vehicle routing problem,” Adv. Eng. Informatics, 18(1), (2004), pp. 41–48.
  [24] Pedersen, Magnus Erik Hvass, and Andrew J. Chipperfield. "Simplifying particle swarm optimization" Applied Soft Computing 10(2), (2010): 618-628.
  [25] Russell C. Eberhart and Yuhui Shi, “Particle swarm optimization: Developments, applications and resources,” in Proc. IEEE Int. Conf. Evolutionary Computation, 01, (2001) pp. 81-86.
  [26] Jens Clausen, Michael Perregaard, “Solving Large Quadratic Assignment Problems in Parallel”, Computational Optimization and Applications, September 1997, 8(2), pp 111-127
  

  Citation
  Vanita Jain, Aarushi Jain, Achin Jain, Arun Kumar Dubey, "Comparative Study between FA, ACO, and PSO Algorithms for Optimizing Quadratic Assignment Problem," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.76-81, 2018

• Open Access   Article

  A Review on Efficient Mac Layer Handoff Protocol to Reduce Handoff Latency for Wi-Fi Based Wireless Network

  Mohini Joshi, Kishore Kumbhare

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.82-86, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.8286

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  Abstract
  In next generation mobile heterogeneous networks and WIFI-based wireless networks are becoming important components, because WIFI devices are widely used in laptops, PDAs and other mobile computing machines. In a WIFI-based wireless network, handoff management is a key service, as the radio range of the WIFI device is limited. Moreover, providing seamless roaming in wireless networks is mandatory for supporting real-time applications in a mobile environment, such as VoIP, online games, and e Conference. Recently, many solutions have been introduced to reduce MAC layer handoff latency; however, these solutions consider every mobile node separately, which would not be suitable for a large-scale environment. This paper proposes a novel MAC layer handoff protocol for large-scale WIFI-based wireless networks to support seamless real-time applications. In our scheme, before the mobile node starts initiating the MAC layer handoff process, it selects several neighboring nodes to help it scan available channels. All channels are divided into groups and scanned by these neighbors separately. Therefore, the number of scanning channels in each node is reduced, and scanning latency is minimized. According to simulation results in ns2, we conclude that our scheme can shorten MAC layer handoff latency greatly, and also achieve seamless handoff in terms of loss ratio of data packets.

  Key-Words / Index Term
  IEEE 802. 11 Wlan Handoff, Access Point Handoff, Latency Selective, Scanning Neighbor Graph

  References
  [1] “IEEE standard for information technology-telecommunications and information exchange between systems-local and metropolitan area networks-specific requirements - part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications,” http://standards.ieee.org/getieee802/802.11.html, 2007.
  
  [2] I. F. Akyildiz, J. Xie, and S. Mohanty, “A survey of mobility manage-ment in next-generation all-IP-based wireless systems,” IEEE Wireless Communications, vol. 11, no. 4, pp. 16–28, August 2004.
  
  [3] W. A. Arunesh Mishra, Minho Shin, “An empirical analysis of the ieee 802.11 mac layer handoff process,” ACM SIGCOMM Computer Communication Review, vol. 33, no. 2, pp. 93–102, April 2003.
  
  [4] V. M. Chintala and Q.-A. Zeng, “Novel mac layer handoff schemes for ieee 802.11 wireless lans,” in IEEE Wireless Communications and Networking Conference, March 2007, pp. 4435–4440.
  
  [5] H. Velayos and G. Karlsson, “Techniques to reduce the ieee 802.11b handoff time,” in 2004 IEEE International Conference on Communica-tions, vol. 7, June 2004, pp. 3844–3848.
  
  [6] M. Shin, A. Mishra, and W. A. Arbaugh, “Improving the latency of
  
  802.11 hand-offs using neighbor graphs,” in Proceedings of the 2nd international conference on Mobile systems, applications, and services, June 2004, pp. 19–26.
  
  [7] S. Shin, A. G. Forte, A. S. Rawat, and H. Schulzrinne, “Reducing mac layer handoff latency in ieee 802.11 wireless lans,” in Proceedings of the second international workshop on Mobility management and wireless access protocols, October 2004, pp. 70–83.
  
  [8] N. Mustafa, W. Mahmood, A. A. Chaudhry, and M. Ibrahim, “Pre-scanning and dynamic caching for fast handoff at mac layer in ieee
  
  802.11 wireless lans,” in IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, November 2005, pp. 1–8.
  
  [9] K. Ramachandran, S. Rangarajan, and J. C. Lin, “Make-before-break mac layer handoff in 802.11 wireless networks,” in 2006 IEEE ICC, vol. 10, June 2006, pp. 4818–4823.
  [10] Y. Liao and L. Cao, “Practical schemes for smooth MAC layer handoff in 802.11 wireless networks,” in WoWMoM, June 2006, pp. 1–10.
  
  [11] I. Ramani and S. Savage, “SyncScan: practical fast handoff for 802.11 infrastructure networks,” in Proceeding of IEEE INFOCOM, vol. 1, March 2005, pp. 13–17.
  
  [12] “The network simulator - ns-2,” http://www.isi.edu/nsnam/ns/.
  

  Citation
  Mohini Joshi, Kishore Kumbhare, "A Review on Efficient Mac Layer Handoff Protocol to Reduce Handoff Latency for Wi-Fi Based Wireless Network," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.82-86, 2018

• Open Access   Article

  Implementation of DevOps Architecture in the project development and deployment with help of tools

  Chellamalla Mamatha, S C V S L S Ravi Kiran

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.87-95, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.8795

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  Abstract
  DevOps is the portmanteau of development and operations. In recent times, the agile transformation was adopted in IT organizations for continuous integration principles in software development life cycle (SDLC) which has improved the efficiency of development in the project. With time being it has been realized that the optimization does not help in only continuous integration to make the software delivery process efficient. Unless all the modules in software delivery lifecycle are well designed, implemented and optimized. This is the problem with previous technologies and DevOps addresses it. This paper explains the various phases of SDLC, business needs and ways to move from continuous integration to continuous delivery.

  Key-Words / Index Term
  DevOps, Continuous Development, Continuous Integration, Continuous Testing, Continuous Deployment, Continuous Delivery, Github, Jenkins, Maven, Ansible

  References
  [1]Carmine Giardino, Nicolò Paternoster, Michael Unterkalmsteiner, Tony Gorschek and PekkaAbrahamsson, “Sofware Development in Startup Companies: The Greenfield Startup Model”, IEEE Transactions on Software Engineering, 2016
  [2]Youssef Bassil, “A simulation Model for the Waterfall Software Development Life Cycle”, International Journal of Engineering & Technology (iJET), ISSN: 2049-3444, Vol. 2, No. 5, 2012
  [3]Dimitris Karagiannis, “Agile Modeling Method Engineering”,Faculty of Computer Science, University of Vienna.
  [4]David P. Harvie, Arvin Agah, “Targeted Scrum: Appling mission command to Agile Software Development”, IEEE Transactions on Software Engineering, 2016.
  [5]Dan Hao, Lu Zhang, Lei Zang, Yanbo Wang, Xingxia Wu, Tao Xie, “To be optimal or not in Test-case prioritization”, IEEE Transactions on Software Engineering, 2016.
  [6]Earl T. Barr, Mark Harman, Phil McMinn, MuzammilShahbaz, Shin Yoo, “The Oracle Problem in Software Testing: A Survey”, IEEE Transactions on Software Engineering, 2015.
  [7]Lucy Ellen Lwakatare, PasiKuvaja and Markku Oivo, “Dimensions of Devops”, Springer International Publishing Switzerland 2015.
  [8]Manish Virmani, “Understanding DevOps & bridging the gap from continuous integration to continuous delivery”, Innovative Computing Technology (INTECH), Fifth International IEEE Conference, 2015.
  [9]S. R. Chidamber ; C. F. Kemerer, “A metric suite for Object Oriented Design”, IEEE Transactions on Software Engineering, 2015.
  [10]Maximilien de Bayser, Leonardo G. Azevedo, Renato Cerqueira, “The case for Devops in scientific applications”, IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015.
  [11]Michael Huttermann, “DevOps for Developers”, Springer International Publishing Switzerland, vol. 1, 2012.
  [12]Sungjoo Kang, Okjoo Choi and JongmoonBaik, “Model-based Dynamic Cost Estimation and Tracking Method for Agile Software Development, IEEE/ACIS Transactions on Software Engineering, 2010.
  [13]M. WasifNisar, Yong-Ji WANG, ManzoorElahi, “Software Development Effort Estimation Using Fuzzy Login - A Survey”, IEEE Transactions on Software Engineering, 2008
  [14]L. Dobrica, E. Niemela, “A survey on software architecture analysis methods”, IEEE Transactions on Software Engineering, 2002.
  [15]A. Remi Jullian, M.Sangeetha, “From Dev to Ops – Introduction to Devops on understanding Continuous Integration and Continuous Delivery”, International Journal of Innovative Research in Computer and Communication Engineering Vol. 4, Issue 6, June 2016.
  

  Citation
  Chellamalla Mamatha, S C V S L S Ravi Kiran, "Implementation of DevOps Architecture in the project development and deployment with help of tools," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.87-95, 2018

• Open Access   Article

  Mining Health Data in Multimodal Data Series for Disease Prediction

  R.Anupriya, P.Saranya, R.Deepika

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.96-99, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.9699

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  Abstract
  Disease Prediction plays a major role in health care community. With data mining process, disease will be predicted from large number of data. Dataset may be structured or unstructured. If the dataset is unstructured then the latent factor model is used to convert unstructured to structured data and it is very complex to predict a disease using unstructured data. Therefore we use synthetic data, which is structured. We concentrate on different kind of diseases. We propose a convolutional neural network based multimodal disease risk prediction (CNN-MDRP). Here datasets are stored as HER records. K-means clustering algorithm is used to group the datasets. Semi-Supervised Heterogeneous algorithm is applied to grouped data to predict the disease.

  Key-Words / Index Term
  Data Mining, Disease Prediction, HealthCare, Multimodal, K-means, SVM classification

  References
  [1] Min Chen, Yixue Hao, Kai Hwang, Fellow, IEEE, Lu Wang, and Lin Wang*, “Disease Prediction by Machine Learning over Big Data from Healthcare Communities”, vol.5, Pages: 8869 – 8879,2017.
  [2] P. B. Jensen, L. J. Jensen, and S. Brunak, “Mining electronic health records: towards better research applications and clinical care,” Nature Reviews Genetics, vol. 13, no. 6, pp. 395–405, 2012.
  [3] D. Oliver, F. Daly, F. C. Martin, and M. E. McMurdo, “Risk factors and risk assessment tools for falls in hospital in-patients: a systematic review,” Age and ageing, vol. 33, no. 2, pp. 122–130, 2004.
  [4] P. Groves, B. Kayyali, D. Knott, and S. V. Kuiken, “The’big data’revolution in healthcare: Accelerating value and innovation,” 2016.
  [5] M. Chen, S. Mao, and Y. Liu, “Big data: A survey,” Mobile Networks and Applications, vol. 19, no. 2, pp. 171–209, 2014.
  [6] B. Qian, X. Wang, N. Cao, H. Li, and Y.-G. Jiang, “A relative similarity based method for interactive patient risk prediction,” Data Mining and Knowledge Discovery, vol. 29, no. 4, pp. 1070–1093, 2015.
  [7] A. Singh, G. Nadkarni, O. Gottesman, S. B. Ellis, E. P. Bottinger, and J. V. Guttag, “Incorporating temporal ehr data in predictive models for risk stratification of renal function deterioration,” Journal of biomedical
  informatics, vol. 53, pp. 220–228, 2015.
  [8] J. Wan, S. Tang, D. Li, S. Wang, C. Liu, H. Abbas and A. Vasilakos, “A Manufacturing Big Data Solution for Active Preventive Maintenance”, IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII. 2017.2670505, 2017.
  [9] W. Yin and H. Sch¨utze, “Convolutional neural network for paraphrase identification.” in HLT-NAACL, 2015, pp. 901–911.
  [10] Nori, H. Kashima, K. Yamashita, H. Ikai, and Y. Imanaka, “Simultaneous modeling of multiple diseases for mortality prediction in acute hospital care,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015,pp. 855–864.
  

  Citation
  R.Anupriya, P.Saranya, R.Deepika, "Mining Health Data in Multimodal Data Series for Disease Prediction," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.96-99, 2018

• Open Access   Article

  Survey on User’s Micro-Environment control using Smartphone Sensors in Android

  P.S. Borkar, K. M. Surve, S. S. Punde, K. S. Gorde, S. A. Jaiswal, S. Y. Jamaiwar

  Survey Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.100-104, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.100104

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  Abstract
  Context-awareness is obtaining more and more necessary for a variety of mobile and pervasive applications on these days good phones. Whereas human-centric contexts (e.g., indoor/ out of doors, at home/in workplace, driving/walking) are extensively researched, few makes an attempt have studied from phones perspective (e.g., on table/sofa, in pocket/bag/hand). We have a tendency to see such immediate surroundings as micro atmosphere, typically many to a dozen of centimetres, around a phone. In this study, we have a tendency to style and implement micro-environment sensing platform that mechanically records detector hints and characterizes the micro-environment of good phones. The platform runs as a daemon method on a wise phone and provides finer-grained atmosphere data to higher layer applications via programming interfaces.

  Key-Words / Index Term
  Context-awareness, micro-environment

  References
  [1] Z. Yang, L. Shangguan, W. Gu, Z. Zhou, C. Wu and Y. Liu, “Sherlock: Micro-environment Sensing for Smartphones”, IEEE Transactions on Parallel and Distributed Systems , Vol 2. Issue 3, 2013
  [2] Derick A. Johnson and Mohan M. Trivedi, “Driving Style Recognition Using a Smartphone as a Sensor Platform”, Laboratory for Intelligent and Safe Automobiles (LISA) University of California, San Diego La Jolla
  [3] Intelligent Transportation Systems Washington, DC, USA. Pp.1609-1615, 2011
  [4] Jilong Liao, Zhibo Wang, Lipeng Wan, Qing Cao and Hairong Qi, “Smart Diary: A Smartphone-based Framework for Sensing, Inferring and Logging Users’ Daily Life”, pp.4-12, 2013
  [5] Jennifer R. Kwapisz, Gary M. Weiss, Samuel A. Moore, “Activity Recognition using Cell Phone Accelerometers”, IEEE 2014
  [6] Activity recognition with smartphone sensors by Stefan Dernbach. Pp. 121-133
  [7] Zhibo Wang, Jilong Liao, Qing Cao, Hairong Qi and Zhi Wang, “Friendbook: A Semantic-based Friend Recommendation System for Social Networks”, IEEE, 2013
  [8] J. Dai, X. Bai, Z. Yang, Z. Shen, D. Xuan. “PerFallD: A Pervasive Fall Detection System Using Mobile Phones.” In PervasiveHealth’10, 2010.
  [9] Prashanth Mohan, Venkata N. Padmanabhan, Ramchandran Ramjee. “Nericell: Rich Monitoring of Road and Traffic Conditionsusing Mobile Smartphones” In Mobisys 2010
  [10] Marjorie Skubic 1, (Senior Member, IEEE), Rainer Dane Guevara 1, 2, and Marilyn Rantz 3, (Member, IEEE). Automated Health Alerts Using In-Home Sensors.
  

  Citation
  P.S. Borkar, K. M. Surve, S. S. Punde, K. S. Gorde, S. A. Jaiswal, S. Y. Jamaiwar, "Survey on User’s Micro-Environment control using Smartphone Sensors in Android," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.100-104, 2018

• Open Access   Article

  REVIEW OF REAL TIME EFFICIENT WEB PAGE SEARCHES BASED ON RANKING

  S. HASEENA, R. LATHA, A.M. BARANI

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.105-108, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.105108

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  Abstract
  Web surfer are explicitly intended for looking website pages, archives and pictures were created to encourage searching through a vast, amorphous blob of unstructured assets. They are built to take after a multi-organize process: creeping the vast reserve of pages and archives to glance through at once in the allegorical froth from their substance, ordering the froth/popular expressions in a kind of semi-organized frame (database or something), and finally, settling client sections/questions to return most precise or logical relevance to the outcomes and connections to those filtered records or pages from the stock.

  Key-Words / Index Term
  look interface, crawler,indexer, and database

  References
  Adriaanse L and Rensleigh C (2013). Web of Science, Scopus and Google Scholar. The Electronic Library, 31(6), 727-744.
  [2] Aniko Hannak, Piotr Sapiezynski, Arash Molavi, Kakhki, Balachander Krishnamurthy, David Lazer, Alan Mislove and Christo Wilson (2013)”Measuring Personalization of Web Search (PDF)” Archived from the original (PDF) on April 25, 2013.
  [3] Bornmann, L, Leydesdorff L and Mutz R (2013). The use of percentiles and percentile rank classes in the analysis of bibliometric data: Opportunities and limits. Journal of Informetrics, 7(1), 158-165.
  [4] D. Coltuc and J.-M. Chassery, “Very fast watermarking by reversible contrast mapping,” IEEE Signal Process. Lett., vol. 14, no. 4, pp. 255–258, Apr. 2007. Christian Quast, Elmar, Pruesse Pelin, Yilmaz Jan, Gerken Timmy, Schweer Pablo, Yarza Jörg, Peplies Frank and Oliver Glöckner (2013) ” The SILVA ribosomal RNA gene database project: improved data processing and web-based tools” at Nucleic Acids Research, Volume 41, Issue D1, 1 January 2013, Pages D590–D596.
  [5] Jayanthi.J and Dr.K.S.Jayakumar (2011) “An integrated Page Ranking Algorithm for Personalized Web Search”. In International Journal of Computer Applications (0975- 8887), Volume 12-No.11, January 2011.
  [6] Manikandan, R and Dr. R.Latha (2017).“A literature survey of existing map matching algorithm for navigation technology,” International journal of Engineering Sciences & Research Technology”, 6(9), 326-331.Retrieved September 15, 2017.
  [7] Mercy paul Selvan, A . Chandra Sekar and A. Priya Dharshini (2012) "Survey on Web page Ranking Algorithms"at International Journal of Computer Applications, Vol.41, No-19, ISSN 0975-8887.
  [8] Northcott, D. and Linacre S (2010). Producing spaces for academic discourse:The impact of research assessment exercises and journal quality rankings. Australian Accounting Review, 20(1), 38- 54.
  [9] Sergey Brin and Lawrence Page (2012) “The anatomy of large scale hypertexual search engine” at Computer Networks Elsevier, vol.56, issue-18, pp3825-3833.
  [10] Mitsuo Yokokawa, Fumiyoshi Shoji, Atsuya Uno, Motoyoshi Kurokawa and Tadashi Watanabe (2011). The K computer: Japanese next-generation supercomputer development project. International Symposium on Low Power Electronics and Design (ISLPED). pp. 371–372. doi:10.1109/ISLPED.2011.5993668 (1–3 August 2011).
  

  Citation
  S. HASEENA, R. LATHA, A.M. BARANI, "REVIEW OF REAL TIME EFFICIENT WEB PAGE SEARCHES BASED ON RANKING," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.105-108, 2018

• Open Access   Article

  A comparative study on the Assortment of Information Retrieval systems

  L. Senthilvadivu

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.109-112, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.109112

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  Abstract
  For thousands of years people have realized the importance of archiving and finding information. With the advent of computers, it became possible to store large amounts of information; and finding useful information from such collections became a necessity. Over the last forty years, the field has matured considerably. Several Information Retrieval (IR) systems are used on an everyday basis by a wide variety of users. This paper presents a brief overview of the comparisons of the few assortments of Information Retrieval (IR) models and the description of the connoisseur in the field. The information retrieval by submitting the queries bring out millions of documents which consume the precious time of the user. This effort gives the information to the user to save their time in retrieving the information from the massive information sources.

  Key-Words / Index Term
  Information Retrieval (IR), assortment, connoisseur

  References
  [1] Zobel, Justin, and Alistair Moffat. “Inverted files for text search engines”, ACM Computing Surveys 38 (2),2006.
  [2] Zobel, Justin, Alistair Moffat, Ross Wilkinson, and Ron Sacks-Davis.”Efficient retrieval of partial documents”. IP&M 31 (3): 361-377. DOI: dx.doi.org/10.1016/0306-457300052 -5, 1995.
  [3] Zaragoza, Hugo, Djoerd Hiemstra, Michael Tipping, and Stephen Robertson. “Bayesian extension to the language model for ad hoc information retrieval”. In Proc. SIGIR, pp. 4-9, 2005.
  [4] Zukowski, Marcin, Sandor Heman, Niels Nes, and Peter Boncz. ”Super -scalar RAM-CPU cache compression” .In Proc. International Conference on Data Engineering, p. 59. IEEE Computer Society. dx.doi.org/10.1109/ ICDE. 2006.150.
  [5] L.Senthilvadivu, K.Duraiswamy “Conniving the Information Assimilation and Retrieval (INAR) system for the heterogeneous, multi related Information Sources”, World of Computer Science and Information Technology Journal pp.357-363,2011.
  [6] Zavrel, Jakub, Peter Berck, and Willem Lavrijssen. “Information extraction by text classification: Corpus mining for features”,2000.
  [7] Shian-hua Lin, Meng Chang Chen, Jan-ming Ho, Yueh-ming Huang - ACIRD: “Intelligent Internet Documents Organization and Retrieval“ IEEE Transactions on Knowledge and Data Engineering 2002
  [8] Lashkari, A.H.; Mahdavi, F.; Ghomi, V. “ A Boolean Model in Information Retrieval for Search Engines”, doi:10.1109/ICIME101, 2009
  [9] Frakes, William B. (1992). Information Retrieval Data Structures & Algorithms, Prentice-Hall, Inc. ISBN 0-13-463837-9.
  [10] Jasminka Dobsa, Faculty of Organization and Informatics, Comparison of information retrieval techniques: Latent semantic indexing (LSI) and Concept indexing (CI) published: Feb. 25, 2007, views: 1308
  [11] Zobel, Justin, and Philip Dart. “Phonetic string matching”. In Proc. SIGIR, pp. 166-173,1996. ACM Press.
  [12] C.D. Manning, P. Raghavan, H. Schütze. Cambridge UP, “Classical and web information retrieval systems: algorithms”, mathematical foundations and practical issues, 2008.
  [14]Managing Gigabytes. I.H. Witten, A. Moffat, T.C. Bell. Morgan Kaufmann, “The authority on index construction and compression”,1999.
  [15]Readings in Information Retrieval. K. Sparck Jones, P. Willett. Morgan Kaufmann, “ A collection of classical IR papers”, 1997.
  [16]Information Storage and Retrieval Systems. G. Kowalski, M.T. Maybury. Springer, "Takes a system approach, discussing all aspects of an Information Retrieval System.", 2005
  [17] G.G.Chowdhury. Neal-Schuman, “Introduction to Modern Information Retrieval”, 2003.
  [18] D.H. Kraft, C.L. Barry, C.T. Meadow, B.R. Boyce “Text Information Retrieval Systems”, 2007, Academic Press.
  [19] Jensen fv. Bayesian Networks and Decision Graphs. Technometrics, Volume 45, Number 2, pp. 178-179(2) 2003.
  [20] Anh, Vo Ngoc, Owen de Kretser, and Alistair Moffat “Vector-space ranking with effective early termination”. In Proc. SIGIR, pp. 35-42,2001. ACM Press.
  [21] Crestani, Fabio, Mounia Lalmas, Cornelis J. Van Rijsbergen, and Iain Campbell. “ A survey of probabilistic models in information retrieval”.ACM Computing Surveys30 (4):528-552,1998.doi.acm.org/10.1145/ 299917.
  

  Citation
  L. Senthilvadivu, "A comparative study on the Assortment of Information Retrieval systems," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.109-112, 2018

• Open Access   Article

  Cloud Computing: Data Storage Protocols and Security Techniques

  R. V. Dharmadhikari, S. S. Turambekar, S. C. Dolli, P K Akulwar

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.113-118, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.113118

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  Abstract
  Cloud computing has emerged as a new technology for hosting and delivering services over the internet. Cloud computing removes the necessity for users to plan ahead for resource provisioning. So it is appealing for business owners. Using cloud computing enterprises can start off with little resources as needed and later on increase the resources when needed. Cloud computing has made a huge impact in IT industry. However cloud computing is at its developing stages only. Still there are many issues to be considered. In this paper an overview of cloud computing is given. The concepts used in cloud computing is also highlighted. This paper also gives an idea about the data storage security and protocols which are used in cloud computing.

  Key-Words / Index Term
  Cloud computing; Virtualization; Data Storage; Security; Architecture

  References
  [1] SurucheeV.Nandagaonkar, A.B Raut, “A Comprehensive Study on Cloud Computing” International Journal of Computer Science and Mobile Computing, vol, 3 Issue. 4, pg. 733-738, April 2014.
  [2] http://cloudcomputingnet.com/cloud-computing-architecture/
  [3] VikasGoyal, “Review: Layers architecture of cloud computing” International Journal of computing and Business research, Proceedings of „I-Society 2012‟at GKU
  [4] https://www.computenext.com/blog/when-to-use-saas-paas-and-iaas/
  [5] Qi Zhang, Lu Cheng, RaoufBoutaba, “Cloud computing: state-of-art and research challenges”, Journal of Internet Services and Applications, pg. 7-18, April 2010.
  [6] https://www.techopedia.com/definition/26814/virtual-private-cloud-vpc.
  [7] http://searchcloudapplications.techtarget.com/definition/virtual-private-cloud-VPC.
  [8] http://whatis.techtarget.com/definition/multi-tenancy
  [9] Keiko Hashiume, David G Rosado, Eduardo Fernandez-medina, Eduardo B Fernandez, “An Analysis of Security Issues of cloud Computing”, Journal of Internet Services and Applications, vol 4 Issue 5, 2013.
  [10] http://searchcloudprovider.techtarget.com/definition/cloud-provisioning
  [11] (http://erpbloggers.com/2013/07/the-five-essential-characteristics-of-cloud-computing/)
  [12] Mladen A. Vouk, “Cloud Computing – Issues, Research and Implementation”, Journal of Computing and Information Technology, pg 235-246, April 2008.
  [13] (http://searchsoa.techtarget.com/definition/service-oriented-architecture)
  [14] (http://www.service-architecture.com/articles/web-services/service oriented_architecture_soa_definition.html)
  [15] (http://searchservervirtualization.techtarget.com/definition/virtualization)
  [16] T. Swathi, K. Srikanth, S. Raghunath Reddy,” Virtualization in Cloud Computing”, International Journal of Computer Science and Mobile Computing, vol, 3 Issue. 5, pg. 540-546, May 2014.
  [17] B. Ramamurthy, K. Madurai,” Concepts, Technologies and Business Implications” Wipro Chennai, 2010.
  [18] (http://searchcloudstorage.techtarget.com/feature/Cloud-storage-architecture-vs-local-storage)
  [19] V. Spoorthy.M. Mamatha, B. Santhosh Kumar,” A Survey on Data Storage and Security in Cloud Computing”, International Journal of Computer Science and Mobile Computing, vol, 3 Issue. 6, pg. 306-313, June 2014.
  

  Citation
  R. V. Dharmadhikari, S. S. Turambekar, S. C. Dolli, P K Akulwar, "Cloud Computing: Data Storage Protocols and Security Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.113-118, 2018