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The proffered paper is presented on the practical implementation of a Configurable Linear Feedback Shift Register using Verilog and assesses its various parameters with respect to its configurable aspects and physical performance. The practical implementation is configurable with respect to Number of Bits, Seed Value, Number of Taps and Tap Position that increases the randomness of the output thus creating a more pseudo-random cycle. Moreover, reversible logic is explored and analysed and the technology is comprehended in this paper as an emerging technology that can be used to implement the designed Configurable Linear Feedback Shift Register. Reversible logic is said to enhance the power efficiency of a logical circuit than the conventional models and thus eases the migration to emerging technologies of Quantum Computing, Portable Embedded Systems and Low Power VLSI. The chosen target for the hardware realization of the CLFSR is Altera Cyclone II FPGA. Furthermore, simulation and synthesis of the design is done using ModelSim-Altera for Quartus II 12.1 Web Edition.

Configurable Linear Feedback Shift Register, Field Programmable Gate Array, Verilog, Reversible Logic, Shift Register, Random Number Generator.

[1] Efficient Shift Registers, LFSR Counters, and Long Pseudo- Random Sequence Generators, Application Note, Xilinx Inc.
[2] S Mishra, R Tripathi and D Tripathi, “Implementation of Configurable Linear Feedback Shift Register in VHDL” 9781-1-5090-2118-5/16/$31.00 ©2016 IEEE
[3] Lama Shaer, Tarek Sakakini, Rouwaida Kanj, Ali Chehab and Ayman Kayssi,” A Low Power Reconfigurable LFSR ”18th Mediterranean Electrotechnical Conference MELECON 2016, Limassol, Cyprus, 18-20 April 2016
[4] C. H. Bennett, R. Landauer, “The fundamentals physical limits of computation”.
[5] H. Thapliyal, M. B. Srinivas, M Zwolinski, A beginning in the reversible logic synthesis of sequential circuits. In proceedings of the Int. Conf. on the military & Aerospace Programmable Logic devices, 2005.
[6] J. Rice, “A new look at reversible memory elements”, In proceedings of the International Symposium on circuit and systems. 243-246, 2006.
[7] K. Morita, “Reversible computing and cellular automata- a survey”, Elsevier Theor. Compt. Sci. 395, 1, 101-131, 2008.
[8] Efficient LFSR, XILINX, XAPP 052 July 7,1996 (Version 1.1)
[9] P. Kaye, R. Laflamme, M. Mosca, An Introduction to Quantum Computing. Oxford University Press Inc., Oxford, 2007
[10] P. Yelekar, S. Chiwande, Introduction to reversible logic gates and its applications, 2nd National Conference on Information and Communication Technology (NCICT) 2011, proceedings published in International Journal of Computer Applications (IJCA)
[11] Reversible logic synthesis methodologies with applications to quantum computing, Springer Publication, 2016, ISBN: 978-3-319-23478-6

Harsh H. Ghelani, Nilesh L. Jha, Rohan Naik, Pragya Gupta, "FPGA Implementation of Configurable Linear Feedback Shift Register using Verilog," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.143-146, 2018.

In order to find out the best fast DCT algorithms presented among numerous algorithms,four Fast DCT Algorithms which are popular and frequently used are considered in the paper. Referring their dataflow graphs 4 architectures are designed using Matlab Simulink. HDL coder is used to generate automated VHDL code. The block setsused in the Simulink design are manually modified tothe fixed point 16-bit data type. VHDL code is generated using HDL coder. The designs are synthesized using Xilinx ISE 14.5. A test bench program is written to test the 4 algorithms with the same set of data. Using the test bench program, a post route simulation up to the pin level is executed. From the timing report and synthesis report, the results are compared to find out the best FDCT algorithm in terms of hardware utilization and simulated timing performance.Loeffler’s Algorithm is performing the best, both in terms of hardware utilization and timing requirement as found from the hardware synthesis report and timing report after post route simulation.

FDCTAlgorithm, Dataflow diagram, Matlab Simulink, Xilinx synthesis, Post Route Simulation, Maximum padding delay, Maximum combinational path delay

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Atri Sanyal, Saloni Kumari, Amitabha Sinha, "Performance Analysis of 4 FDCT Algorithms Using Hardware Synthesis and Simulation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.149-154, 2018.

Cloud computing is a shared pool of compute, storage and network resources that are elastic in nature and can dynamically scale to meet changing demands of an IT organization. Every IT organization has to invest in hardware, software resources to make the business run effectively. Cloud offers guaranteed and reliable access to these resources on pay-as-you-use manner. Increasing demand of cloud services leads to certain challenges. One of the major issue in cloud computing is related to task scheduling in which the goal of service provider is to use the available resources in an efficient manner. A number of meta-heuristic algorithms have been proposed to solve this problem. In this paper, cloud task scheduling policy based on Ant colony Optimization (ACO) and Honey Bee Optimization (HBO) algorithms are combined to improve resource utilization. Minimizing Makespan, Flowtime and reducing cost is the major goal of proposed algorithm.

ACO, HBO, Hybridisation, Makespan, Flowtime, Task scheduling

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[4] Chan, T.S. Felix, M. Kumar, Tiwari,“Swarm
Intelligence: Focus on Ant and Particle Swarm Optimization”, Vienna, Austria: I-Tech Education and Publishing, 2007.

[5] Xiangqian Song, Lin Gao, Jieping Wang, "Job scheduling based on ant colony optimization in cloud computing", In Proceedings of 2011 International Conference on Computer Science and Service System, pp.3309 -3312, 2011.
[6] L.D. Dhinesh Babu, P. Venkata Krishna,“Honey bee behavior inspired load balancing of tasks in cloud computing environments” ,Applied Soft Computing, Vol. 13, 2013; pp. 2292-2303.
[7] D. Karaboga, B. Basturk,“Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems”,Springer-Verlag Berlin Heidelberg, 2007,pp. 789–798.
[8] RubingDuan,RaduProdan, “Performance and Cost Optimization for Multiple Large-scale Grid Workflow Applications”2007 ACM 978-1- 59593-764-3/07/0011.
[9] Calheiros, R.N., Ranjan, R., De Rose, C.A.F., and Buyya, R. (2009). CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services.
[10] Vijindra and Sudhir Shenai. A, “Survey of Scheduling Issues in Cloud Computing”, 2012, Elsevier Ltd.
[11] Isam Azawi Mohialdeen, “A Comparative Study of Scheduling Algorithms in Cloud Computing Environment” 2013 Science Publications.
[12] Lee, M.C., Leu, F.Y., and Chen, Y.P. (2015). ReMBF: A Reliable Multicast Brute-Force Co-allocation Scheme for Multi-user Data Grids. In Proceedings - International Computer Software and Applications Conference, (IEEE Computer Society), pp. 774–783.
[13] Nandagopal, M. and Uthariaraj, R. V.,” Hierarchical status information exchange scheduling and load balancing for computational grid environments,” IJCSNS International Journal of Computer Science and Network Security, 10(2):177-185, 2010.
[14] C.H. Hsu and V. Malyshkin, “Methods and tools of parallel programming multicomputers-2010”, Springer Publication, Russia, pp. 10.1007/978-3-642-14822-4
[15] Wang, Z., Xing, H., Li, T., Yang, Y., Qu, R., and Pan, Y. (2016). A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization. IEEE Transactions on Evolutionary Computation 20, 325–342.
[16] D. Karaboga, B. Akay, "A survey: algorithms simulating bee swarm intelligence", Artif Intell Rev, vol. 31, no. 1, pp. 68-85, 2009.
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[18] Rastkhadiv, F., and Zamanifar, K. (2016). Task Scheduling Based On Load Balancing Using Artificial Bee Colony In Cloud Computing Environment. International Journal of Advanced Biotechnology and Research 7, 1058–1069.
[19] M. Kalra, S. Singh, “ A review of metaheuristic scheduling technique in cloud computing”, Cairo University, Egyptian Informatics Journal, Vol. 16, issue 3, pp 275-295, 2015.

T. Chopra, R. Singh, "Task scheduling algorithm optimization based On hybrid HBO and ACO in cloud computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.155-160, 2018.

Visual saliency endeavors to decide the measure of consideration guided towards different locales in a picture in the human pictorial and intellectual systems. It is thusly a focal issue in knowledge explore, neural science, and PC vision. PC vision examiners spin around influencing computational models for either recreating the human visual idea to process or suspecting visual saliency happens as expected. Visual saliency has been consolidated in a gathering of PC vision and picture getting ready endeavors to improve their execution.In this paper aims to correctly popping up the complete salient object(s). Salient object detection aims to correctly highlight the most salient object(s) in an image. Then we formulate saliency map computation as an regression problem,utilizes the supervised learning approach to map the regional feature vectors to detect the saliency scores. The regional feature vector includes contrast and background details. Random forest regressors with multilevel segmentation algorithms can be used to detect the salient object regions with improved accuracy rate. Experimental results provide improved clustered accuracy for real time datasets and are fit for accomplishing cutting edge execution on all open benchmark datasets.

Salient object detection, Saliency map construction, Regional Feature vectors, Benchmark datasets

[1] D. Walther and C. Koch, “Modeling attention to salient proto-objects,” Neural Networks, vol. 19, no. 9, pp. 1395–1407, 2006.
[2] L. Itti, “Automatic foveation for video compression using a neurobiological model of visual attention,” IEEE TIP, 2004.
[3] L. Marchesotti, C. Cifarelli, and G. Csurka, “A framework for visual saliency detection with applications to image thumbnailing,” in ICCV, 2009, pp. 2232–2239.
[4] S. Goferman, A. Tal, and L. Zelnik-Manor, “Puzzle-like collage,” Comput. Graph. Forum, vol. 29, no. 2, pp. 459–468, 2010.
[5] J. Wang, L. Quan, J. Sun, X. Tang, and H.-Y. Shum, “Picture collage,” in CVPR (1), 2006, pp. 347–354.
[6] Dr.S.Thilagamani, V.Manochitra ,”An Intelligent Region-Based Method for Detecting Objects from Natural Images”, International Journal of Pure and Applied Mathematics , issue Feb. 2018 , pp 473-478.
[7] Dr.S.Thilagamani, N.Shanthi ,”A Survey on image segmentation through clustering ” , International Journal of Research and Reviews in Information Sciences, issue 2011, vol. 1, pp . 14-17.
[8] Dr.S.Thilagamani, N.Shanthi ,”A novel recursive clustering algorithm for image oversegmentation” , in European Journal of Scientific Research , issue 2011, vol. 52, pp. 430-436.
[9] Dr.S.Thilagamani , N.Shanthi ” Literature Survey on enhancing cluster quality” , in International Journal on Computer Science and Engineering , vol. 2, pp. 2010 , 1999.
[10] Dr.S.Thilagamani , N.Shanthi ” Object Recognition based on image segmentation and clustering” , in 2011.
[11] Dr.S.Thilagamani , N.Shanthi ”Gaussian and gabor filter approach for object segmentation” , in Journal on Computing and Information Science in Engineering , issue 2014 , vol. 14, pp. 021006.
[12] Dr.S.Thilagamani , N.Shanthi ” Innovative methodology for segmenting the object from a static frame” , in International Journal of Engineering on Innovative Technology , vol. 2, pp. 52-56, 2013.
[13] Dr.S.Thilagamani, S.Ramesh ponnusamy ,”A Comparative study on Diverse fuzzy logic Techniques in segmenting the color images ” , i-manager’s journal on Image processing, vol. 2, pp. 6-13, 2015.
[14] Dr.S.Thilagamani, N.Kavya”A review: Analysis of the of algorithm and techniques in image segmentation” International Journal, vol. 9 , 2018.

S. Thilagamani, V. Manochitra, "IMPROVED OBJECT SEGMENTATION USING MULTI SCALE SALIENCY APPROACH," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.161-167, 2018.

Data mining is a collection of exploration methods based on advanced analytical tools and techniques for handling huge amount of information. Clustering is a useful technique for discovery of knowledge from a dataset. Distance measure plays an important role in clustering. It is used to measure the similarity or dissimilarity between two data points. Euclidean distance measure is normally used in most clustering methods. Some of the limitations of this measure are inability to handle noise and outlier data points, not suitable for sparse data and clusters with only elliptical shapes. In this paper, fuzzy clustering is proposed using different similarity measures such as non-negative vector similarity coefficient (NVSC), Correlation and Cosine. The performance of the algorithm is compared with various similarity measures using five real life benchmark sets including Wine, Liver Disorders, Pima Indian Diabetes, Haberman’s Survival and Statlog (Heart). Experimental results show that fuzzy clustering based on Cosine similarity measure achieves minimum fitness value, minimum intra-cluster distance and maximum inter-cluster distance on various data sets than other similarity measures.

Fuzzy Clustering, Similarity Measures, Cluster Validity

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[11] Ms. Kothariya Arzoo and Kirit Rathod, “K-Means Algorithm with different distance metrics in spatial data mining with uses of NetBeans IDE 8.2”, International Research Journal of Engineering and Technology (IRJET), Vol. 4, Issue 4, pp. 2363-2368, 2017.
[12] V.P. Mahatme and Dr. K.K. Bhoyar, “Impact of Distance Metrics on the Performance of K-Means and Fuzzy C-means Clustering – An Approach to access Student’s Performance in E-Learning Environment”, International Journal of Advanced Research in Computer Science, Vol. 9, No. 1, pp. 887-892, 2018.
[13] Weina Wang and Yunije Zhang, “On Fuzzy Cluster Validity Indices”, Fuzzy Sets and Systems, Vol. 158, Issue 19, pp. 2095-2117, 2007.

O.A. Mohamed Jafar, "A Study of Different Similarity Measures on the Performance of Fuzzy Clustering," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.168-173, 2018.

The expanding number of programs and platform on which the applications are executed, Cross Browser Incompatibilities (XBIs) are turning into a major issue for organisations to create online programming. Cross Browser Incompatibilities (XBIs) became a common issue which can be watched while accessing a similar Web application in various programs. The expanding number of program executions and the continuous development for Web pages, leads contrasts in how programs act in Web applications. Each component of a Web application concern to be effectively rendered and show a similar content, regardless of showing same content differently.Even if a few systems and appraisers have been proposed to distinguish XBIs, they can`t guarantee a similar execution when the application keeps running crosswise over various programs as just express client movement is considered, and in this manner inclined to creating both false positives and false negatives. This exploration has the objective of explaining an approach for naturally recognizing Visual XBIs in Web applications by improvising the current picture examination and DOM investigation procedures and issues identified with these strategies.

Browser, Cross Browser Inconsistency, Reliability, Web application

[1]C.P.Patidar and Meena Sharma ,”An automated approach for cross browser inconsistency(XBI) detection”, Ninth annual ACM India conference organized by ACM India, Oct 21-23,2016.

[2]Nepal Barskar, C.P.Patidar and Meena Sharma, “Analysis and Identification of Cross Browser Inconsistency Issues in Web Application using Automation Testing”, International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555Vol.6, No3, May-June 2016.

[3] A. Mesbah and M. R. Prasad, “Automated cross-browser compatibility testing,” in Proceeding of the 33rd International Conference on Software Engineering, ser. ICSE ’11. New York, NY, USA: ACM, 2011, pp. 561–570.

[4] Choudhary, S. R., Prasad, M. R., & Orso, A., “X-PERT: A Web Application Testing Tool for Cross-Browser Inconsistency Detection”, 2014.
[5] S. Roy Choudhary, M. R. Prasad, and A. Orso. X-PERT: Accurate Identi_cation of Cross-browser Issues in Web Applications. In Proceedings of the 2013 International Conference on Software Engineering, ICSE `13, pages 702-711. IEEE Press, 2013.
[6]S. R. Choudhary, M. R. Prasad, and A. Orso. X-PERT: Accurate Identification
of Cross-Browser Issues in Web Applications. In Proceedings of the 35th IEEE and ACM SIGSOFT International Conference on Software
Engineering (ICSE 2013), pages 702–711, May 2013.

[7]S. Mahajan and W. G. Halfond. Root Cause Analysis for HTML Presentation Failures Using Search-based Techniques. In Proceedingsof the 7th International Workshop on Search-Based Software Testing(SBST), Hyderabad, India, June 2014

[8] S. Roy Choudhary and A. Orso, “Webdiff: Automated identification of cross-browser issues in web applications,” in ICSM ’10: Proceedings of the International Conference on Software Maintenance. IEEE, September 2010.

[9]Sonal Mahajan and William G. J. Halfond. 2015. Detection and Localization of
HTML Presentation Failures Using Computer Vision-Based Techniques. In Proceedings
of the 8th IEEE International Conference on So.ware Testing, Verifcation and Validation (ICST).

[10]Mesbah, A., & Prasad, M. R., “Automated cross-browser compatibility testing”. 33rd International Conference on Software Engineering, ACM Proceedings, pp. 561-570, 2011.

[11]C.P.Patidar and Meena Sharma ,”An automated approach for cross browser inconsistency(XBI) detection”, Ninth annual ACM India conference organized by ACM India, Oct 21-23,2016.

[12] Nepal Barskar, C.P.Patidar and Meena Sharma, “Analysis and Identification of Cross Browser Inconsistency Issues in Web Application using Automation Testing”, International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555Vol.6, No3, May-June 2016.

[13] Shauvik Roy Choudhary,”Detecting Cross-browser Issues in Web Applications”, ICSE ’11, Waikiki, Honolulu, HI, USA, ACM 978-1-4503-0445-0/11/05, May 21–28, 2011.

[14] Shauvik Roy Choudhary. 2015. X-PERT Code. Retrieved Jan 2017 from h.ps://github.com/gatech/xpert

[15] Sonal Mahajan, Abdulmajeed Alameer, Phil McMinn, and William G.J. Halfond.
2017. XFix: Automated Tool for Repair of Layout Cross Browser Issues. In Proceedings of the 26th International Symposium on So.ware Testing and Analysis (ISSTA) – Tool Track.

C.P.Patidar, Neha Verma, "Comparison of Visual Content for Different Browsers," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.174-178, 2018.

In wireless sensor networks (WSNs), many factors, for instance, mutual interference of wireless connections, battlefield applications and nodes presented to the environment without top physical safety, effects in the sensor nodes being extra powerless in against to the attacked a compromised. For tackling the issues security, an effective appropriated trust model is proposed. They faces some issues, first is system was not focus on other trust metrics Trust is evaluated by the two ways direct and indirect trust on the basis of recommendation from third party. The third issue is offering the trust assessment on-neighbour nodes become very essential. Fourth, trust relationship between sensor nodes frequently modified in wireless sensor networks because of the dynamic topology. For solving all these issues proposed the efficient distributed trust model for wireless sensor networks. This system can estimate dependability of sensor nodes more accurately and prevent the security breaches more considerably. Also for sending the data from subject node to object there are number of paths are generated, in this system we used Dijkastra algorithm for finding the shortest path. Also for the existing system faces the problem of security against the different attacks on network. For security purpose we used ECC algorithm. Experimental result shows that energy consumption for proposed system and existing system.

Trust management, Security in wireless sensor networks, direct trust, indirect trust, Shortest path calculation

[1] guangjie Han, Jinfang Jiang ,Feng Wang, Lei Shu and Mohsen Guizani, "An Efficient Distributed Trust Model for Wireless Sensor Networks" May 2015.

[2] Y. S. Moon, H. S. Lim, and E. Bertino, "Provenance based trustworthiness assessment in sensor networks" 2010

[3] P. Mohapatra, K. Govindan "Trust computations and trust dynamics in mobile ad hoc networks: A survey," 2012

[4] Y. Dong, H. Guo, G. Han, L. Shu, and D. Wu, "Cross-layer optimized routing in WSN with duty-cycle and energy harvesting, Wireless Commun" 2014

[5] T. Schulze, K. Nordheimer and D. Veit, "Trustworthiness in networks:A simulation approach for approximating local trust and distrust values" 2010

[6] L. K. Balzano, S. Ganeriwal, and M. B. Srivastava, "Reputation based framework for high integrity sensor networks" 2004

K. K. Ligade, S. P. Pingat, "Privacy Preserving efficient trusted model in WSN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.179-184, 2018.

Chronic kidney disease (CKD) is an increasing and serious disease impacting public health worldwide. The symptoms of CKD are often appearing too late and many patients inevitably face pain and expensive medical treatments. The ultimate treatment is frequent dialysis or Kidney transplant. Early detection of disease through symptoms can prevent the disease progression by referral to appropriate health care services. Machine Learning (ML) techniques can help in identifying the potential risk by discovering knowledge from medical reports of patient. Thus helps in preventing the disease progression. Several models for detecting the risk of CKD, proposed in the literature are based on Data Mining (DM) techniques like classification, clustering and regression etc. These models are demonstrated using variety of languages like Python, Java and tools like Weka and RapidMiner.This research aims at developing a Cloud based Predictive Model to detect the possibilities of CKD and its progression in patients with some health issues like hypertension and diabetes.

Chronic Kidney Disease (CKD), Health Care, Microsoft Azure, Logistic Regression, Machine Learning, Predictive Model

[1] Jameson K, Jick S, Hagberg KW, Ambegaonkar B, Giles A, O’Donoghue D., “Prevalence and management of chronic kidney disease in primary care patients in the UK”. Int J Clin Pract. 2014;68 (9):1110–21.
[2] www.google.co.in/search?q=Chronic+kidney+disease
[3] Collins GS, Omar O, Shanyinde M, Yu L-M. A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods. J Clin Epidemiol. 2013; 66(3):268–77.
[4] Echouffo-Tcheugui JB, Kengne AP. Risk models to predict chronic kidney disease and its progression: a systematic review. Remuzzi G, editor. PLoS Med. 2012; 9(11):e1001344.
[5] https://msdn.microsoft.com/en-us/library/azure/dn905930.aspx
[6] Sumit Basu, “Empirical Results on the Generalization Capabilities and Convergence Properties of the Bayes Point Machine”, Technical Report, December, 1999.
[7] https://msdn.microsoft.com/en-us/library/azure/dn905994.aspx
[8] http://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch12.pdf
[9] Paolo Fraccaro, Sabine van der Veer, Benjamin Brown, Mattia Prosperi, Donal O’Donoghue, Gary S. Collins, Iain Buchan and Niels Peek, “An external validation of models to predict the onset of chronic kidney disease using population-based electronic health records from Salford, UK”, RESEARCH ARTICLE, BMC Medicine (2016) 14:104.
[10] V. Kunwar, K. Chandel, A. S. Sabitha and A. Bansal, "Chronic Kidney Disease analysis using data mining classification techniques," 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), Noida, 2016, pp. 300-305.
[11] M. D. Başar, P. Sarı, N. Kılıç and A. Akan, "Detection of chronic kidney disease by using Adaboost ensemble learning approach," 2016 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, 2016, pp. 773-776.
[12] P. Soundarapandian and L. J. Rubini, http://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease, UCI Machine Learning Repository, Irvine, 2015.
[13] Z. Sedighi, H. Ebrahimpour-Komleh and S. J. Mousavirad, "Featue selection effects on kidney desease analysis," 2015 International Congress on Technology, Communication and Knowledge (ICTCK), Mashhad, 2015, pp. 455-459.
[14] Subasi A., Alickovic E., Kevric J. (2017) Diagnosis of Chronic Kidney Disease by Using Random Forest. In: Badnjevic A. (eds) CMBEBIH 2017. IFMBE Proceedings, vol 62. Springer, Singapore
[15] N. Chetty, K. S. Vaisla and S. D. Sudarsan, "Role of attributes selection in classification of Chronic Kidney Disease patients," 2015 International Conference on Computing, Communication and Security (ICCCS), Pamplemousses, 2015, pp. 1-6.
[16] https://studio.azureml.net/

Stuti Nathaniel, Anand Motwani, Arpit saxena, "Cloud based Predictive Model for Detection of ‘Chronic Kidney Disease’ Risk," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.185-188, 2018.

Mobile Ad hoc Network (MANET) is a set of wireless devices that can move around freely and cooperate with each other in relaying packets without the support of any fixed infrastructure or centralized administration. The absence of any central administration or base station in the MANET makes the routing between nodes more complex compared to wireless networks. Since MANETs are useful in disaster and military operations, the need for the group communication is vital. Most of the routing protocols proposed for ad hoc networks assume a trusted, non-adversial environment and do not take security issues into account in their design. But in real time a MANET is vulnerable to attacks than a wired or infrastructure wireless network. This thesis investigates the security of Optimized Link State Routing protocol (OLSR), a well known routing protocol in MANET by identifying the impact of malicious nodes called Gray Holes on it. Thus, a security extension to address the gray hole attack in OLSR routing has been proposed and the solution is achieved in a simulation environment using NS-2 simulator.

MANET, infrastructureless network, OLSR, gray holes

[1] P. Sankareswary, R. Suganthi, G. Sumathi “Impact of Gray hole nodes in Multicast Adhoc On Demand Distance Vector Protocol ”, In the Proceedings of the 2010 IEEE International Conference ICWCSC 2010, SSN Engineering College, Chennai, India, 2010
[2] P. Jacquet, P. Muhlethaler, T. Clausen, A. Laouiti, A. Qayyum, L. Viennot, “Optimized Link State Routing Protocol for Ad Hoc Networks”, Hipercom Project, INRIA Rocquencourt, BP 105, 78153 Le Chesnay Cedex, France.
[3] L. M. Mary Jelba, S. Gomathi, “Mitigating Different Attacks in OLSR Protocol – A Survey”, International Journal of Inovative Research in Computer Science and Communication Engineering, Vol.4, Issue 6, June 2016
[4] S. Sharma and R. Gupta, “Simulation Study of black hole attack in the mobile ad hoc networks”, Journal of Engineering Science and Technology, pp.243-250, 2009.
[5] Kshitij Bhargava, Dinesh Goyal, “Packet Dropping Attacks in Manet: A Survey”, Journal of Advanced Computing and Communication Technologies, Vol.2, Issue No.3, June 2014
[6] Rupali Sharma, “Gray-hole Aattack in Mobile Ad-hoc Networks: A Survey”, International Journal of Computer Science and Information Technologies, Vol.7, Issue.3, 2016
[7] Biswaraj Sen, Kalpana Sharma, M.K.Ghose, Achute Sharma “Gray Hole Attack in MANETs”, International Journal of Advances in Electronics and Computer Science, Vol.2, Issue.10, Oct, 2015.
[8] Onkar V. Chandure, V.T.Gaikwad, “Detection & Prevention of Gray Hole Attack in Mobile Ad-Hoc Network using AODV Routing Protocol,” International Journal of Computer Applications , Vol.41, Issue.5, March, 2012
[9] S.V. Vasantha, Dr.A. Damodaram, “A Defense Model for Black hole and Gray hole Attacks in MANET”, IJCSMC, Vol.3, Issue. 11 pp.570-576, 2014.
[10] Madhuri Gupta, Krishna Kumar Joshi, “A Review on Detection and Prevention of Gray-Hole Attack in MANETs”, International Journal of Scientific & Engineering Research, Vol.4, Issue.11, November, 2013.

G. Sumathi, K.C. Aarthi, M. Shanmugapriya, "Impact of Gray Holes on Optimized Link State Routing Protocol," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.189-193, 2018.

Cloud Computing is taking popularity day by day in a various organization as it provides giant and scalable space for public and private use. Cloud computing is now widely used by many of the organization as a service. With the increase in the usage of the cloud, there also increases the concerns of cloud security. Somehow many organizations are hesitating to keep their data in the cloud due to security concerns. This paper understands various security issues related to cloud and resolves the data security issues, based on our proposed framework. Proposed framework is based on securing the various types of data like text, image and videos by applying Encryption Algorithms which reduces the various security issues and loopholes and thus making it more secure. It prevents the data from unauthorized access and also maintains the CIA (Confindentiality , Integrity, Availability). The paper proves to be providing the security in least time.

Cloud Computing, Data Security, CSP(Cloud Service Provider), AES(Advanced Encryption Standard),Data theft, Breach level index,DoS,DDos,CIA(Confidentiality,Integrity,Availability)

[1] V. Chang , M. Ramachandran ,“Towards achieving data security with the cloud computing adoption framework”, IEEE Trans. Serv. Comput. 9 (1) (2016) pp. 138–151.

[2] K. Gai , M. Qiu , L. Chen , M. Liu, “Electronic health record error prevention approach” using ontology in big data”, 17th IEEE International Conference on High Performance Computing and Communications, New York, USA, 2015, pp. 752–757

[3] K. Gai , L. Qiu , H. Zhao , M. Qiu ,“Cost-aware multimedia data allocation for heterogeneous memory using genetic algorithm in cloud computing”, IEEE Trans. Cloud Computing. 1 (2016) pp. 99

[4] M. Rajasekhar Reddy, Akilandeswari R, S. Priyadarshini, B. Karthikeyan, E. Ponmani “ A Modified Cryptographic Approach For Securing Distributed Data Storage in Cloud Computing” 2017 International Conference on Networks & Advances in Computational Technologies (NetACT) |20-22 July 2017| Trivandrum

[5] Akanksha Bansal , Arun Agrawal “Providing Security ,Integrity and Authentication Using ECC Algorithm in cloud storage” 2017 International Conference on Computer Communication and Informatics (ICCCI -2017), Jan. 05 – 07, 2017, Coimbatore, INDIA

[6] Dr. P. Raviraj, Angeline Lydia, Dr. M.Y. Sanavullah “An Accurate Image Segmentation Using Region Splitting Technique” GESJ: Computer Science and Telecommunications 2011|No.2 (31) pp. 16-18 10 June 2010.

[7] Ajay Kulkarni, Saurabh Kulkarni, Ketki Haridas, Aniket More “Proposed Video Encryption Algorithm v/s Other Existing Algorithms: A Comparative Study” International Journal of Computer Applications (0975 – 8887) Volume 65– No.1, pp. 3-5 March 2013.

[8] Jolly Shah and Dr. Vikas Saxena” Video Encryption: A Survey” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 2, pp. 5-8 March 2011

[9] Mohit Kumar, Akshat Aggarwal, Ankit Garg “A Review On Digital Image Encryption Techniques and Security Criteria” International Journal of Computer Applications (0975 – 8887) Volume 96– No.13, pp. 4-8 June 2014

[10] Mark Brinda, Michael Heric "The Changing Faces of the Cloud" Bain Global Technologies, Bain & Company, New York, pp. 5-12, 2017.

[11] Anju Bala, Dr. Aman Kumar Sharma “Split and Merge: A Region Based Image Segmentation” ,International Journal of Emerging Research in Management & Technology ISSN: 2278-9359 (Volume-6, Issue-8), August 2017

[12] Yunchuan Sun, Junsheng Zhang, Yongplng Xlong, Guangyu Zhu “Data Security and Privacy in Cloud Computing”, Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2014, Article ID 190903, 16 April 2016

[13] S. Arul Oli , Dr. L. Arockiam “Confidentiality Technique to Obfuscate the Numerical Data to Enhance Security in Public Cloud Storage”, 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET) pp. 3-5

[14] Swapnil Rajesh Telrandhe, Deepak Kapgate “Authentication Model on Cloud Computing” International Journal of Computer Sciences and Engineering (IJCSE) Volume-2, Issue-10, pp. 33-37

[15] Sajjan R.S., Vijay Ghorpade, Vishvajit Dalimbkar “A Survey Paper on Data security in Cloud Computing” ” International Journal of Computer Sciences and Engineering (IJCSE) Volume-4, Special Issue-4

[16] Dr. Prerna Mahajan, Abhishek Sachdeva, “A Study of Encryption Algorithms AES, DES and RSA for Security” Global Journal Of Computer Science And Technology Network, Web & Security, Volume 13 Issue 15 Version 1.0 Year 2013, Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350, pp. 7-8

G. Shukla, P. Srivastava, R. Kesarwani, H. Neyaz, "Algorithmic Approach To Cloud Data Security," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.194-199, 2018.