CROP YIELD PREDICTION AND SOIL DATA ANALYSIS USING DATA MINING TECHNIQUES IN KRISHNAGIRI DISTRICT
Research Paper | Journal Paper
Vol.06 , Issue.08 , pp.49-55, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.4955
Abstract
The objective of this work is to explore the soil data analysis for crop yield prediction in KrishnagiriDistrict by comparing various data mining techniques which gives the maximum accuracy. Analyzing soil provides major contribution to support the farmers [2]. In this paper one of major parameter which is used to increase crop production is considered – soil, and also explores various proposed algorithms for analyzing soil using data mining techniques and different data mining algorithms are applied to soil data set to predict its soil fertility.
Key-Words / Index Term
Crop Yield,Soil Data, Agricultural Yield Prediction, K-Means, Support Vector Machine (SVM), Multiple Linear Regression (MLR)
References
[1].N Hemageetha , G.M. Nasira,“Analysis of the Soil Data Using Classification Techniques for Agricultural Purpose”International Journal of Computer Sciences and Engineering,4(6)(2016).
[2].Huma Khan ,ShahistaNavaz, Dr. S. M. Ghosh3,” A Survey on Various Data Mining Techniques in Field of Agriculture for Prediction of Crop Yield”,International Journal of Science and Research (IJSR),6(5)(2017).
[3].E. Manjula,S. Djodiltachoumy,”A Model for Prediction of Crop Yield”,International Journal of Computational Intelligence and Informatics, 6(4)(2017).
[4].P. Kanjana Devi.S.Shenbagavadivu” Enhanced Crop Yield Prediction and Soil Data Analysis Using Data Mining”,International Journal of Modern Computer Science 4(6)(2016).
[5]. D Rames , B Vishnu Vardhan“Data Mining Techniques and Applications to Agricultural Yield Data” International Journal of Advanced Research in Computer and Communication 2(9)(2013).
[6].D Ramesh , B Vishnu Vardhan,” Analysis Of Crop Yield Prediction Using Data Mining Techniques”International Journal of Research in Engineering and Technology(IJRET),4(011)2015.
[7].Ramesh A.Medar, “A survey on Data Mining Techniques for Crop Yield Prediction” –A research Article in ijarcsms ,2(9) 2014.
[8].G Ruß, "Data Mining of Agricultural Yield Data : A Comparison of Regression Models", Conference Proceedings, Advances in Data Mining – Applications and Theoretical Aspects, P Perner (Ed.), Lecture Notes in Artificial Intelligence 6171, Berlin, Heidelberg, Springer, 2009, pages : 24-37.
[9].Sellam,V., Poovammal, E., “Prediction of Crop Yield using Regression Analysis”, Indian Journal of Science and Technology, Vol. 9(38), pp.1-5, 2016.
[10].Sujatha, R., Isakki, P., “A study on crop yield forecasting using classification techniques”, International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE), pp.1-4, 2016.
[11].S. Veenadhari, Dr. Bharat Misra, Dr. CD Singh, “Data mining Techniques for Predicting Crop Productivity – A review Article”, International Journal of Computer Science and Technology. (IJCST)2(1)(2011).
[12]. https://en.wikipedia.org/wiki/Krishnagiri_district
[13].https://krishnagiri.nic.in/about-district/history/
Citation
K. Samundeeswari, K. Srinivasan, "CROP YIELD PREDICTION AND SOIL DATA ANALYSIS USING DATA MINING TECHNIQUES IN KRISHNAGIRI DISTRICT", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.49-55, 2018.
Enhanced GMD Technique for Segmentation of Yarn Images based on Parametric Value and Intensity Gradient Analysis
Research Paper | Journal Paper
Vol.06 , Issue.08 , pp.56-63, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.5663
Abstract
Because of the approach of computer based innovation image processing strategies have turned out to be progressively vital in a wide assortment of utilizations. Segmentation is an exemplary subject in the field of image processing. Many works are existing in the area of segmentation and these systems regularly must be joined with area of learning new innovations techniques to achieve the end goal to adequately tackle the segmentation issue. This paper discuss the issue of segmenting yarn image with fuzzy and coherence techniques. The point of segmentation in the considered application is to remove yarn core from the yarn. The technique is guided and compelled by Coherence Enhancing Diffusion (CED) and FCM (Fuzzy C-Means) channel and furthermore the main problem of the yarn image segmentation is considered. For the process of segmentation Gaussian mixture model in enhanced with coherence and fuzzy to acquire a division limit esteem. The Results of segmentation by the CED, FCM and proposed strategy GMD are compared. The correlation demonstrates that the proposed method gives best outcomes. The yarn core and the hairiness segmentation from the proposed algorithm are adequate for assurance of yarn properties performed in the accompanying strides of the estimations of yarn hairiness measurements.
Key-Words / Index Term
Coherence Enhancing Diffusion, Fuzzy C-Means,GMD, Yarn Segmentation, Yarn Hairiness
References
[1]. Kim YK, Langley KD, Avsar F. Quantita¬tive grading of spun yarns for appear¬ ance. Journal of Textile Engineering 2006; 52:13-14.
[2]. Zhong P, Zhang K, Han S, Hu R, Pang JY, Zhang XY, Huang FX. Evaluation method for yarn diameter uneven¬ness based on image sequence pro¬cessing. Textile Research Journal 2014; Advance online publication. doi: 10.1177/0040517514547211.
[3]. Li ZJ, Pan RR, Gao WD. Formation of digital yarn black board using sequence images. Textile Research Journal 2015; Advance online publication. doi: 10.1177/0040517514563725.
[4]. Eldessouki M, Ibrahim S, Militky J. A dynamic and robust image processing based method for measuring the yarn di¬ameter and its variation. Textile Research Journal 2014; Advance online publica¬tion. doi: 10.1177/0040517514530032.
[5] USTER ®, http://www.uster.com/UI/textile-TESTER-5-2-311.aspx, accessed on May 2010.
[6] V. Carvalho, P. Cardoso, M. Belsley, R. Vasconcelos, and F. Soares:“Development of a Yarn Evenness Measurement and Hairiness Analysis System”, IECON`06, The 32nd Annual Conference of the IEEEIndustrial Society, Paris, France, pp. 3621-3626, 2006.
[7]. M. Kuzański: “Measurement Methods for Yarn Hairiness Analysis - TheIdea and Construction of Research Standing”, International Conference MEMSTECH’2006 - Perspective Technologies and Methods in Mems Design, Lviv-Polyana, Ukraine, pp. 87-90, 2006.
[8] Y. A. Ozkaya, M. Acar, and M. R. Jackson: “Computer Vision for Yarn Hairiness and Irregularity Assessment”, ESDA’2002, 6th Biennial Conference on Engineering Systems Design and Analysis Istanbul,Turkey, pp.-, 2002.
[9] S. Pateria: “Design of a New Yarn Hairiness Tester”, M.Sc. Thesis,Department of Mechanical Engineering ,Indian Institute of Technology, Bombay, 2005.
[10] J. Weickert: “Anisotropic Diffusion in Image Processing”, ECMI Series,Teubner-Verlag, Germany, 1998, http://www.mia.uni-saarland.de/ weickert/book.html, accessed on January 2010.
[11] J. Weickert: “Coherence-Enhancing Diffusion Filtering”, International Journal of Computer Vision, vol. 31, pp. 111-127, 1999.
[12] N. Otsu: “A threshold selection method from gray-level histograms”. IEEE Trans. Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66.1979.
[13] P. K. Sahoo, S. Soltani, K. C. Wong, Y. C. Chen: “A Survey of Thresholding Techniques”, Computer Vision, Graphics, and Image Processing, vol. 41, pp. 233-260, 1988.
[14] Zhongjian Li,Ruru Pan, Jing’an Wang,Ziyu Wang, Bianbian Li,Weidong Gao” Real-time Segmentation of Yarn Images Based on an FCM Algorithm and Intensity Gradient Analysis” School of Textiles and Clothing, Jiangnan University, Wuxi 214122, China
[15] Benferhat S, Dubois D, Prade H (1997) Some syntactic approaches to the handling of inconsistent knowledge bases: a comparative study,part 1 :the flat case. Studia Logica 58: 17–45MATHCrossRefMathSciNetGoogle Scholar
[16] Bovens L, Hartmann S (2003) Bayesian epistemology. Oxford University Press, OxfordMATHGoogle Scholar
[17] Bovens L, Olsson E (2000) Coherence, reliability and Bayesian networks. Mind 109: 685–719CrossRefMathSciNetGoogle Scholar
[18] Chen S, Yeh M, Hsiao P (1995) A comparison of similarity measures of fuzzy values. Fuzzy Sets Syst 72: 79–89CrossRefMathSciNetGoogle Scholar
[19] Dubois D, Prade H (1980) Fuzzy set and systems. Academic Press, New YorkGoogle Scholar
[20] Fitelson B (2003) A probabilistic theory of coherence. Analysis 63: 194–199MATHCrossRefMathSciNetGoogle Scholar
[21] Gärdenfors P (1988) Knowledge in flux: modeling the dynamics of epistemic states. MIT Press, CambridgeGoogle Scholar
[22] Glass DH (2002) Coherence, explanation and Bayesian networks. In: Proceedings of the 13th Irish conference on AI and cognitive science. LNAI, vol 2464, pp 177–182Google Scholar
[23] Glass DH (2005) Problems with priors in probabilistic measures of coherence. Erkenntnis 63: 375–385MATHCrossRefMathSciNetGoogle Scholar
[24] Hunter A (2000) Reasoning with inconsistency using quasi-classical logic. J Logic Comput 10: 677–703MATHCrossRefMathSciNetGoogle Scholar
[25] Hunter A (2002) Measuring inconsistency in knowledge via quasi-classical methods. In: Proceedings of the 18th American national conference on AI (AAAI’02), pp 68–73Google Scholar
[26] Hunter A (2004) Making argumentation more believable. In: Proceedings of the 19th American national conference on AI (AAAI’04), pp 269–274Google Scholar
[27] Hunter A, Konieczny S (2004) Approaches to measuring inconsistent information. In: Inconsistency tolerance. LNCS, vol 3300, pp 189–234Google Scholar
[28] Kemeny J, Oppenheim P (1952) Degrees of factual support. Philos Sci 19: 307–324CrossRefGoogle Scholar
[29] Knight KM (2001) Measuring inconsistency. J Philos Logic 31: 77–98CrossRefMathSciNetGoogle Scholar
[30] Knight KM (2003) Two information measures for inconsistent sets. J Logic Lang Inf 12: 227–248MATHCrossRefMathSciNetGoogle Scholar
[31] Konieczny S, Lang J, Marquis P (2003) Quantifying information and contradiction in propositional logic through test actions. In: Proceedings of the 18th international joint conference on AI (IJCAI’03), pp 106–111Google Scholar
[32] Lozinskii E (1994) Information and evidence in logic systems. J Exp Theor Artif Intell 6: 163–193MATHCrossRefGoogle Scholar
[33] Moretti L, Akiba K (2007) Probabilistic measures of coherence and the problem of belief individuation. Synthese 154(1): 73–95MATHCrossRefMathSciNetGoogle Scholar
[34] Olsson EJ (2002) What is the problem of coherence and truth?. J Philos 99: 246–272MathSciNetGoogle Scholar
[35] Olsson EJ (2005) Against coherence. Oxford University Press, OxfordGoogle Scholar
[36] Priest G (2002) Paraconsistent logic. In: Gabbay DM, Guenther F (eds) Handbook of philosophical logic, vol 6. SpringerGoogle Scholar
[37] Qi G, Liu W, Bell DA (2005) Measuring conflict and agreement between two prioritized belief bases. In: Proceedings of the 18th international joint conference on AI (IJCAI’05), pp 552–558Google Scholar
[38] Sancho-Royo A, Verdegay JL (2000) Coherence measures on finite fuzzy sets. Int J Uncertain Fuzziness Knowl Based Syst 8: 641–663MATHMathSciNetGoogle Scholar
[39] Sancho-Royo A, Verdegay JL (2005) Fuzzy coherence measures. Int J Intell Syst 20: 1–11MATHCrossRefGoogle Scholar
[40] Shogenji T (1999) Is coherence truth-conducive?. Analysis 59: 338–345CrossRefGoogle Scholar
[41] Shogenji T (2001) Reply to Akiba on the probabilistic measure of coherence. Analysis 61: 144–150CrossRefMathSciNetGoogle Scholar
[42] Wang X, Baets BD, Kerre E (1995) A comparative study of similarity measures. Fuzzy Sets Syst 73: 259–268MATHCrossRefGoogle Scholar
Citation
R. Sudha Muthusamy, V. Chitraa, "Enhanced GMD Technique for Segmentation of Yarn Images based on Parametric Value and Intensity Gradient Analysis", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.56-63, 2018.
A Significant Assessment of Image Fusion Techniques and its Performance Matrices
Research Paper | Journal Paper
Vol.06 , Issue.08 , pp.64-66, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.6466
Abstract
The main aim of image fusion (IF) is to integrate complementary multisensor, multitemporal and/or multiview information into one new image containing information the quality of which cannot be achieved otherwise. The need of image fusion for high resolution on panchromatic and multispectral images or real world images for better vision. There are various methods of image fusion and some techniques of image fusion such as IHS, PCA, DWT, Laplacian pyramids, Gradient Pyramids, DCT, SF. Several digital image fusion algorithms have been developed in a number of applications. Image fusion extracts the information from several images of a given scene to obtain a final image which has more information for human visual perception and become more useful for additional vision processing. Various performance matrices that used for the evolution of image fusion are Entropy, Standard Deviation, Peak Signal to Noise Ratio (PSNR), and etc.
Key-Words / Index Term
Image fusion, Fused image, Discrete Wavelet Transform, Entropy, PSNR
References
[1] Flusser, Filip Šroubek, and Barbara Zitová,, “Image Fusion: Principles, Methods, and Applications” pp. 1- 3
[2] Mamta Sharma, “A Review: Image Fusion Techniques and Applications“, International Journal of Computer Science and information Technologies, Vol. 7 (3) , 2016, pp.1082-1085
[3] Zhijun Wang, Djemel Ziou, Costas Armenakis, Deren Li, and Qingquan Li, “A comparative Analysis of image fusion methods” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1391–1402,Jun. 2005.
[4] H.B. Mitchell “Image Fusion Theories, Techniques and Applications”.
[5] Mallat SG. “A wavelet tour of signal processing”. Springer New York: Academic Press; 1999. ISBN 978-0-12-466606-1.
[6] Wang Z, Ziou D, Armenakis C, Li D, Li Q. “A comparative analysis of image fusion methods”. IEEE Transactions Geoscience and Remote Sensing. 2005 Jun; 43(6):1391–402.
[7] http://en.wikipedia.org/wiki/Image_fusion.
[8] Vaibhav R. Pandit, R. J. Bhiwani “Image Fusion in Remote Sensing Applications: A Review”, International Journal of Computer Applications (0975 – 8887) Volume 120 – No.10, June 2015
Citation
P. Suresh Babu, "A Significant Assessment of Image Fusion Techniques and its Performance Matrices", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.64-66, 2018.
A CASE STUDY ON DATA MINING APPLICATIONS ON BANKING SECTOR
Research Paper | Journal Paper
Vol.06 , Issue.08 , pp.67-70, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.6770
Abstract
Data mining is becoming strategically important area for many business organizations including banking sector. It is a process of analyzing the data from various perspectives and summarizing it into valuable information. Data mining assists the banks to look for hidden pattern in a group and discover unknown relationship in the data. Today, customers have so many opinions with regard to where they can choose to do their business. Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. These techniques facilitate useful data interpretations for the banking sector to avoid customer attrition. Customer retention is the most important factor to be analyzed in today’s competitive business environment. Fraud is another significant problem in banking sector. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. This paper analyzes the data mining techniques and its applications in banking sector like fraud prevention and detection, customer retention, marketing and risk management.
Key-Words / Index Term
Banking Sector, Customer Retention, Credit Approval, Data mining, Fraud Detection
References
[1].T. F. Bahari, M. S. Elayidom, “An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour”, Procedia Computer Science, Vol. 46, pp. 725-731, 2015
[2] .A. Shrivastava, B. Kumari, “Implementation of classifiers and their performance evaluation”, International Journal of Engineering Research Online, Vol. 3, No. 2, pp. 71-78, 2015
[3]. N. Khan, F. Khan, “Fuzzy based decision making for promotional marketing campaigns”, International Journal of Fuzzy Logic Systems, Vol. 3, No. 1, pp. 64-77, 2013
[4]. H. A. Elsalamony, “Bank Direct Marketing Analysis of Data Mining Techniques”, International Journal of Computer Applications, Vol. 85, No. 7, pp. 12-22, 2014
[5] A. Nachev, “Application of Data Mining Techniques for Direct Marketing”, in: Computational Models for Business and Engineering Domains, pp.86-95, ITHEA, Rzeszow – Sofia, 2014
[6] Vikas Jayasree and Rethnamoney Vijayalakshmi Siva Balan, A REVIEW ON DATA MINING IN BANKING SECTOR.: American Journal of Applied Sciences, 2013.
[7]. Lagazio, M.; Sherif, N.; Cushman, M. A multi-level approach to understanding the impact of cyber crime on the financial sector. Comput. Secur. 2014, 45, 58–74.
[8]. Kharote, M.; Kshirsagar, V.P. Data mining model for money laundering detection in financial domain. Int. J. Comput. Appl. 2014, 85, 61–64. [CrossRef]
[9]. Jayasree, V.; Balan, R.V.S. A review on data mining in banking sector. Am. J. Appl. Sci. 2013, 10, 1160. Pulakkazhy, S.; Balan, R.V.S. Data mining in banking and its applications—A review. J. Comput. Sci. 2013, 1252–1259.
[10]. Amani, F.A.; Fadlalla, A.M. Data mining applications in accounting: A review of the literature and organizing framework. Int. J. Account. Inf. Syst. 2017, 24, 32–58. [CrossRef]
[11].. Wongchinsri, P.; Kuratach,W. A survey-data mining frameworks in credit card processing. In Proceedings of the 2016 13th International Conference on Electrical Engineering ;Electronics,Computer, telecommunications and Information Technology (ECTI-CON), ChiangMai, Thailand, 28 June–1 July 2016; pp. 1–6.
[12]. Bhasin, M.L. Menace of frauds in the Indian banking industry: An empirical study. Aust. J. Bus. Manag. Res. 2015, 4, 1–13. [CrossRef]
[13].Hassani, H.; Huang, X.; Silva, E.S.; Ghodsi , M. A review of data mining applications in crime. Stat. Anal. Data Min. ASA Data Sci. J. 2016, 9, 139–154. [CrossRef]
[14]. Hassani, H.; Huang, X.; Ghodsi, M. Big Data and Causality. Ann. Data Sci. 2018, 5, 133–156. [CrossRef]
[15]. Hassani, H.; Silva, E.S. Big Data: a big opportunity for the petroleum and petrochemical industry. OPEC Energy Rev. 2018, 42, 74–89. [CrossRef]
Citation
M.V. Jisha, D. Vimal Kumar, "A CASE STUDY ON DATA MINING APPLICATIONS ON BANKING SECTOR", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.67-70, 2018.
Density Base Road Accident Control Sensor Monitoring Using Dynamic Network Topology Graph Framework
Research Paper | Journal Paper
Vol.06 , Issue.08 , pp.71-76, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.7176
Abstract
The presence of high-end Internet-connected navigation and infotainment systems is becoming a reality that will easily lead to a dramatic growth in bandwidth demand by in-vehicle mobile users. This will induce vehicular users to resort to resource-intensive applications, to the same extent as today’s cellular customers .The research work considers a system where users aboard communication-enabled vehicles are interested in downloading different contents from Internet-based servers. This scenario captures many of the infotainment services that vehicular communication is envisioned to enable, including news reporting, navigation maps, and software updating, or multimedia file downloading. The project outlines the performance limits of such a vehicular content downloading system by modeling the downloading process as an optimization problem, and maximizing the overall system throughput. The research work investigates the impact of different factors, such as the roadside infrastructure deployment, the vehicle-to-vehicle relaying, and the penetration rate of the communication technology, even in presence of large instances of the problem. Results highlight the existence of two operational regimes at different penetration rates and the importance of an efficient, yet 2-hop constrained, vehicle-to-vehicle relaying.
Key-Words / Index Term
Traffic Monitoring Control, Optimal Content Download, V-to-V Communication Model, Density Base Communication Model, Dynamic Network Monitoring system
References
[1]. J. P. Collomosse, G. McNeill, and Y. Qian, “Storyboard sketches for content based video retrieval,” in Proc. Int. Conf. Computer Vision (ICCV’09), 2009, pp. 245–252
[2]. R. D. Dony, J. W. Mateer, J. A. Robinson, and M. G. Day. Iconic versus naturalistic motion cues in automated reverse storyboarding. In Proc. CVMP, pp. 17–25, 2005
[3]. D. Goldman, B. Curless, D. Salesin, and S. Seitz. Schematic storyboards for video editing and visualization. In Proc. ACM SIGGRAPH, volume 25, pp. 862–871, 2006.
[4]. J. Collomosse, G. McNeill, and L. Watts. Free-hand sketch grouping for video retrieval. In Proc ICPR, 2008.
[5]. E. Tulving. Elements of episodic memory. Oxford Claren-don, 1983. ISBN: 0-198-521251.
[6]. D. B. Goldman, C. Gonterman, B. Curless, D. Salesin, and S. M. Seitz, “Video object annotation, navigation, and composition,” in Proc. 21st Annu. ACM Symp. User Interface Software and Technology, 2008, pp. 3–12.
[7]. P. Dragicevic, G. Ramos, J. Bibliowicz, D. Nowrouzezahrai, R. Balakrishnan, and K. Singh. Video browsing by direct ma-nipulation. In CHI, pages 237–246, 2008.
[8]. T. Karrer, M. Weiss, E. Lee, and J. Borchers. DRAGON: A direct manipulation interface for frame-accurate in-scene video navigation. In CHI , pages 247–250, 2008
[9]. A. Agarwala, M. Dontcheva , M . Agrawala, S. Drucker, A . Col-burn, B. Curless, D. Salesin, and M. Cohen. Interactive dig-ital photomontage. ACM Trans. Graph. (Proc. SIGGRAPH),23(4):294–301, 2004.
[10]. A. Rav-Acha, Y. Pritch, D. Lischinski, and S. Peleg. Dynamo-saicing: Video mosaics with non-chronological time. In Proc. CVPR , pages 58–65, 2005.
[11]. R. L. Guimaraes, P. Cesar, and D. Bulterman, “Creating and sharing personalized time-based annotations of videos on the web,” in Proc. DocEng’10, 2010, pp. 27–36
[12]. Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J. and Ross, K. 2009. Video interactions in online video social networks. In ACM TOMCCAP, 5(4): n. 30, 2009. DOI= http://doi.acm.org/10.1145/1596990.1596994.
[13]. Choudhury, M. D., Sundaram, H., John, A. and Seligmann, D. D. 2009. What makes conversations interesting? Themes, Participants and Consequences of Conversations in Online Social Media. In Proceedings of the International WWW Conference, pp. 331-340. DOI= http://doi.acm.org/10.1145/1526709.1526754
[14]. E. Moxley, T. Mei, and B. S. Manjunath, “Video annotation through search and graph reinforcement mining,” IEEE Trans. Multimedia, vol. 12, no. 3, pp. 184–193, 2010.
[15]. Flickr . [Online]. Available: http://www.flickr.com/
[16]. YouTube . [Online]. Available: http://www.youtube.com/.
[17]. M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, and S. Moon, “I tube, you tube, everybody tubes: Analyzing the world`s largest user generated content video system,” in Proc. ACM SIGCOMM Conf. Internet Measurement , New York, 2007, pp. 1–14.
Citation
K. Gowthami, S. Vijaya kumar, "Density Base Road Accident Control Sensor Monitoring Using Dynamic Network Topology Graph Framework", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.71-76, 2018.
Data Privacy Using Dynamic Multi-Layer Authentication Mechanism in Open Learning Environment
Research Paper | Journal Paper
Vol.06 , Issue.08 , pp.77-80, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.7780
Abstract
Cloud computing, delivers abundant services between Cloud Service Providers (CSP) and Cloud Users (CU) on a demand basis in metered service without having the underlying infrastructure with the help of Internet. This ease will increase the cloud users day by day to adopt cloud model. Technological advancements, specially Information Communication Technology (ICT) and cloud computing, transform traditional teaching and learning practices into blended learning. Due to the fabulous development of Massive Open Online Courses (MOOC) and Open Educational Resources (OER), more attention and efforts are crucial to the security procedures for massive open online courses. Authentication of the users to the cloud service is mandatory, because it eliminates the risks and attacks found in the cloud services. Password is widely used for user authentication because of its simplicity. Frequently changing password is a tedious work. Simple passwords are easily hacked, but complicated one is hard to memorize. To overcome this, we suggest Personally Identifiable Information (PII) for the purpose of Authentication. In this paper, we propose a security model using Dynamic Multi-Layer Authentication Mechanism, which is based on user’s personally identifiable information.
Key-Words / Index Term
Authentication, Access Control, Massive Open Online Course, Open Educational Resource, Responsive Open Learning Environment, Self-Regulated Learning
References
[1] Pranav Vyas, Dr. Bhushan Trivedi, An Analysis of Session Key Exchange protocols, International Journal of Engineering Research and Applications, Vol. 2, Issue 4, June-July 2012, pp.658-663, ISSN: 2248-9622, available at www.ijera.com.
[2] Anuratha. R, Dr. Ganaga durga. M, “Authentication – A part of Identification in the Cloud Environment”, International Journal of Advance Research in Science and Engineering, vol. 07, Special Issue 01, January 2018, ISSN 2319-83554, pp. 25-34
[3] Anuratha. R, Dr. Ganaga durga. M, “Analysis of Data Security in Cloud Computing Using Access Control Technique”, International Conference on Global Talent Management in the Digital Era, ISBN: 978-93-86537-95-9, September, 2017.
[4] Sten Govaerts, Katrien Verbert, C. Delgado Kloos et al. (Eds.), Towards Responsive Open Learning Environments: The ROLE Interoperability Framework, EC-TEL 2011, LNCS 6964, pp. 125–138, 2011. Springer-Verlag Berlin Heidelberg 2011.
[5] L. Das, A. Saxena, and V. P. Gulati, "A Dynamic ID-Based Remote User Authentication Scheme," IEEE Transactions on Consumer Electronics, vol. 50, no. 2, pp. 629 - 631, 2004.
[6] I-En Liao, Cheng-Chi Lee, and Min-Shiang Hwang, "Security Enhancement for a Dynamic ID-Based Remote User Authentication Scheme," In Proceeding of International Conference on Next Generation Web Services Practices, 2005 ( NWeSP 2005), pp. 437-40 , 2005.
[7] Y. Yang, R. H. Deng and F. Bao, “A Practical Password- Based Two-Server Authentication and Key Exchange System,” IEEE Transactions on Dependable and Secure Computing, Vol. 3, No. 2, 2006, pp. 105-114. doi:10.1109/TDSC.2006.16
[8] J. L. Tsai, “Efficient Multi-Server Authentication Scheme Based on One-Way Hash Function without Verification Table,” Computers & Security, Vol. 27, No. 3-4, 2008, pp. 115-121. doi:1 0.1016/j.cose.2008.04.001
[9] Yi-Pin Liao and Shuenn-Shyang Wang, "A Secure Dynamic ID-Based Remote User Authen- tication Scheme for Multi-Server Environment," Computer Standards & Interfaces, vol. 31, no. 1, pp. 24-29, 2009, available at www.sciencedirect.com.
[10] Han-Cheng Hsiang and Wei-Kuan Shih, "Improvement of the Secure Dynamic ID Based Remote User Authentication Scheme for Multi-Server Environment," Computer Standards & Interfaces, vol. 31, no. 6, pp. 1118-1123, 2009.
[11] Cheng-Chi Lee, Tsung-Hung Lin, and Rui-Xiang Chang, "A Secure Dynamic ID Based Re- mote User Authentication Scheme for Multi-Server Environment Using Smart Cards," Expert Systems with Applications, vol. 38, no. 11, pp. 13863-13870, 2011.
[12] N.S Chauhan and A.Saxena, “Energy Analysis of Security for Cloud Application,” in Proc. Annual IEEE India Conference, pp. 1-6, 2011.
Citation
R. Anuratha, M. Ganaga Durga, "Data Privacy Using Dynamic Multi-Layer Authentication Mechanism in Open Learning Environment", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.77-80, 2018.
A BRIEF SURVEY ON SYMBOLIC EXECUTION TEST-SELECTION TECHNIQUES
Survey Paper | Journal Paper
Vol.06 , Issue.08 , pp.81-85, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.8185
Abstract
Symbolic execution techniques decrease the cost of path redundancy by choosing a separation of an existing test suite to use in retesting a customized program. Over the history, Eliminating Path Redundancy via Postconditioned Symbolic Execution techniques has been described in the literature. This paper aims to present a brief survey on symbolic executions in black-box and white-box regression testing under the Software testing and learning techniques that are in use in today`s software engineering of verification and validation tasks. Number of comparative study has been performed to evaluate the performance of predictive accuracy on the test cases and the outcome discloses that Bidirectional Symbolic Analysis for Effective Branch Testing method outperforms having better performance other predictive methods are not performing well.
Key-Words / Index Term
Symbolic execution, testing and debugging, Parallel symbolic execution, AEG
References
[1] J.Jaar, A. E. Santosa, and R. Voicu. An interpolation method for clp traversal. In CP, 2009.
[2] K. L. McMillan. Lazy annotation for program testing and verification. In CAV, 2010.
[3] CADAR, C., DUNBAR, D., AND ENGLER, D. KLEE: Unassisted and automatic generation of high-coverage tests for complex sys tems programs. In Proc. of Symp. on Operating Systems Design and Impl (OSDI) (2008).
[4] M. Staats and C. S. Pasareanu, “Parallel symbolic execution for structural test generation,” in Proc. Int. Symp. Softw. Testing Anal., 2010, pp. 183–194.
[5] T. Avgerinos, S. K. Cha, B. L. T. Hao, and D. Brumley, “AEG: Automatic exploit generation,” in Proc. USENIX Symp. Netw. Distrib. Syst. Secur., Feb. 2011, pp. 283–300.
[6] D.-H. Chu and J. Jaffar, “A complete method for symmetry reduction in safety verification,” in Proc. Int. Conf. Comput. Aided Verification, 2012, pp. 616–633.
[7] E. Bounimova, P. Godefroid, and D. A. Molnar, “Billions and billions of constraints: Whitebox fuzz testing in production,” in Proc. 35th Int. Conf. Softw. Eng., 2013, pp. 122–131.
[8] D. Chu, J. Jaffar, and V. Murali, “Lazy symbolic execution for enhanced learning,” in Proc. 5th Int. Conf. Runtime Verification, 2014, pp. 323–339.
[9] D. A. Ramos and D. R. Engler, “Under-constrained symbolic execution: Correctness checking for real code,” in Proc. 24th USENIX Secur. Symp., 2015, pp. 49–64.
[10] M. Baluda, G. Denaro, and M. Pezze, “Bidirectional symbolic analysis for effective branch testing,” IEEE Trans. Softw. Eng., vol. 42, no. 5, pp. 403–426, May 2016.
Citation
K.K. Nivethithaa, V. Krishnapriya, "A BRIEF SURVEY ON SYMBOLIC EXECUTION TEST-SELECTION TECHNIQUES", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.81-85, 2018.
Artificial Intelligence-A Note on the Present Era Deepmind-Prospects and Aspects
Research Paper | Journal Paper
Vol.06 , Issue.08 , pp.86-90, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.8690
Abstract
An overview on the ruling technology “Artificial Intelligence” whose vision for High level-Perception has been done including its controversies. However, AI has reached its peak, the inventions like “Norman” done by MIT, which is a psychopathic robot has turned the vision or view of the entire community over AI. The AI Machines like “Cozmo” and “Moley”, who are being perfect in their purpose of creation has become an attention seeker in the views of the society. On the other hand “Deepmind” technologies explodes its perspectives and aspects towards every nook and corner of the people’s real time problems in daily life.”Alphago”, the master game being a Deepmind machine, has won the epic strong players of the game, taking AI to the top of the ladder. Notably, Deepmind technology with its amazing Neural Networks is ruling the medical society by completely changing the Lifestyle of patients. An Accurate and undoubted medicine system for everyone will be the future system which will be a dream come true for us. The future scope of AI would be a well-developed ‘AI Precision Farming’ and a strong Cyber security system which reduces the risk of Danger and a Well-developed Medicine system which will be a resolution of all the Social Problems.
Key-Words / Index Term
Deepmind, Artificial Intelligence (AI), Precision medicine, Cyber security
References
[1] Cravier 1993: 22-25
[2] Novet, Jordan (17 June 2017). “Everyone keeps talking about AI-here’s what it really is and why it’s so hot now”. CNBC. Retrieved 16, February 2018.
[3] “Blue Brain” – EPFL.bluebrain.epfl.ch.
[4] Fisher, Adam. “Inside Google’s Quest to Popularize Self-Driving Cars”.Popular Science. Bonnier Corporation. Retrieved 10, October 2013.
[5] “Jamie Shotton at Microsoft Research”. Microsoft Research.
[6] Silver, David; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, loannis; Huang, Aja; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Mathew; Bolton, Adrian; Chen, Yutian; Lillicrap, Timothy; Fan,Hui;Sifre, Laurent; Driessche, George van den; Graepel, Thore; Hassabis, Demis(19 October 2017).”Mastering the game of go without human knowledge”.Nature.550 (7676):354-359.
[7] Alibaba’s AI Outguns Humans in Reading Test. January 15, 2018.
[8] http://Medium.com”
Moley Robotics. June 9, 2016.
[9] www.Media.mit.edu/projects/normal/overview/
Pinar Yanardag. Manual Cebrian, Iyad Rahman.
[10] https://www.artificialintelligence-news.com/2018/06/06mit-psychopathi-ai/
Ryan Daws.
[11] “About Us | DeepMind”. DeepMind.
[12] “The Last AI Breakthrough DeepMind Made Before Google Bought It”. The Physics arXiv Blog. Retrieved 12, October 2014.
[13] Graves, Alex; Wayne, Greg; Danihelka, Ivo (2014). “Neural Turing Machines”. arXiv:1410.5401[cs.NE].
[14] Best of 2014: Google’s Secretive DeepMind Startup Unveils a “Neural Turing Machine”, MIT Technology Review.
[15] Le Monde(in French),27,January 2016.
[16]https://deepmind.com/applied/deepmind-health/working-partners/more-information-statistics/.
[17] Ramesh, Randeep (2016-05-04). “Google’s DeepMind Shouldn’t Suck up our NHS Records in Secret”. TheGuardian.com. The Guardian. Archived from the Original on 2016-10-13. Retrieved 19, October 2016.
[18] Donnelly, Caroline (12 May 2016). “ICO Probes Google DeepMind Patient Data Sharing Deal with NHS Hospital Trust”. Computer Weekly.
[19] Hodson, Hal (25 May 2016). “Did Google’s NHS Patient Data Deal Need Ethical Approval? ”. New Scientist. Retrieved 28 May 2016.
[20] Martin, Alexander J (15 May 2017). “Google received 1.6 million NHS Patients Data on an ‘inappropriate legal basis’ “. Skynews. Retrived 16 May 2017.
[21] Hern, Alex (3 July 2017). “Royal Free Breached UK Data Law in 1.6m Patient Deal with Google’s DeepMind”-Via www.theguardian.com.
[22] https://www.weforum.org/agenda/2018/07/how-to-get-culture-right-when-embedding-it-into-ai.
[23] https://www.artificialintelligence-news.com/2018/06/06/mit-psychopathi-ai/.
[24] “High-level percerption, representation, and analogy; A critique of artificial intelligence methodology” , David J. Chalmers, Robert M. French & Douglas R. Hofstadter. Retrieved 16 May 2007.
Citation
Sangeetha, "Artificial Intelligence-A Note on the Present Era Deepmind-Prospects and Aspects", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.86-90, 2018.
Constraint Programming approach based Virtual Machine Placement Algorithm for Server Consolidation in Cloud data center
Research Paper | Journal Paper
Vol.06 , Issue.08 , pp.91-95, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.9195
Abstract
Server consolidation has recently become a major issue in cloud data centers. Energy efficiency is a key aspect in solving server consolidation issues. Virtual machine placement algorithms play an important role in maximizing the resource utilization may lead to energy efficiency. Constraint programming is an approach used to find the available servers and migrate these servers to the virtual machines by treating them as constraints to find optimal solution. The objective function of this work is to increase resource utilization by minimizing the number of active physical machines and decrease the search time of constraint solver for better energy efficiency. In this paper a Best Fit Resource Utilization ( BFRU) algorithm is proposed for virtual machine placement, it use best fit algorithm technique. However, virtual machine placement problem is formulated as a variant of multidimensional bin packing problem and then a constraint solver is used to solve the problem using the dataset collected from Amazon EC2.The proposed BFRU algorithm implemented through NetBeans IDE to evaluate the data collected. The simulation result shows that constraint programming based virtual machine placement algorithm BFRU can effectively reduce the energy consumption with respect to memory.
Key-Words / Index Term
BFRU algorithm, Amazon EC2, Best Fit Resource Utilization ( BFRU) algorithm
References
[1].Mustafa,K.Bilal,A.Hayat,A.R.Khan,S.A.Madani,”Resource Management in cloud computing: Taxonomy, Prospects and challenges”, Computers and Electrical Engineering,2015,DOI:10.1016/j.compeleceng.2015.07.021
[2] G.Xiao-ming,H.Jie and C.Long,”principle and implementation of virtualization technology”,Electronic Industry Press,Beijing,(2012).
[3] H.Jin,D.Pan,J.Xu and N.Pissinou,”Efficient VM Placement with multiple deterministic and Stochastic resources in datacenters,” in IEEE Global Communications Conference(GLOBECOM),2012,pp.2505-2510.
[4] A.Gupta,L.V.Kale,D.Milojicic,P.Faraboschi,and S.M.Balle,”HPC-aware VM placement in infrastructure clouds”, in IEEE international conference on cloud Engineering,2013,pp.11-20.
[5] X.Li,Z.Qian,R.Chi,B.Zhang and S.Lu,”Balancing resource utilization for continous virtual machine requests in clouds”,in 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing,2012,pp.266-273.
[6] Koomey J.Growth in datacenter electricity use 2005 to 2010.A report by Analytical Press completed the request of ‘The New York Times’,2011.
[7] Cai C,Wang L,Khan S U,et al,”Energy-aware high performance computing:A Taxonomy study,”Parallel and distributed systems(ICPADS),,2011 IEEE 17th International Conference on IEEE,2011:953-958.
[8] Pocket gems on google cloud platform,http://cloud.google.com/customers/pocketgems.
[9] C.Pandiselvi, Dr.S.Sivakumar,” A Review of Virtual machine placement algorithm in cloud datacenters for server consolidation”, International journal of engineering Research in Computer Science and Engineering(IJERCSE),Vol 5,Issue 3,March 2018,pp(182-188).
[10] Verma, A., Ahuja, P., Neogi, A.: pmapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, pp. 243–264. Springer-Verlag New York, Inc. (2008)
[11] Wood, T., Shenoy, P., Venkataramani, A., Yousif, M.: Sandpiper: Black-box and gray-box resource management for virtual machines. Comput. Netw. 53(17), 2923– 2938 (2009)
[12] Kumar, S., Talwar, V., Kumar, V., Ranganathan, P., Schwan, K.: vmanage: loosely coupled platform and virtualization management in data centers. In: Proceedings of the 6th International Conference on Autonomic Computing, pp. 127–136. ACM (2009).
[13] Dhiman, G., Marchetti, G., Rosing, T.: vgreen: A system for energy-efficient management of virtual machines. ACM Transactions on Design Automation of Electronic Systems (TODAES) 16(1), 6 (2010)
[14] Rossi, F., Van Beek, P., Walsh, T.: Handbook of constraint programming, vol. 35. Elsevier Science (2006)
[15] Campegiani, P.: A genetic algorithm to solve the virtual machines resources allocation problem in multi-tier distributed systems. In: Second International Workshop on Virtualization Performance: Analysis, Characterization, and Tools (VPACT 2009), Boston, Massachusett (2009)
[16] Xu, J., Fortes, J.: Multi-objective virtual machine placement in virtualized data center environments. In: 2010 EEE/ACM Int’l Conference on & Int’l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom), pp. 179–188. IEEE (2010)
[17] Gao Y,Guan H,Qi Z,et al. “A multi objective ant colony system algorithm for virtual machine placement in cloud computing, ”Journal of Computer and System Sciences,2013,79(8),pp 1230-1242
[18]Minas L, Ellison B. “Energy Efficiency for Information Technology:How to Reduce Power Consumption in Servers and Data Centers. Intel Press, 2009.
[19] Kusic D, Kephart J O, Hanson J E, et al. “Power and performance management of virtualized computing environments via lookahead control,” Cluster computing, 2009, 12(1): 1-15.
[20] Rivoire S, Ranganathan P, Kozyrakis C. “A Comparison of High-Level Full-System Power Models,” HotPower, 2008, 8, pp.3-3.
[21] Lee Y C, Zomaya A Y. “Energy conscious scheduling for distributed computing systems under different operating conditions,” IEEE Transactions on Parallel and Distributed Systems, 2011, 22(8), pp.1374- 1381.
[22] Amazon EC2,http://aws.amazon.com/ec2/,2013.
Citation
C. Pandi Selvi, S. Sivakumar, "Constraint Programming approach based Virtual Machine Placement Algorithm for Server Consolidation in Cloud data center", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.91-95, 2018.
A Brief Survey Onboolean Expressions in Fault Based Techniques
Survey Paper | Journal Paper
Vol.06 , Issue.08 , pp.96-102, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si8.96102
Abstract
Boolean expressions are major focus of specifications and they are very much prone to introduction of faults, this survey presents various fault based testing techniques.It recognizes that the methods differ in their fault detection capabilities and creation of test suite. The various techniques like Dealing with Constraints in Boolean Expression, Minimal Fault Detecting Test Suites, Reducing logic test set size, A logic mutation approach, SAT and SMT Solvers for Test Generation and Boolean Expressions by Cell Covering has been considered. This survey describes the fundamental algorithms and fault categories used by these strategies for evaluating their performance. Finally, it contains short summaries of the papers that use Boolean expressions used to specify the requirements for detecting faults. These techniques have been empirically evaluated by various researchers on a simplified safety related real time conditionals system.
Key-Words / Index Term
Boolean Expression, BOR, Test suite, MBT
References
[1] K.C. Tai. Theory of Fault Based Predicate Testing for Computer Programs, IEEE Transactions of Software Engineering, vol 22, no 8, pp 552-562, 1996
[2] K.C Tai. M.A Vouk., A. Paradkar., Lu P. , "Predicate Based Testing," IBM Systems Journal, Vol 33 (3), p 445, 1994
[3] M. A. Vouk, K. C. Tai, and A. Paradkar. Empirical Studies of Predicate-based Software Testing. In 5th International Symposium on Software Reliability Engineering, pages 55–64. IEEE, 1994.
[4] A. Gargantini, “Dealing with constraints in boolean expression testing,” in Proc. 3rd Workshop Constraints Softw. Testing Verification Anal., Mar. 25, 2011, pp. 322- -327.
[5] G. Fraser and A. Gargantini, “Generating minimal fault detecting test suites for boolean expressions,” in Proc. 3rd Int. Conf. Softw. Testing Verification Validation Workshops, Apr. 2010, pp. 37–45.
[6] G. Kaminski and P. Ammann, “Reducing logic test set size while preserving fault detection,” Softw. Testing, Verification Rel., vol. 21, pp. 155–193, 2011.
[7] 21] G. Kaminski, U. Praphamontripong, P. Ammann, and J. Offutt, “A logic mutation approach to selective mutation for programs and queries,” Inf. Softw. Technol., vol. 53, pp. 1137–1152, 2011.
[8] Godefroid, P., Levin, M. Y., and Molnar, D. (2012) SAGE: Whitebox fuzzing for security testing. Commun. ACM, 55, 40-44.
[9] Peleska, J. (2013) Industrial-strength Model-Based Testing - state of the art and current challenges. In Petrenko, A. K. and Schlinglo, H. (eds.), Proceedings Eighth Workshop on Model-Based Testing, MBT 2013, Rome, Italy, 17th March 2013, EPTCS, 111, pp. 3-28.
[10] P. Arcaini, A. Gargantini, and E. Riccobene, “How to optimize the use of SAT and SMT solvers for test generation of boolean expressions,” Comput. J., vol. 58, pp. 2900–2920, Jan. 21, 2015.
[11] Lian Yu and Wei-Tek Tsai, "Test Case Generation for Boolean Expressions by Cell Covering", IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 44, NO. 1, JANUARY 2018.
[12] Kaminski G, Ammann P. Using logic criterion feasibility to reduce test set size while guaranteeing fault detection. Proceedings of the 2nd International Conference on Software Testing, Verification and Validation, Denver, CO, April 2009; 167–176.
[13] Kaminski G, Ammann P. Using logic criterion feasibility to reduce test set size while guaranteeing double fault detection. Proceedings of the Mutation Workshop at the 2nd International Conference on Software Testing, Verification and Validation, Denver, CO, April 2009.
[14] G. Kaminski and P. Ammann, “Applications of optimization to logic testing,” in Proc. Softw. Testing, Verification Validation Workshops, 2010, pp. 331–336.
Citation
M. Sivaranjani, D. Gayathri Devi, "A Brief Survey Onboolean Expressions in Fault Based Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.96-102, 2018.