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In this paper authors presents a case based on utilisation of widely known, simple and yet interesting LPP technique for Diet problems i.e. focusing on to optimized product mix with minimum resources. It focuses on developing an innovative product for 12-16 years old children by mixing five different ingredients to formulate a single product that would serve them with the major nutrients needed per day. The objective of work is to minimize the cost of raw material subject to fulfilment of recommended dietary allowance (RDA) values of children. The formulated problem was analysed using LINDO software and gives an optimum solution to minimize the cost for developed product mix. The solution of formulated problem suggests the combination of five ingredients i.e. Milk (200g); Green Beans (200g); Peas (310g); White Rice (200g); Wheat Grains (504g) to achieve the optimum product mix with the minimum cost of INR 64.16 per day or serving.

Linear programing, product mix, Recommended dietry allowance (RDA)

[1] K.H. Cooper; I. Jialal; S. Montgomery; W. C. Willett & J. Selhub, “Multi Vitamin and Mineral Supplement”, Patent No. US 6299896 B, 2000.
[2] Benton D, Roberts G., “Effect of Vitamin and Mineral Supplementation on Intelligence of a Sample of Schoolchildren”, The Lancet, Vol. 1, pp. 140-143, 1988.
[3] M. Okan Özkaya; Mustafa Naziroglu. N., “Multivitamin and mineral supplementation modulates oxidative stress and antioxidant vitamin levels in serum and follicular fluid of women undergoing in vitro fertilization”. Fertility and Sterility, Vol. 94, pp 2465–2466, 2010.
[4] Li Y, Wang C, Zhu K, Feng RN & Sun cH., “Effects of multivitamin and mineral supplementation on adiposity, energy expenditure and lipid profiles in obese Chinese women”, International Journal of Obesity, Vol. 34(6), 2010, 1070–1077, 2010.
[5] C. Gopalan, B. V. Rama Sastri & S.C. Balasubramanian, “Nutritive value of Indian Foods”, National Institute of Nutrition, ICMR, Hyderabad, 2012.
[6] Epstein Henry F; Fischman, Donald A. Molecular Analysis of Protein Assembly in Muscle Development, Science; Washington, 1991.
[7] Michael L. Power et.al. The role of calcium in health and disease. American Journal of Obstetrics and Gynecology, 181(6), 1999, p. 1560–1569.
[8] Swapnil N. Rajpathak et al. The role of iron in type 2 diabetes in humans. Biochimica et Biophysica Acta (BBA) - General Subjects, 1790(7), 2009, p. 671–681.
[9] Jean Claude Cheftel. Nutritional effects of extrusion-cooking. Food Chemistry, 20(4), 1986, p. 263-283.
[10] Gaurav Aggarwal; Kumar Vijay & Gahlawat Vijay. Application of LPP to identify Optimal Production Mix in “Ready to Eat” Snacks Factory. International Journal of Research in Engineering & Applied Sciences, , 2014, p. 95-101.
[11] Soren Koch; Sebastian Konig & Gerhard Wascher. Linear Programming for a Cutting Problem in the Wood Processing Industry- A Case study. Otto-von-Guericke-University Magdeburg, Faculty of Economics and Management, working paper no. 14, 2008.

Vijay Kumar, Akshay Bisht, Kumar Rahul, "Development of Cost Effective Nutritive Diet for Children using Linear Programming Problem," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.573-577, 2018.

The share market is the new business for many individuals and companies.The stock market attracts many as there is a huge profitinvolved. But there can be huge loss as well. The world suffered from many economic crises due to itsdownfall, which may have cost lives and way of living of many. If the share prices couldbe predicted, this can help in avoiding the economic crisis.Many business analysts said that the share market cannot be predicted and it is completelyrandom, but there is no common opinion about that. Many also said that it can bepredicted but with using different measures. In this paper, we have proposed a technique to perform stock market data analysis to find if there exists a relation between stock price change of two companies- TCS and Infosys. This is done using Big data technique by performing sentiment analysis of Tweets from Twitter and finding the correlation. Also, machine learning techniques are applied on the BSE data of the companies to predict the stock prices of next day. The results of allthe factors like sentiments, similar industrial shares, and index shares are combined to get to the conclusion. We found that the share market depends on the numerous factors and considering only certain factorsare insufficient for analysis.

Hadoop, Map-reduce, Machine learning, Sentiment analysis, Stock Market

[1] B. G. Malkiel, “A Random Walk Down Wall Street”, W. W. Norton,
New York. 1973.
[2] R. Iacomin, “Stock Market Prediction,”2015 19th International Conference on System Theory, Control and Computing (ICSTCC), October 14-16, CheileGradistei, Romania
[3] S., Kamath, “Stock Market Analysis,” Master`s Projects, pp. 326, 2012.
[4] V. Rajput, S.Bobde, “Stock Market Forecasting Techniques: Literature Survey,” International Journal of Computer Science and Mobile Computing, 5(6), June 2016, 500-506.
[5] G. S., Navale, N., Dudhwala, K., Jadhav, P., Gabda, & B. J., Vihangam. , “Prediction of Stock Market using Data Mining and Artificial Intelligence,” International Journal of Computer Science and MobileComputing, 134(12), January 2012, pp 9-11.
[6] A. Gupta and B. Dhingra, "Stock market prediction using Hidden Markov Models," 2012 Students Conference on Engineering and Systems, Allahabad, Uttar Pradesh, 2012, pp. 1-4.
[7] P. Somani, S. Talele and S. Sawant, "Stock market prediction using Hidden Markov Model," 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference, Chongqing, 2014, pp. 89-92.
[8] Z. Zhang, Y. Shen, G. Zhang, Y. Song and Y. Zhu, "Short-term prediction for opening price of stock market based on self-adapting variant PSO-Elman neural network," 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 2017, pp. 225-228.
[9] M. Naeini, H. Taremian and H. Hashemi, “Stock Market Value Prediction Using Neural Networks,” International Conference on Computer InformationSystems and Industrial Management Applications (CISIM) 2010.
[10] A. Rao, S. Hule, H. Shaikh, E. Nirwan and P. M. Daflapurkar, "Survey: Stock Predictive Models Using Multilayer Perceptron", International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.111-113, 2016.
[11] S. Asadifar and M. Kahani, "Semantic association rule mining: A new approach for stock market prediction," 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), Kerman, 2017, pp. 106-111.
[12] M. R. Vargas, B. S. L. P. de Lima and A. G. Evsukoff, "Deep learning for stock market prediction from financial news articles," 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Annecy, 2017, pp. 60-65.
[13] Jiahong Li, Hui Bu and Junjie Wu, "Sentiment-aware stock market prediction: A deep learning method," 2017 International Conference on Service Systems and Service Management, Dalian, 2017, pp. 1-6.
[14] W. Chen, Y. Zhang, C. K. Yeo, C. T. Lau and B. S. Lee, "Stock market prediction using neural network through news on online social networks," 2017 International Smart Cities Conference (ISC2), Wuxi, 2017, pp. 1-6.
[15] S. Gour, "Developing Decision Model by Mining Historical Prices Data of Infosys for Stock Market Prediction", International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.92-97, 2016.
[16] J.K.Sharma, “Business Statistics,” Pearson Education, India, pp. 454-455, 2007.
[17] P.Domingos, “A Few Useful Things to Know about Machine Learning,” Communications of the ACM, Vol. 55 No. 10, October 2012, Pages 78-87.

Piyush Jain, Kaustubh Bhat, HarshalKesharwani, Pritesh Bhate, Khushboo P Khurana, "Stock Market Analysis and Prediction using Hadoop and Machine Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.578-584, 2018.

In this growing era of information technology and the Internet, everybody is using interactive media in communication directly or indirectly. Images may have high confidential data.So these images need high security when they are stored somewhere, and when there is a need to send over any insecure communication network, then it is required to provide complete protection from tampering, accessing by illegal party that is not intended receiver. In this research work the problem is to design a novel scheme for 2D image encryption and decryption using an affine cipher, circular rotation of image matrix and the XOR operation. Father the security is enhanced by the fact that main secret key is broken into 3 sub keys and same sub keys are used for encrypting the three different panels of an RGB image. The use of circular shift is also performed by applying a secret operation on a secret key. A special secret key was also generated to be applied during the XOR operation after affine transformation is performed on the original. The cumulative effect of all the above steps results in secure encryption of the image. The encryption process is written in such a manner so that it can be easily reversed and decryption can be achieved by simply running the encryption algorithm in reverse order. This proposed encryption scheme can efficiently be utilized for securing the digital images.

affine, encryption, decryption, key, image, xor

[1] Nag, Jyoti Prakash Singh, Srabani Khan, Sushanta Biswas, D. Sarkar, ParthaPratim Sarkar “Image Encryption Using Affine Transform and XOR Operation” 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN 2011), 21-22 July 2011, pages : 309-312.
[2] Long Bao, Yicong Zhou, C. L. Philip Chen, Hongli Liu “A New Chaotic System for Image Encryption” 2012 International Conference on System Science and Engineering, June 30-July 2, 2012, pages: 69-73 .
[3] Qiudong Sun, Ping Guan, YongpingQiu, YunfengXue “A Novel Digital Image Encryption Method Based on One-dimensional Random Scrambling” 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 29-31 May 2012, page: 1669-1672.
[4] AmneshGoel, Nidhi Chandra “A Technique for Image Encryption Based On Explosive n*n Block displacement Followed By Inter-Pixel Displacement of RGB Attribute of A Pixel” 2012 International Conference on Communication Systems and Network Technologies, 11-13 May 2012, page: 884-888.
[5] Saraswati D. Joshi, Dr. V.R. Udupi, Dr. D.R. Joshi, “A Novel Neural Network Approach for Digital Image Data Encryption/Decryption”, Power, Signals, Controls and Computation (EPSCICON), 2012 International Conference on 3-6 Jan. 2012, pages: 1-4.
[6] Mohammed Abbas Fadhil Al-Husainy, “A Novel Encryption Method for Image Security”, International Journal of Security and Its Applications, vol.6, no.1, January 2012, pages: 1-8.
[7] Anchal Jain, NavinRajpal, “A Two Layer Chaotic Network Based Image Encryption Technique”, Computing and Communication Systems (NCCCS), 2012 National Conference on 21-22 Nov.2012, pages: 1-5.
[8] NidhiSethi, Deepika Sharma, “A New Cryptographic Approach for Image Encryption”, Parallel, Distributed and Grid Computing (PDGC), 2012 2nd IEEE International Conference on 6-8 Dec. 2012, pages: 905-908.
[9] SomdipDey, “SD-AEI: An Advanced Encryption Technique for Images”, Digital Information Processing and Communications (ICDIPC), 2012 Second International Conference on 10-12 July 2012, pages: 68-73.
[10] Hazem Mohammad Al-Najjar, “Digital Image Encryption Algorithm Based on Multi-Dimensional Chaotic System and Pixels Location”, International Journal of Computer Theory and Engineering, Vol. 4, No. 3, June 2012, pages: 354-357.
[11] Dattatherya, S. VenkataChalam&Manoj Kumar Singh, “Unified Approach with Neural Network for Authentication, Security and Compression of Image: UNICAP”, International Journal of Image Processing (IJIP), Volume (6), Issue (1), 25 Feb 2012, pages: 13-25.
[12] Pia Singh, Karamjeet Singh, “Image Encryption and Decryption Using Blowfish Algorithm in MATLAB”, International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-2013, pages: 150-154.
[13] RiahUkurGinting, Rocky YefrencesDillak, “Digital Color Image Encryption Using RC4 Stream Cipher and Chaotic Logistic Map”, Information Technology and Electrical Engineering (ICITEE), 2013 International Conference on 7-8 Oct. 2013, pages: 101-105.
[14] Gurpreet Singh, Amandeep Kaur, “GS-IES: An Advanced Image Encryption Scheme” International Journal of Engineering Research & Technology, Vol. 2 Issue 9, September – 2013, pages: 465-468.
[15] D. R.Stinson, Cryptography, Theory and Practice. Third edition: Chapman & Hall/CRC, 2006.
[16] William Stallings, Cryptography and Network Security, Principles and Practice. Fifth edition.
[17] ShujiangXu, Yinglong Wang, Jizhi Wang, YucuiGuo, “A Fast Image Encryption Scheme Based on a Nonlinear Chaotic Map”, 2010 2nd International Conference on Signal Processing Systems (ICSPS), 5-7 July 2010, pages: v2-326-v2-330.
[18] Linhua Zhang, Xiaofeng Liao, Xuebing Wang, “An image encryption approach based on chaotic maps”, Chaos, Solitons & Fractals. Volume 24, Issue 3, May 2005, Pages 759–765.
[19] http://en.wikipedia.org/wiki/Histogram
[20] Karl Pearson (1895), "Contributions to the Mathematical Theory of Evolution II, Skew Variation in Homogeneous Material". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 186: 343–414.
[21] XuShujiang Wang Yinglong, GuoYucui Wang Cong, “A Novel Chaos-based Image Encryption Scheme”, International Conference on Information Engineering and Computer Science (ICIECS) 2009, 19-20 Dec. 2009, pages: 1- 4.
[22] http://www.waset.org/journals/waset/v3/v3-7.pdf Analysis and Comparison of Image Encryption Algorithms by IsmetÖztürk and Ibrahim Soukpinar.
[23] Abhinav Srivastava, “A survey report on Different Techniques of Image Encryption”, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 6, June 2012, pages: 163-167.
[24] Khaled Loukhaoukha, Jean-Yves Chouinard, and AbdellahBerdai, “A Secure Image Encryption Algorithm Based on Rubik`s Cube Principle”, Journal of Electrical and Computer Engineering, Volume 2012 (2012), Article ID 173931, 13 pages.
[25] J. Bangarraju, V. Rajagopal, and B. Nithin, “Mitigation of supply disturbances using three-leg VSC based DVR from Single Phase P-Q Control Strategy,” in 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015, pp. 1–6.
[26] P. K. Naskar, A. Chaudhuri, and A. Chaudhuri, “A Secure Symmetric Image Encryption Based on Linear Geometry,” in Ieee, 2014, pp. 67–74.
[27] Nurhayati and S. S. Ahmad, “Steganography for inserting message on digital image using least significant bit and AES cryptographic algorithm,” in 2016 4th International Conference on Cyber and IT Service Management, 2016, pp. 1–6.
[28] N. Saikumar, R. B. Krishnan, S. Meganathan, and N. R. Raajan, “An encryption approach for security enhancement in images using key based partitioning technologies (ICCPCT), 2016, pp.-1-4.
[29] K. Sivaranjani and P. B. Prabahar, “Mended algorithm for image encryption based on random shuffling technique,” in 2013 IEEE International Conference on Computational Intelligence and Computing Research, 2013, pp. 1–4.
[30] C. Torres-Huitzil, “Hardware realization of a lightweight 2D cellular automata-based cipher for image encryption,” in 2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS), 2013, pp. 1–4.
[31] J. Vreugdenhil, K. Iverson, and R. S. Katti, “Image encyption using dynamic shuffling and XORing processes,” in 2009 IEEE International Symposium on Circuits and Systems, 2009, no. 2, pp. 734–737.
[32] M. Bin Younas and J. Ahmad, “Comparative analysis of chaotic and non-chaotic image encryption schemes,” in Proceedings - 2014 International Conference on Emerging Technologies, ICET 2014, 2014, pp. 81–86.

Vikas Thada, Utpal Shrivastava, "A Novel Hybrid Digital Image Encryption Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.585-592, 2018.

A huge volume of research have been made in the medical field for finding out inflammatory disorder which is rheumatoid arthritis which is one form of arthritis which affects joints. Identifying the onset of the disease should be done as early as possible to start the proper treatment, to prevent maximum deformity and for better quality of living. Radiography helps in detecting rheumatoid arthritis but there is reduced accuracy and is slower in detecting as it starts detecting after deformity(irreversible process) of joints have started. Magnetic resonance imaging may be more precise and accurate in identifying deformities. In Magnetic resonance imaging, images are analyzed through different image processing techniques. This paper is primarily aimed at stating the advantages of Magnetic resonance imaging over other techniques and after Magnetic resonance imaging scan has been done, making use of image processing techniques to obtain the final image for detection of the disorder.

Erosion, Dilation, Magnetic resonance imaging, Perimeter, arthritis, Skeletonization

[1] Bhavyashree K G, Sheela Rao, “Determination And Analysis Of Arthritis Using Digital Image Processing Techniques” International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084, Vol. 2, Issue 9, Sept 2014.
[2] Arpita Mittal, Sanjay Kumar Dubey, “Analysis of Rheumatoid Arthritis through Image Processing”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 2, November 2012.
[3] Kelvin Ka-fai Leung, “Longitudinal analysis of MRI images in rheumatoid arthritis”, IJCSI, November 16, 2007.
[4] SD. Kasute, M. Kolheka, “ROI Based Medical Image Compression”, IJSRNSC, Vol. 5, Issue 1, April 2017.
[5] Gilkeson G, Polisson R, Sinclair H, “Early detection of carpal erosions in patients with rheumatoid arthritis: a pilot study of magnetic resonance imaging”, J Rheumatol 1988; pp. 1361-1366.
[6] Laxman Singh, R.B.Dubey, Z.A.Jaffery , Zaheeruddin, “Segmentation and characterization of brain tumor from MR images”, IEEE International Conference on Advances in Recent Technologies in Communication and Computing, 2009.
[7] Paul Emery, “Magnetic Resonance Imaging: Opportunities For Rheumatoid Arthritis Disease Assessment And Monitoring Long-Term Treatment Outcomes”, Arthritis Research & Therapy, Vol. 4, 2002.
[8] Namrata Ghuse, Yogita Deore, Amol Potgantwar, “Efficient Image Processing Based Liver Cancer Detection Method”, IJSRNSC, Vol. 5, Issue 3, June 2017
[9] Arpita Mittal , Sanjay Kumar Dubey, “A Literature Review on Analysis of MRI Images of Rheumatoid Arthritis through Morphological Image Processing Techniques”, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 3, March 2013.
[10] J.Vijay, J.Subhashini, “An Efficient Brain Tumor Detection Methodology Using K-Means Clustering Algorithm,” IEEE International conference on Communication and Signal Processing, pp. 653-657, April 3-5, 2013.
[11] Lau E, Symmons D, Bankhead C, MacGregor A, Donnan S, Silman A., “Low prevalence of rheumatoid arthritis in the urbanized Chinese of Hong Kong”, J Rheumatol 1993; pp. 1133-1137.

Sheba Pari, "Analysing the Detection of Rheumatoid Arthritis using Image Processing Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.593-596, 2018.

Job scheduling is one of the major task in the federated cloud computing environment. In this paper a method using the existing job sequencing with deadline algorithm is proposed. The presented model including the existing job sequencing with deadline algorithm, sequences the jobs according to the optimization criterion of minimum energy consumption by the data center server, with the constraint to complete the jobs within deadline. The study mainly emphasizes to develop a system that maximizes the utilization of computing resources on one hand, while on other hand provides proper load balancing and task scheduling to get the optimal solution in the cloud federation for e-Governance services.

Cloud computing, Job scheduling, Cloud federation, Cloud service broker

[1] Ashutosh Gupta, Praveen Dhyani, O.P.Rishi, “Cloud based e-Voting: one step ahead for Good Governance in India”, In International Journal of Computer Applications(0975-8887),Volume 67-No.6, April 2013.
[2] National Institute of Standards and Technology (NIST), The NIST Definition of Cloud Computing, http://csrc.nist.gov /publications/drafts800-145/Draft-SP-800-145_cloud-definition.pdf.
[3] Dr. M. Srivenkatesh, K. Vanitha, “Task Scheduling in Federated Cloud by Multilevel Queue based on Genetic Algorithm”, In International Journal of Advanced Research in Computer and Communication Engineering, Vol.4, Issue 3, March 2015.
[4] Preyesh Kanungo, “Design Issues in Federated Cloud Architectures”, In International Journal of Advanced Research in Computer and Communication Engineering, Vol.5, Issue.5, May 2016.
[5] Himanshu Goel, Narendra Chamoli, “Job Scheduling algorithm in Cloud Computing : a survey”, In International Journal of Computer Applications (0975-8887), Vol.95, No.23, June 2014.
[6] Ziqian Dong, Ning Liu and Roberto Rojas-Cessa, “Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers”, In the Journal of Cloud Computing:Advances, Systems and Applications (2015) .
[7] Kyong Hoon Kim, Rajkumar Buyya and Jong Kim, “Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enable Clusters”.
[8] Cong Liu,Xiao Qin and shuang Li “PASS: Power-Aware Scheduling of Mixed Applications with Deadline Constraints on Clusters”, Proc. the 17th int’l Conf. Computer Communications and Networks(ICCCN) St. Thomas, Vergin Islands, Aug. 2008.
[9] Shruti and Meenakshi Sharma, “ Task Scheduling and Resource Optimization in Cloud Computing using Deadline-Aware Particle Sworm Technique”, in International Journal of Computer Sciences and Engineering(2347-2693), Volume-5, Issue-6, PP 227-231, June 2017.
[10] Indukuri R. Krishnan Raju, Penmasta Suresh Varma, M.V.Rama Sundari, G.Jose Moses “ Deadline Aware Two stage scheduling Algorithm in Cloud computing”, In Indian journal of science and Technology, Vol 9(4), Jan 2016.
[11] Horowitz E., Sahni S. and Rajasekaran S., “Fundamental of computer algorithms”, Hyderabad, A.P., Universities Press(India) Private Limited, India, 2008. page no. 231.

Ashutosh Gupta, Praveen Dhyani, O.P. Rishi, Vishwambhar Pathak, "Service Request Approach for e-Governance using Federation of Cloud," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.597-601, 2018.

Malignant melanoma is the deadliest form of skin cancer. Researches are attempting for the early automatic diagnosis of Melanoma, a lethal form of skin cancer, from dermoscopic images. The process includes different stages like pre-processing, lesion segmentation, dermoscopic feature detection within a lesion, feature extraction and disease classification. In this paper, we review the state-of-the-art computer aided diagnosis system for melanoma detection and examine recent practices in different steps of these systems. Statistics and results from the most important and recent implementations are analyzed and reported. We compared the performance of recent works based on different parameters like accuracy, sensitivity, specificity, machine learning techniques, dataset etc. Research challenges regarding the different parts of computer aided skin cancer diagnosis systems are also highlighted in this paper.

Skin cancer, Melanoma, Dermoscopy, Preprocessing, Image segmentation, Feature extraction, Classification, Ridgelet, K-Means, GLCM, SVM

[1] David Gutman, Noel C. F. Codella, Emre Celebi, Brian Helba, Michael Marchetti, Nabin Mishra, and Allan Halpern. Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC).CoRR, abs/1605.01397, 2016.
[2] Nurulhuda Firdaus Mohd Azmi, Haslina Md Sarkan, Yazriwati Yahya, and Suriayati Chuprat. Abcd rules segmentation on malignant tumor and benign skin lesion images. In Computer and Information Sciences (ICCOINS), 2016 3rd International Conference on, pages 66–70. IEEE, 2016.
[3] ”International Skin Imaging Collaboration: Melanoma Project Website, ” https://isic-archive. com, note = Accessed: 2010-09-30.
[4] Brian D’Alessandro, Atam P Dhawan, and Nizar Mullani. Computer aided analysis of epi-illumination and transillumination images of skin lesions for diagnosis of skin cancers. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, pages 3434–3438. IEEE, 2011.
[5] Wen-Yu Chang, Adam Huang, Chung-Yi Yang, Chien-Hung Lee, Yin- Chun Chen, Tian-Yau Wu, and Gwo-Shing Chen. Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study. PloS one, 8(11):e76212, 2013.
[6] Thanh-Toan Do, Yiren Zhou, Haitian Zheng, Ngai-Man Cheung, and Dawn Koh. Early melanoma diagnosis with mobile imaging. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pages 6752–6757. IEEE, 2014.
[7] O. Abuzaghleh, B. D. Barkana, and M. Faezipour. Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. IEEE Journal of Translational Engineering in Health and Medicine, 3:1–12, 2015.
[8] Maria Joa˜o M Vasconcelos, Lu´ıs Rosado, and Ma´rcia Ferreira. A new risk assessment methodology for dermoscopic skin lesion images. In Medical Measurements and Applications (MeMeA), 2015 IEEE Interna- tional Symposium on, pages 570–575. IEEE, 2015.
[9] O. Abuzaghleh, M. Faezipour, and B. D. Barkana. A comparison of feature sets for an automated skin lesion analysis system for melanoma early detection and prevention. In 2015 Long Island Systems, Applica- tions and Technology, pages 1–6, May 2015.
[10] U. Jamil, S. Khalid, and M. U. Akram. Dermoscopic feature analysis for melanoma recognition and prevention. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH), pages 290–295, Aug 2016.
[11] J Premaladha and KS Ravichandran. Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms. Journal of medical systems, 40(4):96, 2016.
[12] Teck Yan Tan, Li Zhang, and Ming Jiang. An intelligent decision support system for skin cancer detection from dermoscopic images. In Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD),2016 12th International Conference on, pages 2194–2199. IEEE, 2016.
[13] Nazia Hameed, Anglia Ruskin, Kamal Abu Hassan, and MA Hossain. A comprehensive survey on image-based computer aided diagnosis systems for skin cancer. In Software, Knowledge, Information Management & Applications (SKIMA), 2016 10th International Conference on, pages 205–214. IEEE, 2016.
[14] Nguyen Hong Quang et al. Automatic skin lesion analysis towards melanoma detection. In Intelligent and Evolutionary Systems (IES), 2017 21st Asia Pacific Symposium on, pages 106–111. IEEE, 2017.
[15] L. Yu, H. Chen, Q. Dou, J. Qin, and P. A. Heng. Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Transactions on Medical Imaging, 36(4):994–1004, April 2017.
[16] Q. Ul Ain, Bing Xue, H. Al-Sahaf, and M. Zhang. Genetic programming for skin cancer detection in dermoscopic images. In 2017 IEEE Congress on Evolutionary Computation (CEC), pages 2420–2427, June 2017.
[17] Sabrina Conoci, Francesco Rundo, Salvatore Petralta, and Sebastiano Battiato. Advanced skin lesion discrimination pipeline for early melanoma cancer diagnosis towards poc devices. In Circuit Theory and Design (ECCTD), 2017 European Conference on, pages 1–4. IEEE, 2017.
[18] Yuexiang Li and Linlin Shen. Skin lesion analysis towards melanoma detection using deep learning network. Sensors, 18(2):556, 2018.
[19] Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester. Image inpainting. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 417–424. ACM Press/Addison-Wesley Publishing Co., 2000.
[20] Jean-Luc Starck, Emmanuel J Cande`s, and David L Donoho. The curvelet transform for image denoising. IEEE Transactions on image processing, 11(6):670–684, 2002.
[21] Tony F Chan, Jianhong Shen, and Hao-Min Zhou. Total variation wavelet inpainting. Journal of Mathematical imaging and Vision, 25(1):107–125, 2006.
[22] APV Ranganathan and RF Von Borries. Sliced ridgelet transform for image denoising. In Digital Signal Processing Workshop, 12th-Signal Processing Education Workshop, 4th, pages 209–213. IEEE, 2006.
[23] Anne Cuzol, Kim Steenstrup Pedersen, and Mads Nielsen. Field of particle filters for image inpainting. Journal of Mathematical Imaging and Vision, 31(2):147–156, 2008.
[24] M Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer, and William V Stoecker. Lesion border detection in dermoscopy images. Computerized medical imaging and graphics, 33(2):148–153, 2009.
[25] Supot Sookpotharom. Border detection of skin lesion images based on fuzzy c-means thresholding. In Genetic and Evolutionary Computing, 2009. WGEC’09. 3rd International Conference on, pages 777–780. IEEE, 2009.
[26] H Noori and Saeid Saryazdi. Image inpainting using directional median filters. In Computational Intelligence and Communication Networks (CICN), 2010 International Conference on, pages 45–49. IEEE, 2010.
[27] Sachin D Ruikar and Dharmpal D Doye. Wavelet based image denoising technique. International Journal of Advanced Computer Science and Applications, 2(3):49–53, 2011.
[28] Sandeep Palakkal and KMM Prabhu. Poisson noise removal from images using the fast discrete curvelet transform. In Communications (NCC), 2011 National Conference on, pages 1–5. IEEE, 2011.
[29] Mohammad Taghi Bahreyni Toossi, Hamid Reza Pourreza, Hoda Zare, Mohamad-Hoseyn Sigari, Pouran Layegh, and Abbas Azimi. An effective hair removal algorithm for dermoscopy images. Skin Research and Technology, 19(3):230–235, 2013.
[30] Adam Huang, Shun-Yuen Kwan, Wen-Yu Chang, Min-Yin Liu, Min- Hsiu Chi, and Gwo-Shing Chen. A robust hair segmentation and removal approach for clinical images of skin lesions. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 3315–3318. IEEE, 2013.
[31] Santosh Kumar Yadav, Rohit Sinha, and Prabin Kumar Bora. Image denoising using ridgelet transform in a collaborative filtering framework. In Signal Processing and Communications (SPCOM), 2014 International Conference on, pages 1–5. IEEE, 2014.
[32] Azadeh Noori Hoshyar, Adel Al-Jumaily, and Afsaneh Noori Hoshyar. The beneficial techniques in preprocessing step of skin cancer detection system comparing. Procedia Computer Science, 42:25–31, 2014.
[33] Shehzad Khalid and Uzma Jamil. Valuable pre-processing and segmen- tation techniques used in automated skin lesion detection systems. 2015.
[34] Sumit Kumar and Mantosh Biswas. New method of noise removal in images using curvelet transform. In Computing, Communication & Automation (ICCCA), 2015 International Conference on, pages 1193–1197. IEEE, 2015.
[35] Tomas Majtner, Kristına Lidayova, Sule Yildirim-Yayilgan, and Jon Yn- gve Hardeberg. Improving skin lesion segmentation in dermoscopic images by thin artefacts removal methods. In Visual Information Processing (EUVIP), 2016 6th European Workshop on, pages 1–6. IEEE, 2016.
[36] Ph Schmid and S Fischer. Colour segmentation for the analysis of pigmented skin lesions. In Image Processing and Its Applications, 1997., Sixth International Conference on, volume 2, pages 688–692. IET, 1997.
[37] Heng-Da Cheng, X H Jiang, Ying Sun, and Jingli Wang. Color image segmentation: advances and prospects. Pattern recognition, 34(12):2259–2281, 2001.
[38] Wan-Ting Lin, Chuen-Horng Lin, Tsung-Ho Wu, and Yung-Kuan Chan. Image segmentation using the k-means algorithm for texture features. World Academy of Science, Engineering and Technology, 65:612–615, 2010.
[39] Xiang Li, Ben Aldridge, Robert Fisher, and Jonathan Rees. Estimating the ground truth from multiple individual segmentations incorporating prior pattern analysis with application to skin lesion segmentation. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pages 1438–1441. IEEE, 2011.
[40] R Yogamangalam and B Karthikeyan. Segmentation techniques com- parison in image processing. International Journal of Engineering and Technology (IJET), 5(1):307–313, 2013.
[41] Nameirakpam Dhanachandra, Khumanthem Manglem, and Yambem Jina Chanu. Image segmentation using k-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54:764–771, 2015.
[42] Fahimeh Sadat Saleh and Reza Azmi. Automatic multiple regions segmentation of dermoscopy images. In Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on, pages 24–29. IEEE, 2015.
[43] Mohammad H Jafari, Nader Karimi, Ebrahim Nasr-Esfahani, Shadrokh Samavi, S Mohamad R Soroushmehr, K Ward, and Kayvan Najarian. Skin lesion segmentation in clinical images using deep learning. In Pattern Recognition (ICPR), 2016 23rd International Conference on, pages 337–342. IEEE, 2016.
[44] Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al. Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1710.05006, 2017.
[45] Chiranjeev Sagar and Lalit Mohan Saini. Color channel based segmenta- tion of skin lesion from clinical images for the detection of melanoma. In Power Electronics, Intelligent Control and Energy Systems (ICPEICES), IEEE International Conference on, pages 1–5. IEEE, 2016.
[46] Dao Nam Anh. Detection of lesion region in skin images by moment of patch. In Computing & Communication Technologies, Research, Inno- vation, and Vision for the Future (RIVF), 2016 IEEE RIVF International Conference on, pages 217–222. IEEE, 2016.
[47] Neda Zamani Tajeddin and Babak Mohammadzadeh Asl. A general algorithm for automatic lesion segmentation in dermoscopy images. In Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016 23rd Iranian Conference on, pages 134–139. IEEE, 2016.
[48] Behzad Bozorgtabar, Zongyuan Ge, Rajib Chakravorty, Mani Abedini, Sergey Demyanov, and Rahil Garnavi. Investigating deep side layers for skin lesion segmentation. In Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, pages 256–260. IEEE, 2017.
[49] Rashika Mishra and Ovidiu Daescu. Deep learning for skin lesion segmentation. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1189–1194. IEEE, 2017.
[50] Yading Yuan, Ming Chao, and Yeh-Chi Lo. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE Transactions on Medical Imaging, 2017.
[51] Mohamed Attia, Mohamed Hossny, Saeid Nahavandi, and Anousha Yazdabadi. Skin melanoma segmentation using recurrent and convolu- tional neural networks. In Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, pages 292–296. IEEE, 2017.
[52] Oludayo O Olugbara, Tunmike B Taiwo, and Delene Heukelman. Segmentation of melanoma skin lesion using perceptual color differ- ence saliency with morphological analysis. Mathematical Problems in Engineering, 2018, 2018.
[53] Miroslav Bencˇo and Robert Hudec. Novel method for color textures features extraction based on glcm. Radioengineering, 16(4):65, 2007.
[54] Giuseppe Di Leo, Gabriella Fabbrocini, Alfredo Paolillo, Orsola Rescigno, and Paolo Sommella. Towards an automatic diagnosis system for skin lesions: estimation of blue-whitish veil and regression structures. In Systems, Signals and Devices, 2009. SSD’09. 6th International Multi- Conference on, pages 1–6. IEEE, 2009.
[55] R Sumithra, Mahamad Suhil, and DS Guru. Segmentation and classifi- cation of skin lesions for disease diagnosis. Procedia Computer Science, 45:76–85, 2015.
[56] Omar Abuzaghleh, Buket D Barkana, and Miad Faezipour. Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention. In Systems, Applications and Technology Conference (LISAT), 2014 IEEE Long Island, pages 1–6. IEEE, 2014.
[57] Sinan Kockara, Mutlu Mete, Tansel Halic, Nurcan Yuruk, Muhyeddin Ercan, and Ashley Lawrence. Fractals for malignancy detection in der- moscopy images. In Healthcare Informatics (ICHI), 2015 International Conference on, pages 115–121. IEEE, 2015.
[58] M Suryapraba, G Rajanarayanee, Priyanka Kumari, and P Santhi. Analysis of skin cancer classification using glcm based on feature extraction in artificial neural network. International Journal of Emerging Technology in Computer Science & Electronics, 13.
[59] Seema Kolkur and DR Kalbande. Survey of texture based feature extraction for skin disease detection. In ICT in Business Industry & Government (ICTBIG), International Conference on, pages 1–6. IEEE, 2016.
[60] Md Khalad Abu Mahmoud, Adel Al-Jumaily, and Maen Takruri. The automatic identification of melanoma by wavelet and curvelet analysis: study based on neural network classification. In Hybrid Intelligent Systems (HIS), 2011 11th International Conference on, pages 680–685. IEEE, 2011.
[61] Bareqa Salah, Mohammad Alshraideh, Rasha Beidas, and Ferial Haya- jneh. Skin cancer recognition by using a neuro-fuzzy system. Cancer informatics, 10:CIN–S5950, 2011.
[62] Maen Takruri, Adel Al-Jumaily, and Mohamed Khaled Abu Mahmoud. Automatic recognition of melanoma using support vector machines: A study based on wavelet, curvelet and color features. In Industrial Au- tomation, Information and Communications Technology (IAICT), 2014 International Conference on, pages 70–75. IEEE, 2014.
[63] Diwakar Gautam and Mushtaq Ahmed. Melanoma detection and classi- fication using svm based decision support system. In India Conference (INDICON), 2015 Annual IEEE, pages 1–6. IEEE, 2015.
[64] RS Shiyam Sundar and M Vadivel. Performance analysis of melanoma early detection using skin lession classification system. In Circuit, Power and Computing Technologies (ICCPCT), 2016 International Conference on, pages 1–5. IEEE, 2016.
[65] Muhammad Ali Farooq, Muhammad Aatif Mobeen Azhar, and Rana Hammad Raza. Automatic lesion detection system (alds) for skin cancer classification using svm and neural classifiers. In Bioinformatics and Bioengineering (BIBE), 2016 IEEE 16th International Conference on, pages 301–308. IEEE, 2016.
[66] Wiem Abbes and Dorra Sellami. High-level features for automatic skin lesions neural network based classification. In Image Processing, Applications and Systems (IPAS), 2016 International, pages 1–7. IEEE, 2016.
[67] Adria Romero Lopez, Xavier Giro-i Nieto, Jack Burdick, and Oge Marques. Skin lesion classification from dermoscopic images using deep learning techniques. In Biomedical Engineering (BioMed), 2017 13th IASTED International Conference on, pages 49–54. IEEE, 2017.
[68] Saeid Amouzad Mahdiraji, Yasser Baleghi, and Sayed Mahmoud Sakhaei. Skin lesion images classification using new color pigmented boundary descriptors. In Pattern Recognition and Image Analysis (IPRIA), 2017 3rd International Conference on, pages 102–107. IEEE, 2017.
[69] A Rajesh. Classification of malignant melanoma and benign skin lesion by using back propagation neural network and abcd rule. In Electrical, Instrumentation and Communication Engineering (ICEICE), 2017 IEEE International Conference on, pages 1–8. IEEE, 2017.
[70] S Chatterjee, D Dey, and S Munshi. Development of a superpixel based local color feature extraction technique for the classification of skin lesions. In Calcutta Conference (CALCON), 2017 IEEE, pages 210–214. IEEE, 2017.
[71] Hiam Alquran, Isam Abu Qasmieh, Ali Mohammad Alqudah, Sajidah Alhammouri, Esraa Alawneh, Ammar Abughazaleh, and Firas Hasayen. The melanoma skin cancer detection and classification using support vector machine. In Applied Electrical Engineering and Computing Technologies (AEECT), 2017 IEEE Jordan Conference on, pages 1–5. IEEE, 2017.
[72] Youssef Filali, Assia Ennouni, My Abdelouahed Sabri, and Abdellah Aarab. Multiscale approach for skin lesion analysis and classification. In Advanced Technologies for Signal and Image Processing (ATSIP),2017 International Conference on, pages 1–6. IEEE, 2017.
[73] Bill S Lin, Kevin Michael, Shivam Kalra, and HR Tizhoosh.Skin lesion segmentation: U-nets versus clustering. arXiv preprint arXiv:1710.01248, 2017.
[74] Nay Chi Lynn and Zin Mar Kyu. Segmentation and classification of skin cancer melanoma from skin lesion images. In Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2017, 18th International Conference on, pages 117–122. IEEE, 2017.
[75] MA Rahman, MT Haque, C Shahnaz, SA Fattah, WP Zhut, and MO Ahmedt. Skin lesions classification based on color plane-histogram- image quality analysis features extracted from digital images. 2017.
[76] Ivan Gonzalez Diaz. Dermaknet: Incorporating the knowledge of der- matologists to convolutional neural networks for skin lesion diagnosis. IEEE Journal of Biomedical and Health Informatics, 2018.
[77] Roberta B Oliveira, Joa˜o P Papa, Aledir S Pereira, and Joa˜o Manuel RS Tavares. Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Computing and Applica- tions, 29(3):613–636, 2018.

S. Sreena, A. Lijiya, "Survey on Skin Lesion Analysis towards Melanoma Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.602-615, 2018.

Software-as-a-Service (SaaS) paradigm is one of the most popular forms of cloud services in today’s multi-tenant technological architecture. The role of multi-tenancy architecture is to offer services to its tenants with customized features of applications they need. Data isolation and resource sharing between multiple tenants in such architecture is more complicated task. Access control models takes accountability of the verification mechanism, the administration and the proper governance of the resources and related services. New architectural model is therefore required to maintain simple relation between the providers and multiple tenants in the system with a strong security feature. SaaS paradigm also needs an effective portability and orchestration mechanism over a virtualized infrastructure. To address these issues, we present a novel architecture called the E-RBAC (Enhanced- Role Based Access Control) model to enhance the security and access control over the services in the SaaS infrastructure by calculating the trust of the roles assigned. We also present a comparative analysis of SaaS provisioning with and without E-RBAC security model.

Cloud Computing, SaaS, Multi-Tenancy Architecture, RBAC

[1] A. Almutairi, M. Sarfraz, S. Basalamah, W. Aref, A. Ghafoor, “A distributed access control architecture for cloud computing,”. IEEE software, vol. 29(2), pp. 36-44, March 2012.
[2] X. Chen, W. Xu, W. Shen, “Trustworthiness-based dynamic access control for grid application,” Journal of Hunan University (Natural Sciences), vol. 35(7), pp. 859, July 2008.
[3] M. Srivatsa, A. Iyengar, T. Mikalsen, I. Rouvellou, and J. Yin, “An access control system for Web service compositions,” in Proc. IEEE International Conference on Web services, pp. 1-8, 2007.
[4] W. Wang, J. Han, M. Song, X. Wang, “The design of a trust and role based access control model in cloud computing,” In Pervasive Computing and Applications (ICPCA), 6th International Conference on IEEE, pp. 330-334, October 2011.
[5] Qiong Zuo, Meiyi Xie, Guanqiu Qi, Hong Zhu, “Tenant Based Access Control Model for Multi-tenancy and Sub-tenancy Architecture in Software-as-a-Service”, Frontiers of Computer Science, vol. 11(3), 2017.
[6] Masood R, Shibli M A, Ghazi Y, Kanwal A, Ali A. Cloud authorization: exploring techniques and approach towards effective access control framework. Frontiers of Computer Science, vol. 9(2), pp. 297–321, 2015.
[7] Li xia Xie, Chong Wang, “Multi domain Access Control Model Based on Role and Trust Degree”, Journal of Electrical and Computer Engineering, vol. 16,2016.
[8] Chaitali Uikey, D.S. Bhilari, “TrustRBAC: Trust Role Based Access Control Model in Multi-domain Cloud Environments”, International Conference on Information, Communication, Instrumentation and Control (ICICIC), 2017.
[9] L. Xia, J. Jing An administrative model for role-based access control using hierarchical namespace. Journal of Computer Research and Development, vol. 44(12), pp. 2020-2027, 2007,
[10] M. Lorch, S. Proctor, R. Lepro, D. Kafura, and S. Shah, “First experiences using XACML for access control in distributed systems,” in Proc. ACM workshop on XML security. ACM, pp. 25-37, 2003.
[11] C. Uikey, D. S. Bhilar, “Interaction Modelling using Trust and Recommendation in Cloud Computing Environment”. International Journal of Computer Applications. vol. 124(17), pp. 37-44, January 2015.
[12] M. Azarmi, B. K. Bhargava, P. Angin, R. Ranchal, N. Ahmed, A. Sinclair, M. Linderman, and L. B. Othmane, “An endto-end security auditing approach for service oriented architectures,” in Proc. IEEE Symposium on Reliable Distributed Systems, pp. 279-284, 2012.
[13] E. M. Ei, T. N. Thinn, The privacy-aware access control system using attribute-and role-based access control in private cloud. Proceedings of the 2011 4th IEEE IC-BNMT. pp. 447-451, 2011.
[14] T. Tavizi, M. Shajari, P. Dodangeh, A usage control based architecture for cloud environments. Parallel and Distributed Processing Symposium Workshops & Ph.D Forum (IPDPSW), 2012 IEEE 26th International. pp. 1534-1539, IEEE (2012)
[15] C. Jincui, J. Liqun “Role-based access control model of cloud computing,” Energy Procedia 13, pp. 1056-61, December 2011.
[16] Rohit Ranchal, Bharat Bhargava, Ruchith Fernando, Hui Lei and Zhongjun Jin, “Privacy Preserving Access Control in Service-Oriented Architecture”, IEEE International Conference on Web Services, 2016.
[17] Dr. P. Neelakantan, “A Study on E-Learning and Cloud Computing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Volume 3, Issue 1, ISSN : 2456-3307.
[18] Geetha.N and M.S.Anbarasi, “Enhanced Role Based Access Control System for Cloud Service Composition in Multi-Tenant Environment”, International Journal of Pure and Applied Mathematics, volume 118(11), pp. 349-355, 2018.
[19] R. Sood , R. Sharma, “Cloud Security Threats and Issues-A Review”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-4, E-ISSN: 2347-2693.

N. Geetha, M. S. Anbarasi, "Enhanced-Role Based Access Control (E-RBAC) with Trust Factor for Cloud Software- as-a-Service Paradigm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.616-621, 2018.

In ancient, dynamic secret writing systems it provides solely information confidentiality, however, it`s incompatible with dynamic multi-keyword over encrypted information by causing secret keys to the user through the mail. In traditional we`ll be going to be operative on plain information however in our project we tend to encrypt the information. The info by generating secret keys and activity search on encrypted data by user request. During this paper, we tend to propose a dynamic multi-keyword hierarchical search theme over encrypted cloud information, that at the same time supports dynamic update operations like deletion and insertion of documents. Intensive experiments area unit conducted to demonstrate the potency of the projected theme.

MultiKeyword,Ranked,Efficient,Searchable

K.Ren, C.Wang, Q.Wangetal., “Security challenges for the public cloud”, IEEE Internet Computing, vol. 16, No. 1, pp. 69–73, 2012.
[2] S. Kamara and K. Lauter, “Cryptographic cloud storage,” in Financial Cryptography and Data Security. Springer, 2010, pp. 136– 149.
[3] C. Gentry, “A fully homomorphic encryption scheme”, Ph.D. dissertation, Stanford University, 2009.
[4] O. Goldreich and R. Ostrovsky, “Software protection and simulation on oblivious rams”, Journal of the ACM (JACM), vol. 43, No. 3, pp. 431–473, 1996.
[5] D. Boneh, G. Di Crescenzo, R. Ostrovsky, and G. Persiano, “Public key encryption with keyword search”, in Advances in Cryptology Eurocrypt 2004. Springer, 2004, pp. 506–522.
[6] D. Boneh, E. Kushilevitz, R. Ostrovsky, and W. E. Skeith III, “Public key encryption that allows pir queries”, in Advances in Cryptology-CRYPTO 2007. Springer, 2007, pp. 50–67.
[7] Y. H. Hwang and P. J. Lee, “Public key encryption with conjunctive keyword search and its extension to a multi-user system,” in Proc. 1st Int. Conf. Pairing-Based Cryptography, 2007, pp. 2–22
[8] D. X. Song, D. Wagner, and A. Perrig, “Practical techniques for searches on encrypted data”, in Proc. IEEE Symp. Secur. Privacy, 2000, pp. 44–55.
[9] E.-J. Goh, “Secure indexes”, IACR Cryptol. ePrint Archive, vol. 2003, pp. 216.
[10] Y.-C. Chang and M. Mitzenmacher, “Privacy preserving keyword searches on remote encrypted data,” in Proc. 3rd Int. Conf. Appl. Cryptography Netw. Secur., 2005, pp. 442–455.
[11] J. Li, Q. Wang, C. Wang, N. Cao, K. Ren, and W. Lou, “Fuzzy keyword search over encrypted data in cloud computing,” in IEEE Proc. INFOCOM, 2010, pp. 1–5.

P.Sai Sirisha, K.Praveen Kumar, M.Harsha Vardhan, J.Karthik, "A Novel Approach for Searchable Keyword in Cloud Computing Using Efficient Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.622-625, 2018.

Our research goal is to develop an augmented reality application to improve the learning experience of the kindergarten students. We are developed an android application which includes augmented reality features which will help kindergarten students learn things in more easy and interesting way. This application scans the environment using device camera and search for markers. If Marker is detected it shows the 3D model corresponding to that marker. OpenCV includes algorithms of feature detection. We used natural feature detection with some improvement. Natural feature detection algorithms help to identify images or markers. This algorithm good for capturing and detecting images with greater accuracy and speeds. This project proposes a method for teaching early childhood kindergarten students by designing augmented reality. With the help of AR technology to generate learning interest in kindergarten students.

Augmented Reality, Kindergarten Education, OpenCV, Natural Feature Detection Algorithm

[1] Application of Augmented Reality for Early Childhood English Teaching,2017, Lap-Kei Lee, Cheuk-Him Chau, Chun-Hin Chau, Chun-Tim Ng School of Science & Technology the Open University of Hong Kong Ho Man Tin, Kowloon, Hong Kong.
[2] Mobile Augmented Reality for Learning Z. Mobile Learning Seminar. 2011, T. Roffmann, T. Friese.
[3] Educational Magic Toys Developed with Augmented Reality Technology for Early Childhood Education J. Computers in Human Behavior, R.M Yilmaz.
[4] R. N. Chai, H. L. Fu, A Study on Current Situations and Patterns of Kids English Teaching J. Journal of Tongren University. Vo l.15, pp.103-105, 2013.
[5] Chiang, T.H.C., S.J.H. Yang and G. Hwang, An Augmented Reality-based Mobile Learning System to Improve Students Learning Achievements and Motivations in Natural Science Inquiry Activities J. Journal of Educational Technology Society. pp. 352-365, 2014.

S.S. Deshmukh, Akshay Gaikwad, Mukesh Mahajan, Harshali Bonde, Sanjana Kolge, "Teaching Kindergarten Student using Augmented Reality," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.626-629, 2018.

Biometric is considered as an authentic system to recognize a human with respect to their behavior and body features. Automatic verification of features like finger print, palm print, iris recognition is considered a proficient way to grant an access to any system. Among all those, iris is taken as one of the admired technique of recognition which needs precise recognition to execute the whole system. To extract those features which exists in the texture of eye and identify it with the existing database requires various methods to get performed like segmentation, preprocessing, normalization etc. For all those methods, various algorithms have been developed and their effectiveness varies according to the circumstances in which they have been applied. This paper proposes a review on various systems and their developed technique on which researchers have previously worked. Due to several issues, methods which have been developed, till now, can’t consider for wide implementation. So, the system which has been proposed in this paper provides an iris recognition or authentication system using Savitzky-Golay filter for iris feature extraction. A Savitzky–Golay filter is a digital filter that can be applied to a set of digital data points for the purpose of smoothing or enhancing the data without distorting the information. The approach also proves that the symbolic representation effectively handles noise and degradations, including low resolution, specular reflection, and occlusion of eyelids present in the eye images and uses minimum number of features to represent iris image. This system can be implemented in various fields such as banking, security concern areas and many more. Major Canadian Airports have been using Iris recognition systems to expedite passengers through customs.

Biometric System, IRIS recognition, Savitzky-Golay Filter, Eye Lids, Feature Extraction

[1] Fabián Rolando Jiménez López et al., “Biometric Iris Recognition Using Hough Transform”, IEEE- 2013.
[2] Arezou Banitalebi Dehkordi et al., “Noise Reduction in Iris Recognition Using Multiple Thresholding”, International Conference on Signal and Irnage Processing Applications, IEEE 2013.
[3] P.Thirumurugan et al., “Iris Recognition using Wavelet Transformation Techniques”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.1, January- 2014.
[4] Navjot Kaur and Mamta Juneja, “A Review on Iris Recognition”, IEEE 2014.
[5] Amena Khatun, A. K. M. Fazlul Haque et al., “Design and Implementation of Iris Recognition Based Attendance Management System”,IEEE 2015.
[6] Mateusz Trokielewicz et al., “Iris Recognition with a Database of Iris Images Obtained in Visible Light Using Smartphone Camera”, IEEE -2016.
[7] Sarika B. Solanke et al., “Biometrics: Iris Recognition System, A Study of Promising Approaches For Secure Authentication”, IEEE 2016.
[8] Jagadeesh N. et al., “Iris recognition system development using Matlab”, International Conference on Computing Methodologies and Communication, IEEE 2017.
[9] tedmontgomery.com/the_eye/iris.html.
[10] github.com/ghazi94/IRIS-Segmentation.
[11] Raghavender ReddyJillela et al. “Segmenting iris images in the visible spectrum with applications in mobile biometrics”, Science Direct, 2014.
[12] Iqra Altaf Mattoo and Parul Agarwal, “Iris Biometric Modality: A Review”, OJCST, 2017.
[13] https://geektimes.ru/post/247634/

Monika Singh, Sanjeev Kumar Sharma, "A Review on Implementation of Biometric Iris Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.630-635, 2018.