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A new pixel selection technique has been introduced where data embedding starts from middle region (one or more than one pixel) of the image. Successively, diagonal pixels of middle region are selected for further embedding the data. The diagonal pixels form a quadrilateral where data are embedded through four edges of quadrilateral with clockwise manner. A hash based method is also initiated for embedding the data into the pixel of an image taking one bit position out of four LSB bits where one bit of secret data is embedded. We propose a variable hash key function for the selection of bit position so that intruder cannot guess the actual bit position of secret information easily. Our proposed method has achieved good PSNR and high capacity of secret information compared to different pixel value differencing method (PVD).

LSB, middle region, Cryptography, Image Steganography, e-Banking

[1] Behrouz A. Forouzan , “Data Communication and Networking “,MacGraw Hill Education ( India) Private Limited.
[2] N. Provos, P. Honeyman, “Hide and Seek: An Introduction to Steganography”, IEEE Security and Privacy, Vol. 1, No. 3, pp. 32–44,2003 .
[3] A. Kumar ,K. M. Pooja , “Steganography a Data Hiding Technique” , International Journal of Computer Applications, Vol-9,No-7,Nov 2010.
[4] W. bender, D. Gruhl, N. Morimoto, A.Lu, “Techniques for data hiding”, IBM Systems Journal Vol. 35(3-4),pp. 313-336, 1996.
[5] A. Sarkar, S.Karforma, “ A new pixel selection Technique of LSB based steganography for data hiding”,IJRCS, Vol-5,Issue-3,pp.12-125,March 2018.
[6] D.C. Wu, and W.H. Tsai, “A Steganographic method for images by pixel-value differencing”, Pattern Recognition Letters, Vol. 24, pp. 1613-1626, 2003.
[7] J. K. Mandal ,D. Das, “Colour Image Steganography Based on Pixel Value Differencing in Spatial Domain”, International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.4, July 2012.
[8] H.C. Wu, N.I. Wu, C.S. Tsai , M.S. Hwang, “Image Steganographic Scheme Based on Pixel Value Differencing and LSB Replacement Method”, IEEE Proceedings on Vision, Image and Signal processing, Vol. 152, No. 5,pp. 611-615, 2005.
[9] G.R. Manjula ,A. Danti “A novel hash based LSB (2-3-3) image steganography in spatial domain”, International Journal of Security, Privacy and Trust Management (IJSPTM) Vol. 4, No 1, February 2015 .
[10] P. Bharti ,R. Soni, “ A new approach of data hiding in images using cryptography and Steganography”,IJOCA,vol. 58,no. 8,Nov 2012.
[11] Ali Akbar Nikoukar, “An Image Steganography Method with High Hiding Capacity Based on RGB Image”, International Journal of Signal and Image Processing, Vol. 1,Issue.4, pp. 238-241,2010.
[12] Ran Zan Wang, Chi Fang Lib, Ja Chen Lin, “Image hiding by optimal LSB substitution and Genetic algorithm”, Pattern Recognition Society. Published by Elsevier Science Ltd., pp.671-683, 2001.
[13] N. Kaur, A .Garg, “Steganography Using PSO Based Hybrid Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering,Vol-4,Issue.1,Nov 2014.

Atanu Sarkar, Sunil Karforma, "A Steganographic Approach Using New Pixel Selection Combined With Hash Function for Secure Data Transmission in e-Banking," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.379-385, 2018.

Mobile ad-hoc networks are the self-configuring mobile nodes which are connected through the wireless links with the decentralised networks. The nodes in MANET communicate with each other on the basis of mutual trust. Nodes dynamically form a temporary network and are protected using many firewalls and encryption software. But number of them is not sufficient and effective. So the main aim is to provide security services such as authentication, confidentiality, integrity, availability etc. to the mobile users. In this paper the effect of Black hole attack and Worm attack is analysed on the AODV routing protocol in MANET and prevention mechanism is presented to secure the network.

MANET, Wormhole Attack, Collaborative black hole Attack, Security, AODV

[1] J. Gronkvist, A. Hansson, and M. Skold, “Evaluation of a Specification-Based Intrusion Detection System for AODV”. 2007.
[2] M. Abolhasan, T. Wysocki, and E. Dutkiewicz “A review of routing protocols for mobile ad hoc networks.” Ad hoc networks, Vol.2 (1):pp.1–22, 2004.
[3] Kaur, Er. Sandeep Kaur Dhanda, “Analyzing the effect of Wormhole Attack on Routing Protocol in Wireless Sensor Network”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 8, August 2013.
[4] B. Bharagava, R. Oliveria, Y. Zhang and N. C. Idika, “ Addressing Collaborative Attacks and Defense in Ad Hoc Wireless Networks,” In Proc. of 2009 29th IEEE Int. Conf. on Distributed Computing Systems Workshops, pp. 447-450, 2009.
[5] C.E. Perkins and E.M. Royer. “Ad-hoc on-demand distance vector routing”. In Second IEEE Workshop on Mobile Computing System and Application, WMCSA 99, pages 90 –100, Feb. 1999.
[6] Lui K.-S., sChiu H.S., “DelPHI: Wormhole Detection mechanism for Adhoc Wireless Networks” Proceedings of the 1st International Symposium on Wireless Pervasive Computing; Phuket, Thailand. 16–18 January 2006.
[7] Viren Mahajan, Maitreya Natu, and Adarshpal Sethi, “Analysis of wormhole Intrusion Attacks In MANETS”,IEEE Military Communications Conference,MILCOM. Nov 2008.
[8] Rashid Hafeez Khokhar, Md Asri Ngadi and Satira Mandala, “A Review of Current Routing Attacks in Mobile Ad Hoc Networks”, International Journal of Computer Science and Security, Volume-2 Issue-3 pp. 18-29, , 2009.
[9] C. E. Perkins and E. M. Royer, “Ad Hoc On-Demand Distance Vector Routing,” In Proc. Of IEEEWorkshop on Mobile Comp. Sys.and Apps. Feb. 1999, pp. 90–100.
[10] S. Ramaswamy, H. Fu, M. Sreekantaradhya, J. Dixon, and K. Nygard, “Prevention of Cooperative Blackhole Attack in Wireless Ad Hoc Networks,” In Proc. of 2003 Int. Conf. on Wireless Networks, ICWN’03, Las Vegas, Nevada, USA, 2003, pp. 570–575.
[11] Ramaswamy, S., Fu, H., Sreekantaradhya, M., Dixon, j., and Nygard, K.“Prevention of cooperative black hole attack in wireless ad hoc networks”. In Proceedings of the International Conference on Wireless Networks, 2003
[12] H. Weerasinghe and H. Fu. “Preventing cooperative black hole attacks in mobile ad hoc networks: Simulation implementation and evaluation. In Future generation communication and networking” (fgcn 2007), volume 2, pages 362–367. IEEE, 2007.
[13] A. Baadache and A. Belmehdi.”Avoiding black hole and cooperative black hole attacks in wireless ad hoc networks.” Arxiv preprint arXiv: 1002.1681, 2010.
[14] D. Eastlake and P. Jones, “Us secure hash algorithm 1 (sha1),” 2001.
[15] Yih-Chun Hu, Adrian Perrig, David B. Johnson, “Packet Leashes: A Defence against Wormhole Attacks in Wireless Ad Hoc Networks”, IEEE 2003.

R.Sharma, R.Thakur, "Detection and Prevention of Wormhole & Black Hole Attacks in MANET Using AODV Protocol: Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.386-389, 2018.

This study demonstrates an EOQ model with partial backorders over the finite time horizon assuming imprecise demand which is characterized by triangular fuzzy number. Learning effect is considered to reduce the impreciseness of demand as inventory planners get experienced by collecting knowledge from previous cycles. This paper aims to find out the optimal number of replenishments and an optimal fraction of the cycle during the positive inventory to minimize the total annual cost. The optimal policy is derived using analytical approach for crisp and fuzzy model whereas algorithmic procedure is adopted for the fuzzy-learning model. To show the significance of learning effect, numerical analysis is executed and compared results from the crisp, fuzzy and fuzzy-learning case which shows that increasing human learning reduces fuzziness of the demand and approaches to the crisp model.

EOQ model, partial backordering, fuzzy demand, learning in fuzziness, centroid method

[1] F.W. Harris. "How many parts to make at once", Factory, Mag. Manag. Vol.10, Issue.2, pp.135–136. 152, 1913.
[2] C.H. Glock, E.H. Grosse, J.M. Ries Carlo. "The lot sizing problem: A tertiary study", Intern. J. Prod. Econ. Vol.155, pp.39–51, 2014.
[3] D.W. Pentico, M.J. Drake. "A survey of deterministic models for the EOQ and EPQ with partial backordering", Eur. J. Oper. Res. Vol.214, Issue.2, pp.179–198, 2011.
[4] W.J. Fabrycky, J. Banks. Procurement and Inventory Systems, Theory and Analysis, (pp 92-95). Reinhold Publishing Corporation, New York.
[5] A.M. Ali. Inventory problems, in: F.C. Jelen (Ed.), Cost Optim. Eng., McGraw-Hill, New York, pp. 191– 193.
[6] D.C. Montgomery, M.S. Bazaraa, A.K. Keswani. "Inventory models with a mixture of backorders and lost sales", Nav. Res. Logist. Q. Vol.20, Issue.2, pp.255–263, 1973.
[7] C.Y. Dye, H.J. Chang, J.T. Teng. "A deteriorating inventory model with time-varying demand and shortage-dependent partial backlogging", Eur. J. Oper. Res. 2006.
[8] Y.W. Zhou, H.S. Lau, S.L. Yang. "A finite horizon lot-sizing problem with time-varying deterministic demand and waiting-time-dependent partial backlogging", Int. J. Prod. Econ. Vol.91, Issue.2, pp.109–119, 2004.
[9] P.S. You, M.T. Wu. "Optimal ordering and pricing policy for an inventory system with order cancellations", OR Spectr. Vol.29, Issue.4, pp.661–679, 2007.
[10] R. Uthayakumar, K. V. Geetha. "A replenishment policy for non-instantaneous deteriorating inventory system with partial backlogging", Vol.25, Issue.3, pp.313–332, 2009.
[11] H.L. Yang. "A comparison among various partial backlogging inventory lot-size models for deteriorating items on the basis of maximum profit", Int. J. Prod. Econ. Vol.96, Issue.1, pp.119–128, 2005.
[12] H.M. Wee. "Joint pricing and replenishment policy for deteriorating inventory with declining market", Int. J. Prod. Econ. Vol.40, Issue.2–3, pp.163–171, 1995.
[13] L.Y. Ouyang, T.P. Hsieh, C.Y. Dye, H.C. Chang. "An inventory model for deteriorating items with stock-dependent demand under the conditions of inflation and time-value of money", Eng. Econ. Vol.48, Issue.1, pp.52–68, 2003.
[14] H.J. Chang, J.T. Teng, L.Y. Ouyang, C.Y. Dye. "Retailer’s optimal pricing and lot-sizing policies for deteriorating items with partial backlogging", Eur. J. Oper. Res. Vol.168, Issue.1, pp.51–64, 2006.
[15] Y.C. Tsao. "Two-phase pricing and inventory management for deteriorating and fashion goods under trade credit", Math. Methods Oper. Res. Vol.72, Issue.1, pp.107–127, 2010.
[16] A. Zadeh. "Fuzzy S e t s", Inf. Control. Vol.8, pp.338–353, 1965.
[17] K.S. Park. "Fuzzy set theoretic interpretation of economic order quantity", IEEE Trans. Syst. Man Cybern. Vol.17, pp.1082–1084, 1987.
[18] M. Vujosevic, D. Petrovic, R. Petrovic. "EOQ formula when inventory cost is fuzzy", Int. J. Prod. Econ. Vol.45, pp.499–504, 1996.
[19] D. Yadav, S.R. Singh, R. Kumari. "Application of Minimax Distribution Free Procedure and Chebyshev Approach in Mixed Inventory Model Involving Reducible Lead-Time and Setup Cost With Imprecise Demand", Asia-Pacific J. Oper. Res. Vol.30, Issue.04, pp.1350009, 2013.
[20] L.Y. Ouyang, H.C. Chang. "A minimax distribution free procedure for mixed inventory models involving variable lead time with fuzzy lost sales", Int. J. Prod. Econ. Vol.76, Issue.1, pp.1–12, 2002.
[21] H.-C. Chang, J.-S. Yao, L.-Y. Ouyang. "Fuzzy mixture inventory model with variable lead-time based on probabilistic fuzzy set and triangular fuzzy number", Math. Comput. Model. Vol.39, Issue.2–3, pp.287–304, 2004.
[22] H.-C. Chang, J.-S. Yao, L.-Y. Ouyang. "Fuzzy mixture inventory model involving fuzzy random variable lead time demand and fuzzy total demand", Eur. J. Oper. Res. Vol.169, pp.65–80, 2006.
[23] M. Yang, T. Huang, Y. Lin. "Variable lead time fuzzy inventory system under backorder rate with allowed shortage", Int. J. Innov. Comput. Inf. Control. Vol.6, Issue.11, pp.5015–5034, 2010.
[24] L. Wang, Q.L. Fu, Y.R. Zeng. "Continuous review inventory models with a mixture of backorders and lost sales under fuzzy demand and different decision situations", Expert Syst. Appl. Vol.39, Issue.4, pp.4181–4189, 2012.
[25] J.J. Buckley, T. Feuring, Y. Hayashi. "Solving fuzzy problems in operations research: inventory control", Soft Comput. Vol.7, Issue.2, pp.121–129, 2002.
[26] S.K. De, A. Goswami. "An EOQ model with fuzzy inflation rate and fuzzy deterioration rate when a delay in payment is permissible", Int. J. Syst. Sci. Vol.37, Issue.5, pp.323–335, 2006.
[27] K. Maity, M. Maiti. "Possibility and necessity constraints and their defuzzification-A multi-item production-inventory scenario via optimal control theory", Eur. J. Oper. Res. Vol.177, Issue.2, pp.882–896, 2007.
[28] G. Yazgı Tütüncü, O. Aköz, A. Apaydın, D. Petrovic. "Continuous review inventory control in the presence of fuzzy costs", Int. J. Prod. Econ. Vol.113, Issue.2, pp.775–784, 2008.
[29] E. Shekarian, S.H. Abdul-Rashid, E. Bottani, S.K. De, N. Kazemi, S.H. Abdul-Rashid, et al. "Fuzzy inventory models: A comprehensive review", Appl. Soft Comput. J. Vol.55, pp.588–621, 2017.
[30] M. Jaber. Learning Curves: Theory, Models, and Applications.
[31] E.H. Grosse, M. Calzavara, C.H. Glock, F. Sgarbossa. "Incorporating human factors into decision support models for production and logistics: current state of research", IFAC-PapersOnLine. Vol.50, Issue.1, pp.6900–6905, 2017.
[32] C.H. Glock, K. Schwindl, M.Y. Jaber. "An EOQ model with fuzzy demand and learning in fuzziness", Int. J. Serv. Oper. Manag. Vol.12, Issue.1, pp.90–100, 2012.
[33] N. Kazemi, E.U. Olugu, S.H. Abdul-Rashid, R.A.B.R. Ghazilla. "Development of a fuzzy economic order quantity model for imperfect quality items using the learning effect on fuzzy parameters", J. Intell. Fuzzy Syst. Vol.28, Issue.5, pp.2377–2389, 2015.
[34] N. Kazemi, S.H. Abdul-Rashid, E. Shekarian, E. Bottani, R. Montanari. "A fuzzy lot-sizing problem with two-stage composite human learning", Int. J. Prod. Res. Vol.54, Issue.16, pp.5010–5025, 2016.
[35] H.N. Soni, B. Sarkar, M. Joshi. "Demand uncertainty and learning in fuzziness in a continuous review inventory model", J. Intell. Fuzzy Syst. Vol.33, pp.2595–2608, 2017.
[36] A.A. Taleizadeh, M. Nematollahi. "An inventory control problem for deteriorating items with back-ordering and financial considerations", Appl. Math. Model. Vol.38, Issue.1, pp.93–109, 2014.
[37] S.-H. Chen, C.-C. Wang, A. Ramer. "Backorder Fuzzy Inventory Model under Function Principle.", Inf. Sci. (Ny). Vol.95, pp.71–79, 1996.
[38] E.H. Grosse, C.H. Glock, C.H. Glock Carlo. "Production Economics and the Learning Curve: A Meta-Analysis", Intern. J. Prod. Econ. 2015.
[39] T.P. Wright. "Factors Affecting the Cost of Airplanes", J. Aeronaut. Sci. Vol.3, Issue.4, pp.122–128, 1936.

Shivangi N. Suthar, Hardik N. Soni, "An EOQ Model with Partial Backorder for Fuzzy Demand and Learning in Fuzziness," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.390-397, 2018.

In Word Sense Disambiguation (WSD) much research has been carried out and are still being made today. If a sentence has ambiguity or ambiguous word, then the meaning of this sentence may or may not differ from context. If the meaning of the sentence is inferred from the context, then the concept of WSD comes to remove the ambiguity. Here we will discuss ambiguity which comes after Machine translation. In our experiment, we have collected different types of questions for analyzing the impact of ambiguity for wh-questions with respect to other questions (objective, match, fill in the blank and keyword specific). Some machine translators do not understand the type of the question and treated as a normal question/sentence. In this paper, we will discuss the five different types of questions and their machine translation with five standard online/offline translators. This paper describes our work on the impact of ambiguity from English to Hindi translation of different types of questions and main focus on wh-questions versus other questions translation. In this paper also have some experimental analysis and their result

Ambiguity, Questions, BLEU score, Machine translation, English Language and Hindi language

1. Bouziane, Abdelghani, et al.: Question answering systems: survey and trends. pp. 366-375. Procedia Computer Science (2015).
2. Pechsiri, C., and R. Piriyakul.: Developing a Why–How Question Answering system on community web boards with a causality graph including procedural knowledge. pp. 36-53 Information Processing in Agriculture (2016).
3. Zayaraz, Godandapani.: Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systems. pp. 13-24. Journal of King Saud University-Computer and Information Sciences (2015)
4. Hartawan, Andrei, and Derwin Suhartono.: Using vector space model in question answering system. pp. 305-311. Procedia Computer Science (2015)
5.Mishra, Amit, and Sanjay Kumar Jain.: A survey on question answering systems with classification. pp. 345-361. Journal of King Saud University-Computer and Information Sciences (2016)
6. Dwivedi, Sanjay Kumar, and Shweta Vikram.: Word Sense Ambiguity in Question Sentence Translation: A Review. International Conference on Information and Communication Technology for Intelligent Systems. Springer, Cham, 2017.
7. Vikram, S., & Dwivedi, S. K. (2018). Ambiguity in Question Paper Translation. International Journal of Modern Education and Computer Science, 10(1), 13. (2018)
8. Deepak Gupta, Surabhi Kumari, Asif Ekbal and Pushpak Bhattacharyya,: MMQA: A Multi-domain Multi-lingual Question-Answering Framework for English and Hindi, pp. 7-12. LREC, Miyazaki, Japan, (2018).
9. Hao, Tianyong, and Liu Wenyin.: Automatic Question Translation Based on Semantic Pattern. Semantics, Knowledge and Grid. SKG`08. Fourth International Conference on. IEEE, (2008).
10. Papineni, Kishore, et al.: BLEU: a method for automatic evaluation of machine translation. Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, (2002).
11. Chaplot, D. S., & Salakhutdinov, R.: Knowledge-based word sense disambiguation using topic models. (2018)
12. DurgaPrasad, P., Sunitha, K. V. N., & Rani, B. P.: Context-Based Word Sense Disambiguation in Telugu Using the Statistical Techniques. In Proceedings of the Second International Conference on Computational Intelligence and Informatics. pp. 271-280. Springer, Singapore. (2018).
13. Hermjakob, U., Li, Q., Marcu, D., May, J., Mielke, S. J., Pourdamghani, N., ... & Murray, K. Incident-driven machine translation and name tagging for low-resource languages. pp. 59-89. Machine Translation. (2018)
14. https://www.jagranjosh.com/articles/uppsc-uppcs-prelims-exam-2017-question-paper
15. https://www.jagranjosh.com/articles/uppsc-uppcs-exam-prelims-question-paper-2017
16. https://iasexamportal.com/upsc-mains/papers
17. http://ncert.nic.in/textbook/textbook.htm
18. http://epathshala.nic.in/e-pathshala-4/flipbook
19. NCERT: http://ncert.nic.in/NCERTS/textbook/textbook.htm
20. https://www.bing.com/translator/help/#About Microsoft Translator
21. http://translation.babylon-software.com/english/to-french
22. https://www.babelfish.com/success
23. https://anusaaraka.iiit.ac.in/drupal/node/2
24. https://translate.google.com

Shweta Vikram, Sanjay K. Dwivedi, "Ambiguity in Different Types of Question Translation: An Experimental Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.398-405, 2018.

Cloud computing enables internet based data storage, accessing, portability and processing. The flexibility cloud endows comes with a few security challenges. Though plans like "Proofs of Retrievability" and "Provable Data Possession" has been created to ascertain security they can`t bolster dynamic information. By and large a significant number of the peril models accept that having a fair data owners are concentrating on the exploitative cloud authority organization. In certain scenarios the client might be untrustworthy for getting the advantages by means of pay from the supplier. This research work focuses on an open inspecting plan that endows information support and reasonable discretion in data safety issues. This paper primarily focuses on developing a signature based plan to configure reasonable discretion conventions that ensures data integrity and security. The security verification establishes that the proposed method is secure and the information flow and dispute arbitration are sensible.

Cloud computing, thrid party auditor, Third party arbitrator, Data Verification, dispute arbitration

[1] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, “Provable data possession at untrusted stores,” in Proc. 14th ACM Conf. Computer and Comm. Security (CCS07), 2007, pp. 598–609.
[2] H. Shacham and B. Waters, “Compact proofs of retrievability,” in Proc. 14th Intl Conf. Theory and Application of Cryptology and Information Security: Advances in Cryptology (ASIACRYPT 08), 2008, pp. 90–107.
[3] Q. Wang, C. Wang, J. Li, K. Ren, and W. Lou, “Enabling publicverifiability and data dynamics for storage security in cloud computing,”in Proc. 14th European Conf. Research in Computer Security(ESORICS 08), 2009, pp. 355–370.
[4] A. Juels and B. S. Kaliski Jr, “Pors: Proofs of retrievability forlarge files,” in Proc. 14th ACM Conf. Computer and Comm. Security(CCS07), 2007, pp. 584–597.
[5] M. A. Shah, R. Swaminathan, and M. Baker, “Privacy-preservingaudit and extraction of digital contents.” IACR Cryptology ePrintArchive, Report 2008/186, 2008.
[6] Q. Zheng and S. Xu, “Fair and dynamic proofs of retrievability ” inProc. 1st ACM Conf. Data and Application Security and Privacy(CODASPY 11), 2011, pp. 237–248.
[7] A.Kupcu¸ “Official arbitration with secure cloud storage application”, The Computer Journal, pp. 138–169, 2013.
[8] Vishnu Patidar, Makhan Kumbhkar, “Analysis of Cloud Computing Security Issues in Software as a Service” International Journal of Scientifi International Journal of Scientific Research , Vol.2 , Issue.3, pp.1-4, 2014.

Ramanjaiah Ganji, Suresh Babu Yalavarthi, "Cloud Audit Server based verification for enhancing Information Security in cloud," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.406-413, 2018.

With the advances of the internet technology, people are more relying on the e-commerce transaction. However, due to security paucities, it is often subjected to many controversies. There are different existing cryptographic techniques to provide security for the transactions in e-commerce. Nevertheless, the enhancement in technology causes different attacks on the conventional cryptographic schemes, which resulted many security threats to the transactions. A new symmetric key cryptographic algorithm for e-commerce transaction has been proposed in this paper which can provide better security for the transactions over the internet. The new randomized key generation, substitution box generation and permutation box generation algorithms have been proposed for this cryptographic technique. This proposed algorithm has been proved as the cryptographic process with randomness as its avalanche effect is more than 50%. Additionally, correlation coefficient of this method is also better than the original AES. Moreover, the encryption and decryption time of this proposed algorithm is much less than the original AES. So, this proposed algorithm would ensure better security to the e-commerce transaction with less time to make the transaction more efficient.

E-commerce, symmetric key cryptography, Substitution-Box, Permutation-Box, key generation

[1] S.Chatterjee and K.Gupta, ”A Comparative Study of Security in E-commerce:Review”, International Journal of Computer Sciences and Engineering,pp. 143-148,2016.
[2] R.C.Marchany and J.G.Tront, ”E-Commerce Security Issues”, Proceedings of the 35th Hawaii International Conference on System Sciences – 2002, Big Island, HI, USA, pp. 2500-2508. IEEE,2002.
[3] H.Ortok , R. Haraty, and A. N. El-Kassar , ”Improving the Secure Socket Layer Protocol by modifying its Authentication Function”, World Automation Congress(WAC) , Budapest, Hungary, pp. 1-6. IEEE,2016.
[4] W.Stallings, ”Cryptography and Network Security”, 4/E. Pearson Education India, pp. 63-68, 2006.
[5] A.Alabaichi and A.I.Salih, “Enhanced Security of Advanced Encryption Standard Algorithm Based on Key-dependent S-Box”, In Digital Information Processing and Communications (ICDIPC), 2015 Fifth International Conference, Sierre, Switzerland, pp. 44-53. IEEE, 2015.
[6] E.P.Nugroho, R..R.J.Putra and I.M.Ramadhan, ”SMS Authentication Code Generated by Advanced Encryption Standard(AES) 256-bits Modification Algorithm and One Time Password(OTP) to Activate New Applicant Account”, 2nd International Conference on Science and Information Technology(ICSITech), Balikpapan, Indonesia, pp. 175-180 .IEEE, 2016.
[7] Dilna.V and C.Babu, ”Area Optimized and High Throughput AES Algorithm based on Permutation Data Scramble Approach”, In Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference , Chennai, India, pp. 3056-3060. IEEE, 2016.
[8] J.Mary and D.S.Kumar , ”An Implementation of Modified Lightweight Advanced Encryption Standard in FPGA”, Procedia Technology 25 (2016), pp. 582-589,2016.
[9] P.Kumar and S.B.Rana, “Development of Modified AES Algorithm for Data Security”, ScienceDirect, www.elsevier.de/ijleo, Optik-International Journal for Light and Electron Optics 127, no. 4 (2016), pp. 2341-2345,2016.
[10] M.Dara and K.Manochehri , “ Using RC4 and AES Key Schedule to Generate Dynamic S-Box in AES” , Information Security Journal: A Global Perspective 23, no. 1-2(2014), pp. 1-9,2014.
[11] S.Mondal and S.Maitra, “ Data Security-modified AES algorithm and its applications”, ACM SIGARCH Computer Architecture News 42, no. 2(2014),pp. 1-8, 2014.
[12] S.M.Wadi and.N.Zainal “ A low cost implementation of modified advanced encryption standard algorithm using 8085A microprocessor”, Journal of Engineering Science and Technology 8, no. 4, pp. 406-415,2013.
[13] A.Khelifi, M. Aburrous,M.A.Talib and P. V. S. Shastry, ” Enhancing Protection Techniques of E-banking Security Services Using Open Source Cryptographic Algorithms”, In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2013 14th ACIS International Conference, Honolulu, HI, USA, pp. 89-95 . IEEE, 2013.
[14] C.P.Dewangan , S.Agarwal, A.K.Mandal and A.Tiwari, “Study of Avalanche Effect in AES Using Binary Codes”, IEEE Conference on Advanced Communication Control and Computing Technologies (ICACCCT), Ramnathapuram, India, pp. 23-25, 2012.
[15] D. O. Vadaviya and P. H. Tandel, “Study of Avalanche Effect in AES”, National Conference on Recent Advances in Engineering for Sustainability, 2015.
[16] A.Alabaichi, F.Ahmad and R.Mahmod, “Security Analysis of Blowfish Algorithm”, Informatics and Applications (ICIA), 2013 Second International Conference, Lodz, Poland, pp. 12-18. IEEE, 2013.

B. Biswas, A.K. Khan, "Confirming Secured E-Commerce Transaction Environment Supported by A New Symmetric Key Cryptographic Scheme," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.414-423, 2018.

Association rule mining is a procedure which is meant to find frequent patterns from data sets found in various kinds of databases such as relational databases, transactional databases, etc. It has a great importance in data mining. Extracting relevant information from a huge collection of data by exploitation of data is called data mining. There is an increasing need of data mining by business people to extract valid and useful information from large datasets. Thus, data mining has its importance to discover hidden patterns from huge data stored in databases as well as data warehouse. Apriori algorithm has been one of the key algorithms in association rule mining. Classical Apriori algorithm is inefficient as it takes considerable amount of time to generate the desired output for mining the frequent itemsets owing to multiple scans on the database. In this research paper, a method has been proposed to improve the efficiency of Apriori algorithm by reducing the size of the database as well as reducing the time complexity for scanning the transactions.

Itemsets, Apriori algorithm, Association rule mining, Minimum support

[1] J. Han, M. Kamber, “Conception and Technology of Data Mining”, China Machine Press, China, 2007.
[2] U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, “From data mining to knowledge discovery in databases”, Vol 17, Issue 3, AI magazine, pp. 37-54, 1996.
[3] S. Rao, R. Gupta, “Implementing Improved Algorithm over APRIORI Data Mining Association Rule Algorithm”, International Journal of Computer Science And Technology,Vol 3, Issue 1, pp. 489-493, 2012.
[4] H. H. O. Nasereddin,“Stream data mining”, International Journal of Web Applications, Vol 1, Issue 4, pp. 183–190, 2009.
[5] M. Halkidi, “Quality assessment and uncertainty handling in data mining process”, In Proceedings of EDBT Ph.D. Workshop, Germany, 2000.
[6] R. Agarwal, R. Srikant, “Fast Algorithm for mining association rules”, In Proceedings of 20th VLDB Conference, pp 487-499, 1994.
[7] Sakshi Aggarwal, Ritu Sindhu, “An Approach of Improvisation in Efficiency of Apriori Algorithm”, In Proceedings of International Journal of Computer and Communication System Engineering, Vol 2, Issue 5, pp. 659-664, 2015.
[8] S. Kumar , S. Karanth , A. Prabhu, and B. Kumar , “Improved Apriori Algorithm Based On Bottom Upapproach Using Probability And Matrix”,IJCSI,2012.
[9] F. H. AL-Zawaidah, Y. H. Jbara, A. L. Marwan, “An Improved Algorithm for Mining Association rules in Large Databases”,World of Computer Science and Information Technology, Vol 1, Issue 7, pp. 311-316, 2011.
[10] X. Wu, V. Kumar, J. Ross Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z.-H. Zhou, M. Steinbach, D. J. Hand, D. Steinberg, “Top 10 algorithms in data mining,”, Knowledge and Information Systems, Vol 14, Issue 1, pp. 1–37, 2007.
[11] F. Crespo, R. Weber, “A methodology for dynamic data mining based on fuzzy clustering”, Fuzzy Sets and Systems, Vol 150, Issue 2, pp. 267–284, 2005.
[12] R. Agrawal, T. Imielinski, A. Swami, “Mining association rules between sets of items in large database,”, In Proceedings of ACM SIGMOD International Conference on Management of Data, Vol 22, Issue 2, pp. 207–216, 1993.
[13] R. Bhaskar, S. Laxman, A. Smith, A. Thakurta, “ Discovering frequent patterns in sensitive data”, In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, , pp. 503-512, 2010.
[14] B. Vo, M. Chi, H. C. Minh, “Fast Algorithm for Mining Generalized Association Rules”, International Journal of Database Theory and Application, Vol 2, Issue 12, pp. 161–180, 1994.

D. Datta, M.P. Dutta, R. Mukherjee, "Improvisation in Efficiency of Apriori Algorithm for Mining Frequent Itemsets," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.424-428, 2018.

Data mining is a process using high volume of data for needful information. Most popular data mining techniques are rule mining, clustering, classification and sequence pattern. Number of tests should be done for a patient to detect a disease. So, large volume of information is stored by the health care information system for further reference. Due to the complication of healthcare information and the slow acquisition of technology, this industry lags behind other industries in implementing effective data analysis and extraction strategies. Mining information from the large health databases gives the best healthcare information, reduces time and saves the humans from complicated diseases like cancer. In this circumstance proper data mining technique is needed for the best performance. This research work focuses on the advantages and disadvantages of various data mining prediction algorithms.

Data mining, Healthcare system, Prediction, Techniques

[1] Larsson EG, Selén Y. “Linear regression with a sparse parameter vector”. IEEE Transactions on Signal Processing. 2007 Feb;55(2):451-60.
[2] Wu J, Huo Q. “A study of minimum classification error (MCE) linear regression for supervised adaptation of MCE-trained continuous-density hidden Markov models”. IEEE Transactions on Audio, Speech, and Language Processing. 2007 Feb;15(2):478-88.
[3] Zhang S, Zhang L, Qiu K, Lu Y, Cai B. “Variable selection in logistic regression model. Chinese Journal of Electronics”. 2015 Oct 1;24(4):813-7.
[4] Zhang J, Jiang J. “Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification”. Neural computation. 2018 Feb;30(2):505-25.
[5] Li C, Chiang TW. “Complex neurofuzzy ARIMA forecasting—a new approach using complex fuzzy sets”. IEEE Transactions on Fuzzy Systems. 2013 Jun;21(3):567-84.
[6] Gong S, Gao Y, Shi H, Zhao G. “A practical MGAARIMA model for forecasting real‐time dynamic rain induced attenuation”. Radio Science. 2013 May 1;48(3):208-25.
[7] Fu C. “Business Valuation Based on Intellectual Capital: A Hierarchical Clustering-MARS Approach. In Management and Service Science (MASS)”, 2011 International Conference on 2011 Aug 12 (pp. 1-6). IEEE.
[8] Crino S, Brown DE. “Global optimization with multivariate adaptive regression splines”. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2007 Apr;37(2):333-40.
[9] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, "A Review: Design and Development of Novel Techniques for Clustering and Classification of Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.19-22, 2018
[10] Nikita Jain, Vishal Srivastava “Data Mining Techniques: A Survey Paper” IJRET: International Journal of Research in Engineering and Technology, Volume: 02 Issue: 11 | Nov-2013.
[11] Pawan S. Wasnik, S.D.Khamitkar, Parag Bhalchandra, S. N. Lokhande, Ajit S. Adte, "An Observation of Different Algorithmic Technique of Association Rule and Clustering", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.28-30, 2018.
[12] Suárez A, Lutsko JF. “Globally optimal fuzzy decision trees for classification and regression”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1999 Dec;21(12):1297-311.
[13] Goin JE. “Classification bias of the k-nearest neighbor algorithm”. IEEE transactions on pattern analysis and machine intelligence. 1984 May(3):379-81.
[14] J. Han and M. Kamber. “Data Mining, Concepts and Techniques”, Morgan Kaufmann, 2000.
[15] D. J. Higham and N. J. Higham. MATLAB Guide. Siam, second edition edition, 2005.
[16] R Development Core Team.. R: A language and environment for statistical computing [Computer software and manual]. Vienna, Austria: R Foundation for Statistical Computing. Available from http://www .r-project.org. 2007
[17] Jindal and Dutta Borah, ―A Survey On Educational Data Mining and ResearchTrends, In International Journal Of Database Management Systems,2013, Vol.5, No.3. http://dx.doi. org/10.5121/ijdms.2013.5304.
[18] Ha, S., Bae, S., and Park, S , Web mining for distance education, In Proc.Int. Conf. On Management of Innovation and Technology, IEEE.,2000, pp.715-719. http://dx.doi. org/10.1109/EmbeddedCom-ScalCom.2009.98.
[19] Zhang, H., Raitoharju, J., Kiranyaz, S., & Gabbouj, M. (2016). Limited random walk algorithm for big graph data clustering. Journal of Big Data, 3(1), 26.
[20] Witten, I., H., Frank, E., Hall, M., A.. Data mining: practical machine learning tools and techniques.2011.
[21] Ma X, Ye Q, Yan H. “L2P-Norm Distance Twin Support Vector Machine”. IEEE Access. 2017;5:23473-83.
[22] Kampa K, Hasanbelliu E, Cobb JT, Principe JC, Slatton KC. “Deformable Bayesian network: A robust framework for underwater sensor fusion”. IEEE Journal of Oceanic Engineering. 2012 Apr;37(2):166-84.
[23] Aarti Sharma et al, “Application of Data Mining – A Survey Paper”, International Journal of Computer Science and Information technologies’, Vol. 5 (2), 2014.

K. Mohan Kumar, S. Jamuna, "Comparative Study on Data Mining Algorithms for Healthcare Information System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.429-433, 2018.

Data mining is the computational procedure for discovering routines within big files portions ("big files") pertaining to techniques in the intersection involving synthetic contemplating capability, unit learning, data, as well as collection programs. In this paper, we have proposed a new method in order to improve the accuracy of diabetes classification rate. The proposed technique have integrated Particle swarm optimization (PSO) with support vector machine (SVM) based machine learning technique. The proposed technique also verified by using the various standard diabetes classification data sets. The comparison drawn among the proposed and the existing technique based upon the various standard quality metrics of the data mining. Experimental results indicate that the proposed algorithm is more efficient than existing techniques.

Data Mining, Particle Swarm Optimization, Suppport Vector Machine, Diabetes Mining

[1] Prather, J. C., Lobach, D. F., Goodwin, L. K., Hales, J. W., Hage, M. L., & Hammond, W.E.: Medical data mining: knowledge discovery in a clinical data warehouse. In Proceedings of the AMIA annual fall symposium (p. 101). American Medical Informatics Association(1997).
[2] Parpinelli, R. S., Lopes, H. S., &Freitas, A. A.: An ant colony based system for data mining: applications to medical data. In Proceedings of the genetic and evolutionary computation conference (GECCO-2001) (pp. 791-797)(2001,July).
[3] Ghazavi, S. N., & Liao, T. W.: Medical data mining by fuzzy modeling with selected features. Artificial Intelligence in Medicine, 43(3), 195-206(2008).
[4] Delen, D., Walker, G., &Kadam, A.:Predicting breast cancer survivability: a comparison of three data mining methods. Artificial intelligence in medicine, 34(2), 113-127(2005).
[5] Moses, D.:A survey of data mining algorithms used in cardiovascular disease diagnosis from multi-lead ECG data. Kuwait Journal of Science, 42(2)(2015).
[6] Ji, S., Wang, Z., Liu, Q., & Liu, X.:Classification Algorithms for Privacy Preserving in Data Mining: A Survey. In International Conference on Computer Science and its Applications (pp. 312-322). Springer Singapore(2016,December).
[7] Rani, G., Gladis, D., &Mammen, J.Classification and Prediction of Breast Cancer Data derived Using Natural Language Processing. In Proceedings of the Third International Symposium on Women in Computing and Informatics (pp. 250-255). ACM(2015,August).
[8] Das, T. K.:A customer classification prediction model based on machine learning techniques. In 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (pp. 321-326). IEEE(2015,October).
[9] Yu Zhou, Xiaokang Yang, Yongzheng Zhang, Xiang Xu, Yipeng Wang, Xiujuan Chai, Weiyao Lin, Unsupervised adaptive sign language recognition based on hypothesis comparison guided cross validation and linguistic prior filtering, Neurocomputing, Volume 149, Part C, 3 February 2015, Pages 1604-1612.
[10] WalidMagdy, Tamer Elsayed, Unsupervised adaptive microblog filtering for broad dynamic topics, Information Processing & Management, Volume 52, Issue 4, July 2016, Pages 513-528.
[11] Shiqiang Du, Yide Ma, Shouliang Li, Yurun Ma, Robust unsupervised feature selection via matrix factorization, Neurocomputing, Volume 241, 7 June 2017, Pages 115-127.
[12] Daniel Carlos GuimarãesPedronette, Ricardo da S. Torres, Unsupervised Rank Diffusion for Content-Based Image Retrieval, Neurocomputing, Available online 16 May 2017.
[13] Herman Kamper, Aren Jansen, Sharon Goldwater, A segmental framework for fully-unsupervised large-vocabulary speech recognition, Computer Speech & Language, Available online 18 May 2017.
[14] Wei He, Xiaofeng Zhu, Debo Cheng, Rongyao Hu, Shichao Zhang, Unsupervised feature selection for visual classification via feature-representation property, Neuro-computing, Volume 236, 2 May 2017, Pages 5-13.
[15] ZouhairMbarki, HasseneSeddik, Ezzedine Ben Braiek, A rapid hybrid algorithm for image restoration combining parametric Wiener filtering and wave atom transform, Journal of Visual Communication and Image Representation, Volume 40, Part B, October 2016, Pages 694-707.
[16] Gloria Re Calegari, EmanuelaCarlino, Diego Peroni, Irene Celino, Filtering and windowing mobile traffic time series for territorial land use classification, Computer Communications, Volume 95, 1 December 2016, Pages 15-28.
[17] Mostafa Mohammadpourfard, Ashkan Sami, AlirezaSeifi, A statistical unsupervised method against false data injection attacks: A visualization-based approach, Expert Systems with Applications, Volume 84, 30 October 2017, Pages 242-261.
[18] EliahuKhalastchi, Meir Kalech, LiorRokach, A hybrid approach for improving unsupervised fault detection for robotic systems, Expert Systems with Applications, Volume 81, 15 September 2017, Pages 372-383.
[19] Chong Yang, Xiaohui Yu, Yang Liu, YanpingNie, Yuanhong Wang, Collaborative filtering with weighted opinion aspects, Neurocomputing, Volume 210, 19 October 2016, Pages 185-196.
[20] Pengfei Zhu, Wencheng Zhu, Qinghua Hu, Changqing Zhang, WangmengZuo, Subspace clustering guided unsupervised feature selection, Pattern Recognition, Volume 66, June 2017, Pages 364-374.

Ramandeep Kaur, Prabhdeep Singh, "Particle Swarm Optimization based Support Vector Machine for Diabetes Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.434-439, 2018.

Any Software which is created for some purpse will be true software if it is tested properly. Testing is very essential component of Software Development Life Cycle .Testing is process of finding whether the program meets required specification or not.Testing is very important for performance and usability.It is process of analysing and evaluating the system components.In this we analyse difference between actual result and expected result is studied.

Testing, Tools, Software, Error, System

[1] Priyanka Rathi, Vipul Mehra, “Analysis of Automation and Manual Testing Using Software Testing Tool”, International Journal of Innovations & Advancement in Computer Science,Vol. 4, Special Issue, March 2015.
[2] Gautam Kumar Saha, “Understanding Software Testing Concepts”, ACM Ubiquity Vol 9, Issue 6, February12-18, 2008.
[3] M. Prasanna, S.N. Sivanandam, R. Venkatesan, R. Sundarrajan, “A survey on automatic test case generation”, Academic Open Internet Journal, www.acadjournal.com , Vol 15, 2005.
[4] Mark Utting,Alexander Pretschner, Bruno Legeard, “A Taxonomy of Model-based testing”, April 2006.
[5] Shahid Mahmood, “A Systematic Review of Automated Test Data Generation Techniques” , Master Thesis Software Engineering Thesis no: MSE-2007:26 October 2007.
[6] Shaveta, Sachinkumar, Nitika, Snehlata, “Comparative Study of Automated Testing Tools: Quick Test Pro and Load Runner”, International Journal of Computer Science and Information Technologies, Vol. 3, Issue 4, 2012

Vivek Chaplot, "Study of Various Testing Techniques With Respect To Application Software," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.440-442, 2018.