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Cloud computing is a new modern and broad concept of the world. Users store huge amount of data on cloud storage for future use. Now a day’s privacy to the data stored in cloud is an aspect and security is a very major challenging mission in cloud computing. These challenges have huge effect in the development of secure data storage qualities of cloud system. The objective of this paper is to provide the privacy two factor authentication (2FA) techniques algorithm for the cloud environment with an attempt to bring solutions of such problems. The main aim of this paper is to design and propose Two-Factor Authentication (2FA) technology with One-Time Password (OTP) and finger print for providing strong security system to the data stored in cloud system. This paper, presents two modules, the first module deals with ensuring the storing security of data in encrypted format by using the (2FA) technique and the second module with digital signature algorithm (DSA). We have proposed framework which provides, verifies user authenticity using two-step verification, which is based on password, CSP level key establishment between the users and the cloud server.

Cloud Computing, 2FA Secure Data Storage Cloud Server, DSA, Encryption and Decryption Algorithm Cloud Storage

[1] M.S. Monisha, S. Chidambaram,“Enhanced Data Security using RSA Digital Signature with Robust Reversible Watermarking Algorithm in Cloud Environment”, International Journal of Electronics & Co mmunication Technology, Vol.8, Issue 1, pp.20-24, 2017.
[2] M. Singhal, S. Tapaswi,“Software Tokens Based Two Factor Authentication Scheme”,International Journal of Information and Electronics Engineering, Vol.2, No. 3,pp.383-386, 2012.
[3] N. Mashhadi,“Authentication in mobile cloud computing by combining the tow factor Authentication and one time password token”,Vol.37, Part 2 pp. 220−229, 2015.
[4] Y. Kale, A. B. Patankar, “Enhanced Data Security Mechanism on Cloud using two-factor authentication, data encryption and key Sharing Mechanism”, Proceedings of 11th IRF International Conference, ISBN: 978-93-84209-27-8, pp.158- 161, 2014.
[5] A.Padmapriya, P.Subhasri, “Cloud Computing: Reverse Caesar Cipher Algorithm to Increase Data Security”, International Journal of Engineering Trends and Technology, Vol.4 Issue4, pp.1067- 1071, 2013.
[6] L. Arockiam, S. Monikandan,“Data Security and Privacy in Cloud Storage using Hybrid Symmetric Encryption Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Vol.2, Issue 8, pp. 3064-3070, 2013.
[7] T. Sivasakthi, N. Prabakaran, “Applying Digital Signature with Encryption Algorithm of User Authentication for Data Security in Cloud Computing”, International Journal of Innovative Research in Computer and Communication Engineering,Vol. 2, Issue 2, pp. 3102- 3107,2014.
[8] M. A. Acha, M. J. Po, “Two (2)-Factor Authentication for Cloud Storage”, researchGate,2016.
[9] G. Saini, N. Sharma, “Triple Security of Data in Cloud Computing” ,International Journal of Computer Science and Information Technologies, Vol.5 Issue 4, pp. 5825-5827, 2014.
[10] A. Chaturvedi, D. N. Goswami, R. P. Sarang, “Privacy Algorithms to Improve the Secure Framework for Cloud Computing Environment”,
International Journal of Advanced Research in Computer and Communication Engineering, Vol.6, Issue 4,pp.63-69, 2017.
[11] D. Patil, P.K.Deshmukh, “Data Security in Cloud Using Attribute Based Encryption with Efficient Keyword Search”, International Journal of Scientific & Engineering Research, Vol.7, Issue 1, pp. 986-991, 2016.
[12] N. Nagar, U. Suman, “A Secure Mobile Cloud Storage Environment using Encryption Algorithm”, International Journal of Computer Applications, Vol.140, No.8, pp.33-43, 2016.
[13] A. Khodwe, V.R.Wadhankar, “Security Enhancement for Privacy Preservation in Cloud Computing by Anonymous Request Access”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol.4, Issue 1, pp. 233-237, 2016.
[14] An Braeken, A. Touhafi, “ Efficient Anonymous User Authentication on Server Without Secure Channel During Registration”, IEEE, 978-1-4673-8894-8, 2016.
[15] R. Sugumar, K. A. M. Joycee, “DSCESEA: Data Security in Cloud using Enhanced Symmetric Encryption Algorithm”, International Journal of Engineering Research & Technology, Vol.6, Issue 10, pp. 292-295, 2017.

Rakesh Prasad Sarang, Anshu Chaturvedi, D.N. Goswami, "Privacy Secure Data Authentication in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.723-731, 2018.

Data reduction has become increasingly very important in storage systems thanks to the explosive growth of digital information among the globe that has ushered among the large information era. In existing system cloud suppliers offer less method capability and thus displease their users for poor service quality. Therefore, it is vital for a cloud provider to select out applicable servers to provide services; such it reduces worth the most quantity as potential wherever as satisfying its users at the same time. Here the foremost disadvantage duplication therefore to beat of those problems we tend to tend to pick planned model. Throughout this paper, we tend to gift DARE, a low-overhead Deduplication-Aware likeness detection and Elimination theme that effectively exploits existing duplicate-adjacency information for terribly economical likeness detection in information deduplication based backup/archiving storage systems. Our experimental results and backup data sets show that DARE only consumes concerning 1/4 and 1/2 severally of the computation and classification overheads required by the conventional super-feature approaches whereas investigating 2-10% extra redundancy and achieving an improved turnout, by exploiting existing duplicate-adjacency information for likeness detection and finding the “sweet spot” for the super-feature approach.

Data deduplication, delta compression, storage system, index structure, performance evaluation.

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K. Reddy Pradeep, G. Sreehitha, "A Deduplication -Aware similarity finding and removal system for Cloud Provider and Its Users," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.732-736, 2018.

Recently wireless ad hoc network is an emerging new technology. Due to its emerging capability it faces spectrum scarcity problem. Resources and channels in CRN (cognitive radio network) would be allocated based on dynamic access methods with respected to sensed radio environment. Cognitive Radio (CR) technology provides a smart and optimistic solution to the problem of spectrum scarcity through Dynamic Spectrum Allocation (DSA). Due to the nature of Cognitive Radio Networks (CRNs), where two networks area unit active at the same time, a significant quantity of control messaging is essential in order to coordinate channel access, schedule sensing, and establish release connections. Efficient Control Plane messaging can be achieved by the selection of a suitable Control Channel (CC). This paper provides a comparative study of probable systems for providing reliable channels dedicated to the coordination and information distribution in License-Exempt (LE) bands. This involves determining the potential and limitations of every method.

CRN, CC, Spectrum Management, Spectrum Allocation

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Tamilarasan.S, Kumar. P, "Study and Performance Analysis of Dedicated In-Band Control Channels for Cognitive Radio Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.737-740, 2018.

As we all know the data security is the biggest concern for all the domains and it has very deep impact on the data and its security. Here in this paper we have tried to carry out the analytical study of the different cryptography and steganography technique which is used to maintain the integrity of the data. Any type of cover object can be taken that may be text, image or video to embed the secret information. In this paper a brief analysis of different image stegnography techniques and their comparison is done.

Steganography, Embedding, LZW, Stego-Key, PSNR

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2012.
[2] Kumar and R. Sharma, “A Secure Image Steganography Based on RSA Algorithm and Hash-LSB Technique”, International Journal of Advanced Research in computer science and Software Engineering, vol3, pp
363-372,2013.
[3] G. Kaur and A. Kochhar,“A Steganography Implementation based on LSB and DCT”, International Journal for Science and Emerging Technologies, vol4,
pp 36-41, 2012.
[4] A.M.AL-Shatnawi, “A New method in Image Steganography with improved Image Quality”, Applied Mathematical Sciences, vol6, pp 3908-3915, 2012.
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[6] R. Jain and N. Kumar, “Efficient data hiding scheme using lossless data compression and image Steganography”, International journal of engineering
science and technology, vol4, pp 3908-3915,2012
[7] Hemalatha S, U Dinesh Acharya, Renuka A and PriyaR.
Kamath, “A Secure and High Capacity Image Steganography Technique”, International Journal (signal and image processing), vol4, pp 83-89, 2013.
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Security Using Image Steganography and Weighing Its Techniques”, International Journal of Scientific and Technology,vol2, pp 238-241, 2013.
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Introduction to Steganography”, pp 32-44, IEEE 2003. [15] S.Channalli and A .Jadhav, “Steganography an art of
Hiding Data”, International Journal on Computer
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Hitendra Donga, Kishor Atkotiya, "An analytical study of Cryptography and Steganography technique for robust Security and integrity of the data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.741-746, 2018.

Test case prioritization is the process of ordering the test case executions to meet various testing goals. Improved software quality is one of the ultimate goals of software testing process. While dealing with object oriented testing specific features like inheritance, polymorphism and abstraction play important role in test case prioritization. Measures of object oriented features in a software component can be used as quality indicators. Some of the object oriented features may contribute positively while some may contribute negatively to the software quality. Software components can be ranked on the basis of their contribution in overall quality of software. These rankings may be helpful in test case prioritization. This study presents a test case prioritization approach based on software quality aspect. We performed an empirical investigation on six sequential versions of open source software and analyzed the contribution of various object oriented metrics in quality of the software. The work presents a novel technique of test case prioritization that would not only enhance the quality of the software but also prioritize the test cases as per fault proneness of the software modules. Proposed approach first investigates the impact of coupling metrics on software quality then provides ranking to component classes as per coupling measures. It is observed that coupling metrics potentially correlate with the change impact of software.

Regression Testing,Test case Prioritization, Machine Learning,Object Oriented metrics, Software Quality, Faults Prediction, Object Oriented Testing

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Ajmer Singh, Rajesh Kumar Bhatia, Anita Singhrova, "Object Oriented Coupling based Test Case Prioritization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.747-754, 2018.

Quality of agile software is one of the major issues in software vigorous systems, and it is important to examine methodically it as early as possible. An increasingly important quality attribute of complicated software systems is adaptability. Agile software development methodologies are very useful since their beginning to improve the quality of the software product. In this paper an innovative practice has been presented for evaluating agile software quality of adaptation using the Fuzzy Inference System in order to determine the developed software quality acceptance degree.

Agile Software Quality of Adaptation, Risk Indicators, Fuzzy Rule Base, Fuzzy Inference System

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[2]Alawairdhi M,” Agile Development as a change Management Approach in Software Projects: AppliedCase Study”, Information Management (ICIM),IEEE,UK, Pages 100-104, 7-8 May 2016, ISBN: 978-1-5090-1471-2.
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[5]Kropp M., Meier A. and Perellano G.,” Experience Report of Teaching Agile Collaboration and values.Software Engineering Education and Training (CSEET)”, 2016 IEEE 29th International Conference,USA, Pages 76-80, 5-6 April, ISBN-13: 978-1-5090-0765-3
[6] L.R. Vijayasarathy and D.Turk, “Agile Software Development: A survey of early adopter,” Journal of Information Technology Management, Vol. 11( 2), Pages 1-8, 2008.
[7]Lotfi A. Zadeh, “Is there a need for fuzzy logic?”, Information Sciences, Elsevier, Vol 178( 13),Pages 2751–2779,2008.
[8] Misra C. S., Kumar V. and Kumar U.” Identifying some important success factors in adopting agile softwaredevelopment practices”, In The Journal of Systems and Software, Elsevier,Vol 82 Pages 1869–1890,2009.
[9] Padmini K.V., Dilum H.M. and Perera I.,” Use of Software Metrics in Agile Software Development Process”,Moratuwa Engineering Research Conference (MERCon),IEEE, Shri Lanka Pages 312-317,7-8 April 2015, ISBN: 978-1-4799-1740-2.
[10] Sishar M. and Arif M.(2012),” Evaluation of QualityAssurance Factors in Agile Methodologies”, InternationalJournal of Advanced Computer Science, Vol. 2(2), Pages .73-78, Feb. 2012.
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[12] Sagheer. M, Zafar. T &Sirshar.M (2015, Februray),” A Framework for Software Quality Assurance”, International Journal Of Scientific & Technology Research Vol 4(2), Pages 44-50, February 2015.
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[14]Xu.B.(2009),”Towards high quality software development with extreme programming methodology: practices from real software projects”. In Management and Service Science, 2009.MASS `09. International Conference, IEEE,China, 20-22 September 2009, ISBN: 978-1-4244-4638-4.

Anand Kumar Rai, Shalini Agrawal, Mazahar Khaliq, "Agile Software Quality of Adaptability Risk Measurement using Fuzzy Inference System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.755-759, 2018.

The image processing is the technique which can propose the information stored in the form of pixels. The plant disease detection is the technique which can detect the disease from the leaf. The plant disease detection algorithms has various steps like pre-processing, feature extraction, segmentation and classification. The KNN classifier technique is applied which can classify input data into certain classes. The performance of KNN classifier is compared with the existing techniques and it is analyzed that KNN classifier has high accuracy, less fault detection as compared to other techniques

GLCM, KNN, K-means, Plant Disease Detection

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[10] Shivani K. Tichkule, Prof. Dhanashri. H. Gawali, “Plant Diseases Detection Using Image Processing Techniques”, 2016 Online International Conference on Green Engineering and Technologies (IC-GET)
[11] Boikobo Tlhobogang and Muhammad Wannous, “Design of Plant Disease Detection System: A Transfer Learning Approach Work in Progress”, 2018, IEEE
[12] Rutu Gandhi Shubham Nimbalkar Nandita Yelamanchili Surabhi Ponkshe, “Plant Disease Detection Using CNNs and GANs as an Augmentative Approach” , 2018, IEEE
[13] Zia Ullah Khan1, Tallha Akra , Syed Rameez Naqvi , Sajjad Ali Haider , Muhammad Kamran , Nazeer Muhammad, “Automatic Detection of Plant Diseases; Utilizing an Unsupervised Cascaded Design” , 2018, IEEE

Varinderjit Kaur, Ashish Oberoi, "Development of an Efficient Image Processing Technique for Wheat Disease Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.760-764, 2018.

According to Risk Base Security, during last few years leakage of sensitive data record has increased. Human mistake is one of the important reason for data exposure. There is an approach in which data is monitored during transmission to detect the inadvertent data leak cause by human mistakes. However it makes the detection process difficult. There is a need of method that support accurate detection without revealing sensitive data. In particular system, Human identity i,e fingerprint is applied to data file for authentication. It is the process in which original fingerprint matrix is compressed using novel down sampling technique. In the technique, original matrix is compressed by calculating arithmetic mean of the sum of the pixel values on each input row matrix to generate a unit input vector for artificial neural network. The fingerprint samples are matched using back propagation technique. The evaluation result shows improved accuracy and detection time.

Data leakage, Downsampling, Backpropagation algorithm

[1] Xiaokui Shu, Danfeng Yao and Elisa Bertino, fellow," Privacy-Preserving Detection of Sensitive Data Exposure" IEEE Trans. on Information Forensics and Security, Vol. 10, No. 5,pp.1092-1103, May 2015.
[2] Liu F., Shu X., Yao D., and A. R. Butt, "Privacy-preserving scanning of big content for sensitive data exposure with MapReduce," in Proc. ACM Conference on Data Application Security and Privacy, pp.195-206, 2015.
[3] Jagtap V. and Mishra S.," Fast efficient artificial neural network for handwritten digit recognition," International Journal of Computer Science and Information Technologies, vol. 5, pp. 2302-2306., 2014.
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[5] Lei Yu, Mohamed Laaraiedh, Stephane Avrillon, "Fingerprint localisation based on neural networks and ultra-wide band signals," IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, pp. 184-189, 14-17 December 2011.
[6] Shu X. and Yao D., "Data leak detection as a service," in Proc. 8th Int. Conf. Secur.Privacy Commun. Netw"., pp. 222-240, 2012.
[7] K. Borders, E. V. Weele, B. Lau, and A. Prakash, "Protecting con_dential data on personal computers with storage capsules," in Proc. 18th USENIX Secur. Symp. , pp. 367-382 ,2009.
[8] A. Nadkarni and W. Enck, "Preventing accidental data disclosure in modern operating systems," in Proc. 20th ACM Conf. Comput. Commun. Secur., pp. 1029-1042, 2013.
[9] Risk Based Security. (Feb. 2017). Data Breach Quick- View: An Executive`s Guide to 2013 Data Breach Trends. [Online]. Available: https://www.riskbasedsecurity.com/reports /2016 DataBreachQuickView.pdf, accessed on Oct. 2017.

R.J. Patil, Y.S. Borse, "Detection of Sensitive Data Leakage for Privacy Preserving," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.765-769, 2018.

Sentiment analysis is useful for multiple tasks including customer satisfaction metrics, identifying market trends for any industry or products, analyzing reviews from social media comments. These kinds of data assets, which are a broad stage of people`s sentiment, suggestions, input, and audits, are viewed as intense witnesses and have become a valuable resource for big industries, research and technology markets, news service providers, and numerous domains where sentiment analysis became a useful tool. This paper discusses on deep learning algorithms applied in recent years for sentiment analysis. The main goal of this paper is to analyze how deep learning research is growing in different application areas and can be helpful for sentiment analysis.

Computer Vision, Deep Learning, Machine Learning, Natural Language Processing Sentiment Analysis

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Sukanya Ledalla, Tummala Sita Mahalakshmi, "An Investigation on Sentiment Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.770-779, 2018.

Semantic search tool is a user-friendly tool which helps to improve search accuracy by understanding what the user wants to search in the search space on the web or a closed system. But there are large number of challenges for translating the data query to SPARQL for better readability and visual ability. SPARQL is a RDF query language i.e. a semantic query language for database, which is able to retrieve and manipulate data stored in Resource Description Framework(RDF) format. Present work provides the optimum result of running queries over different SPARQL end points. This paper presents the comparative study of different algorithms for optimization and also discusses the two aspects of the result optimization like ranking and readability and it concludes the result for the user data.

Relational Database, SPARQL , RDF, Basic Graph pattern

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Rakesh Kumar Pandey, Sachindra Kumar Azad, "Comparative Study of Optimization of data query for SPARQL for Distributed Queries," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.780-785, 2018.