iosr-JCE   Volume-19 ~ Issue-3 ~ May-June 2017

Abstract: In this project, we are trying to promote security to the data using developed encryption techniques. The image is encrypted using Magic Rectangle (MR) encryption technique, and text using the RSA algorithm with LSB as data concealment technique. The data hiding technique uses LSB replacement algorithm for hiding the secret message bits into encrypted image. By choosing the decryption keys, both the image and encrypted text will be extracted.

Keyword: Reversible data hiding, MR encryption, LSB replacement, RSA key encryption.

[1]. Champakamala B.S Padmini.K, Radhika D.K Asst Professor "Least Significant Bit algorithm for Image Steganography",International Journal of Advance Computer Technology,volume 3,Number 4.
[2]. KedaMa,Weiming Zhang, Xianfeng Reserving room before Encryption".IEEETrans.Information Forensics and Security, vol 8 No.3 March 2013.
[3]. M. Johnson, P. Ishwar, +V.M.Prabhakaran,D.Schonberg, and K.Ramchandran,"On compressing encrypted data,"IEEE Trans. Signal Process.,vol.52,no. 10,pp.2992-3006,Oct. 2004.
[4]. W. Liu, W.Zeng, L.Dong, and Q.yao,"Efficient compression of encrypted grayscale images," IEEE Trans. Image Process., vol 19,no.4,pp.107-1102, Apr.2010
[5]. X. Zhang," Lossy compression and iterave reconstruction for encrypted image,"IEEE Trans. Inform. Forensics Sceurity, vol.6,no. 1,pp. 53-58,Feb.2011.

Abstract: In today's fast moving world, shopping has become increasingly online. The benefits provided by online shopping outperform the need to shop in person. One such enticing benefit is the personalised recommendations provided specifically to each user. These recommendations guide the users in their shopping process and also unveil new range of products suiting their tastes. This system we intend to develop stores the user's purchase and rating data in a backend database. It also includes a smart beacon that connects to the offline shopper's mobile phone and gains data about the particular customer from backend database...........

Keywords: Clustering, Cold start problem, Demographic Data, Hybrid recommender systems, Recommender system

[1]. Shani G, Gunawardana, A Evaluating Recommendation Systems.In: Recommender Systems Handbook, pp. 257–297, 2011.

[2]. Wojtek Kowalczyk,Recommender systems for e-shops,faculty of Sciences, Vrije Universiteit,Amsterdam, 2011.

[3]. Harpreet Kaur Virk, Er.Maninder Singh, Er. Amritpal Singh, Analysis and Design of Hybrid Online Movie Recommender System, International Journal of Innovations in Engineering and Technology, 5(2), 2015

[4]. Jyoti Gupta, Jayant Gadge, Performance Analysis of Recommendation System Based on Collaborative Filtering and Demographics, International Conference on Communication, Information & Computing Technology (ICCICT), Jan. 16-17, 2015 Mumbai, India.

[5]. Manoj Kumar,D.K Yadav,Ankur Singh,Vijay Kr. Gupta, A Movie Recommender System: MOVREC, International Journal of Computer Applications (0975 – 8887), 124(3), August 2015.

Abstract: The main aim of this work is to propose a novel Computer-aided detection (CAD) system based on a Contextual clustering combined with region growing for assisting radiologists in early identification of lung cancer from computed tomography(CT) scans. Instead of using conventional thresholding approach, this proposed work uses Contextual Clustering which yields a more accurate segmentation of the lungs from the chest volume. Following segmentation GLCM features are extracted which are then classified using three different classifiers namely Random forest, SVM and k-NN.

Keywords: Computer aided detection(CAD);computed tomography(CT) imaging;lung cancer ;support vector machine(SVM);

[1]. B. S. Morse, Lecture 18: Segmentation (Region Based), 1998-2000.
[2]. Giorgio De Nunzio, Eleonora Tommasi, Antonella Agrusti, Rosella Cataldo, Ivan De Mitri, Marco Favetta, Silvio Maglio, Andrea Massafra, Maurizio Quarta, Massimo Torsello, Ilaria Zecca, Roberto Bellotti, Sabina Tangaro, Piero Calvini, Niccolò Camarlinghi, Fabio Falaschi, Piergiorgio Cerello, and Piernicola Oliva, "Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region", Journal of digital imaging, Vol 24, No 1, pp 11-27, 2011.
[3]. J. Quintanilla-Dominguez, B. Ojeda-Magaña, M. G. Cortina-Januchs, R. Ruelas, A. Vega- Corona, and D. Andina, "Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications," Sharif University of Technology Scientia Iranica, vol. 18, pp. 580–589, Received 21 July 2010; revised 26 October 2010 accepted 8 February 2011.
[4]. Eero Salli, Hannu, J, Aronen, Sauli Savolainen, Antti Korvenoja & Ari Visa 2001, 'Contextual Clustering for Analysis of Functional MRI Data', IEEE transactions on Medical Imaging, vol. 20, no. 5, pp.403-414, 2001.
[5]. Linda G. Shapiro and George C. Stockman (2001): "Computer Vision", pp 279-325, New Jersey, Prentice-Hall, ISBN 0-13- 030796-3.

Abstract: The goal of image watermarking is to hide the specific information into host image and the presence of the watermark cannot be realized by human visual. The watermarking algorithm design should meet the criteria such as the rightful ownership protection, robustness against the image processing attacks and watermark imperceptibility. In this paper an optimal watermarking scheme based on singular value decomposition (SVD) and Discrete wavelet transform (DWT) using multi-objective genetic algorithm (GA) optimization is presented. The singular values of the watermark are embedded in a detail sub-band of host image after third level approximation............

Keywords: Digital Image watermarking, DWT, SVD, GA, Robust, Imperceptibility

[1]. Liu RZ, Tan. TN. An SVD-based watermarking scheme forProtecting rightfulownership. IEEE Trans Multimedia
2002;4(1):121–8.
[2]. Zhang XP, Li. K. Comments on an SVD-based watermarking scheme for protec-ting rightful ownership. IEEE Trans Multimedia
2005;7(3):593–4.
[3]. Rykaczewski R. Comments on SVD-based watermarking scheme for protectingrightful ownership. IEEE Trans Multimedia
2007;9(2):421–3.
[4]. Changzhen X, Fenhong G, Zhengxi L. Weakness analysis of singular valuebased watermarking. In: Int. Conf. Mechantronics
and Automation.2009. p.2596–601.
[5]. Changzhen X, Ward RK, Xu J. On the security of singular value based water-marking. In: IEEE Int. Conf. Image Proc. 2008. p.
437–40.

Abstract: The "Integrated Data Warehouse Architecture for Financial Institutes with Security Levels" is proposed data warehouse architecuture for overall Indian Finacial System [1][2][3][4][5]. The performance of architecture has been measure in SQL Server Management Studio by observing results[5]. The paper focus on the performance of architecture results and proved the executed queries have been given satisfactory performance. The performance of result is functionally correct because it behaves according to its stated functional specifications and gets the result according to our proposed expectation because it does not produce dangerous or costly failures [6]. It is used in a reasonable manner, that is, in a manner that a typical user expects is normal [7].

Keywords: ATM, BUID

[1] Vaibhav R Bhedi, Shrinivas P Deshpande and Ujwal A Lanjewar," Secured Integrated Data Warehouse Architecture for Financial Institutes", International Journal of Electronics, Communication & Soft Computing Science and Engineering (IJECSCSE), Special Issue-ATCON-2015, ISSN: 2277-9477, Impact Factor: 4.5.
[2] Vaibhav R Bhedi. "Design and Study of Financial Transaction Model for BUID". International Journal of Computer Applications (0975 – 8887) ISBN: 973-93-80870-28-,57(13):12-15, November 2012. Published by Foundation of Computer Science, New York, USA, Impact Factor: 0.821.
[3] Vaibhav R Bhedi, Shrinivas P Deshpande and Ujwal A Lanjewar "Data Warehouse Architecture for Financial Institutes to Become Robust Integrated Core Financial System using BUID". International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 3, March 2014. ISSN (Online) : 2278-102, ISSN (Print) : 2319-5940, Impact Factor: 1.770.
[4] Vaibhav R Bhedi, Shrinivas P Deshpande and Ujwal A Lanjewar, "Design and Study of Security Model for Core Financial System". International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 3, Issue 6, November-December 2014.ISSN: 2278-6856, Impact Factor:3.258.

Abstract: Data storage and retrieval is major issue in cloud environments. For data storage and retrieval used authentication and authorization process. The authentication and authorization process used cryptography technique. The cryptography technique provides symmetric and asymmetric crypto systems. The symmetric technique is week in compression of asymmetric technique. Various authors used the concept of asymmetric cryptography technique for the process of data over the cloud network. Authors also suggested the concepts of data dynamics for the proof of data retrieval..............

Keywords: Cloud, Data Storage, Cryptography, RSA, Proof of Retrieval

[1] Shucheng Yu, Cong Wang, KuiRen and Wenjing Lou, Achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing, IEEE, 2010, 1-9.
[2] Cong Wang, Sherman S.-M. Chow, Qian Wang, Kui Ren and Wenjing Lou, Privacy-Preserving Public Auditing for Secure Cloud Storage, IEEE, 2013, 1-12.
[3] Sherman S.M. Chow, Cheng-Kang Chu, Xinyi Huang, Jianying Zhou and Robert H. Deng, Dynamic Secure Cloud Storage with Provenance, Springer, 2011, 442-464.
[4] Zhihua Xia, Xinhui Wang, Xingming Sun and Qian Wang, A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data, IEEE, 2015, 1-13.
[5] Cong Wang, Qian Wang, Kui Ren and Wenjing Lou, Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing, IEEE, 2010, 1-9.

Abstract: Job allocation and balancing of load is major issue in public cloud network. The allocation of job and resource used queening principle, the queueing principle creates the job wanting condition and finally job allocation process is failed. For the improvement of job allocation process used meta-heuristic function and dynamic load balancing technique. the dynamic load balancing technique offers various methods for the job allocation and resource management. In this paper evaluate the performance of job allocation technique in cloud network based on different heuristic function such as genetic algorithm, ant colony optimization algorithm and particle swarm optimization. The heuristic function increases the performance of cloud network in interdisciplinary environments. The evaluation process used MATLAB software and design different scenario of load over public cloud network.

Keywords: Cloud Computing. Job Allocation, heuristic function, Load Design, MATLAB

[1] Kui Ren, Cong Wang and Qian Wang, Security Challenges for the Public Cloud, IEEE, 2012, 69-73.
[2] Yongjun Ren, Jian Shen, Jin Wang, Jin Han and Sungyoung Lee, Mutual Verifiable Provable Data Auditing in Public Cloud Storage, Mutual Verifiable Provable Data Auditing in Public Cloud Storage, 2015, 317-323.
[3] Wayne Jansen and Timothy Grance, Guidelines on Security and Privacy in Public Cloud Computing, NIST, 2011, 1-70.
[4] Ang Li, Xiaowei Yang, Srikanth Kandula and Ming Zhang, Comparing Public- Cloud Providers, IEEE, 2011, 50-53.
[5] Gaochao Xu, Junjie Pang and Xiaodong, A Load Balancing Model Based on Cloud Partitioning for the Public Cloud, Tsinghua Science And Technology, 2013, 34-39.

Abstract: Now a day's data mining technique used in the field of medical diagnose of critical diesis and clinical data. The prediction of mining technique is major issue. For the enhancement of mining technique used various approach such as fuzzy logic, feature optimization and machine learning based classification technique. in this classification proceed based on classifier selection to medical disease data and propose a clustering-based classifier selection method. In the method, many clusters are selected for an ensemble process. Then, the standard presentation of each classifier on selected clusters is calculated and the classifier with the best average performance is chosen to classify the given data. In the computation of normal act, weighted average is technique is used.............

Keywords: Medical Diseases, Data Mining, Classification, Clustering

[1] Mai Shouman, Tim Turner and Rob Stocker, using data mining techniques in heart disease diagnosis and treatment, IEEE, 2012, 189-193.
[2] M. L. Kowalski, J. S. Makowska, M. Blanca, S. Bavbek, G. Bochenek, J. Bousquet, P. Bousquet, G. Celik, P. Demoly, E. R. Gomes, E. Ni_zankowska-Mogilnicka, A. Romano, M. Sanchez-Borges, M. Sanz, M. J. Torres, A. De Weck, A. Szczeklik and K. Brockow, Hypersensitivity to nonsteroidal anti-inflammatory drugs (NSAIDs) – classification, diagnosis and management: review of the EAACI/ENDA and GA2LEN/HANNA, John Wiley & Sons A/S, 2011, 818-829.
[3] Chaitrali S. Dangare and Sulabha S. Apte, Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques", International Journal of Computer Applications, 2012, 44-48.
[4] Asha Rajkumar and Mrs. G.Sophia Reena, Diagnosis Of Heart Disease Using Datamining Algorithm, Global Journal of Computer Science and Technology, 2010, 38-43.
[5] Nidhi Bhatla and Kiran Jyoti, An Analysis of Heart Disease Prediction using Different Data Mining Techniques, International Journal of Engineering Research & Technology, 2012, 1-4.

Abstract: Humans usually have some default shapes which the eye could recognize and know what they are, so vision is an important thing in human knowledge. The same operation applied in machines and computers because the software must recognize the shape before doing any operation on it. In the current paper we proposed a new algorithm for detecting shapes and even detecting the kind of shapes. In addition, the proposed system detected all the types of shapes which are known usually.............

Keywords: Recognition shapes.

[1]. F. Pedro, "Representation and Detection of Shapes in Images," Massachusetts Institute of Tecnology, 2003.
[2]. K. Karthik and ,. S. Wesley, "A Shape Recognition Algorithm Robust to Occlusion: Analysis and Performance Comparison," 2013.
[3]. K. M. S., "Shape recognition for image scene analysis," Université de Nice - Sophia-Antipolis, 2009.
[4]. M. Hankyu, C. Rama and R. Azriel, "Optimal Edge-Based Shape Detection," IEEE Transcations On Image Processsing NG, vol. 11, no. 11, pp. 1209-1226, 2002.
[5]. R. Mar, c. D. Philippe and L. Josep, "Boundary Shape Recognition Using Accumulated Length and Angle Information," Pattern Recognition and Image Analysis, vol. 4478, no. 27, p. 210–217, 2007.