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);

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