iosr-JCE   National Conference on Emerging Trends in Engineering & Technology (NCETET17)

Abstract: Nowadays, businesses are evolving. For having business people needs to deal with much amount of data and this data needs to be delicate and confidential. So, to secure and preserve our data there are plenty of technologies used one of them is Data Mining. Data Mining is the technique in which it tries to find out interesting patterns or knowledge from database such as association or correlation etc. Frequent Itemset Mining is the critical problem in data mining. The frequent can contains valuable and research purpose. Frequent itemsets are items or patterns like itemset, substructures or subsequences that occurs frequently in transaction. To find out frequent itemset there are many Frequent Itemset Mining Algorithms used such as Apriori, FP-growth, Elcat...............

Keywords - Frequent Item Mining, Apriori algorithm, FP- growth

[1] Li Zhou, Zhaohui Yang and Qing Yuan "Salient Region Detection via Integrating Diffusion-Based Compactness and Local Contrast," IEEE Trans. Image processing, vol. 24, no. 11, Nov. 2015
[2] L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254–1259, Nov. 1998.
[3] J. Harel, C. Koch, and P. Perona, "Graph-based visual saliency," in Proc. Adv. Neural Inf. Process. Syst., 2006, pp. 545–552.
[4] Y. Zhai and M. Shah, "Visual attention detection in video sequences using spatiotemporal cues," in Proc. 14th Annu. ACM Int. Conf.Multimedia, 2006, pp. 815–824.
[5] X. Hou and L. Zhang, "Saliency detection: A spectral residual approach," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2007, pp. 1–8.

Abstract: Detection of visually salient image regions is useful for applications like object segmenta- tion, adaptive compression, and object recognition. Visual saliency is the perceptual quality that makes an object, person, or pixel stand out relative to its neighbors and thus capture our attention. Visual attention results both from bottom-up visual saliency as well as top-down methods. Bottom-up salient region detection methods can be broadly classified into uniqueness, compactness and background based and furthermore uniqueness based methods can be roughly divided into local and global contrast based techniques. Thus bottom up salient region detection method is introduced that integrates compactness and local contrast cues. Furthermore, in order to produce a pixel accurate saliency map that more uniformly covers the salient objects output is propagated through the diffusion process.

Keywords - Compactness, Local contrast ,saliency map.

[1] Li Zhou, Zhaohui Yang and Qing Yuan "Salient Region Detection via Integrating Diffusion-Based Compactness and Local Contrast," IEEE Trans. Image processing, vol. 24, no. 11, Nov. 2015
[2] L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254–1259, Nov. 1998.
[3] J. Harel, C. Koch, and P. Perona, "Graph-based visual saliency," in Proc. Adv. Neural Inf. Process. Syst., 2006, pp. 545–552.
[4] Y. Zhai and M. Shah, "Visual attention detection in video sequences using spatiotemporal cues," in Proc. 14th Annu. ACM Int. Conf.Multimedia, 2006, pp. 815–824.
[5] X. Hou and L. Zhang, "Saliency detection: A spectral residual approach," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2007, pp. 1–8.

Abstract: To improve the performance of document clusters ,it is very important to find effectiveand efficient mechanism to minimize the processing time without affecting the total numberof documents given as input required application. The Semi-supervised mechanism usedhere addressed the above need.The proposed approach is designed 1) to group documents into a set of clusters and the number of document clusters formed is determined automatically. 2) To distinguish the discriminative words and non-discriminative words and separate them from unrelated noise words.

Keywords- Semi-supervised Clustering, feature partition, Pattern Recognition.

[1] C. Elkan, "Clustering Documents with an Exponential-Family Approximation of the Dirichlet Compound Multinomial Distribution," Proc. Int'l Conf. Machine Learning, pp. 289-296, 2006.
[2] R. Madsen, D. Kauchak, and C. Elkan, "Modeling Word Burstiness Using the Dirichlet Distribution," Proc. Int'l Conf. Machine Learning, pp. 545-552, 2005.
[3] K. Nigam, A.K. McCallum, S. Thrun, and T.M. Mitchel, "Text Classification from Labeled and Unlabeled Documents Using Em," J. Machine Learning, vol. 39, no. 2, pp. 103-134, 2000.
[4] Ruizhang Huang, Guan Yu, Zhaojun Wang, Jun Zhang, and Liangxing Shi," Dirichlet Process Mixture Model for Document Clustering with Feature Partition", IEEE Trans. On knowledge and data engineering, vol. 25,no. 8, August 2013
[5] K. Rose, "Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems," Proc. IEEE, vol. 86, no. 11, pp. 2210-2239, Nov. 1998.

Abstract: Smartphones and mobile tablets are fast becoming necessary in daily life. Android has been the most popular mobile operating system since 2012. However, due to the open nature of Android, immeasurable malwares are hidden in a large number of kindly apps in Android markets that dangerously pressure Android security. Deep learning is a new area of machine learning research that has gained increasing detect in artificial intelligence. In this study, we propose to connect the features from the static analysis with features from dynamic analysis of Android apps and differentiate malware using deep learning techniques. We execute an Online deep-learning-based Android malware detection engine (DroidDetector) that can automatically..............

Keywords: Android security; malware detection; characterization; deep learning; Evaluation.

[1] S. Poeplau, Y. Fratantonio, A. Bianchi, C. Kruegel, and G. Vigna, Execute this! Analyzing unsafe and malicious dynamic code loading in Android applications, in Proceedings of the 21th Annual Symposium on Network and Distributed System Security (NDSS), 2014.
[2] Y. Zhou and X. Jiang, Dissecting Android malware: Characterization and evolution, in Proceedings of the 33rd IEEE Symposium on Security and Privacy (Oakland), 2012, pp. 95–109.
[3] D. Barrera, H. G. Kayacik, P. C. van Oorschot, and A. Somayaji, A methodology for empirical analysis of permission-based security models and its application to Android, in Proceedings of the 17th ACM Conference on Computer and Communications Security (CCS), 2010, pp. 73–84.
[4] Y. Aafer, W. Du, and H. Yin, Droidapiminer: Mining apilevel features for robust malware detection in Android, in Proceedinds of the 9th International Conference on Security and Privacy in Communication Networks (SecureComm), 2013, pp. 86–103.
[5] D. Arp, M. Spreitzenbarth, M. Hbner, H. Gascon, K. Rieck, and C. Siemens, Drebin: Effective and explainable detection of Android malware in your pocket, in Proceedings of the 21th Annual Symposium on Network and Distributed System Security (NDSS), 2014.

Abstract: Crowd sourcing is most emerging and popular technology which is use currently. It provides easy gathering and manipulation of huge data hence it becomes the most favorable choice among big organization. In this paper we have done search on quick notifications about the events for nearby user. With the help of this application user can provide the event description along with the image to better understand what exactly incident it held. Application is get used with few details after registering user can feed and access the application as per their need. Events are sudden part of the life to deal with them it's the way to get interacted along with technical world, social media is now part of our life so this is the easy way to interact and notified.

Keywords: Crowd Sourcing, Emergency Events, Social Media.

[1] ZHANG Qingsong , ZHAO Guomin , LIU Jinlan "Performance-Based Design for Large Crowd Venue Control Using a Multi-Agent Model", Num- ber 3, June 2009.
[2] Nigel P. Melville, "Crowd-Sourced Peer Feedback (CPF) for Learning Community Engagement: Results and Reections from a Pilot Study", 2014.
[3] Imed Boughzala1, Triparna de Vreede2, Cuong Nguyen2, Gert-Jan de Vreede2, "Towards a Maturity Model for the Assessment of Ideation in CrowdsourcingProjects",2014.
[4] Hamed Tajedin, Dorit Nevo," Value-Adding Intermediaries in Software Crowd sourcing ", 2014.
[5] Je_rey V. Nickerson, Don Steiny , Harri Oinas-Kukkonen,"Introduction to the Humanized Web: Networks, Crowds and their Output",2014.

Abstract: Data de- duplication is nothing but data compression method which is used to eliminate the duplicate copies of repeating data. This approach is frequently used for reducing the storage space and save bandwidth under cloud server. Dynamic Proof of Storage (POS) is a useful cryptographic primitive that enables a user to check the integrity of outsourced files and to efficiently update the files in a cloud server. Although researchers have proposed many dynamic POS schemes in single-user environments, the problem in multi-user environments has not been investigated sufficiently.............

Keywords - Cloud storage, dynamic proof of storage, de-duplication, authorized duplicate check, confidentiality.

[1] Kun He, Jing Chen, Ruiying Du, Qianhong Wu, GuoliangXue, and Xiang Zhang" DeyPoS: Deduplicatable Dynamic Proof of Storage for Multi-User Environments" IEEE Transactions on Computer, Volume: 65, Issue: 12, pp. 3631 - 3645, 2016.
[2] Zhihua Xia, X. sun ,Qian Wang " A secure and dynamic multi-keyword ranked search scheme over Encrypted cloud data".
[3] Minqui zhoul,Rong Zhang, Wei xie,Weining Qian "security and privacy in cloud computing :A survey"
[4] R.Gennaro and D. Wichs ," Fully Homomorphic message authenticators, " in proc. of ASIACRYPT, pp.
[5] D.Boneh and D.M.Freeman, "Homomorphic signatures for polynomial functions" in proc. of EUROCRYPT, pp.

Abstract: In this paper we have discussed numerous of papers which are based on mobile computing we also have done comparative analysis of those paper with different parameter which include domain application Since system user never transmit actual location information, Location of Mobile device does not consider this issue .The message processing time may become a critical issue and Privacy is a big issue .Previous studies proposed methods to preserve a user's privacy Successfully system user transmit actual location information. We have solved user privacy problem .In this paper, we focus on considers traceability of the user's locations to quickly recover from an roller reveal of the user's location..To observe movements of a user and dummies and try to find the real user.

Keywords— Privacy, Location-Based-Service, User, Dummy user

[1] G. Acs, C. Castelluccia, and R. Chen, "Differentially private histogram publishing through lossy compression," in Data Mining (ICDM), 2012 IEEE 12th International Conference on. IEEE, 2012.
[2] A Perez, M. Labrador and P. Wightman, Location-Based Information Systems. USA. CRC Computer and Information Science Series. CRC Press, 2011.
[3] F . Liu, K. Hua, Y. Cai. Query I-Diveristy in LocationBased Services.International Conference On Mobile Data Management,2009.
[4] B Gedik and L. Liu, "Protecting location privacy with personalized k anonymity: Architecture and algorithms," IEEE TMC, 2008.
[5] G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, Tan, and KianLee. Private queries in location based services: Anonymizers are not necessary. In SIGMOD, 2008.

Abstract: Cloud storage is a storage service provide to user, where they can transfer their data anytime and any- where. With the great development of cloud computing in modern era, the massively increasing number of data, the chunk of information storage and the application requirement for high availability of data, network backup is facing an extraordinary challenge. On the other hand, a cloud server normally performs a data compression technique (data deduplication) to eliminate duplicate data because the storage is not infinite. Data deduplication, which makes possible for data holder to share a copy of the duplicate data, can be performed to reduce the consuming of storage space..............

Keywords - Deduplication, Cloud storage, Avoid Duplication in Cloud System

[1] Amazon EC2. http://aws.amazon.com/ec2/.
[2] Amazon Glacier. http://aws.amazon.com/glacier/.
[3] AWS Cloud HSM. http://aws.amazon.com/cloudhsm/.
[4] Dropbox. http://www.dropbox.com.
[5] Google Drive. http://drive.google.com/
[6] Opendedup. http://opendedup.org/.