iosr-JCE   Volume-16 ~ Issue-3 ~ May-June 2014

Abstract: Now-a-days Renal Calculi is becoming a most common disease in both men and women. Calculi are due to abnormal collection of certain chemicals like oxalate, phosphate and uric acid. These calculi can be present in kidney, urethra or in urinary bladder. Most of the previous study in diagnosis of Renal Calculi spots out the presence or absence of the calculi in the kidney. In this paper we propose an algorithm to detect the renal calculi and to find the level of the calculi. It is more helpful to change the diet conditions. In this paper, we presented a method for ultrasound kidney image diagnosis for calculi and to find the size of the calculi using Advanced seeded region growing based segmentation. With segmented portions of the images the intensity threshold values helps to classify the level of the calculi as early calculi, medium calculi and large calculi. The Advanced Seeded Region Growing based image segmentation process homogeneous region depends on the image features. First, the regions of interest (ROIs) extracted from the pre-processed image. Second, the initial seed is selected based on ROIs extracted from the image. The size of the growing regions depends on this look up table entries. After completing region growing, region merging is used to suppress the high frequency artifacts. The diagnosis process is done based on the intensity threshold variation obtained from the segmented portions of the image and level of the calculi is found out.
Keywords: Renal Calculi, Advanced seeded region growing, intensity threshold, region merging, segmentation

[1]. Steve WH Chan, CF Ng CW Man, Robert Chung, SK Li, "A report on a randomly sampled questionnaire survey about renal stone disease in Hong Kong", Hong Kong Med J 2008;14:427-31.
[2]. Ms.P.R. Tamilselvi, Dr.P. Thangaraj, "Computer Aided Diagnosis System for Stone Detection and Early Detection of Kidney Stones", Journal of Computer Science 7 (2): 250-254, 2011.
[3]. Dr. P. R. Tamiselvi, "Segmentation of Renal Calculi Using Squared Euclidean Distance Method", International Journal of Scientific Engineering and Technology, Volume No.2, Issue No.7, pp : 651-655.
[4]. Dr. P. R. Tamiselvi, "Effective Segmentation Approaches for Renal Calculi Segmentation", International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

Abstract: The goal of Machine Learning is to construct a computer system that can adapt and learn from their experience. Machine Learning approach helps to integrate the computer based system into the healthcare field in order to obtain best and accurate results for the system. Here the system deals with automatic identification of informative sentences from medical published by medical journals. Our main aim is to integrate machine learning in medical field and build an application that is capable of automatically identifying and disseminating disease and treatment related information, further it also identifies semantic relations that exists between diseases and treatments. In the proposed work user will search for the disease summary (disease and treatment related information) by giving symptoms as a query in the search engine. Initially when a pdf is downloaded and saved in the system it first performs per processing on the data in the document and the extracted relevant data is stored in the database. The symptoms entered by the user are further classified using SVM classifier to make the further process easier to find the semantic keyword which helps to identify the disease easily and quickly. Then the semantic keyword found is matched with the stored medical input database to identify the exact disease related to that keyword present. Once the disease related to the symptom is identified, it is sent to medical database to extract the articles pertaining to that disease. The preprocessing process involves tokenization, removal of stop words and stemming. Followed by that, relevant information is extracted using the keyword searching algorithm. The combination of BOW, NLP and biomedical concepts are put together toe identifying semantic relations that exist between diseases and treatments in biomedical sentences. Till now the best result obtain is 98.51% F-measure by OanaFrunza, for the extraction of cure and prevents relations. In our implementation of our proposed system we have used SVM classifier which gives us an improved result. The problem statement of the existing system was, it didn't identify the best disease treatment. So the proposed solution used data mining concepts using voting algorithm to resolve the problem and find the best treatment for disease out of the treatment identified by the system.
Keywords: Datasets, Medline, HealthCare, Diseases and Treatment relation, Keyword Searching Algorithm, Rule Based classifier, Ranking Algorithm.

[1] Wernick, Yang, Brankov, Yourganov and Strother, ―Machine Learning in Medical Imaging‖, IEEE Signal Processing Magazine, vol. 27, July 2010, pp. 25-38
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[3] S. Ray and M. Craven, ―Representing Sentence Structure in Hidden Markov Models for Information Extraction,‖ Proc. International Joint Conférence Artificial Intelligence, 2001.
[4] R. Bunescu and R. Mooney, ―A Shortest Path Dependency Kernel for Relation Extraction,‖ Human Language Technology and Empirical Methods in Natural Language Processing 2005, pp. 724-731.
[5] Srinivasan P. and T. Rindflesch, ―Exploring text mining from Medline,‖ Proceedings of the AMIA Symposium, 2002, pp.722–726.

Abstract: Opportunistic routing scheme is used to route data packet from source to destination. Broadcasting nature of the wireless networks is utilized here to send data packets. A suitable forwarder node is selected from the neighboring node based on the rank ordering of the nodes. The nodes are ranked according to a score called Estimated Best Score (EBS). This score is calculated by considering the network congestion which is one of the major problems in adhoc networks. Probability of unsuccessful delivery of the packet become very less. Different types of packets are used to communicate between different nodes. A distributed and asynchronous routing scheme is introduced to route the packet. The proposed routing scheme doesn't require any initial knowledge about the network topology. Another problem with adhoc wireless networks is the presence of selfish nodes. For the successful delivery of the packet at the destination we have to avoid the presence of selfish nodes in the networks. So each node calculate the selfishness of its neighboring nodes. The data packet is forwarded by considering the selfishness of the node.
Keywords: opportunistic routing,wireless adhoc networks

[1]. Abhijeet A. Bhorkar, Mohammad Naghshvar, Student Member, IEEE, Tara Javidi, Member, IEEE, and Bhaskar D. Rao, Fellow, IEEE Adaptive Opportunistic Routing for Wireless Ad Hoc Networks, IEEE/ACM transactions on networking, vol. 20, no. 1, february 2012.
[2]. C. Lott and D. Teneketzis, "Stochastic routing in ad hoc wireless networks," in Proc. 39th IEEE Conf. Decision Control, 2000, vol. 3, pp. 2302–2307, vol. 3.
[3]. S. Biswas and R. Morris, "ExOR: Opportunistic multi-hop routing for wireless networks," Comput. Commun. Rev., vol. 35, pp. 33–44, Oct. 2005..
[4]. S. Jain and S. R. Das, "Exploiting path diversity in the link layer in wireless ad hoc networks," in Proc. 6th IEEE WoWMoM, Jun. 2005, pp. 22–30.
[5]. J. Boyan and M. Littman, "Packet routing in dynamically changing networks: Areinforcement learning approach," in Proc. NIPS, 1994, pp. 671–678.

Abstract: Lip is used as an identifier from person to person as the size of the upper and lower lips, furrows, grooves, the distances between the lines and the edges vary individualistically. These unique parameters are experimented to distinguish individuals and their uniqueness is also verified. The highlighting advantage in devising the biometric system based on lip recognition is that the data acquisition and handling are simple and hence storing and processing the data becomes an unconstrained task leading to drastic increase in the efficacy of the system. The main focus of this work is to track the lips in the given face image and then extract suitable features from the lips to perform the differentiation task. Accurately and robustly tracking lip motion in image sequences is especially difficult because lips are highly deformable, and they vary in shape, colour, and relation to surrounding features across individuals; in addition, they are subject to both non -rigid (expression, utterance of speech) and rigid motion (head movement). The use of colour space transformation has shown very well for lip image segmentation.
Keywords: Lip Corner Detection, Lip Corner Extraction, Lip Features Classification, Lip Segmentation, Region of Interest

[1]. "Automatic lip contour extraction from color images"S.L.Wanga,W.H. Laua,∗, S.H. Leungb
[2]. "A Lip Contour Extraction Method Using Localized Active Contour Model with Automatic Parameter Selection " Xin Liu,Yiu-ming Cheung, Meng Li
[3]. "Automatic Outer Lip Contour Extraction in Facial Images" Juan-Bernardo Gómez Mendoza, Flavio Prieto
[4]. G. Chiou and J. N. Hwang, "Lip reading from color video," IEEE Transactions on Image Processing", vol. 6, no. 8,
[5]. X. Zhang, R. M. Mersereau, M. Clements, and C. C.Broun, "Visual speech feature extraction for improved speech recognition," in Proc. International Conference on Acoustics

Abstract: Platform as a Service (PAAS) is a new initiative that is changing the structure of the organizations from traditional infrastructure services to the cloud based services. With the growing demand for these cloud services, organizations that offer these services need effective management of the resources provided. The paper provides an efficient solution in this regard by providing a common resource manager across all the platforms, coupled with administrative and reporting capabilities, further empowered with additional features like security and middleware messaging provided as optional add-ons. The resource manager automates the entire lifecycle of application workflow starting from its development until production. This multifaceted resource manager consolidated with a plethora of features, supplemented by the power of cloud, transforms into a tool that can modify and scale itself according to the ever-changing demands of the customers providing the right competitive business edge to the organization.
Index Terms: Cloud based services, Platform as a Service (PaaS), Resource Management, Workflow Automation.

[1]. William Y. Chang, Hosame Abu-Amara, Jessica Feng Sanford, "Transforming Enterprise Cloud Services", Springer, pp. 55, Nov. 2010
[2]. Han Xingye, Li Xinming, Liu Yinpeng, "Research on Resource Management for Cloud Computing Based Information System", 2010 International Conference on Computational and Information Sciences (ICCIS), pp. 491-494, Dec. 2010.
[3]. Erwin Fischer, "APACHE Web Server in BS2000/OSD", Fujitsu White Paper, pp.1-4, April 2009.
[4]. David Baum, Ruma Sanyal Ayalla, Goldschmidt Mike Lehmann, "Introducing Web Logic Server 12C", ORACLE White Paper, pp. 10-16, January 2012.
[5]. Younkwan Lee, Kangchan Lee, Seungyun Lee, "Web Services Deployment Model Based on WSG (Web Services Gateway) in NGN", International Journal of Software Engineering and Its Applications, Vol. 7, No. 1, pp. 229-236, January 2013.

Abstract: Composite sketches are frequently used to help track down suspects in a crime in which there is no photographic evidence. Automatic retrieval of photos of suspects from police mug-shot databases can help police narrow down potential suspects quickly. This paper describes a novel approach for matching facial composite sketches to police mug-shot images. Proposed method is based on Geometric Features. To recognize a facial composite we focussed on face geometric features like eyes, eyebrows, nose, lips, and face cutting along with their length and width ratios. This system has five components 1) Extract components from a face image/composite sketch 2) Compute Ratios of Length, Width and Area 3) Represent each image component as a vector 4) Mean of the vectors are computed and subtracted from each component vector to get a set of vectors called zero-mean vectors 5) Searching for mug shot images in the database based on a query sketch drawn by an artist. It has useful applications for both law enforcement and digital entertainment. We assume that mug shot images to be studied are in frontal pose, under normal lighting with neutral expression, and have no occlusion. Extensive experiment has been conducted with 50 male and female face/sketch database from CUHK face database.
Keywords: Composite sketches, Geometric features, Mean vectors, Face image components, CUHK face database.

[1] S. Pramanik, D. Bhattacharjee, "Geometric feature based face sketch Recognition, "Proc.IEEE Int'l Conf. Pattern Recognition, Informatics and Medical Engineering, Mar.2012.
[2] D. Bhattacharjee, S. Halder, M. Nasipuri, D.K. Basu, M. Kundu"Construction of human faces from textual description", Springer, Nov.2009.
[3] Wu H, Chen Q, Yachida M (1995) An application of fuzzy theory: face detection. In: Proceedings of IWAFGR'95, pp 314-319.
[4] Yang MH, Kriegman, DJ, Ahuja N (2002) Detecting faces in images: a survey. IEEE Trans Pattern Anal Mach Intell 24(1):34-58.

[5] Rowley HA, Baluja S, Kanade (1998) neural network-based face detection. IEEE Trans Pattern Anal Mach Intell 20(1):23-38.

Abstract: The decision process for selecting the best-suited follow-up treatment for suspected breast cancer cases are strongly dependent upon the correct diagnosis and assessment of the breast cancer risk. This study will develop an expert system with soft computing methods by using Fuzzy logic and Bayesian network. This system has five input variables and one output variable (risk status). The output variable is a value from 1 to 4; representing low risk status, intermediate risk status ,high risk status and very high risk. This study will help to diagnosis and monitor breast cancer at home.
Keywords: Breast cancer diagnosis, fuzzy intelligent technique, Soft Computing, Expert system, Probabilistic reasoning, Bayesian Network.

[1]. Freddie Bray, P. M. (2004). The changing global patterns of female breast cancer incidence and mortality. BioMedCentral , 229-239.
[2]. Carol DeSantis, M. ,. (2011). Breast Cancer Facts & Figures . American Cancer Societ .
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[4]. HussainSaleem&et. al., (2010), "Traceability Management Framework for Patient Data in Healthcare Environment", In Proc. IEEE Intl. Conf. on Computer Science & Information Technology, Chengdu, China, pp.264-8
[5]. Masood, A. (2011). MEDICAL EXPERT SYSTEM Theory, Implementation and Application: Knowledge Discovery from Medical Data Using a Bayesian Network and Fuzzy Logic . LAP LAMBERT.

Abstract: Ad hoc wireless network is defined as a collection of mobile platforms or nodes where each node is free to move. Each node is logically consisting of a router that may have multiple hosts and that also may have multiple wireless communications devices.An ad hoc wireless network has the features like autonomous terminals, distributed operation, multihop routing and dynamic network topology.As nodes are mobile and their battery power is less there are lots of possibilities to design a protocol. To test these routing protocols, there are some simulator like NS2 and Qualnet available. These routing protocols are augmented on NS2 and Qualnet for standard results. a power aware routing protocol methodology is proposed in this paper. The major aim of proposed work is to design a simulator for the routing protocols so that we will find out the some parameters like average number of nodes needed to get path from source to destination, average number of retransmission during finding the path from source to destination, average number of throughput and average power remaining of each node. In this work a routing protocol is designed in C++ and run on standard condition.

1. Number of nodes
2. Transmission Radius of each node
3. Number of iterations

Abstract: In many applications, users specify target values for certain attributes, without requiring exact matches to these values in return. Instead, the result to such queries is typically a rank of the top-k tuples that best match the given attribute values. Focusing on the top-k items according to a ranking criterion constitutes an important functionality in many different query answering scenarios. The idea is to read only the necessary information mostly from secondary storage with the ultimate goal to achieve low latency. In this paper, we study the advantages and limitations of processing a top-k query by translating it into a single range query that traditional relational database management systems can process efficiently. In this work, we also consider processing such top-k queries under the constraint that the result items are members of a specific set, which is provided at query time it is also known as set-defined selection criterion.
Keywords: top-k query processing, index partitioning, mapping strategy, index partitioning

[1] G. Salton and M. J. McGill. Introduction to modern information retrieval. McGraw-Hill, 1983.
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[5] R. Fagin. Combining fuzzy information from multiple systems. In Proceedings of the Fifteenth ACM Symposium on Principles of Database Systems (PODS'96), June 1996.

Abstract: Cloud computing provides the shared infrastructure to the user this lead to the privacy and security violation. Privacy is regarding to the user's sensitive information that resides onto the cloud, unauthorized secondary usage of the data. Windows Cardspace is a digital identity management system that deals with the privacy. It is managed the digital identities inform of Security Token. This paper described the shortcoming of the windows Cardspace that direct to a privacy violation. First issue is reliance on the single layer authentication, second is relying on service provider judgment and third is token storage on cloud. This proposed work will overcome the limitation of the windows cardspace identity management solution.
Keyword: windows Cardspace

[1]. Understanding Ws-security (2011); http://msdn.microsoft.com
[2]. Bhargav, Noopur Singh, Asher Sinclair (2011) "Privacy in cloud computing through identity Management", Computer Science ,Purude University west Lafayette, IN47907 united state.
[3]. Walees A, Alrodhan and Chris J.Mitchell (2009) "Improve the security of CardSpace" EURASIP Journal on information Security;
[4]. Bharat Bhargav, Rohit.R, Noopur.S "An Entity-centric approach for and identity Management in cloud computing" Computer Science , Purude University west Lafayette, IN47907 united state.
[5]. Kumar Gunjan, G.Sahoo, R.K.Tiwari (2012) "Identity Management in Cloud Computing"; International Journal of Engineering Research & Technology (IJERT); Dept. of Information Technology ; B.I.T. Mesra, Ranchi, India

Abstract: Anomaly detection is a crucial analysis topic in the field of data mining as well as machine learning. Several real-world applications like Intrusion or MasterCard fraud detection need a good and efficient framework to spot deviated data instances. A good anomaly detection methodology must be able to accurately establish many varieties of anomalies, robust, need comparatively very little resources, and perform detection in period of time. In this paper we proposed the idea of combining the two different algorithms i.e. Median Based Outlier Detection and Online Oversampling PCA for effective detection of anomaly in online updating mode. Median Based outlier detection uses the interquartile range which is a measure of statistical dispersion being equal to the difference between the upper and lower quartiles. Whereas oversampling PCA does not need to store the entire covariance matrix or data matrix and thus this approach is a more useful in online or large scale problem. Compared with other anomaly detection algorithm our experimental result verifies the feasibility of our proposed method.

Keywords: Anomaly, Leave One Out, Median, Oversampling and Principal Component Analysis

[1]. Hawkins, D.M. 1980. Identification of Outliers. Chapman and Hall Publication.
[2]. Angiulli, F., Basta, S., and Pizzuti, C. 2006.Distance-Based Detection and Prediction of Outliers. IEEE Trans. Knowledge and Data Engg. vol. 18, no. 2, pp. 145-160, 2006.
[3]. Jin, W., Tung, A.K.H., Han, J., and Wang, W. 2006. Ranking Outliers Using Symmetric Neighborhood Relationship. Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining, 2006.
[4]. Aggarwal, C. C., and Yu, S. P. 2005. An effective and efficient algorithm for high-dimensional outlier detection, The VLDB Journal, vol. 14, pp. 211–221, 2005.

[5]. Kriegel, H.P., Schubert, M. and Zimek, A. 2008. Angle-Based Outlier Detection in High-Dimensional Data. Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2008

Abstract: Software Process Improvement (SPI) is a systematic approach and continuous improvement of software development organization's ability to produce and deliver quality software with in time and budget constraints [1]. This paper describes the principle of the software process improvement and the factors which influence on the Software Process Improvement on the basis of survey and give solutions.
Keywords: Software Process Improvement, Software Process Improvement Model, Software Quality

[1]. Jesper Arent, Jacob Nørbjerg "Software Process Improvement as Organizational Knowledge Creation: A Multiple Case Analysis," Proceedingsof the 33rd Hawaii International Conference onSystem Sciences – 2000
[2]. The American Corporation Today<http://www.questia.com/googleScholar.qst?docId=80352426>
[3]. Laurie Honour Werth, "Lecture Notes on Software Process Improvement", document number CMU/SEI-93-EM-8, copyright by Carnegie Mellon University, "Introduction to Software Process Improvement", 1993.
[4]. Mohd. Hairul Nizam, Md. Nasir, Rodina Ahmad, Noor Hafizah Hassan, "Resistance Factors in the Implementation of Software Process Improvement Project", Volume: 4, pp. 1-10, 26-28 Aug. 2008.
[5]. Anthony Finkelstein, "Software Process Immaturity Model", Software engineering notes 17,vol. no. 4,Oct 1992 pp 22-23.
[6]. Zhedan Pan, Hyuncheol Park, Jongmoon Baik, HojinChoi, "A Six Sigma Framework for Software Process Improvements and its Implementation", Proceedings of the 14th Asia-Pacific Software Engineering Conference, ISBN ~ ISSN: 1530-1362 , 0-7695-3057-5, pp. 446-453 2007.

Abstract: Speech is a natural mode of communication for people. Yet people are so comfortable with speech, they would also interact with the computers via speech, and various interfacing devices such as keyboards and pointing devices. Outstanding work in speech recognition and computing has produced the commercial speech recognition systems for voice driven computing and word-processing systems. The analysis within the space of speech recognition lots of work is done for English language and European language and the opposite hand very little work done for Indian language and really little in Marathi. Only Because of This We Develop Automatic Speech Recognition of Isolates Marathi Words For Agriculture Purpose. For developing ASR system we use hybrid feature extraction technique i.e. MFCC with RASTA and for recognition Dynamic Time Wrapping is used.
Keywords: DTW, MFCC, RASTA, Speech Recognition.

[1] Pukhraj P. Shrishrimal, Ratnadeep R. Deshmukh and Vishal B. Waghmare, "Indian Language Speech Database: A Review," International Journal of Computer Applications, Volume 47, No.5, 2012.
[2] C. Vimala and V. Radha, "A Review on Speech Recognition Challenges and Approaches," World of Computer Science and Information Technology Journal (WCSIT), Volume 2, No. 1, 2012.
[3] S.Ananthi and P. Dhanalakshmi, "Speech Recognition System and Isolated Word Recognition based on Hidden Markov Model (HMM) for Hearing Impaired", International Journal of Computer Applications, Volume 73, No. 20, 2012.
[4] P. V. Lieshout, "PRAAT Short Tutorial," Volume 4.2.1, 2003.
[5] W. Ding and G. Marchionini, "A Study on Video Browsing Strategies," Technical Report, University of Maryland at College Park 1997.

Abstract: This paper focuses on the various neural network techniques including Multilayer perceptron (MLP) neural network, classifier and self-organizing maps (SOMs). Various aspects of neural network techniques are mentioned in this paper along with the advantages and drawbacks. The neural network models are trained with measured values of the field strength at arbitrary points. The back propagation training algorithm is used for the learning process in MLP. The mechanism of supervised and unsupervised learning are also specified.
Keywords: MLP, Self-organizing maps (SOM),Neural network, Classifier, Applications

[1] ] Harun, N.; Woo, W.L. ; Dlay, S.S. ;" Performance of keystroke biometrics Authentication system using Artificial Neural Network (ANN) and Distance Classifier Method " International Conference on Computer and Communication Engineering (ICCCE), 2010
[2] Schclar, A.; Rokach, L.; Abramson, A.; Elovici Y,"User Authentication Based on Representative Users" IEEE Transactions on systems, man and cybernetics part C, Vol 42, NO. 6, pp.1669-1678,( NOVEMBER 2012)
[3] BioPassword: History and Science of keystroke dynamics. http://www.biopassword.com/resources/
[4] D.Shanmugapriya and G.Padmavathi, "An Efficient Feature Selection Technique for User Authentication using Keystroke Dynamics," IJCSNS, vol. 11, no.10, 2011.
[5] L. K. Maisuria , C. S. Ong and W. K. Lai, " A comparison of artificialneural network and cluster analysis for typing biometricsauthentication", International Joint Conference on Neural Network, IJCNN'99, vol.5, pp 3295-3299, (1999).

Abstract: The designation of the mobile ad hoc networks (MANET) is, particularly to build up a dynamic wireless network, which has no antecedent and strictly defined infrastructure, within areas with limited or no available organized infrastructure, is possible for the parties to participate in MANET - authentic network users additionally as malicious attackers. This fact certainly raises the question regarding the protection. In this work we tend to concentrate on the common attacks within MANET, that differ in their essence like Sleep Deprivation attack, DoS etc. and what can be done as to stop them.
Keywords: MANET, AD-HOC network, Attacks, Protocols

[1] Jian-Ming Chang, Po-Chun Tsou, Isaac Woungang, Han-Chieh Chao, and Chin-Feng Lai, Member, IEEE."Defending Against Collaborative Attacks by Malicious Nodes in MANETs: A Cooperative Bait Detection Approach" IEEE Systems Journal,2014
[2] S.B.Aneith Kumar, S.Allwin Devaraj, S.Allwin Devaraj J. Arun kumar."Efficient Detection of Denial of Service Attacks in MANET" International Journal of Advanced Research in Computer Science and Software Engineering, ,Volume 2, Issue 5, May 2012Bing Wu, Jianmin Chen, Jie Wu, Mihaela Cardei."A Survey on Attacks and Countermeasures in Mobile Ad Hoc Networks" Wireless/Mobile Network Security, Springer, 2006
[3] Mohammad Al-Shurman, Seong-Moo Yoo, Seungjin Park. "Black Hole Attack in Mobile Ad Hoc Networks " ACMSE'04, April 2-3, 2004
[4] Yi Tan, Shamik Sengupta,Member, IEEE and K.P. Subbalakshmi,Senior Member, IEEE. "Analysis of Coordinated Denial-of-Service Attacksin IEEE 802.22 Networks" August 13, 2010
[5] Kemal Bicakci, Bulent Tavli, "Denial-of-Service attack s and countermeasures in IEEE 802.11 wireless network" ELSEVIER, 28 september 2008,pp- 931– 941

Abstract: Data mining is the process of analyzing data. Data Privacy is collection of data and dissemination of data. Privacy issues arise in different area such as health care, intellectual property, biological data, financial transaction etc. It is very difficult to protect the data when there is transfer of data. Sensitive information must be protected. There are two kinds of major attacks against privacy namely record linkage and attribute linkage attacks. Research have proposed some methods namely k-anonymity, ℓ-diversity, t-closeness for data privacy. K-anonymity method preserves the privacy against record linkage attack alone. It is unable to prevent address attribute linkage attack. ℓ-diversity method overcomes the drawback of k-anonymity method. But it fails to prevent identity disclosure attack and attribute disclosure attack. t-closeness method preserves the privacy against attribute linkage attack but not identity disclosure attack. A proposed method used to preserve the privacy of individuals' sensitive data from record and attribute linkage attacks. In the proposed method, privacy preservation is achieved through generalization by setting range values and through record elimination. A proposed method overcomes the drawback of both record linkage attack and attribute linkage attack
Keywords: Anonymization , data privacy, , data publishing, data mining, privacy preservation

[1] Mahesh R, Meyyappan T, "Anonymization technique through record elimination to Preserve Privacy of Published data", International workshop on pattern recognization, Informatics and mobile engineering, proceedings,978-1-4673-5845-3,2013
[2] TamirTassa,ArnonMazza and Aristides Gionis,"k-Concealment: An Alternative Model of k-Type Anonymity", transactions on data privacy 5, 2012, pp189–222
[3] XinJin,MingyangZhang,Nan Zhang and Gautam Das, "Versatile Publishing For Privacy Preservation",2010,KDD10,ACM
[4] QiangWang,ZhiweiXu and ShengzhiQu,"An Enhanced K-Anonymity Model against Homogeneity Attack", Journal of software,2011, Vol. 6, No.10, October 2011;1945-1952
[5] Benjamin C.M.Fung,KEWang,AdaWai-Chee Fu and Philip S. Yu, Introduction to Privacy-Preserving Data Publishing Concepts and techniques, ISBN:978-1-4200- 9148-9,2010

Abstract: With the rapid growth in the field of technology, there is always a requirement of development of fast and secure algorithms. Steganography is used to provide confidentiality over the transmitted data or the stored data. There were many steganography algorithms proposed to provide security but there is always a competition to develop a algorithm which is fast, secure, having high PSNR value and less cover file size. In this paper, authors have discussed such papers and compare it with each others.
Keywords: Computer Security, Network, Encryption, Decryption, Algorithm, Cryptography, Symmetric Key

[1] Manoj Kumar Ramaiya, Naveen Hemrajani, Anil Kishore Saxena, "Security Improvisation in Image Steganography using DES", IEEE-2012.
[2] Thomas Leontin Philjon. J, Venkateshvara Rao. N, Metamorphic Cryptography -A Paradox between Cryptography and Steganography Using Dynamic Encryption, IEEE-International Conference on Recent Trends in Information Technology, ICRTIT 2011.
[3] Yambin Jina Chanu , Themrichon Tuithung , Kh Manglem singh," A Short Survey on Image Steganography and Steganalysis Technique " , IEEE Trans, 2012 science and Management (ICAESM- 2012) 709 -713.
[4] W. Luo, F. Huang, J. Huang, Edge adaptive image steganography based on LSB matching revisited, IEEE Trans. Inf. Forens. Security 5 (2) (2010) 201-214.