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Data mining is the process of discover useful information from large datasets. The data mining techniques are used to analyze and evaluate diabetic dataset in the field of bio-medical. One of the most important techniques of data mining is clustering which is used to analyzing data from different perspectives and summarizing into useful information. Clustering is the task of assigning a set of objects into group called clusters. This paper discusses different clustering algorithms like cobweb, DBSCAN, EM, Farthest first, filtered cluster hierarchical cluster, OPTICS, simple Kmeans. The algorithms are used to compare its performance by Time taken to build the clusters, the cluster differentiated by its true positive and true negative values. Our main aim to show the comparison of the different cluster algorithms are evaluated in weka tool (Data mining Tool) and find out which algorithm will be most suitable for the diabetes dataset.

Cluster, Diabetes , Weka ,Data Mining

[1] Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, second edition, Morgan Kaufmann Publishers an imprint of Elsevier.
[2]A.K. JAIN Michigan State University, M.N.MURTY Indian Institute of Science AND P.J. FLYNN The Ohio State University: “Data Clustering”.
[3] P. Vijaya, M N Murthy and D K Subramanian. Leaders-sub leaders, “An efficient hierarchical clustering algorithm for large data sets”,Pattern Recognition Letters (2004) 505-513.
[4] Rama. B,“A Survey on clustering Current status and challenging issues” (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 09, 2010, 2976-2980.
[5] M. Pramod Kumar “Simultaneous Pattern and Data Clustering Using Modified K-Means Algorithm” International Journal on Computer Science and Engineering Vol. 02, No. 06, 2010, 2003-2008.
[6] Miroslav Marinov, M.S.,1 Abu Saleh Mohammad Mosa, M.S.,1 Illhoi Yoo, Ph.D.,1,2 and Suzanne Austin Boren, Ph.D., MHA1,2 “ Data-Mining Technologies for Diabetes: A Systematic Review” Journal of Diabetes Science and Technology Volume 5, Issue 6, November 2011 © Diabetes Technology Society.
[7] Celeux, G. and Govaert, G. (1992). “A classification EM algorithm for clustering and two stochastic versions. Computational statistics and data analysis”, 14:315–332
[8] Narendra Sharma , Aman Bajpai , Mr. Ratnesh Litoriya, “Comparison the various clustering algorithms of weka tools” International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 5, May 2012).
[9] Dr. Wenjia Wang, “Tutorial for DM tool Weka 1 CMP: Data Mining and Statistics within the Health Services”.
[10] K. Rajesh, V. Sangeetha ,“ Application of Data Mining Methods and Techniques for Diabetes Diagnosis” International
Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 3, September 2012

G.G.Gokilam and K.Shanthi, "Comparing clustering Algorithms with Diabetic Datasets in WEKA Tool," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.1-5, 2015.

License plate identification or recognition has become an important part of vehicle surveillance systems. This paper, reviews the basic methodologies used for identification of vehicle number plates. Over main concern in this paper is to review the edge based methods. Since edge detection based methods are simple and thus widely used for real time recognition of the license number plate Paper also briefly describes the various problems and shortcomings of the existing methods. Paper also addresses the problem of automatic number plate detection. Paper also reviews the various color spaces used for number plate identification. The basic results of the various edge detectors are also presented for different input images.

License plate identification, Edge detection, Thresholding, Segmentation, Fuzzy logic

[1] Sarmad Majeed Malik, and Rehan Hafiz, “Automatic Number Plate Recognition Based on Connected Components Analysis Technique”, IEEE 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET'2014), UK, 30-31, 2014.
[2] Bai Hongliang , Liu Changping , “A hybrid License Plate Extraction Method Based on Edge Statistics and Morphology”, Proceeding ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Vol. 2 831-834, 2004.
[3] Mohammad Ghazal, Hassan Hajjdiab, “License Plate Automatic Detection and Recognition”, IEEE International Conference 2013..
[4] S. Adebayo daramola1, E. Adetiba1, A. U. Adoghe1, j. A. Badejo1, I. A samuel1, T. Fagorusi1, “Automatic vehicle identification system using license plate”, International Journal of Engineering Science and Technology (IJEST), Vol. 3 No. 2 Feb 2011
[5] R. Radha, C. P. Sumath, “A novel approach to extract text from License plate of vehicles”, Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.4, August 2012.
[6] Leandro Araújo, Sirlene Pio, David Menotti, "Seg0ent5ng and rec6gn5z5ng the 35cense *3ate Charecters”, IEEE international conference.
[7] W. Osman, H. A. Obaida, E. A. Khater, and D. Hataba, “Vision-based automatic detection of unauthorized vehcile for oil field surveillance,” Abu Dhabi University, Tech. Rep. 2012/1, Oct. 2012.
[8] J.S. Chittode and R. Kate, “Number plate recognition using segmentation,” International Journal of Engineering Research & Technology, Vol. 1 Issue 9, November- 2012.
[9] C N Paunwala and S Patnaik, ”A novel multiple license plate extraction technique for complex background in Indian traffic conditions,” International Journal of Image processing, Vol-4,Issue-2,pp 106-118 2011.
[10] Rajesh k. Rai, Puran Gour, Balvant Singh,”Underwater image segmentation using CLAHE enhancement and thresholding”, International Journal of Emerging Technology and Advanced Engineering (IJETAE)) Vol. 2 Issue 1, 2012.
[11] Manisha Rathore and Saroj Kumari, “Tracking Number Plate from Vehicle using MATLAB”, International Journal in Foundations of Computer Science & Technology (IJFCST), Vol. 4, No. 3, May 2014
[12] Yassin M.Y.Hasan and Lina J.Karam, “Morphological text extraction from images,” IEEE Transactions on Image Processing, vol. 9, no. 11, pp. 1978-1983, November 2000.
[13] Ching Tang Hsieh, Y.S.J., K.M. Hung, “Multiple license plate detection for complex background”. Proceedings of International Conference on Advanced Information Networking and Applications, 2:389-392, 2005.
[14] Neetu Sharma, Paresh Rawat,, “E book on Underwater Image Segmentation using thresholding, Lambart publication Germany 2011.
[15] S Kranti and K Pranathi, ”Automatic number plate recognition”, International Journal of Advancements in Technology,vol-2, no-3, pp408-423, July 2011
[16] Onorej Martinsky, “Algorithmic and Mathematical principals of automatic number plate recognition system”, Thesis, Brono University of Technology, 2007.
[17] Pandya and M Sing,” Morphology based approach to recognize number plates in India,” International Journal of Soft Computing and Engineering,Vol-1, Issue-3, pp 107-113, June2011
[18] Anish Lazrus, Siddhartha Choubey, Sinha G.R., “An Efficient Method Of Vehicle Number Plate Detection And Recognition”, International Journal Of Machine Intelligence, Volume 3, Issue 3, Pp-134-13, 2011.

Pankaj Sharma and Jai karan sing, "Challenges and Overview of License Plate Character Segmentation," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.6-9, 2015.

The main aim of this survey is to compare different image enhancement methods. The basic purpose of enhancement is to increase the interpretability of pixels in images so that a good quality image can produce as an output image. Removal of noise from an image using filtering is known as image enhancement. When we talk about human perception we can’t say directly that which image is good or noisy because there is no slandered measurement through which we can measure the quality of good image. Image enhancement plays important role when we use enhanced image for further processing like for number plate recognition. This paper will provide idea about already existing enhancement techniques which are frequently used in these days. This paper will provide idea that which technique is better in which case.

Noise, Filtering, Image Enhancement, Digital Image Processing

[1] RAMAN Maini and Himanshu Aggarwal, “Comprehensive Review of Image Enhancement,” Journal of Computing, ISSN: 2151-9617, 2010, pp 8-13.
[2] Rajesh Garg, Bhawana Mittal, Sheetal Garg, “Histogram Equalization Techniques for Image Enhancement,” IJECT, ISSN: 2230-7109, 2011, pp 107-111.
[3] H. K. Sawant, Mahentra Deore, “Comprehensive Review of Image Enhancement,” IJCTEE, ISSN: 2249-6343, 2013.

Anshu Vashisth and Rohitt Sharma, "A Survey of Various Image Enhancement Methods on Different Types of Images," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.10-14, 2015.

The Internet of Things (IOT) is a scenario in which objects, animals or people are provided with unique identifiers and the ability to sense analyses and transfer data over a network without requiring human-to-things interaction. IOT has evolved from the convergence of wireless technologies, micro-electromechanical systems and the Internet. Various scenario in which human-physical objects interaction and physical object-physical object interaction can be predicted with the help of past temporal information. IOT provide flow of information whenever a things are interacting with other things and human as well. A physical thing like RFID is implanted with its unique address using private and public network which is known to the end user. Based on the evaluation of temporal information human behavior is studied for particular thing of interest and things on the other hand could interact with the end user.

Internet of Things; Architecture;RFID,Challenges

[1] Evans, D. The Internet of Things. 2011. Cisco Blog. Available online: “http://blogs.cisco.com/ news/the-internet-of-things-infographic/”, accessed on 22 September 2014.
[2] J. P. Conti, “The Internet of Things”, Communications Engineer, 2006, pp.20-25.
[3] Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, Marimuthu Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions”, Future Generation Computer Systems 29, Elsevier B.V., 2013, pp 1645–1660.
[4] Jian An, Xiao-Lin Gui, Xin He, “Study on the Architecture and Key Technologies for Internet of Things”, International Conference on Electrical and Computer Engineering Advances in Biomedical Engineering, Vol.11, 2012, pp 329- 335.
[5] M. Zorzi, A. Gluhak, S. Lange, A. Bassi, “From today’s Intranet of Things to a future Internet of Things: a wireless- and mobility-related view”, IEEE Wireless Communications 17, 2010, pp 43–51.
[6] Sandra Dominikus and J¨ orn-Marc Schmidt, “Connecting Passive RFID Tags to the Internet of Things”, IAIK, Graz University of Technology.

Pranay Kujur and Kiran Gautam, "Smart Interaction of Object on Internet of Things," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.15-19, 2015.

Researchers are regularly trying to figure out secure encryption algorithms so that the data transmitted over the computer networks are not intercepted by an unwanted entity. The two methods to encrypt data are Transposition and Substitution. Transposition refers to changing the position of characters in a given text. On the other hand, substitution is the process of replacing each character in the plaintext with some other character. One main concern area for researches is to analyze the existing encryption systems and overcome the flaws in them. In this paper, emphasis is paid on the enhancement of hill cipher algorithm. This algorithm uses simple substitution for encryption. The resistance of this algorithm is improved by introducing columnar and Myszkowski transpositions.

Cryptography, Encryption, Modified Hill Cipher, Columnar Transposition, Myszkowski Transposition

[1] Behrouz A. Forouzan, “Cryptography and Network Security” special Indian Edition 2007, Tata McGraw- Hill Publishing Company Limited, New Delhi
[2] Ayushi, “A Symmetric Key Cryptographic Algorithm” International Journal of Computer Applications (0975 8887) Volume 1 – No. 15
[3] Atul Kahate, “Cryptography and Network Security”, 2nd Edition
[4] Panduranga H T, Naveen Kumar S K. “Advanced Partial Image Encryption using Two-Stage Hill Cipher Technique”, International Journal of Computer Applications (0975 – 8887) Volume 60– No.16, December 2012
[5] Yuri Borissov and Moon Ho Lee.: Bounds on Key Appearance Equivocation for Substitution Ciphers. IEEE Transactions on Information Theory Vol. 53, No.6, pp. 2294-2296 (2007).
[6] Malay B. Pramanik, “Implementation of Cryptography Technique using Columnar Transposition”, International Journal of Computer Applications (0975 – 8887) Second National Conference on Recent Trends in Information Security, GHRCE, Nagpur, India, Jan-2014
[7] An article on Myszkowski Transposition: cryptospecs.googlecode.com/svn/trunk/classical/specs/myszkowski.pdf
[8] Shahrokh Saeednia, “How to Make Hill cipher Secure,” Cryptologia 24:4, pp. 353-360, Oct 2000.
[9] Chefranov, A. G., “Secure Hill Cipher Modification,” Proc. Of the First International Conference on Security of Information and Network (SIN2007) 7-10 May 2007, Gazimagusa (TRNC) North Cyprus, Elci, A., Ors, B., and Preneel, B (Eds) Trafford Publishing, Canada, 2008: pp 34-37, 2007.
[10] P. L. Sharma, M. Rehan, “On Security of Hill Cipher using Finite Fields”, International Journal of Computer Applications (0975 – 8887) Volume 71– No.4, May 2013
[11] Kondwani Magamba, Solomon Kadaleka, Ansley Kasambara, “Variable-length Hill Cipher with MDS Key Matrix”, International Journal of Computer Applications (0975 – 8887) Volume 57– No.13, November 2012
[12] Neha Sharma, Sachin Chirgaiya, “A Novel Approach to Hill Cipher”, International Journal of Computer Applications (0975 – 8887) Volume 108 – No. 11, December 2014
[13] Gary C. Kessler, “An Overview of Cryptography 2014” - http://www.garykessler.net/library/crypto.html
[14] Andrew S Tanenbaum, “Computer Networks”, 4th Edition

Anirban Bhowmick and Geetha M, "Enhancing Resistance of Hill Cipher Using Columnar and Myszkowski Transposition," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.20-27, 2015.

Fingerprint matching algorithm is a key issue of the fingerprint acceptance, and there already exist many fingerprint matching algorithms, Give to the dependence of the core point, fingerprint matching algorithms are branched into two groups, core-based contest algorithms and noncore-based contest algorithms. Most of the noncore-based matching algorithm is time consuming; therefore, they are not convenient for online application; meantime, the core-based matching algorithm is efficient than the noncore-based matching algorithm, but it deeply depends on the core detection care. In this paper, we present a new core-based structure matching algorithm which considers both efficient and precision. Firstly we used core disclosure algorithm to get the core space, then we define some local structure of the core field. Used these local structure, we can find some contributor points of the two fingerprint image. Secondly, we adoption the correspondent points in the first stage to match the global feature of the fingerprint. Empirical results show that the performance of the proposed algorithm is good.

Component; Formatting; Style; Styling; Insert

[1] JunTaoXue, Yini Guo, ShaoFang Xing ZhengGuang Liu, School of Electrical Engineering and Automation, Tianjin University, Tianjin, China, Fingerprint Generation Method Based on Gabor Filter, 201O International Conference on Computer Application and System Modeling (ICCASM 2010).
[2] Quratulain Shafi, Javaria Khan, Nosheen Munir, Naveed Khan
Baloch, Computer Engineering Department, University of Engineering and Technology, Taxila, Pakistan, Fingerprint Verification over the Network and its Application in Attendance Management, 2010 International Conference on Electronics and Information Engineering, (ICEIE 2010).
[3] "Guide to Fingerprint Recognition" Digital Persona, Inc. 720
Bay Road Redwood City, CA 94063 USA, http:// www.digitalpersona.com
[4] R. Heidn!' "A world history of fingerprint," Chinese People Public Security University Press, (2008), l.ISBN: 978-7-81109-789-4.
[5] Automatic fingerprint Recognition System, by Nalini Kantha Ratha, Rudde Bole, Department of Computer science, Michigan State univercity, East Lansing, MI48824, U.S.A.
[6] Handbook of Fingerprint Recognition by Konda Jayashree,Chapter 1and 2.
[7] Digital Persona, white paper "Guide to Fingerprint Recognition".
[8] Introduction to Biometrics, http://ics1.mk.co.kr/file/cd104/ biometrics1.pdf.
[9] Pankanti, S., Bolle, R.M. and Jain, A., Biometrics: The Future of Identification. IEEE Computer magazine, February
(2000).
[10] L. Hong, A.K. Jain,"Classification of Fingerprint Images", MSU Technical Report, MSU Technical Report MSUCPS:
TR98-18, June (1998).

Usha Nandhini.B, Suganraj. L , Selvapriya.P.B ,Gomathi.P, "Staff Attendance System Based on Fingerprint Recognition," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.28-30, 2015.

In today scenario all data store in digital form and data size is too large. So problem is that how to manage this big data or extract information with speed and efficiency. Information extraction is a technique which using in text mining. Information extraction extract required information whose user demand from unstructured text. Information extraction use NLP (Natural Language Processing) and NER (Name entity recognition). NER systems help to machine recognize proper noun (entity), events, relationships and so on. There are several NER systems in the world. Such as GATE, CRFClassifier, OpenNLP and Stanford NLP (Natural Language Processing ). The NER system works fast for limited amount of documents but drawback of this system is that it works slows for huge/large amount of data. To overcome the drawback of NER system, this paper, report the implement of a NER which is based on Map Reduce, a distributed programming model. This improvement helps to achieve the fast extraction and reduce storage cost with better performance.

Distributed computing, Big textual data, Named Entity Recognition (NER) , Natural Language Processing (NLP), MapReduce, Hadoop and Maxent Tagger

[1]. Nigam, Jigyasa, and Sandeep Sahu. "An Effective Text Processing Approach With MapReduce."
[2]. James J. (Jong Hyuk) Park et al. (eds.), Mobile, Ubiquitous, and Intelligent Computing,Lecture Notes in Electrical Engineering 274,DOI: 10.1007/978-3-642-40675-1_41, © Springer-Verlag Berlin Heidelberg 2014
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[5]. Dean, J., Ghemawat, S.: MapReduce: simplified
data processing on large clusters. In: OSDI, pp. 137–150 (2004)
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[7]. Seo, D., Hwang, M.-N., Shin, S., Choi, S.: Development of Crawler System Gathering Web Document on Science and Technology. In: The 2nd Joint International SemanticTechnology Conference (2012) Morphological features help POS tagging of unknown words across language varieties
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[9]. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Databases (VLDB-94), pages 487–499, Santiago, Chile, Sept. 1994.
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Jigyasa Nigam and Sandeep Sahu, "Fast and Effective System for Name Entity Recognition on Big Data," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.31-35, 2015.

These days there are number of tools available for assistance in implementation of data mining. Some of them are RapidMiner, WEKA, Orange, Rattle, and KNIME. WEKA tool includes a number of techniques like classification, clustering, regression, etc. For solving classification problems this tool provide variety of strategies such as decision tree, neural networks, lazy classifiers etc. For each strategy, the tool allows the user to select specific values for large number parameters for e.g. in case of a neural network classifier, parameters are to be provided by user such as epochs, learning rate, momentum etc. With WEKA an expert user could study which strategy could be the best compatible for the any particular dataset. For that test run can be performed on the test dataset of the user using the various strategies and the resultant outputs are to be evaluated by the expert user its own. This is tedious task to be performed. This paper aims at developing a system that could work on its own even on the evaluation part and thus make it possible for effective implementation of data mining by developing a database to record the nature of data such as number and type of attributes, presence or absence of missing values etc. along with different values for developing classifier models and the accuracy of the classifier. Such a database can then be made available to the novice users to build a model based on past experience.

Classification, data mining, WEKA, ARFF

[1] Data Mining: A Knowledge Discovery Approach, K. Cios, W. Pedrycz, R. Swiniarski, L. Kurgan, Springer, ISBN: 978-0-387-33333-5, 2007.
[2] Data mining: concepts, models, methods, and algorithms, mehmed kantardzic, ISBN: 0471228524, Wiley-IEEE Press, 2002.
[3] Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Morgan Kaufmann, ISBN 0120884070, 2005.
[4] WEKA manual.
[5] Zdravko Markov, Ingrid Russell, An Introduction to the Weka DataMining System.

S P Rajendra, B K Ramkrushna, S C Giridhar, V G Jayvant and S. N. Bhadane, "Automated Assistance for Data Mining Implementation," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.36-39, 2015.

This paper tries to present an overview of different face recognition techniques and study the characteristics of various algorithms developed for “feature selection” and “feature extraction”. Study and analysis of the face recognition rate of various face recognition algorithms, used currently, is imperative for designing and developing a new Algorithm. In his paper we report performance comparison analysis of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), ICA, SVM & SVD for face recognition. Various PCA and LD, ICA; SVM & SVD based face recognition algorithms were studied and compared in this paper. Standard public database was utilized for this purpose.

Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), Support Vector Machine (SVM) & Support vector Discriminant (SVD).

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Rashmi Ravat and Namrata Dhanda, "A Review on Comparison of Face Recognition Algorithm Based on Their Accuracy Rate," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.40-44, 2015.

Operating systems are very inflexible towards modification of already existing functionalities such as security, dynamic re-configurability, robustness etc. In such functionalities if need arises for any enhancements then it effects on large fractions of the code. Thus results in difficult to implement. Such functional enhancements in any component of the OS that affect large fractions of the program code, are often called crosscutting concerns. Such cross-cutting concerns can be solved by the new emerging extension to object oriented paradigm i.e. Aspect Oriented Programming (AOP). The main idea in AOP is the programmer’s ability to affect the execution of core code by writing aspects. Aspects are pieces of code that are run before, or after core function for which aspect is written. For example logging is a good example of using aspects. To log all function calls the programmer simply needs to define a logging aspect that is executed before and after each function call in the program. In this work aspects are introduced in the Operating System for implementing various concerns and analyzing the performance based on various metrics.

Aspect; Pointcut; Before; After; Aspectj; Cross Cutting; Concerns; Dependency

[1] S. Hanenberg, S. Kleinschmager and M. Walter, “Does Aspect-Oriented Programming Increase the Development Speed for Crosscutting Code”, Third International Symposium on Empirical Software Engineering and Measurement, IEEE, Feb 2009
[2] Y. Zhang, et.all, “Implementing and Testing Producer-Consumer Problem using AOP”, Fifth International Conference on Information Assurance and Security, IEEE, 2009
[3] Y.Coady, G. Kiczales, and M. Feeley, “Exploring an Aspect-Oriented Approach to Operating System Code”, September 2000.
[4] A. Rashid, T. Cottenier, P. Greenwood and R. Chitchyan, “Aspect-Oriented Software Development in Practice”, Computer Society IEEE, Feb 2010.
[5] G,C. Murphy, R, J. Walker, and E. L.A. Baniassad, “Evaluating Emerging Software Development Technologies: Lessons Learned from Assessing Aspect-Oriented Programming”. IEEE transactions on software engineering, vol. 25, no. 4, July 1999
[6] G. Murphy and C. Schwanninger, “Aspect-Oriented Programming”, Computer Society, IEEE, 2006
[7] W. A. Christopher, S. J Procter and T. E. Anderson, “The NachOS Instructional Operating System”, 2005.
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Jatin Arora, Jagandeep Singh Sidhu and Pavneet Kaur, "Applying Dependency Injection Through AOP Programming to Analyze the Performance of OS," International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.45-50, 2015.