This article presents a novel approach for color image segmentation using two different algorithms with respect to color features. Color Image Segmentation separates the image into distinct regions of similar pixels based on pixel property. It is the high level image description in terms of objects, scenes, and features. The success of image analysis depends on segmentation reliability. Here presented an adaptive masking method based on fuzzy membership functions and a thresholding mechanism over each color channel to overcome over segmentation problem, before combining the segmentation from each channel into the final one. Our proposed method ensures accuracy and quality of different kinds of color images. Consequently, the proposed modified fuzzy approach can enhance the image segmentation performance by use of its membership functions. Similarly, it is worth noticing that our proposed approach is faster than many other segmentation algorithms, which makes it appropriate for real-time application. According to the visual and quantitative verification, the proposed algorithm is performing better than existing algorithms.
Key-Words / Index Term
Segmentation, Fuzzy Membership Functions, Fuzzy Inference System, Edge Detection, Region Growing and Thresholding
Digital universe has been menaced by plenty of bugs but only a few seemed to pose a great hazard. Y2K,Y2K10 were the most prominent bugs which were blown away. And now we have Y2K38. The Y2K38 bug, if not resolved, it will get hold off the predictions that were made for the Y2K bug would come face reality this time. Y2K38 bug will affect all the system applications and most of the embedded systems which use signed 32 bit format for representing the internal time. The number of seconds which can be represented using this signed 32 bit format is 2,147,483,647 which will be equal to the time 19, January, 2038 at 03:14:07 UTC(Coordinated Universal Time),where the bug is expected to hit the web. After this moment the systems will stop working correctly. This could wipe out programs that rely on the internal clock to make measurements. There have been some solutions which delayed this problem, that we can have some more time to find a universal solution and so does our proposed solution.
Key-Words / Index Term
. M. Schumacher,”Year 2000, Y2K, millennium bug”, Power Engineering Society Summer Meeting, 1999. IEEE, ISBN: 0-7803-5569-5
. Zijiang Yang,J.C.Paradi,”DEA evaluation of a Y2K software retrofit program”, IEEE Transactions on Engineering Management , Vol.51(3),ISSN: 0018-9391,2004.
. Vikas Chandra Sharma,”YK-2028 Bug”,Journal Of Image Processing And Artificial Intelligence, Vol .1(3),2015.
. Diomidis Spinellis,”Code quality: the open source perspective. Effective software development series in Safari Books Online”. Adobe Press. ISBN 0-321-16607-8.
. Margaret Ross,”The Y2K Legacy—The Time Bomb of Tomorrow”, Kluwer Academic Publishers, Vol 10(4), pp 281–283,2002 Print ISSN:0963-9314
. Carrington, Damia “Was Y2K bug a boost?”BBC News. Archived from the original on 22 April 2004.
. Raymond B.Howard,”The Case for Windowing: Techniques That Buy 60 Years”, Year/2000 Journal, Mar/Apr 1998.”
. “Issue 16899: Year 2038 problem,”Android, 16 May 2011.
. J. L. Smoot and P. J. Meyer, “What is UTC?,”NASA - Marshall Flight Space Center, 29 March 1995.
. Jet Propulsion Laboratory, “Mission Fantastic to Mars (Part 4),”California Institute of Technology,
. The Open Group and IEEE, “The Open Group Base Specifications Issue 6, IEEE Std 1003.1, 2004 Edition (definition of epoch),”The Open Group; IEEE;, 2004
. “Y2K Bug,”National Geographic Society 1996-2013
. Dutton, Denis , ”It`s Always the End of the World as We Know It”, The opinion Pages,The New York Times. 31 December 2009.
. R. M. Wilcox, “The Year 2038 Problem,”23 October 2003.
. Rae Zimmerman,”Y2K readiness helped NYC on 9/11”, MIT News, 19 November 2002.
. Arnd Bergmann,”The End Of Time(32bit edition)”,Embedded Linux Conference,San Jose,CA,2015.
S. Harshini, K.R. Kavyasri, P. Bhavishya, T. Sethukkarasi, "Digital World Bug: Y2k38 an Integer Overflow Threat-Epoch", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.106-109, 2017.
We present the design of a C++ framework for building custom Web agent applications. Our framework includes abstractions for networking and communications as well as a format-independent set of classes for representing document components. We discuss the design and parts of the implementation of the framework and present possible extensions till date.
Key-Words / Index Term
www; Agent; Abstraction; Object; Class; Framework
 The Adobe Portable Document Format (PDF) IEEE Explore Version 4.01B, May 1995.
 Grady Booch, Object-Oriented Design with Applications, Benjamin-Cummings, 1995.
 Booch, G., “Object-Oriented Development”, IEEE Transactions on Software Engineering 12(2), February 1996.
 Coad, P. and Yourdon, E., Object-Oriented Analysis, Prentice Hall, 1998.
 A. Dave, M . Sefika and R. Campbell, “Proxies, application interfaces, and distributed systems,” Proc. Second Annual Workshop on Object Orientation an Operating Systems, Paris, France, September 2000, pp. 212-220.
 Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software, Addison-Wesley, 2005.
 L. Lamport, BQY: A Document Preparation System, Addison-Wesley, 2009.
 D.C . Schmidt and T . Suda, “An object-oriented framework for dynamically configuring extensible.
 Distributed systems,” 1EE Distributed Systems Engineering Journal, 2011.
 M. A . Sridhar, Building Portable C++ Applications with YACL, Addison-Wesley, 2015.
A. Chagi, "Framework for Object Oriented WWW Applications using Embedded Concepts", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.110-113, 2017.
Medical documents are rich in information and such information can be useful in building many health applications. Since information in medical documents is often unstructured and in nonstandard natural language so it is difficult to collect and present this information in a structured way. Structured information can be present as named-entity in the text, relationship between clinical entities, summary of the text, etc. To get the specific information from the text, many rule based and machine learning techniques are widely used. In this paper, we present several existing techniques for relation classification from unstructured medical text. We focus on rule based approaches, feature based relation classification approaches and convolutional neural network based approach in context of relation classification from unstructured text. We will also discuss semi supervised approaches for the cases where tagged data set is not much available to train the classifier.
Key-Words / Index Term
Data Mining, Relation Classification, Natural Language Processing
 Collobert, Ronan, "Natural language processing (almost) from scratch." Journal of Machine Learning Research, Vol. (12), pp.2493-2537, 2011.
 Bach N, Badaskar S. “A review of relation extraction”. Literature review for Language and Statistics II. 2007.
 Hearst, Marti A. "Automatic acquisition of hyponyms from large text corpora." Proceedings of the 14th conference on Computational linguistics, Association for Computational Linguistics, Vol. (2), pp.539-545, 1992.
 Rindflesch, Thomas C., et al. "Medical facts to support inferencing in natural language processing." AMIA. 2005.
 Hong, Gumwon. “Relation extraction using support vector machine." In International Conference on Natural Language Processing, pp. 366-377, 2005.
 Nguyen, Thien Huu, and Ralph Grishman. "Relation extraction: Perspective from convolutional neural networks." In Proceedings of NAACL-HLT, pp. 39-48, 2015.
 Kambhatla, Nanda. "Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations." In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions, pp. 22-23, 2004.
 Gormley, Matthew R., Mo Yu, and Mark Dredze. "Improved relation extraction with feature-rich compositional embedding models." arXiv preprint arXiv:1505.02419 (2015).
 Nguyen, Thien Huu, and Ralph Grishman. "Relation extraction: Perspective from convolutional neural networks." In Proceedings of NAACL-HLT, pp. 39-4,. 2015.
 Carlson, Andrew, et al. "Toward an Architecture for Never-Ending Language Learning." AAAI. Vol. (5), 2010.
 Lodhi, Huma, Craig Saunders, John Shawe-Taylor, Nello Cristianini, and Chris Watkins. "Text classification using string kernels." Journal of Machine Learning Research, Vol. (2), pp 419-444, 2002.
S. Gupta, A.K. Manjhvar, "A Survey on Relation Classification from Unstructured Medical Text", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.114-118, 2017.
Blocking artefacts occurs almost in every compression technology including the most renowned JPEG compression. To minimize the blocking artefact problem, several researches have been done. But adaptively lacks in those algorithms which leads to complex calculation and distortion in the image. In this paper, we have proposed adaptive neighbourhood selection in a way that balances the exactness of approximation. The proposed method is iterative and spontaneously adapts to the degree of underlying smoothness. Our proposed method also restores distorted cracked images along with compressed blocking artefacts.
Key-Words / Index Term
JPEG, Artefacts , Image, DCT
 T. Brox, O. Kleinschmidt, and D. Cremers, “Efficient nonlocal means for denoising of textural patterns”, IEEE Trans. on Imag. Proc., Vol. 17(7), pp. 1083–1092, 2008
 J. Grazzini and P. Soille, “Edge-preserving smoothing using a similarity measure in adaptive geodesic neighbourhoods”, Pattern Recogn., Vol. 42(10), pp. 2306–2316, 2009.
 L. I. Rudin, S. Osher, and E. Fatemi, “Non-linear total variation based noise removal algorithms”, Physica D: Nonlinear Phenomena, Vol. 60, pp. 259 – 268, 1992.
 L. Zhang, W. Dong, D. Zhang, and G. Shi, “Two-stage image denoising by principal component analysis with local pixel grouping” Pattern Recogn., Vol. 43(4), pp. 1531–1549, 2010.
 Y. Wang, M. Orchard, V. Vaishampayan, and A. Reibman,“Multiple description coding using pairwise correlating transforms,” IEEE Transactions on Image Processing ,vol. 10, pp. 351–366, 2001.
 Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity”, IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004.
 A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” CVPR, vol. 2, pp. 60–65, 2005.
 K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian,“Image denoising by sparse 3-d transform-domain collaborative filtering”, IEEE Trans. on Image Processing, vol. 16, no. 8, pp. 2080–2095, Aug. 2007
 J. G. Apostolopoulos and N. S. Jayant, “Post processing for Very Low Bit-Rate Video Compression”, IEEE Transactions on Image Processing. Vol. 8, NO. 8, pp. 1125-1129, (Aug. 2012).
 C. Wang, P. Xue, W. Lin, W. Zhang and S. Yu, “Fast Edge-Preserved Postprocessing for Compressed Images”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 16, NO. 9, pp. 1142-1147, (Sep. 2006).
. D. G. Sheppard, A. Bilgin, M. S. Nadar, B. R. Hunt and M. W. Marcellin, “A Vector Quantizer for Image Restoration”, IEEE Transactions on Image Processing, Vol. 7, NO. 1, pp. 119-124, (Jan. 1998).
. R. Nakagaki and A. K. Katsaggelos, “A VQ-Based Blind Image Restoration Algorithm”, IEEE Transaction on Image Processing. Vol. 12, NO. 9, pp. 1044-1053, (Sep. 2003).
. Y. Liaw, W. Lo and J. Z. Lai, “Image Restoration of Compressed Image using Classified Vector Quantization.”, Pattern Recognition. Vol. 35, pp. 329-340, 2002.
. W. T. Freeman, E. Pasztor, O. Caemichael, “Learning Low-level Vision”, International Journal of Computer Vision, Vol. 48, pp. 25-47, 2011.
. J. Sun, N. N Zheng, H. Tao and H. Y. Shum, “Image Hallucination with Primitive Sketch Priors”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. (2009).
. L. Ma, Y. Zhang, Y. Lu, F. Wu and D. Zhao, “Three-Tiered Network Model for Image Hallucination”, Accepted by International Conference on Image Processing, (2008).
. S. Roweis and L. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embeddings”, Science. Vol. 290, NO. 5500, pp. 2323-2326, (Dec. 2000).
 S. Schulte, V. D. Witte and E. E. Kerre, “A Fuzzy Noise Reduction Method for Color Images”, IEEE Transactions on Image Processing, Vol. 16, No.5, pp.1425–1436, 2007.
. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithm”, Physica D: Nonlinear Phenomena, Vol. 60, pp. 259 – 268, 1992.
K. Singh, J. Shaveta, "A Review on Patch Based Image Restoration or Inpainting", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.119-123, 2017.
Data mining is the procedure of find or concentrates new patterns from extensive data sets including techniques from data and counterfeit consciousness. Arrangement and gauge are the procedures used to make out imperative data classes and conjecture plausible pattern .The Decision Tree is a critical scientific categorization technique in data mining grouping. It is generally utilized as a part of showcasing, reconnaissance, misrepresentation location, logical disclosure. As the established calculation of the decision tree ID3, C4.5, C5.0 calculations have the benefits of high group speed, solid learning capacity and straightforward development. In any case, these calculations are additionally unacceptable in viable application. Data mining is the method of find or focus new cases from immense instructive accumulations including methodologies from data and fake awareness. course of action and guess are the strategies used to make out basic data classes and gauge conceivable example .The Decision Tree is a basic logical order procedure in data mining portrayal. While using it to arrange, there does exists the issue of inclining to pick trademark which have more values, and neglecting properties which have less values. This paper gives focus on the diverse counts of Decision tree their trademark, troubles, ideal position and injury.. This work shows the strategy of WEKA examination of record converts, all around requested technique of weka use, decision of attributes to be mined and examination with Knowledge Extraction of Evolutionary Learning . I took database  and execute in weka programming. The complete of the paper shows the relationship among all kind of decision tree figurings by weka mechanical assembly.
Key-Words / Index Term
Data Minning, Classification Algorithm, Decision Tree, J48, Random Forest, Random Tree, LMT, WEKA 3.7
 J. Han and M. Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann publisher, Third editon -2001 , ISBN: ISBN: 978-0-12-381479-1.
 Swasti singhal and monika jena, “a study on weka tool for data pre-processing, classification and clustering”, international journal of innovation technology and exploring enginnering, Vol.2, Issue.6, pp.250-253, 2013 .
 King, M., A., and Elder, J., F., “Evaluation of Fourteen Desktop Data Mining Tools”, IEEE International Conference on Systems, mans, cybernetics, SMC, Newyork, oct 11th and 14th ,1998, ISBN:0-7803-4778-1.
 N. Landwehr , M. Corridor, and E. Forthcoming, ―Logistic model trees,‖ Mach. Learn., vol. 59, no. 1–2, pp. 161–205, 2005. .
 L. Breima , “Random forests, Mach. Learn”, Springer, volume- 45, Issue no- 1, Page no-( 5–32), Oct 2001.
 E. Frank, M. Hall, G. Holmes, R. Kirkby, B. Pfahringer, I. H. Witten, and L. Trigg, “Weka in Data Mining and Knowledge Discovery Handbook”, Springer, pp. 1305 –1314, 2005.
 Pallavi, Sunila Godara , “A Comparative Performance Analysis of Clustering Algorithms”, International Journal of Engineering Research and Applications , Volume- 1, Issue no- 3, Page no- (441-445), ISSN: 2248-9622.
 E. Straight to the point, M. Corridor, G. Holmes, R. Kirkby, B. Pfahringer, I. H. Witten, and L. Trigg, ”Weka,in Data Mining and Knowledge Discovery Handbook”, Springer, 2005, pp. 1305 –1314.
P. Tomar, A.K. Manjhvar, "Survey Report on Various Decision Tree Classification Algorithm Using Weka Tool", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.124-128, 2017.
Apriori calculation has been basic calculation in association rule mining. Principle proposition of this calculation is to discover valuable examples between various arrangements of information. It is the least complex calculation yet having numerous downsides. Numerous specialists have been accomplished for the improvement of this calculation. This paper does a study on couple of good improved methodologies of Apriori calculation. This will be truly exceptionally supportive for the up and coming specialists to locate some new thoughts of this methodology.
Key-Words / Index Term
component Apriori algorithm ,frequent pattern, association rule mining. Support, minimum support threshold, multiple scan. FP Growth algorithm,regression technique
. S. Paul, “An Optimized Distributed Association Rule Mining Algorithm In Parallel And Distributed Data Mining With XML Data For Improved Response Time”, International Journal of Computer Science and Information Technology, Volume 2, Number 2, April 2010.
. M.N. Moreno, S. Segrera and V.F. López, “Association Rules: Problems”, Solutions and new application Universidad de Salamanca, Plaza Merced S/N, 37008, Salamanca.
. K.P. Kumar and S. Arumugaperumal, “Association Rule Mining and Medical Application; A Detailed Survey”, International Journal of Computer Application(0975-8887), Volume 80, number 17, October 2013.
. E. Bala Krishna, B. Rama, A. Nagaraju, "A Survey on Effective Mining of Negative Association Rules from Huge Databases", International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp-220-223, 2015.
. V. Kavi, D. Joshi , "A Survey on Enhancing Data Processing of Positive and Negative Association Rule Mining", International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.139-143, 2014.
. C. Wang, R. Li, and M. Fan, “Mining Positively Correlated FrequentItemsets,” Computer Applications, vol. 27, pp. 108-109, 2007
. J. Pei, J. Han, and H. Lu, “Hmine: Hyper-structure mining of frequent patterns in large databases”, In ICDM, 2001, pp441–448.
. N. Sethi, P. Sharma, "Mining Frequent Pattern from Large Dynamic Database Using Compacting Data Sets", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.31-34, 2013.
. R. Trikha, J. Singh, “Improving the efficiency of apriori algorithm by adding new parameters”, International Journal for Multi-Disciplinary Engineering and Business Management, Volume-2, Issue-2, June-2014
. M. Al-Maolegi, B. Arkok, “An improved apriori algorithm for association rules”, International Journal on Natural Language Computing (IJNLC) Vol. 3, No.1, February 2014
M. Shridhar, M. Parmar, "Survey on Association Rule Mining and Its Approaches", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.129-135, 2017.