Volume-5 , Issue-3 , Mar - 2017 Go Back

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.

Segmentation, Fuzzy Membership Functions, Fuzzy Inference System, Edge Detection, Region Growing and Thresholding

[1] Rafael C Gonzalez and Richard E Woods, “Digital image processing”, 2013, Pearson Education, ISBN 978-81-317-2695-2.
[2] Rafael C Gonzalez, Richard E Woods and Steven L Eddins, “Digital image processing using MATLAB”, 2011, Tata McGraw Hill Education , ISBN – 13: 978-0-07-070262-2.
[3] Firas Ajil Jassim, Fawzi H. Altaani, “Hybridization of Otsu Method and Median Filter for Color Image Segmentation”, International Journal of Soft Computing and Engineering, Vol. 3(2), 2013.
[4] Song Gao, Chengcui Zhang, and Wei-Bang Chen, “An Improvement of Color Image Segmentation through Projective Clustering”, IEEE, 2012.
[5] Mrs. C. Mythili, Dr. V. Kavitha, “Efficient Technique for Color Image Noise Reduction”, The Research Bulletin of Jordan, ACM, Vol. 2(3), 2011.
[6] Nikita Sharma, Mahendra Mishra, Manish Shrivastava, “Color Image Segmentation Techniques and Issues: An Approach”, International Journal of Scientific & Technology Research Vol. 1(4), 2012.
[7] Jan Puzicha and Serge Belongie, “Model–based Halftoning for Color Image Segmentation”, UC Berkeley, Department of Computer Science, 2002.
[8] Soumya Dutta, Bidyut B. Chaudhuri, “Homogenous Region based Color Image Segmentation”, Proceedings of the World Congress on Engineering and Computer Science, Vol. 2, 2009.
[9] E. Boopathi Kumar and M. Sundaresan, “Edge Detection Using Trapezoidal Membership Function Based on Fuzzy‘s Mamdani Inference System”, IEEE, 2014.
[10] E. Boopathi Kumar and M. Sundaresan, “Fuzzy Inference System based Edge Detection using Fuzzy Membership Functions”, International Journal of Computer Applications, Vol. 112(4), 2015.
[11] M. Jogendra Kumar, Dr. GVS Raj Kumar, R. Vijay Kumar Reddy, “Review on Image Segmentation Techniques”, International Journal of Scientific Research Engineering & Technology, Vol. 3(6), 2014.
[12] Rozy Kumari, Narinder Sharma, “A Study on the Different Image Segmentation Technique”, International Journal of Engineering and Innovative Technology, Vol. 4(1), 2014.
[13] H.D. Cheng, X.H. Jiang, Y. Sun, Jingli Wang, “Color image segmentation: advances and prospects”, Pattern Recognition, pp.2259-2281, 2001.
[14] M. Borsotti, P. Campadelli, R. Schettini, “Quantitative evaluation of color image segmentation results”, Pattern Recognition Letters, pp.741–747, 1998.
[15] Navkirat Kaur, V. K. Banga, Avneet Kaur, “Image Segmentation Based on Color”, International Journal of Research in Engineering and Technology, Vol. 02(11), 2013.
[16] Frank Y. Shih, Shouxian Cheng, “Automatic seeded region growing for color image segmentation”, Image and Vision Computing, pp.877–886, 2005
[17] Khande Bharath Kumar and D. Venkataraman, “Object Detection Using Robust Image”, © Springer India 2015.
[18] Mihir Narayan Mohanty and Subhashree Rout, “An Intelligent Method for Moving Object Detection”, © Springer India 2015.
[19] Emmanuel Joy and J. Dinesh Peter, “Tracking of Unique Colored Objects: A Simple, Fast Visual Object Detection and Tracking Technique”, © Springer India 2015.
[20] Simranjit Singh Walia, Gagandeep Singh, “Color based Edge detection techniques– A review”, International Journal of Engineering and Innovative Technology, Vol. 3(9), 2014.
[21] Amiya Halder, Nilabha Chatterjee, Arindam Kar, Swastik Pal and Soumajit Pramanik, “Edge Detection: A Statistical approach”, 3rd International Conference on Electronics Computer Technology, 2011.
[22] Suryakant, Neetu Kushwaha, “Edge Detection using Fuzzy Logic in Matlab”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2(4), 2012.
[23] Shikha Bharti, Sanjeev Kumar, “An Edge Detection Algorithm based on Fuzzy Logic”, International Journal of Engineering Trends and Technology, Vol. 4(3), 2013.
[24] Mehul Thakkar, Prof. Hitesh Shah, “Edge Detection Techniques Using Fuzzy Thresholding”, IEEE, 2011.
[25] Anil K Jain, “Fundamentals of Digital Image Processing”, Pearson Education, ISBN 978-81-203-0929-6, 2014.
[26] E.Boopathi Kumar and V.Thiagarasu, “Evaluation of Color Image Segmentation Novel Methods”, IJIRCCE, Pp.21323 – 21328, Vol. 4(12), 2016.
[27] E.Boopathi Kumar and V.Thiagarasu, “Segmentation using Masking Methods in Color Images: an Approach”, IJESRT, Pp. 104-110, Vol. 6(2), 2017.
[28] A.Kalaivani, Dr.S.Chitrakala, “Automatic Color Image Segmentation”, International Conference on Science, Engineering and Management Research, IEEE, 2014.
[29] Md. Habibur Rahman, Md. Rafiqul Islam, “Segmentation of Color Image using Adaptive Thresholding and Masking with Watershed Algorithm”, IEEE, 2013.
[30] Rafael Guillermo Gonzalez, Junli Tao, “Generalization of Otsu’s Binarization into Recursive Color Image Segmentation”, IEEE, 2015.

E. B. Kumar, V. Thiagarasu, "Segmentation Using Fuzzy Membership Functions: An Approach," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.101-105, 2017.

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.

Date,Time,Y2K,Y2K38 bug

[1]. M. Schumacher,”Year 2000, Y2K, millennium bug”, Power Engineering Society Summer Meeting, 1999. IEEE, ISBN: 0-7803-5569-5
[2]. 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.
[3]. Vikas Chandra Sharma,”YK-2028 Bug”,Journal Of Image Processing And Artificial Intelligence, Vol .1(3),2015.
[4]. Diomidis Spinellis,”Code quality: the open source perspective. Effective software development series in Safari Books Online”. Adobe Press. ISBN 0-321-16607-8.
[5]. Margaret Ross,”The Y2K Legacy—The Time Bomb of Tomorrow”, Kluwer Academic Publishers, Vol 10(4), pp 281–283,2002 Print ISSN:0963-9314
[6]. Carrington, Damia “Was Y2K bug a boost?”BBC News. Archived from the original on 22 April 2004.
[7]. Raymond B.Howard,”The Case for Windowing: Techniques That Buy 60 Years”, Year/2000 Journal, Mar/Apr 1998.”
[8]. “Issue 16899: Year 2038 problem,”Android, 16 May 2011.
[9]. J. L. Smoot and P. J. Meyer, “What is UTC?,”NASA - Marshall Flight Space Center, 29 March 1995.
[10]. Jet Propulsion Laboratory, “Mission Fantastic to Mars (Part 4),”California Institute of Technology,
[11]. 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
[12]. “Y2K Bug,”National Geographic Society 1996-2013
[13]. Dutton, Denis , ”It`s Always the End of the World as We Know It”, The opinion Pages,The New York Times. 31 December 2009.
[14]. R. M. Wilcox, “The Year 2038 Problem,”23 October 2003.
[15]. Rae Zimmerman,”Y2K readiness helped NYC on 9/11”, MIT News, 19 November 2002.
[16]. 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.

www; Agent; Abstraction; Object; Class; Framework

[1] The Adobe Portable Document Format (PDF) IEEE Explore Version 4.01B, May 1995.
[2] Grady Booch, Object-Oriented Design with Applications, Benjamin-Cummings, 1995.
[3] Booch, G., “Object-Oriented Development”, IEEE Transactions on Software Engineering 12(2), February 1996.
[4] Coad, P. and Yourdon, E., Object-Oriented Analysis, Prentice Hall, 1998.
[5] 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.
[6] Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software, Addison-Wesley, 2005.
[7] L. Lamport, BQY: A Document Preparation System, Addison-Wesley, 2009.
[8] D.C . Schmidt and T . Suda, “An object-oriented framework for dynamically configuring extensible.
[9] Distributed systems,” 1EE Distributed Systems Engineering Journal, 2011.
[10] 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.

Data Mining, Relation Classification, Natural Language Processing

[1] Collobert, Ronan, "Natural language processing (almost) from scratch." Journal of Machine Learning Research, Vol. (12), pp.2493-2537, 2011.
[2] Bach N, Badaskar S. “A review of relation extraction”. Literature review for Language and Statistics II. 2007.
[3] 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.
[4] Rindflesch, Thomas C., et al. "Medical facts to support inferencing in natural language processing." AMIA. 2005.
[5] Hong, Gumwon. “Relation extraction using support vector machine." In International Conference on Natural Language Processing, pp. 366-377, 2005.
[6] Nguyen, Thien Huu, and Ralph Grishman. "Relation extraction: Perspective from convolutional neural networks." In Proceedings of NAACL-HLT, pp. 39-48, 2015.
[7] 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.
[8] Gormley, Matthew R., Mo Yu, and Mark Dredze. "Improved relation extraction with feature-rich compositional embedding models." arXiv preprint arXiv:1505.02419 (2015).
[9] Nguyen, Thien Huu, and Ralph Grishman. "Relation extraction: Perspective from convolutional neural networks." In Proceedings of NAACL-HLT, pp. 39-4,. 2015.
[10] Carlson, Andrew, et al. "Toward an Architecture for Never-Ending Language Learning." AAAI. Vol. (5), 2010.
[11] 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.

JPEG, Artefacts , Image, DCT

[1] 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
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” CVPR, vol. 2, pp. 60–65, 2005.
[8] 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
[9] 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).
[10] 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).
[11]. 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).
[12]. 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).
[13]. 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.
[14]. W. T. Freeman, E. Pasztor, O. Caemichael, “Learning Low-level Vision”, International Journal of Computer Vision, Vol. 48, pp. 25-47, 2011.
[15]. 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).
[16]. 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).
[17]. S. Roweis and L. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embeddings”, Science. Vol. 290, NO. 5500, pp. 2323-2326, (Dec. 2000).
[18] 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.
[19]. 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 [1] and execute in weka programming. The complete of the paper shows the relationship among all kind of decision tree figurings by weka mechanical assembly.

Data Minning, Classification Algorithm, Decision Tree, J48, Random Forest, Random Tree, LMT, WEKA 3.7

[1] J. Han and M. Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann publisher, Third editon -2001 , ISBN: ISBN: 978-0-12-381479-1.
[2] 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 .
[3] 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.
[4] N. Landwehr , M. Corridor, and E. Forthcoming, ―Logistic model trees,‖ Mach. Learn., vol. 59, no. 1–2, pp. 161–205, 2005. .
[5] L. Breima , “Random forests, Mach. Learn”, Springer, volume- 45, Issue no- 1, Page no-( 5–32), Oct 2001.
[6] 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.
[7] 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.
[8] 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.

component Apriori algorithm ,frequent pattern, association rule mining. Support, minimum support threshold, multiple scan. FP Growth algorithm,regression technique

[1]. 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.
[2]. 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.
[3]. 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.
[4]. 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.
[5]. 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.
[6]. C. Wang, R. Li, and M. Fan, “Mining Positively Correlated FrequentItemsets,” Computer Applications, vol. 27, pp. 108-109, 2007
[7]. J. Pei, J. Han, and H. Lu, “Hmine: Hyper-structure mining of frequent patterns in large databases”, In ICDM, 2001, pp441–448.
[8]. 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.
[9]. 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
[10]. 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.

Dustbins are containers used for collecting household waste all around the world. In our day-to-day life, we dispose variety of waste materials categorized as industrial waste, sewage wastes, domestic wastes etc. Dustbins are used for collecting the domestic waste materials. Indoor dustbins are used to collect wastes from household, which are then disposed into the outdoor dustbins maintained by the Corporation or Municipality. Indoor dustbins are smaller in size, whereas municipal dustbins present outdoors are so big in size since it has to accommodate all the wastes from many household users in that area. Hence our main focus is on the dustbins placed outside every corner in the streets in order to keep the environment clean. Road side dustbins are not monitored and cleaned properly most of the times. In this paper we propose a new system for managing garbage within Smart Cities. This Efficient Waste disposal or Management System is considered as an essential for Modern Smart Cities (MSC). Internet of Things (IoT) can be implemented both in IS and MSC creating an highly developed proposal for future Operations. Special methods can be applied to enhance technology used for high Quality of Service (QoS) in our waste management system. Specifically, IoT components like sensors, detectors, and actuators are integrated into Intelligent System (IS) and Inspection systems for efficient waste management. We recommend a sophisticated IS for efficient waste management in Smart Cities. The proposed system is an automated alert based smart bin or garbage collection system and to alert the authorities like corporation or local waste disposal team. Using this, we can monitor the complete waste disposal in an efficient way.

Smart cities, Smart bin IOT Sensors, UV infra-red automated, Aurdino UNO, Ethernet module, alert buzzer, cost efficient, Rain detector, Ethernet, Html web page

[1] “A state of the art reviews on The Internet of Things (IOT)” P.Suresh1J. Vijay Daniel2, R.H. Aswathy3 Dr.V.Parthasarathy4 International Conference on Science, Engineering and Management Research (ICSEMR 2014).
[2] “Internet of Things: Challenges and State-of-the-art Solutions in Internet-scale Sensor Information Management and Mobile Analytics”, Arkady Zaslavsky, Dimitrios Georgakopoulos. 16th
[3] “Top–for IOT enabled Smart City Waste Collection” by Theodoros, Anagno stopoulos1, Arkady. Zaslavsky 2, 1, Alexey Medvedev1, Sergei Khoruzhnicov1. 16th IEEE International Conference on Mobile Data Management. 2015.
[4] “City Garbage collection indicator using RF (ZigBee) and GSM technology” Hindustan Embedded Systems.2015.
[5] Vikrant Bhor, PankajMorajkar, Maheshwar Gurav and Dishant Pandya. “Smart Garbage Management System”. International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 IJERTV4IS031175 Vol. 4 Issue 03, March-2015.
[6] Insung Hong, Sunghoi Park, Beomseok Lee, Jaekeun Lee, DaebeomJeong, and Sehyun Park. “IoT-Based Smart Garbage System for Efficient Food Waste Management”. The Scientific World Journal Volume 2014 (2014), Article ID 646953.
[7] Chaitanya More1, Darshan Mestry2, ParagKedia3, Reshma4- “Efficient Garbage Collection Using Wsn” International Journal of Research in Engineering and Technology eISSN: 2319 -1163- pISSN: 2321-7308., Volume: 05 Issue: 01- Jan-2016.
[8] Akshay Bandal, Pranay Nate,Rohan Manakar,Rahul Powar. “Smart Wi-Fi Dustbin System”. International Journal of Advance Research, Ideas and Innovations in Technology. 2016.
[9] Michael Batty, Kay Axhausen, et al., “Smart Cities of the Future,”ISSN 1467-1298, Paper 188 - Oct 12.
[10] Narayan Sharma,, “Smart Bin Implemented for Smart City”, International Journal of Scientific & Engineering Research,sep- 2015.
[11] Kanchan Mahajan, “Waste Bin Monitoring System Using Integrated Technologies”, International Journal of Innovative Research in Science,Engineering and Technology.
[12] Gaikwad Prajakta, Jadhav Kalyani, Machale Snehal,” “Smart Garbage Collection System In Residential Area”Ijret.2014.
[13] Twinkle Sinha, K.Mugesh Kumar, P. Saisharan,“SMART DUSTBIN”, International Journal of Industrial Electronics and Electrical Engineering, May-2015
[14] Narendra Kumar G., ChandrikaSwamy, and K. N. Nagadarshini. “Efficient Garbage Disposal Management in Metropolitan Cities Using VANETs” Journal of Clean Energy Technologies, Vol. 2, No. 3, July 2014.
[15] Meghana K C, Dr. K R Nataraj, "IOT Based Intelligent Bin for Smart Cities” International Journal on Recent and Innovation Trends in Computing and Communication –May 2016.

Sunu Ann Thomas, Neethan Elizabeth Abraham, Resma Chandran, Jyothysree K R, "Highly Efficient IOT Based Smart Waste Disposal System," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.136-140, 2017.

This article proposes a new high-gain transformerless dc/dc boost converter. Although they possess the ability to boost voltage at higher voltage levels, converter switching devices are under low voltage stress. The voltage stress on active switching devices is lower than the output voltage. Therefore, low-rated components are used to implement the converter. The proposed converter can be considered as a promising candidate for PV microconverter applications, where high voltage-gain is required. The principle of operation and the steady-state analysis of the converter in the continuous conduction mode are presented. A hardware prototype for the converter is implemented in the laboratory to prove the concept of operation.

High gain dc/dc converter; low voltage stress; photovoltaic (PV)

[1]. Abdel-Rahim, O.; Funato, H.; Haruna, J. A comprehensive study of three high gain dc-dc topologies based on cockcroft-walton voltage-multiplier for reduced power PV applications. IEEJ Trans. Electr. Electron. Eng. 2018, 13, 642–651. [CrossRef]
[2]. Zaid, M.; Lin, C.-H.; Khan, S.; Ahmad, J.; Tariq, M.; Mahmood, A.; Sarwar, A.; Alamri, B.; Alahmadi, A. A Family of transformerless quadratic boost high gain dc-dc converters. Energies 2021, 14, 4372. [CrossRef]
[3]. Barbosa, E.A.O.; Carvalho, M.R.S.D.; Rodrigues Limongi, L.; Cavalcanti, M.C.; Barbosa, E.J.; Azevedo, G.M.D.S. High-gain high-efficiency dc–dc converter with single-core parallel operation switched inductors and rectifier voltage multiplier cell. Energies 2021, 14, 4634. [CrossRef]
[4]. Pereira, A.V.C.; Cavalcanti, M.C.; Azevedo, G.M.; Bradaschia, F.; Neto, R.C.; Carvalho, M.R.S.D. A novel single-switch high step-up dc–dc converter with three-winding coupled inductor. Energies 2021, 14, 6288. [CrossRef]
[5]. Li, W.; He, X. Review of nonisolated high-step-up DC/DC converters in photovoltaic grid-connected applications. IEEE Trans. Ind. Electron. 2011, 58, 1239–1250. [CrossRef]
[6]. Souza, L.C.; Morais, D.C.; Silva, L.D.S.D.C.E.; Seixas, F.J.M.D.; Arenas, L.D.O. DC-DC 3SSC-a-based boost converter: Analysis, design, and experimental validation. Energies 2021, 14, 6771. [CrossRef]
[7]. Gholizadeh, H.; Gorji, S.A.; Afjei, E.; Sera, D. Design and implementation of a new cuk-based step-up DC–DC converter. Energies 2021, 14, 6975. [CrossRef]
[8]. Chub, A.; Vinnikov, D.; Blaabjerg, F.; Peng, F.Z. A review of galvanically isolated impedance-source DC-DC converters. IEEE Trans. Power Electron. 2016, 31, 2808–2828. [CrossRef]
[9]. Abdel-Rahim, O.; Wang, H. A new high gain DC-DC converter with model-predictive-control based MPPT technique for photovoltaic systems. CPSS Trans. Power Electron. Appl. 2020, 5, 191–200. [CrossRef]
[10]. Arango, E.; Ramos-Paja, C.A.; Calvente, J.; Giral, R.; Serna-Garces, S.I. Asymmetrical interleaved dc/dc switching converters for photovoltaic and fuel cell applications—Part 2: Control-oriented models. Energies 2013, 6, 5570–5596. [CrossRef]
[11]. Abdel-Rahim, O.; Chub, A.; Blinov, A.; Vinnikov, D. New high-gain non-inverting buck-boost converter. In Proceedings of the IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada, 13 October 2021; pp. 1–6.
[12]. Ojeda-Rodríguez, Á.; González-Vizuete, P.; Bernal-Méndez, J.; Martín-Prats, M.A. A survey on bidirectional dc/dc power converter topologies for the future hybrid and all electric aircrafts. Energies 2020, 13, 4883. [CrossRef]
[13]. De Souza, A.F.; Tofoli, F.L.; Ribeiro, E.R. Switched capacitor dc-dc converters: A survey on the main topologies, design characteristics, and applications. Energies 2021, 14, 2231. [CrossRef]
[14]. Forouzesh, M.; Siwakoti, Y.P.; Gorji, S.A.; Blaabjerg, F.; Lehman, B. Step-up DC-DC converters: A comprehensive review of voltage-boosting techniques, topologies, and applications. IEEE Trans. Power Electron. 2017, 32, 9143–9178. [CrossRef]
[15]. Andrade, A.M.S.S.; Martins, M.L.D.S. Quadratic-boost with stacked zeta converter for high voltage gain applications. IEEE J. Emerg. Sel. Top. Power Electron. 2017, 5, 1787–1796. [CrossRef]
[16]. Ai, J.; Lin, M. Ultralarge gain step-up coupled-inductor dc-dc converter with an asymmetric voltage multiplier network for a sustainable energy system. IEEE Trans. Power Electron. 2017, 32, 6896–6903. [CrossRef]
[17]. Vighetti, S.; Ferrieux, J.P.; Lembeye, Y. Optimization and design of a cascaded DC/DC converter devoted to grid-connected photovoltaic systems. IEEE Trans. Power Electron. 2012, 27, 2018–2027. [CrossRef]
[18]. Zhang, Y.; Gao, Y.; Zhou, L.; Sumner, M. A switched-capacitor bidirectional dc-dc converter with wide voltage gain range for electric vehicles with hybrid energy sources. IEEE Trans. Power Electron. 2018, 33, 9459–9469. [CrossRef]
[19]. Ballo, A.; Grasso, A.D.; Palumbo, G.; Tanzawa, T. Linear distribution of capacitance in Dickson charge pumps to reduce rise time. Int. J. Circ. Theor. Appl. 2020, 48, 555–566. [CrossRef]
[20]. Ballo, A.; Grasso, A.D.; Palumbo, G. A simple and effective design strategy to increase power conversion efficiency of linear charge pumps. Int. J. Circ. Theor. Appl. 2020, 48, 157–161. [CrossRef]
[21]. Ahmed, M.E.; Orabi, M.; AbdelRahim, O.M. Two-stage micro-grid inverter with high-voltage gain for photovoltaic applications. IET Power Electron. 2013, 6, 1812–1821. [CrossRef]
[22]. Axelrod, B.; Berkovich, Y.; Ioinovici, A. Switched-capacitor/switched-inductor structures for getting transformerless hybrid DC–DC pwm converters. IEEE Trans. Circuits Syst. 2008, 55, 687–696. [CrossRef]
[23]. Young, C.-M.; Chen, M.-H.; Chang, T.-A.; Ko, C.-C.; Jen, K.-K. Cascade Cockcroft–Walton Voltage Multiplier Applied to Transformerless High Step-Up DC–DC Converter. IEEE Trans. Ind. Electron. 2013, 60, 523–537. [CrossRef]
[24]. Andrade, A.M.S.S.; Hey, H.L.; Schuch, L.; da Silva Martins, M.L. Comparative Evaluation of Single Switch High-Voltage Step-Up Topologies Based on Boost and Zeta PWM Cells. IEEE Trans. Ind. Electron. 2018, 65, 2322–2334. [CrossRef]
[25]. Li, W.; Xiang, X.; Li, C.; Li, W.; He, X. Interleaved high step-up ZVT converter with built-in transformer voltage doubler cell for distributed PV generation system. IEEE Trans. Power Electron. 2013, 28, 300–313. [CrossRef]
[26]. Liu, H.; Li, F. A novel high step-up converter with a quasi-active switched-inductor structure for renewable energy systems. IEEE Trans. Power Electron. 2016, 31, 5030–5039. [CrossRef]
[27]. De Paula, W.J.; Júnior, D.D.O.; Pereira, D.D.; Tofoli, F.L. Survey on non-isolated high-voltage step-up dc–dc topologies based on the boost converter. IET Power Electron. 2015, 8, 2044–2057.
[28]. Stauth, J.T.; Seeman, M.D.; Kesarwani, K. Resonant switched-capacitor converters for sub-module distributed photovoltaic power management. IEEE Trans. Power Electron. 2013, 28, 1189–1198. [CrossRef]
[29]. Waradzyn, Z.; Stala, R.; Mondzik, A.; Penczek, A.; Skala, A.; Pirog, S. Efficiency Analysis of MOSFET-Based Air-Choke Resonant DC–DC Step-Up Switched-Capacitor Voltage Multipliers. IEEE Trans. Ind. Electron. 2017, 64, 8728–8738. [CrossRef]
[30]. Cervera, A.; Evzelman, M.; Peretz, M.M.; Ben-Yaakov, S. A high-efficiency resonant switched capacitor converter with continuous conversion ratio. IEEE Trans. Power Electron. 2015, 30, 1373–1382. [CrossRef]
[31]. Stala, R.; Waradzyn, Z.; Mondzik, A.; Penczek, A.; Ska?a, A. DC–DC high step-up converter with low count of switches based on resonant switched-capacitor topology. In Proceedings of the 2019 21st European Conference on Power Electronics and Applications (EPE’19 ECCE Europe), Genova, Italy, 2–5 September 2019; pp. P.1–P.10.
[32]. Shoyama, M.; Naka, T.; Ninomiya, T. Resonant switched capacitor converter with high efficiency. In Proceedings of the 2004 IEEE 35th Annual Power Electronics Specialists Conference 2004, Aachen, Germany, 20–25 June 2004; Volume 5, pp. 3780–3786. [CrossRef]
[33]. Kesarwani, K.; Sangwan, R.; Stauth, J.T. Resonant-switched capacitor converters for chip-scale power delivery: Design and implementation. IEEE Trans. Power Electron. 2015, 30, 6966–6977. [CrossRef]
[34]. Erickson, R.W.; Maksimovic, D. Fundamentals of Power Electronics; Wiley: Hoboken, NJ, USA, 2013.
[35]. Nguyen, M.; Duong, T.; Lim, Y. Switched-Capacitor-Based Dual-Switch High-Boost DC–DC Converter. IEEE Trans. Power Electron. 2018, 33, 4181–4189. [CrossRef]
[36]. Ardi, H.; Ajami, A.; Kardan, F.; Avilagh, S.N. Analysis and implementation of a nonisolated bidirectional DC-DC converter with high voltage gain. IEEE Trans. Ind. Electron. 2016, 63, 4878–4888.
[37]. Chen, M.; Hu, J.; Li, K.; Ioinovici, A. A new switched-capacitor based hybrid converter with large step-up DC gain and low voltage on its semiconductors. In Proceedings of the 2016 IEEE International Symposium on Circuits and Systems (ISCAS), Montréal, QC, Canada, 22–25 May 2016; pp. 1190–1193. [CrossRef]
[38]. Ahmad, J.; Zaid, M.; Sarwar, A.; Lin, C.-H.; Asim, M.; Yadav, R.K.; Tariq, M.; Satpathi, K.; Alamri, B. A New High-Gain DC-DC Converter with Continuous Input Current for DC Microgrid Applications. Energies 2021, 14, 2629. [CrossRef]

Jeneesh Scaria, Preethi Sebastian, Susan V Nainan, "High Gain Transformer less Boost Converter for Solar PV Application," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.141-145, 2017.

Water is an essential component for the development of plants in agriculture or irrigation. The paper stresses on the need of an externally hosted cloud computing platform to manage the database, android and the isolated server by the users across the country for irrigation. The system proposed in this paper uses information and communication technologies, allowing the user to consider and examine the information obtained by different sensors. Here we are using different sensors like humidity, temperature, moisture, light etc. These sensors give signal to the micro controller. Micro-controller gives the data to the isolated server through a serial communication. According to sensor values graph will be display on PC and Smart phone side and by using this graph user can on or off dripdevices. In this we keep threshold value for each sensor. The data is sent and processed on an isolated server, which stores the information from the sensors in a database, allowing further interpretation of data in a simple and flexible way. The intendedsystem may lead to enhance the farming practices, overcomingthe water crises and developing an upgraded agricultural systemforthecountry.

Cloud, Embedded, Android, Remote Monitoring, Wireless Sensor Network

[1] J. Broeders, D. Croux, M. Peeters, T. Beyens, S. Duchateau, T. J.Cleij, P.Wagner, R. Thoelen, and W. De Ceuninck, “Mobile Application forImpedance-Based Biomimetic Sensor Readout, "IEEE Sensors J., vol.13, no.7, pp.2659-2665, July2013.
[2] Mahir Dursun and SemihOzden, 1573-1582, April2011.Shen Jin,SongJingling, WanfShengde, Remote Measurement and Control System for Greenhouse Based on GSM SMS, IEEE international conference onElectronicandMeasurement,2007.
[3] ShenJin, SongJingling, HanQiuyan, WangShengde, YangYan, ”School of Electric and Electronic Engineering”
[4] Mark E. Casada, P.E.,"Wheat Moisture Measurement With A Fringing Field Capacitive Sensor", USDA ARS Grain Marketing and Production Research Center Manhattan
[5] Santoshkumar; Udaykumar; R.Y. , “Development of WSN system for precision agriculture", embedded and communication system, 2015.
[6] X. Wang, W. Yang, A. Wheaton, N. Cooley, and B. Moran, “E_cient registration of optical and IR images for automatic plant water stress assessment", Comput. Electron. Agricult.,vol.74,no.2, pp.
[7] Xihai Zhang, Junlong Fang, Xiao Yu 2010 “Design and Implementation of codes Based on CC2430 for the Agricultural Information Wireless Monitoring", IEEE.
[8] AWATIJ.S.,PATILV.S.(Automatic Irrigation Control by using wireless sensor networks) Journal of Exclusive Management Science–June2012-Vol 1 Issue 6- ISSN 2277.
[9] AbreuVM, PereiraLS (2002). Sprinkler irrigation systems design using ISAM im.
[10] Associate Prof.B.T.Salokhe, Miss Shilpa G.Gadekar,“ A wireless application of drip irrigation automation supported by soil moisture sensors ” , International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4,Issue4, April 2015.
[11] Venkata Narayana Eluri, K. Madhusudhana Rao, A. Srinag, ”Wireless Solution for Water Saving In Agriculture Using Embedded System”,International Journal of Computer Science and Business Informatics, ISSN:1694-2108—Vol.2,No.1.JUNE 2013.
[12] Prabha,TanujabaiJ.M,S.Krupesh,”Real-Time Atomization of Agricultural Environment for Social Modernization of Indian Agricultural System Using Arm 7” International Journal of AdvancedResearch in Electrical, Electronics and Instrumentation Engineering (An ISO 3297:2007 Certified Organization) Vol. 3, Issue 6, June 2014.
[13] Tameem Ahmad, Shamim Ahmad, Mohammed Jamshed, “A knowledge based Indian Agriculture: With Cloud ERP Arrangement”2015 IEEE.
[14] S.Sumeetha, D.Sharmila, “Embedded based Remote Control Application using Mobile Phonein Irrigation”, International Journal of Power Control Signal and Computation (IJPCSC), Vol3. No1. Jan-Mar 2012 ISSN:0976-268X.
[15] Santoshkumar, Udaykumar R.Y, “Development of WSN System for Precision Agriculture”, IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS’15 , 2016 IEEE.

Sanu Ann Thomas, Neethan Elizabeth Abraham, Reshma Chandran, Anu Philip, "Drip Irrigation and Monitoring Based On Smart Irrigation," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.146-150, 2017.