Segmentation Using Fuzzy Membership Functions: An Approach
Research Paper | Journal Paper
Vol.5 , Issue.3 , pp.101-105, Mar-2017
Abstract
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
References
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Citation
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 World Bug: Y2k38 an Integer Overflow Threat-Epoch
Review Paper | Journal Paper
Vol.5 , Issue.3 , pp.106-109, Mar-2017
Abstract
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
Date,Time,Y2K,Y2K38 bug
References
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Citation
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.
Framework for Object Oriented WWW Applications using Embedded Concepts
Review Paper | Journal Paper
Vol.5 , Issue.3 , pp.110-113, Mar-2017
Abstract
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
References
[1] The Adobe Portable Document Format (PDF) IEEE Explore Version 4.01B, May 1995.
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Citation
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.
A Survey on Relation Classification from Unstructured Medical Text
Survey Paper | Journal Paper
Vol.5 , Issue.3 , pp.114-118, Mar-2017
Abstract
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
References
[1] Collobert, Ronan, "Natural language processing (almost) from scratch." Journal of Machine Learning Research, Vol. (12), pp.2493-2537, 2011.
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[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.
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Citation
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.
A Review on Patch Based Image Restoration or Inpainting
Review Paper | Journal Paper
Vol.5 , Issue.3 , pp.119-123, Mar-2017
Abstract
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
References
[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
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Citation
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.
Survey Report on Various Decision Tree Classification Algorithm Using Weka Tool
Survey Paper | Journal Paper
Vol.5 , Issue.3 , pp.124-128, Mar-2017
Abstract
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.
Key-Words / Index Term
Data Minning, Classification Algorithm, Decision Tree, J48, Random Forest, Random Tree, LMT, WEKA 3.7
References
[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.
Citation
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.
Survey on Association Rule Mining and Its Approaches
Survey Paper | Journal Paper
Vol.5 , Issue.3 , pp.129-135, Mar-2017
Abstract
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
References
[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
Citation
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.
Highly Efficient IOT Based Smart Waste Disposal System
Survey Paper | Journal Paper
Vol.5 , Issue.3 , pp.136-140, Mar-2017
Abstract
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.
Key-Words / Index Term
Smart cities, Smart bin IOT Sensors, UV infra-red automated, Aurdino UNO, Ethernet module, alert buzzer, cost efficient, Rain detector, Ethernet, Html web page
References
[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.
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[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.
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[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.
Citation
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.
High Gain Transformer less Boost Converter for Solar PV Application
Research Paper | Journal Paper
Vol.5 , Issue.3 , pp.141-145, Mar-2017
Abstract
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.
Key-Words / Index Term
High gain dc/dc converter; low voltage stress; photovoltaic (PV)
References
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Citation
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.
Drip Irrigation and Monitoring Based On Smart Irrigation
Review Paper | Journal Paper
Vol.5 , Issue.3 , pp.146-150, Mar-2017
Abstract
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.
Key-Words / Index Term
Cloud, Embedded, Android, Remote Monitoring, Wireless Sensor Network
References
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Citation
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.