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In Model Driven Architecture (MDA), model and meta-model are the primary artifacts. In this work, a detailed analysis of the existing meta-model-based transformation tools is done for the declarative model using an exhaustive criterion. The evaluation of the eleven chosen tools, which are open source and has download page available using search engine like Google Scholar and Github, is analyzed; like UML–RSDS, Tefkat, JTL, PTL etc. Analysis is performed over fourteen different parameters like language, model query, type of transformation, compatibility, cardinality etc. Results show that all selected tools produce platform specific target model which mostly transform PSM to PSM and none produces platform independent target model transforming a PSM into PIM.

Model Driven Re-engineering, Model Transformation, Declarative User Interface, Transformation tools

[1] S. Agarwal and A. Agarwal. "Model driven reverse engineering of user interface — A comparative study of static and dynamic model generation tools."in International Conference on Parallel, Distributed and Grid Computing.pp 268 – 273, 2014.
[2] K. Lano, S. Kolahdouz-Rahimi. “Specification and verification of model transformations using UML-RSDS.”International Conference on Integrated Formal Methods (pp. 199-214). Berlin, Heidelberg: Springer., 2010.
[3] M. Lawley, J. Steel. (2005). “Practical declarative model transformation with Tefkat”. International Conference on Model Driven Engineering Languages and Systems (pp. 139-150). Berlin, Heidelberg.: Springer. 2005.
[4] D. Cicchetti, D. Di Ruscio, R. Eramo, & A. Pierantonio. “ JTL: a bidirectional and change propagating transformation language”. International Conference on Software Language Engineering (pp. 183-202). Berlin: Springer. 2010
[5] J.M. Almendros-Jiménez, , L. Iribarne, J. López-Fernández, and Á. Mora-Segura. "PTL: A model transformation language based on logic programming." Journal of Logical and Algebraic Methods in Programming 85, 332-366. 2016
[6] L.Bondé, C. Dumoulin, and J.L. Dekeyser. "Metamodels and MDA transformations for embedded systems." Advances in design and specification languages for SoCs, Springer, 89-105.2005.
[7] N. Macedo, T. Guimaraes, A. Cunha. "Model repair and transformation with Echo." 28th IEEE/ACM International Conference on Automated Software Engineering. IEEE Press, 694-697, 2013.
[8] D. Li,X. Li, V. Stolz. "QVT-based model transformation using XSLT." ACM SIGSOFT Software Engineering Notes 36, no. 1, 1-8, 2011.
[9] S. Reddy, R. Venkatesh, A. Zahid. "A relational approach to model transformation using QVT Relations." TATA Research Development and Design Centre, 1-15, 2006.
[10] B. Schätz,"Formalization and rule-based transformation of EMF Ecore-based models." International Conference on Software Language Engineering,. Berlin, Heidelberg: Springer, 227-244.2008.
[11] M.Brambilla, J. Cabot, and M.Wimmer. "Model-driven software engineering in practice." 1-182. Synthesis Lectures on Software Engineering 1, no. 1, 2012.
[12] L. Lúcio. "Model transformation intents and their properties." Software & systems modeling 15, no. 3 , 647-684.2016.
[13] Prince Singha, Aditya, Kunal Dubey, Jagadeeswararao Palli, “Toolkit for Web Development Based on Web Based Information System,” Isroset-Journal (IJSRCSE), 6, no. 5, pp.1-5. 2018..
[14] Shubham, Deepak Chahal, LatikaKharb, “Security for Digital Payments: An Update,” Journal (IJSRNSC), 6, no. 5 , pp. 51-54. 2018.

Smita Agarwal, S. Dixit, Alok Aggarwal, "Model to Model Transformation for Declarative Models," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.164-170, 2018.

In industrial sector, employee performance evaluation has paramount importance which will be used for predicting the performance of an employee or a number of employees by considering various aspects like employee’s task type, job skills, working quality, interpersonal relationship, creativity, adherence to policy, productivity, attendance, performance etc. Data mining consists of different techniques used to complete our objectives. Using this particular research, the industry superiors will have the ability to predict the performance of employees in an organization. The decision tree method is used on the database of an employee or a number of employees in order to analyze an employee data to make a prediction. Data mining is a tool which allows us to manage the data in a superior way.

Havrda and Charvat entropy, Improved ID3 algorithm, Information gain

[1] Shubham Tupe, Chetan Mahajan, Dnyaneshwar Uplenchwar, Pratik Deo,“ Employee Performance Evaluation System Using ID3 Algorithm”, International Journal of Innovative Research in Computer & Communication Engineering, Vol. 5, Issue 2, February 2017
[2] Prof. Mr. A.M Bhadgale, Ms. Sharvari Natu, Ms. Sharvari G. Deshpande, Mr. Anirudha J. Nilegaonkar., “Implementation of Improved ID3 Algorithm Based on Association Function,” Volume 114, No. 10 2017, 1-9
[3] Rahul A. Patil, Prashant G. Ahire, Pramod. D. Patil, “A Modified Approach to Construct decision Tree in Data Mining Classification “, International Journal of Engineering and Innovative Technology (IJEIT) , Volume 2, Issue 1, July 2012
[4] Sartaj Sahni, “Algorithms analysis and design”, Galgotia Publications Pvt. Ltd., New Delhi, 1996
[5] Nishant Mathur, Sumit Kumar, Santosh Kumar, And Rajni Jindal, “The Base Strategy For ID3 Algorithm Of Data Mining Using Havrda And Charvat Entropy Based On Decision Tree”, International Journal Of Information And Electronics Engineering, Vol. 2, No. 2, March 2012
[6] L.Surya Prasanthi1, R.Kiran Kumar2, “ID3 and Its Applications in Generation of Decision Trees across Various Domains- Survey”, International Journal of Computer Science and Information Technologies, Vol. 6 (6) , 2015
[7] Kirandeep, Mrs. Neena Madan, “Analysis of Improved ID3 algorithm using Havrda and Charvat entropy”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 3 | ISSN : 2456-3307

Kirandeep, Neena Madan, "Deployment of Improved ID3 algorithm with Havrda and Charvat entropy for Employees performance evaluation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.171-177, 2018.

Rice crop production contributes to the food security of India, more than 40% to overall crop production. Variability from season to season is detrimental to the farmer’s income and livelihoods. Improving the ability of farmers to predict crop productivity. In our method aimed to use of machine learning techniques Support Vector Machine (SVM), Bayesian Networks (BN) and Artificial Neural Networks (ANN) to predict rice production yield and investigate the factors affecting the rice crop yield. Data are sourced from publicly available in Indian Government’s records. The attributes are used for the present studies are rainfall, minimum temperature, average temperature, maximum temperature, area, production and yield . The results showed the accuracy of, SVM is 78.76% , BN is 85.78% and ANN is 97.54% using the WEKA tool. The aim of this study are used evaluated in agriculture for predicting the crop yield production.

Crop yield prediction, Crop analysis Support Vector Machine , Bayesian Networks, Artificial Neural Network

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Science and Management Studies, vol. 2, no. 9, pp. 59-64, 2014.
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Florida(US): CRC Pr, 2012.
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[14] Auer, Peter, Harald Burgsteiner, Wolfgang Maass , "A learning rule for very simple universal approximators consisting of a single layer of perceptrons" (PDF). Neural Networks. 21 (5): 786–795. doi:10.1016/j.neunet.2007.12.036. PMID 18249524,2008.
[15] A. Abraham, “Artificial Neural Networks: Handbook of Measuring System Design”, John Wiley & Sons, Ltd., 2005,ISBN: 0-470-02143-8.
[16]https://en.wikipedia.org/wiki/Types_of_artificial_neural_networks.
[17] Weka 3:Data Mining Software in Java, Machine Learning Group at the University of Waikato, Official Web: http://www.cs.waikato.ac.nz/ml/weka/index.html, accessed on 26th March 2016.

P. Anitha, T. Chakravarthy, "Agricultural Crop Yield Prediction using Artificial Neural Network with Feed Forward Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.178-181, 2018.

A Mobile operating system is a software platform which is typically designed for mobile devices like phone, tablet, PDAs, Handheld devices, etc. to enables its hardware components and helps to execute various programs and applications. A mobile OS specifically starts when the device switch on and loads with its default configuration. Mobile OS has normally combined the features of the personal computer as well as phone devices. Many Mobile operating systems are available in the market for the last quarter century. Presently Android Mobile OS is using more than 75% of mobile devices. Many other mobile OS also become popular based on various criteria like user-friendly, security, sophistication, etc. Mobile device consumers are aggressively looking forward to a mobile device with a stable and secure operating system to use for a longer duration, at least for 8-10 years. This study targets to give a better visibility to consumers on presently available operating systems in the market. Here analyzed the architecture, capabilities, versions, current status, active duration, etc. for most mobile operating systems launched in the past quarter-century.

Mobile OS, Mobile Device, Mobile OS architecture, Android, iOS, Windows, Ubuntu, Tizen, webOS, Maemo, Firefox OS, Fire OS, MeeGo, Bada, Symbian, BlackBerry

[1] Mobile OS overview [online] https://www.shoutmeloud.com/top-mobile-os-overview.html
[2] Wikipedia [online] https://en.wikipedia.org/wiki/Mobile_operating_system
[3] Techopedia [online] https://www.techopedia.com
[4] DigitalSeoGuide [online] https://www.digitalseoguide.com/technology/top-mobile-phones-operating-systems-os/

Shinto Kurian K, K.Nirmala, "Mobile OS - High Level Glance," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.182-193, 2018.

Breast cancer is one of the most common cancers all around the world and an early diagnosis of breast cancer plays a very vital role in the survival of the patient. Though there are plenty of experienced doctors, top range imaginary devices and advanced radiological techniques etc. but still computer assisted decision support system for the diagnosis of breast cancer can help a lot to medical staff for the said decease. This paper introduces a fuzzy logic (FL) based decision support system (DSS) for identifying the risk of breast cancer a person can have. The primary focus of the paper is on the algorithm used to identify the risk of breast cancer that a patient may have based on seven input parameters. The proposed system uses seven input parameters; namely age, genetic factor, menopause age, HER2, age of first pregnancy, alcohol intake & body mass index (BMI) which is based on diagnosis risk degree and one output which identify risk status of breast cancer recurrence or mortality in early diagnosed patients. Different medical practitioners dealing with the said decease were consulted before setting up the rule base. Through decision support system, the meaning of transferred data is translated into linguistic variables that can be understood by non-experts. Mamdani inference engine is used to deduce from the input parameters to stage the risk level of breast cancer.

Fuzzy Logic, Fuzzy Inference Systems (FIS), Decision support system, Breast Cancer, risk analysis

[1]. Chen H-L, Yang B, Liu J, Liu D-Y, “A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis,” Expert Systems with Applications: An International Journal, vol. 38, no. 7, July 2011, 9014–9022. 10.1016/j.eswa.2011.01.120
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[10]. Aruna S, Rajagopalan SPA, Nandakishore LV, “An Empirical Comparasion of Supervised Learning Algorithms in Disease Detection,” International Journal of Information Technology Convergence and Services – IJITCS, vol. 1, pp. 81–92, 2011. 10.1016/S0019-9958(65)90241-X
[11]. Mohamed MA, Hegazy AE-F, Badr AA, “Evolutionary Fuzzy ARTMAP Approach for Breast Cancer Diagnosis,” International Journal of Computer Science and Network Security, vol. 11, no. 4, pp. 77-84, 2011.
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[14]. Lukasiewicz J, O logice trójwartościowej (in Polish). Ruch filozoficzny 5:170–171. English translation: On three-valued logic, in L. Borkowski (ed.). Selected works by Jan Lukasiewicz, North–Holland, Amsterdam 1970, 87–88. ISBN 0–7204–2252–3
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[28]. Prince Singha, Aditya, Kunal Dubey, Jagadeeswararao Palli, “Toolkit for Web Development Based on Web Based Information System,” Isroset-Journal (IJSRCSE), 6, no. 5, pp. 1-5. 2018..
[29]. Shubham, Deepak Chahal, Latika Kharb, “Security for Digital Payments: An Update,” Journal (IJSRNSC), 6, no. 5 , pp. 51-54. 2018.

Monika Varshney, Azad Kumar Srivastava, Alok Aggarwal, "A Rule based Fuzzy controlled Decision Support System for Management of Breast Cancer," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.194-199, 2018.

This paper is concerned with the study and analysis of Data Emplacement and Redistribution (DER) in large set of databases called Big Data and proposes a model for improving the efficiency of data processing and storage utilization for dynamic load imbalance among nodes in a heterogeneous cloud environment. With the era of explosive information and data receiving, more and more fields need to deal with massive, large scale of data. A method has been proposed with an improved Data Placement algorithm called Effective Data Emplacement and Redistribution approach (EDER) with computing capacity of each node as a predominant factor that promotes and improves the efficiency in data processing in a short duration time from large set of data. The proposed solution improves the performance of the heterogeneous cluster environment by effectively distributing data based on the performance oriented sampling as the experimental results made with word count applications.

Cloud Computing, Big Data, HDFS, MapReduce, Data emplacement, MapReduce applications

[1] Jiong Xie, Shu Yin, Xiaojun Ruan, Zhiyang Ding, Yun Tian, “ Improving MapReduce Performance through Data Placement in Heterogeneous Hadoop Clusters”, 19th International Heterogeneity in Computing Workshop, Atlanta, Georgia, April 2010.
[2] Yuanquan Fan, Weiguo Wu, Haijun Cao, Huo Zhu, Xu Zhao, Wei Wei, “A heterogeneity-aware data distribution and rebalance method in Hadoop cluster”, Seventh ChinaGrid Annual Conference, 2012.
[3] Mahesh Maurya, Sunita Mahajan “Performance analysis of MapReduce Programs on Hadoop Cluster” IEEE World Congress on Information and Communication technologies,2012.
[4] Wentao Zhao, Lingjun Meng, Jiangfeng Sun, Yang Ding, “An Improved Data Placement Strategy in a Heterogeneous Hadoop Cluster”, The Open Cybernetics & Systemics Journal, 2014.
[5] Chia-Wei Lee, Kuang-Yu Hsieh, Sun-Yuan Hsieh, Hung-Chang Hsiao , “A Dynamic Data Placement Strategy for Hadoop in Heterogeneous Environments”, Big Data Research, 2014.
[6] Dipayan Dev, Ripon Patgiri “Performance Evaluation of HDFS in Big Data Management”, International Comference on High Performance Computing and Applications (ICHPCA), 2014.
[7] Suhas V. Ambade, Priya R. Deshpande, “Heterogenity-based files placement in Big Data Cluster”, International Conference on Computational Intelligence and Communication Networks, 2015.
[8] Vrushali Ubarhande, “Novel Data-Distribution Technique for Hadoop in Heterogeneous Cloud Environments”, IEEE Transactions 2015.
[9] Ch. Bhaskar VishnuVardhan and Pallav Kumar Baruah, “Improving the Performance of Heterogeneous Hadoop Cluster”, Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), 2016.
[10] Anton Spivak and Denis Nasonov “Data Preloading and Data Placement for MapReduce Performance Inproving” Procedia Computer Science 101, 2016.
[11] Ramchandani Hema Megharajbhai, Viral Parmar, “Heterogeneity based Fairly Data Distribution in Cluster Environment”, International Journal of Advance Engineering and Research Development, 2018.
[12] S. Annapoorani, Dr. B. Srinivasan, “Initial Dynamic Data Allocation for Heterogeneous hadoop clusters” International Journal of Scientific Research in Computer Science Applications and Management Studies, Volume 7, Issue 3, 2018.
[13] S. Annapoorani, Dr. B. Srinivasan, “Improving performance of data in Hadoop clusters using dynamic data replication” International Journal of Engineering sciences & Research Technology, Feb, 2018.

S. Annapoorani, B. Srinivasan, "Improvement of an Effective Data Emplacement and Redistribution Algorithm among Nodes in Cloud Based Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.200-202, 2018.

Software has become an indispensible element of day-to-day life. As users of the software are increasing, the need of the software is also getting increased. Developing software that meets the needs of the users is an cumbersome task for a software developer. Since the complexity of the software has increased, the intricacy of the requirement also has increased. As the market needs meet changes, software development team has to meet the changes in the requirements. Hence the developing team faces the challenge to prioritize the requirements in order to produce a risk-free software. This study describes the techniques and methods used in prioritizing the requirements.

software requirement prioritizing, software requirement prioritizing techniques, Analytical Hierarchical Process, Numerical method, Fuzzy logic

[1] Chetna Sisodiya, Pradeep Sharma, “Scrutiny to Effectiveness of Various Software Process Model”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol.5, Issue.2, pp.88-93, April (2017), ISSN: 2320-7639.
[2] Wiegers, “First things first: Prioritizing requirement,”, Software development Online, vol. 7, pp. 48-53, Sep. 1999.
[3] Karlsson J., Wohlin C., Regnell B., “An evaluation of methods for prioritizing software requirement.” Elsevier, New York, 7 Feb. 1997, pp 939-947.
[4] M. Asaem, M. Ramzan and A. Jaffar, "Analysis and optimization of software requirements prioritization techniques," International Conference on Information and Emerging Technologies, Pakistan, June 2010, pp. 1-6.
[5] J. Azar, R. K. Smith and D. Cordes, “Value Oriented Requirements Prioritization in a Small Development Organization,” IEEE Software , 2007, pp. 32-73.
[6] Saaty, T. L., “The Analytic Hierarchy Process”. Mc-Graw-Hill, 1980. .
[7] Persis Voola, A Vinaya Babu, “Comparison of Requirements Prioritization Techniques Employing Different Scales of Measurement”, ACM SIGSOFT Software Engineering Notes, July 2013 Volume 38 Number 4.
[8] Luay Alawneh, “Requirements Prioritization Using Hierarchical Dependencies”, Advances in Intelligent Systems and Computing , 2017, pp. 459-464.
[9] Manju Khari and Nikunj Kumar “Comparisons of Techniques of Requirement prioritization,” Journal of Global Research in Computer Science 2013,Volume 4, No. 1, pp.38-43
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[11] Ruby, Dr. Balkishan, “Fuzzy Logic based Requirement Prioritization(FLRP) - An Approach”, International Journal of Computer Science And Technology, Vol. 6, Iss ue 3, July - Sept 2015.

A. Sandanasamy, R. Thamarai Selvi, "Software Requirement Prioritization: A Critical Study on Its Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.203-206, 2018.

The handwriting of a person is an important biometric attribute of a human being which can be used to authenticate human identity. A number of biometric techniques have been proposed for personal identification in the past. Handwriting by an authorized person is considered to be the “seal of approval” and remains the most preferred means of authentication. Handwritten recognition has been an active and challenging problem. Most of the traditional methods have two challenges, due to the large variations of written text and the dependency relationship between letters. First, in real applications, words may be written cursively, so it is hard to identify the words automatically. Even if the words are neat, different people may write the same words in different styles. Since there are large shape variations in human handwriting, recognition accuracy of handwritten words is very difficult. The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verification. A verification stage includes applying the extracted features of test handwriting to a neural network which will classify it as a genuine or forged. To recognize the handwritten words, the proposed work combines Artificial Neural Network (ANN) and Hidden Markov Model (HMM).

HMM, ANN

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Vinita .B. Patil, Rajendra R. Patil, "User Identification Using HMM and ANN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.207-213, 2018.

Data security can be achieved in a variety of ways. Often encryption algorithms are prone to attacks and hence can’t ensure high data security for classified and highly confidential data, the reason being the fact that encrypted data is often perceptible and hence more prone to attacks due to its existence on the application layer level of the OSI model. Hence the necessity for physical layer security arises. The proposed technique uses partially transmitted sequences along with the fast Fourier transform for physical layer security. The performance of the proposed system is based on the complementary cumulative distribution function (CCDF) of the crest factor of the data. It has been shown that the proposed system attains a low crest factor therefore reducing the perceptibility and increasing the level of security.

Complementary Cumulative Distribution Function (CCDF), Crest Factor (CF), Fast Fourier Transform (FFT), Partially Transmitted Sequences (PTS), Peak to Average Power Ratio (PAPR)

[1]. Adnan A. E. Hajomer, Xuelin Yang, Weisheng Hu, “Secure OFDM Transmission Precoded by Chaotic Discrete Hartley Transform”, IEEE 2017
[2]. Chongfu Zhang, Wei Zhang, Xiujun He, Chen Chen, Huijuan Zhang, Kun Qiu,, “Physically Secured Optical OFDM-PON by Employing Chaotic Pseudorandom RF Subcarriers”, IEEE 2017
[3]. Wei Zhang, Chongfu Zhang, Chen Chen, Wei Jin, Kun Qiu, “Joint PAPR Reduction and Physical Layer Security Enhancement in OFDMA-PON”,IEEE 2016
[4]. Wei Zhang ,Chongfu Zhang, Wei Jin, Kun Qiu, Chen Chen, ” Hybrid time-frequency domain chaotic interleaving for physical-layer security enhancement in OFDM-PON systems”, IEEE 2016
[5]. Xiaonan Hu, Xuelin Yang, Zanwei Shen ,Hao He, Weisheng Hu ,Chenglin Bai, ” Chaos-Based Partial Transmit Sequence Technique for Physical Layer Security in OFDM-PON”,IEEE 2015
[6]. Xuelin Yang, Xiaonan Hu ,Zanwei Shen, Hao He, Weisheng Hu, Chenglin Bai, “Chaotic signal scrambling for physical layer security in OFDM-PON”,IEEE 2015
[7]. Bo Liu, Lijia Zhang, Xiangjun Xin, Yongjun Wang, ”Physical Layer Security in OFDM-PON Based on Dimension-Transformed Chaotic Permutation”, IEEE 2014
[8]. Wei Zhang , Chongfu Zhang, Wei Jin, Chen Chen, Ning Jiang, Kun Qiu, ”Chaos Coding-Based QAM IQ-Encryption for Improved Security in OFDMA-PON”, IEEE 2014

Prateek Kumar Dohare, Preeti Ahirwar, "Data Security using Partially Transmitted Sequences and Fast Fourier Transform," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.214-218, 2018.

Noise is a natural property of medical imaging, and it commonly tends to diminish the image resolution as well as contrast, thus dropping the diagnostic rate of this imaging modality, there is a developing attentiveness in using noise reduction techniques in a variety of medical imaging applications. This paper presents a hybrid nonlinear filtering algorithm in which the proposed method has two stages. In the first stage, the rank-ordered sequence is used to decide whether a pixel is corrupted or not based on a decision measure which considers the differences of adjacent pixel values in the input image. In the second stage, the replacement is done by the weighted median value of uncorrupted pixels in the filte1ing window. The visual and experimental results show that the proposed filter can provide very high quality restored images with image detail preservation for various level noise density images.

Alzheimer’s disease, Denoising, MRI, Non – linear, Median filter

[1] Rejina A.S, M.Selin, A.Neela Madheswari "Performance Analysis of Nonlinear Decision Based Filter and Weighted Median ε-Filter for Salt and Pepper Noise Removal" IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555,Vol. 3, No.5, 2013.
[2] Raju Anitha, S.Jyothi, P.Ramesh Babu"Detection of Brain Abnormality for Alzheimer`s Disease Using Image Processing Techniques" https://www.researchgate.net/publication/305640527, January 2015.
[3] Benjamin Y.M.Kwan ” Impulse Noise Reduction in Brain Magnetic Resonance Imaging using Fuzzy Filters” World Academy of Science, Engineering and Technology 2011.
[4] Kamboj P. & Rani V.,“A Brief study of various noise models and filtering techniques,” Journal of Global Research in Computer Science, Vol. 4, No. 4, 2013.
[5] S. Shrestha, “Image Denoising Using New Adaptive Based Median Filter”, Signal & Image Processing: An International Journal (SIPIJ), vol. 5, no. 4, 2014.
[6] Ping Kuang, Lei Sun, “An Improved Two-Dimensional Multi-Level Median Filtering Algorithm” IEEE International Conference on Apperceiving Computing and Intelligence Analysis Proceeding, 2010.
[7] Zhu Youlian, Huang Cheng, “An Improved Median Filtering Algorithm Combined with Average Filtering” IEEE Third International Conference on Measuring Technology and Mechatronics Automation, 2011.
[8] Raghuram Kunsoth, Mantosh Biswas "Modified Decision Based Median Filter for Impulse Noise Removal" IEEE International conference on Wireless Communication, Signal Processing and Networking-WiSPNET, 2016.
[9] K. S. Srinivasan, and D. Ebenezer, ”A New Fast and Efficient Decision Based Algorithm for Removal of High Density Impulse Noises”, Signal Processing Letters, Vol. 14, No. 3, pp. 189-192, 2007.
[10] Aparna Rajesh Lokhande, Munmun Das, Dr.D.B.Salunke " Efficient Removal of Impulse Noise and Analyze Performance of Median Filter and Center Weighted Median Filter" International Journal of Engineering Science and Computing, 2016.
[11] Zhu Youlian, Huang Cheng, “An Improved Median Filtering Algorithm Combined with Average Filtering”, ICMTMA `11 Proceedings of Int. Conference on Measuring Technology and Mechatronics Automation, IEEE, vol1, pp 420-423, 2011.
[12] Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Transaction on Image Processing vol. 13, No. 4, pp.600-611, 2004.
[13] Debdoot Sheet, Santanu ParI, Arindam Chakraborty , Jyotirmoy Chatterjee and Ajoy K. Ray t “Visual Importance Pooling for Image Quality Assessment of Despeckle Filters in Optical Coherence Tomography” IEEE International Conference on Systems in Medicine and Biology, 2010.

R. Senthilnathan, A. Marimuthu, "Non-linear Based Hybrid Denoising filter for Alzheimer’s disease Magnetic Resonance Imaging," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.219-223, 2018.