Volume-6 , Issue-11 , Nov - 2018 Go Back

There are number of energy efficient techniques used to enhance the network life time one of them is gateway based energy-efficient routing protocol (M-GEAR) this protocol is based on the node which is rechargeable and divides network into four logical regions on the basis of the location from the Sink. In this paper we use the Enhanced gateway based energy-efficient technique which works on the basis of multilevel multihop technique with CHs and gateway nodes in region 2 and region 3 and the selection of the cluster heads in these region is based on the energy concept, which improves the network life time by minimizing the data loss and cluster failure.

Sensors, Gateway Node, TDMA, Homogeneous Network

[1] Q. Nadeem1, M. B. Rasheed1 et al, “M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol for WSNs”, ieee july 2016.
[2] Nazia anjum, Maood ahmed et al,” Gateway Based Energy Efficient Routing: GEER”, International Journal of Advance Research, Ideas and Innovations in Technology, ISSN: 2454-132X, 2017.
[3] Pallavi Jain1 and Harminder kaur2, ” An Improved Gateway Based Multi Hop Routing Protocol for Wireless Sensor Network”, International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 15 (2014), pp. 1567-1574.
[4] Velanati Mohana Gandhi1, M.V.H.Bhaskara Murthy2,M.Lakshmu Naidu3, “ Performance Analysis of Multihop-Gateway Energy Aware Routing (M-Gear) Protocol for Wireless Sensor Networks”, IOSR Journal Of Humanities And Social Science (IOSR-JHSS) Volume 21, Issue11, Ver. 9 (Nov. 2016) PP 01-07.
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[12] Li, Hongjuan, Kai Lin, and Keqiu Li. "Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks." Computer Communications 34.4 (2011): 591-597.

Preeti Jamwal, Sonam Mahajan, "Region Refinement Technique In MGEAR Protocol To Enhancing Sensor Node Life Time," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.867-870, 2018.

Nowadays remote sensing image classification process has been most commonly used for object identification. It identifies the object in the remote sensing images by assigning the land cover classes to pixels. In this paper, a review on conventional and advanced remote sensing image classification techniques such as supervised, unsupervised, per pixel, sub pixel and object based image analysis processes has been provided. Further, a brief description about the effective features of different image classification algorithms like Fuzzy classifier, classification based on Artificial Neural Network (ANN), classification based on Support Vector Machine (SVM), Evolutionary Algorithms (EA) and Optimum Path Forest classification algorithms were also given. In the next section of paper various classification methodologies with their characteristics and examples of classifiers are explained. Moreover, this study compares the frequently used image classification algorithms and suggests the remote sensing image classifier to choose the best image classification technique based on the performance of classification that improves the accuracy range.

Remote sensing, Image classification, ANN, SVM, Optimum Path Forest

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Gandla Shivakanth, Prakash Singh Tanwar, "Review On Conventional and Advanced Classification Approaches in Remote Sensing Image Processing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.871-879, 2018.

In today’s digital world, privacy concerns for data over the internet have increased. In many communications we transmit digital images and these images contain confidential information. Making is vulnerable towards unauthorized personals attacking the image and leaking our information which demands higher privacy. So, there are a number of methods available for achieving this privacy, one of them being Steganography. This make use of Mosaic image creation which has two distinct techniques, DWT (Discrete wavelet Transform) and DCT (Discrete Cosine Transform). The cover image is arbitrarily selected and uses of this image to hiding of the secret image. Secret image and cover image is split into tiny fragments called tile image and target block respectively. A secret image hiding scheme is proposed with new security features. This scheme utilizes the mosaic images, which is created from the secret and target images. A mosaic image is similar to that of the target image. The secret image fragments are hidden in the target image by performing appropriate color transformations. In these paper we are describe different types of watermarking techniques and differentiate using advantage and disadvantages for future research direction.

Steganography, Mosaic Image, DCT (Discrete Cosine Transform), DWT (Discrete Wavelet Transform)

[1] Mr.Indrajeet Phutanr and Dr. Sanjay Nalbalwar “A New Method For Secret Image Transmission Via Secret Fragment Visible Mosaic Image”, IEEE 2016.
[2] Ya-Lin Lee And Wen-Hsiang Tsai “A New Secure Image Transmission Technique via Secret-Fragment – Visible Mosaic Images by Nearly Reversible Color Transformations”.IEEE 2014
[3] Anitha Devi M.D and K B Shivakumar “Secured Covert Color Image Transmission using Secret Fragment Visible Mosaic Image and Reversibile Color Transformation Technique”,IEEE 2016
[4] Saravanan Chandran and Koushik Bhattacharya “Performance Analysis of LSB,DCT and DWT for Digital Watermarking Application using Steganography”.
[5] Shahanaz N and Greeshma R “Secret Image Transmission Through Mosaic Image”.
[6] Tamanna and Ashwani Sethi “Steganography: A Juxtaposition between LSB, DCT and DWT.”
[7] Arthe Henriette Pascaline, Li Chun Fong Christopher, Maleika Heenaye-Mamode Khan, Sameerchand Pudaruth,” Using Photomosaic and Steganographic Techniques for Hiding Information inside Image Mosaics”
[8] Senthilarut selvi balu, Mr.Shai Sanmuga Raja,” A Method for Creating Mosaic Image with Small Size Database”.
[9] Manisha Ghortale” Secret-Fragment Visible Mosaic Image Technique for ImageHiding”.
[10] Shabana Vathelil Subair, Timna P Elizabeth, “Secret-Fragment-Mosaic-Images: A Secure Method for Image Transmission”. International Journal of Science and Research (IJSR) ISSN(Online): 2319-7064, Index Copernicus Volume (2013).
[11] Deepali G. Singhavi and Dr. P. N. Chatur, “A New Method for Creation of Secret - Fragment–Visible–Mosaic Image for Secure Communication” IEEE 2nd international conference on innovations in information embedded and communication systems ICIIECS’15.
[12] I. J. Lai and W. H. Tsai, “Secret-Fragment-visible mosaic image-A new computer art and its application to information hiding”, IEEE Trans. Inf. Forens. Secur., vol. 6, no. 3. Pp. 936-945. Sep. 2011.

Dhruval Kachhiya, Keyur N Upadhayay, Tejaskumar P Bhatt, "Privacy Preserving Image Transmission Using Random Pattern Mosaic Images Steganography - Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.880-883, 2018.

The enlargement in development of web 2.0 and web enabled devices smacked huge user generated data hence attracted many researchers in the past years in the field of social media mining. The focal point in mining social media is for obtaining the important decision making opinions, attitudes, sentiments, and emotions. This paper uses naïve bayes algorithm to classify the sentiments and polarity of the tweets of Bengaluru traffic in detail with the help of opinion lexicon through R studio. The tweets on Bengaluru traffic are first accessed from twitter through streaming API, then preprocessed and functions containing naïve bayes classifier is used to classify the tweets into emotions and polarity, through classify emotions and classify polarity functions. Classify emotions functions makes use of naïve bayes algorithm for classifying the emotions into seven categories such as anger, disgust, fear, joy, sadness, surprise, and best fit. Classify polarity function receives two arguments, cleaned tweets and naïve bayes algorithm for classifying the polarity into positive sentiment and negative sentiment. The results are represented through plots in R studio.

Sentiment analysis, Naïve bayes, R programming, Data mining and Polarity detection

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[15] Abhijit Janardan Patankar, Kshama V. Kulhalli, and Kotrappa Sirbi, "Emotweet: Sentiment Analysis tool for twitter," in Advances in Electronics, Communication and Computer Technology (ICAECCT), 2016 IEEE International Conference on, 2016, pp. 157-159.
[16] Fotis Aisopos, Dimitrios Tzannetos, John Violos, and Theodora Varvarigou, "Using n-gram graphs for sentiment analysis: an extended study on Twitter," in Big Data Computing Service and Applications (BigDataService), 2016 IEEE Second International Conference on, 2016, pp. 44-51.
[17] Chintan Dedhia and Jyoti Ramteke, "Ensemble model for Twitter sentiment analysis," in Inventive Systems and Control (ICISC), 2017 International Conference on, 2017, pp. 1-5.
[18] Mohit Mertiya and Ashima Singh, "Combining naive bayes and adjective analysis for sentiment detection on Twitter," in Inventive Computation Technologies (ICICT), International Conference on, vol. 2, 2016, pp. 1-6.
[19] Anurag P. Jain and Vijay D. Katkar, "Sentiments analysis of Twitter data using data mining," in Information Processing (ICIP), 2015 International Conference on, 2015, pp. 807-810.
[20] David Zimbra, Manoochehr Ghiassi, and Sean Lee, "Brand-related Twitter sentiment analysis using feature engineering and the dynamic architecture for artificial neural networks," in System Sciences (HICSS), 2016 49th Hawaii International Conference on, 2016, pp. 1930-1938.
[21] Huma Parveen and Shikha Pandey, "Sentiment analysis on Twitter Data-set using Naive Bayes algorithm," in Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on, 2016, pp. 416-419.
[22] Mondher Bouazizi and Tomoaki Ohtsuki, "Opinion mining in Twitter: How to make use of sarcasm to enhance sentiment analysis," in Advances in Social Networks Analysis and Mining (ASONAM), 2015 IEEE/ACM International Conference on, 2015, pp. 1594-1597.
[23] M. S. Neethu and R. Rajasree, "Sentiment analysis in twitter using machine learning techniques," in Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on, 2013, pp. 1-5.
[24] Omar Abdelwahab, Mohamed Bahgat, Christopher J. Lowrance, and Adel Elmaghraby, "Effect of training set size on SVM and Naive Bayes for Twitter sentiment analysis," in Signal Processing and Information Technology (ISSPIT), 2015 IEEE International Symposium on, 2015, pp 46-51.
[25] Angelpreethi, P. kiruthika , S. BrittoRameshKumar, “A Methodological Framework for Opinion Mining” in International Journal of Computer Sciences and Engineering, 2018, Vol.6(2)

Annie Syrien, M. Hanumanthappa, B. Sundaravadivazhagan, "Sentiment Analysis of Tweets using Naïve Bayes Algorithm through R Programming," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.884-889, 2018.

The rapid growth in digital imaging techniques have resulted in the generation of large volume of diverse medical images. Most of these image corpus is either unlabeled or partially annotated. To ex-tract relevant information from such large-scale image corpus, it is necessary to have an efficient and scalable image retrieval techniques. In this article, we present an effective approach for retrieving images from large-scale Chest X-Ray dataset that have the similar disease conditions or severity as that of the query image. We tested our approach on NIH chest x-ray image dataset, that contains images of pneumonia affected patients. The Histogram of Gradients (HoG) features are found to give better results in classifying the disease. The dimensionality of dense HoG features is reduced by using level decomposition of Haar wavelet and using random projection. The performance degradation happened due to the feature reduction is rectified by using a hybrid approach. The proposed features are compact and capable of conveniently outperforming several existing approaches in image retrieval. To find the nearest match to the query image, the feature space is reduced further by applying k-means clustering. The implementation results are presented to test efficacy of the proposed approach.

Medical image retrieval, pneumonia detection, hand-crafted features, classification, Histogram of Gradient, feature reduction, clustering

[1] B. Venkataramana, L. Padmasree, M. Srinivasa Rao, G. Ganesan, K. Rama Krishna, “Implementation of Clustering Algorithms for real datasets in Medical Diagnostics using MATLAB”, Journal of Soft Computing and Applications, Vol.2017, Issue.1, pp.53-66, 2017.
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Irene Getzi S, D. Christopher Durairaj, V Joseph Raj, "Efficient Image Retrieval approach for Large-scale Chest X Ray data using Hand-Crafted Features and Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.890-896, 2018.

The era of Web has resulted in generation of vast amount of user-generated content and analysis of all data by the viewer is time consuming process and viewers are interested towards the specific features of an entity, so the aspect based sentiment analysis became more important. Aspect term Extraction is the prime aim for the aspect based sentiment analysis. Aspect based sentiment Analysis have explored the rule based approach to extract the aspect term . By Experimented approach we have achieved average results for aspect term extraction.

lexicon,aspect,lemma

[1] Mining and summarizing customer reviews; Minqing Hu and Bing Liu; In Proceedings of the ACMSIGKDD International Conference on Knowledge Discovery & Data Mining; 2004 ; pages 168–177, Aug.
[2] Extracting product features and opinions from reviews ; Ana-Maria Popescu and Oren Etzioni; In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2005); 2005; pages 3–28.
[3] Opinion Word Expansion and Target Extraction through Double Propagation ; Guang Qiu, Bing Liu, Jiajun Bu, and Chun Chen; Computational linguistics; 2011 ; 37(1):9–27.
[4] Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews;Su Su Htay and Khin Thidar Lynn; The ScientificWorld Journal Volume 2013, Article ID 394758, 5 pages
[5] A Rule-Based Approach to Aspect Extraction from Product Review; Soujanya Poria, Erik Cambria, Lun-Wei Ku, Chen Gui, Alexander Gelbukh ;ACM ;2014.
[6] An Approach to Perform Aspect level Sentiment Analysis on Customer Reviews using SentiscoreAlgorithm and Priority Based Classification ;Aishwarya Mohan, Manisha.R, Vijayaa.B, Naren.J ; International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4145-4148
[7] Sentiment Analysis of Movie Reviews -A new Feature-based Heuristic for Aspect-level Sentiment Classification;,V.K. Singh, R. Piryani, A. Uddin,P. Waila; Researchgate;2015
[8] Aspect Based Analysis for Rating Prediction of the Restaurant Reviews;Namita Mittal, Basant Agarwal, Shalini Laddha, Manish Sharma;International Journal of Computer System (ISSN: 2394-1065), Volume 02– Issue 03, March, 2015
[9] Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation; Md Shad Akhtar, Asif Ekbal and Pushpak Bhattacharyya; LRTC ; 2015
[10] Hindi Dependency Parsing and Treebank Validation ; Bharat Ram Ambati ;LRTC;2005
[11] Solving Data Sparsity for Aspect based Sentiment Analysis using Cross-linguality and Multi-linguality ;Md Shad Akhtar , Palaash Sawant, Sukanta Sen ,Asif Ekbal and Pushpak Bhattacharyya;Proceedings of NAACL-HLT 2018, pages 572–582 New Orleans, Louisiana, June 1 - 6, 2018. c 2018 Association for Computational Linguistics
[12] https://bitbucket.org/sivareddyg/hindi-dependency-parser

Deepali Mishra Tiwari, "Aspect retrieval in Hindi language feedback using Rule based method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.897-902, 2018.

Information Security ensures mathematical techniques and related aspects to provide for confidentiality, data security, entity authentication and data origin authentication. Visual cryptography is a new technique which provides information security using simple algorithm unlike the complex, computationally intensive algorithms used in other techniques like traditional cryptography. This technique allows visual information to be encrypted in such a way that their decryption can be performed by the Human Visual System (HVS), without any complex cryptographic algorithms. Circular Random Grids extends the functionality by hiding more data in circular grids to provide confidentiality and secrecy without risking suspicion of an intruder. The proposed scheme complies with the methodology of secret sharing scheme where secret information is divided into various shares in meaningless form and is further recovered on overlapping printed transparencies with the shared information on it. Each of them is then validated for authenticity. An attempt has been made to use circular rings to embed the secret information with certain angular rotation and validation of the individual cipher shared in order to avoid cheating. In this paper we are describe every methods of VCS and presented its comparative study using advantages and disadvantages.

Visual cryptography, Random grid, Circular girds, Q’tron neural networks traditional visual cryptography, circular visual cryptography, hierarchical visual cryptography

[1] Sandeep Gurung and Mrinaldeep “ChakravortMultiple Information Hiding in General Access Structure Visual Cryptography Using Q’tron Neural Network”. Springer-2018[1]
[2] Abhishek Mishra & Ashutosh Gupta “ Multi secret sharing scheme using iterative Method”. 12 Apr 2018 Elsevier[2]
[3] Bibhas Chandra Das, Md Kutubuddin Saradr, Avishek Adhikari “Efficient Constructions for t-(k; n)-Randon Grid Visual Cryptographic Schemes”. 2017 IEEE[3]
[4] Tzung-Her Chen , Kuang-Che Li “Multi-image encryption by circular random grids”. 2009 Elsevier[4]
[5] Sandeep Gurung, Mrinaldeep Chakravorty, Abhi Agarwal, M K “Multiple Information Hiding using Circular Random Grids”.2015Elsevier[5]
[6] Sandeep Gurung, Bijoy Chhetri, Mrinal Kanti Ghose “A Novel approach for Circular Random Grid with Share Authentication”. 2015 IEEE[6]
[7] S. D. Degadwala and S. Gaur “Metadata of the chapter that will be visualized in SpringerLink”. Springer-2018[7]
[8] Shyong Jian Shyu “ Visual Cryptograms of Random Grids for General Access Structures”. 2012 IEEE[8]
[9] Sandeep Gurung,Gaurav Ojha,M K Ghose “ Multiple Image Encryption using Random Circular Grids and Recursive Image Hiding”. May 2013 IJETAE[9]
[10] Sandeep Gurung, Mrinaldeep Chakravorty, Abhi Agarwal, M K Ghose “Multiple Information Hiding using CircularRandomGrids”.2015Elsevier[10]

Sudhir Parmar, Sheshang D Degadwala, Nimit Modi, "Secret sharing scheme Circular Visual Cryptography for Color Images - Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.903-906, 2018.

This paper shows the analysis of reliability of a system composed of the two- N.I.U., NO and NS in which NO is operative and NS is kept in standby mode upon failure of operative units NO units NS become operative instantaneously. Unit-1 has two types of failures. Let failure time distribution of type 1 and type 2 are assumed to be exponential with parameters λ1 and λ2 respectively, and the repair time is taken as general. When the second unit is failed it goes for replacement.

Reliability, MTSF, Availability, Busy period, Mean Sojourn Time

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Praveen Gupta and Pooja Vinodiya, "Analysis of Reliability of A Two-Non-Identical Units Cold Standby Repairable System Has Two Types of Failure," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.907-913, 2018.

Since the growth of internet is increasing day by day, hence the amount of data that is storing in Web Server is also increasing rapidly. The growth of number of users of internet is also increasing at a rapid rate, this in turn increasing the Web traffic, so we need some type of strategies or mechanism that can handle this rapid growth of Web traffic. Web Prefetching and caching are techniques that can be used to deal with this increased growth of Web Traffic. Web prefetching and caching are processes that prefetch frequent pages which are likely to be requested in near future and caching is used to store these pages in Proxy Cache Server. Here we have proposed some cache replacement policies by which the hit ratio is likely to get increased. We have proposed novel pre fetching and caching scheme to access frequent data items. It helps in improving pattern analysis, and pattern generation process. Proposed techniques will be useful in E-commerce, Web personalization for customer requirement & satisfaction. This will reduce the user overall access time in future.

World wide web, Web log, Web mining, Web usage mining, Web Transactions, Caching policies, LRU, LFU, Web Prefetching, Web caching, Hit Ratio

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[12]. Koskela, J. Heikkonen, ,and K. Kaski, (2003). "Web cache optimization with nonlinear model using object feature", Computer Networks journal, elsevier , 43(6), ( 2003), pp. 805-817
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Pushpraj Singh Chauhan , Sarvottam Dixit, Suresh Jain, "Concept of Prefetching and Caching in Web Usage Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.914-919, 2018.

Nowadays, Data Mining is a popular tool for extracting hidden knowledge from huge amount of data. To find hidden knowledge in the data without revealing sensitive information is one of the major challenges in data mining. There are many strategies have been proposed to hide the sensitive information. Association rule mining is one of the data mining techniques used to extract hidden knowledge from large datasets. This hidden knowledge contains most of the times confidential information that the users want to keep private or do not want to disclose to public. Therefore, privacy preserving data mining (PPDM) techniques are used to preserve such confidential information or restrictive pattern from unauthorized access. In this paper, all the approaches for hiding sensitive association rules in PPDM have been compared theoretically and points out their pros and cons.

Data Mining, Association rule mining, privacy preserving data mining (PPDM)

[1] Shubhra Rana, Dr. P. anthi Thilagam, “Hierarchical Homomorphic Encryption based Privacy Preserving Distributed Association Rule Mining”. IEEE 13th International Conference on Information Technology, 2014.
[2] Rachit V. Adhvaryu, Nikunj H. Domadiya, “Privacy Preserving in Association Rule Mining On Horizontally Partitioned Database”. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 3, Issue 5, May 2014.
[3] Vaishali Patil, Ramesh Vasappanavara, Tushar Ghorpade, “Securing association rule mining with FP growth algorithm in horizontally partitioned database”. International Conference on Control, Computing, Communication and Materials (ICCCCM), 2016.
[4] Lichun Li, Rongxing Lu, Kim-Kwang Raymond Choo, Anwitaman Datta, and Jun Shao, “Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases”. IEEE, 2016.
[5] Yaoan Jin, Chunhua Su, Na Ruan, and Weijia Jia, “Privacy-Preserving Mining of Association Rules for Horizontally Distributed Databases Based on FP-Tree”. Springer International Publishing AG, 2016.
[6] Golnar Assadat Afzali, Shahriar Mohammadi Jia, “Privacy preserving big data mining: association rule hiding using fuzzy logic approach”. The Institution of Engineering and Technology, 2017.
[7] Umesh Kumar Sahu, Anju Singh, “Approaches for Privacy Preserving Data Mining by Various Associations Rule Hiding Algorithms – A Survey”. International Journal of Computer Applications, 2016.
[8] Shabnum Rehman and Anil Sharma, “Privacy Preserving Data Mining Using Association Rule Based on Apriori Algorithm”. Springer Nature Singapore Pte Ltd, 2017.
[9] Narges Jamshidian Ghalehsefidi,, Mohammad Naderi Dehkord, “A Hybrid Algorithm based on Heuristic Method to Preserve Privacy in Association Rule Mining”. Indian Journal of Science and Technology,2016.
[10] D. Menaga, S. Revathi, “Least lion optimisation algorithm (LLOA) based secret key generation for privacy preserving association rule hiding”. The Institution of Engineering and Technology, 2018.
[11] Chun-WeiLin, Tzung-PeiHong, Hung-ChuanHsu, “Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining”. Hindawi Publishing Corporation the Scientific World Journal, 2014.
[12] Bettahally N. Keshavamurthy, Asad M. Khan, Durga Toshniwal, “Privacy preserving association rule mining over distributed databases using genetic algorithm”. Neural Comput & Applic, 2013.
[13] Baocang Wang, Yu Zhan, and Zhili Zhang, “Cryptanalysis of a Symmetric Fully Homomorphic Encryption Scheme”. IEEE, 2017.
[14] P. Amaranatha Reddy, MHM Krishna Prasad, “Challenges to find Association Rules over various types of data items: a Survey”. International Conference on Computing, Communication and Automation(ICCCA), 2017.
[15] Chan Man Kuok, Ada Fu, Man Hon Wong, “Mining Fuzzy Association Rules in Databases”.
[16] Harendra chahar, B N keshavamurthy, chirag modi, “Privacy-preserving distributed mining of association rules using Elliptic-curve cryptosystem and Shamir’s secret sharing scheme”. Indian Academy of Sciences, 2017.

Janki Patel, Priyanka Shah, "A Review Approaches for Hiding Sensitive Association Rules in Data Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.920-924, 2018.