Volume-6 , Issue-5 , May - 2018 Go Back

Aluminium (Al6061) possesses excellent Castability and considered as the prominent materials widely used for various mechanical applications. Lower mechanical properties such as strength, hardness and wear resistance of the Aluminium alloy is major limitation for their use in many areas. Their limitation can be prevailed by producing hybrid metal matrix aluminium composites with appropriate selection of reinforcement material and process. In this present experimental work, Graphite is chosen as reinforcement material owing to its exceptional wear and heat resistant properties and the other reinforcement material Silicon Carbide - for its Hardness, tensile strength and enhancing yield strength characteristics. The reinforcement materials were added and by stir-casting method the cast components were produced and are subjected to various tests to find their improved Properties. Wear test was carried out using pin-on-disk wear testing machine and the microstructure analysis was studied on an Inverted optical metallurgical microscope at a magnification factor of 200X. The values obtained from the test result shows that Al6061 alloy hybrid metal matrix composite processed with addition of different weight percentage of reinforcement particles Silicon Carbide (SiC) and Graphite (Gr) in volume percentage combinations resulted in the significant improvement in mechanical properties.

Castability, hybrid metal matrix, Stir casting, mechanical properties, wear behavior

[1] Saravanan C, Subramanian K, Ananda Krishnan V, Sankara Narayanan R (2015) “Effect of particulate reinforced Aluminium metal matrix composite – A Review” Mechanics and Mechanical Engineering Vol. 19, No.1 pp23-30.
[2] Baradeswaran A, Vettivel S.C, Elaya Perumal A, Selvakumar N, Franklin Issac R (2014) “Experimental investigation on mechanical behaviour, modelling and optimization of wear parameters of B4C and graphite reinforced aluminium hybrid composites” Materials and Design 63 pp620-632.
[3] Dinesh kumar, Jasmeet Singh (2014) “Comparitive investigation of mechanical properties of Aluminium based hybrid metal matrix composites” International Journal of Engineering Research and Applications ISSN: 2248-9622 pp5-9.
[4] Maheswaran P, Thomas Renald C.J (2014) “Investigation on wear behaviour of Al6061 - Al2O3–graphite hybrid metal matrix composites using Artificial Neural Network” International Journal of Current Engineering and Technology E-ISSN 2277-4106, pp363-367.
[5] Fakruddinali J Y, Noor Ahmed R, BadarinarayanK S, Abrar Ahamed (2015) “Wear Behaviour of Al 6061-SiC-Gr Hybrid Composites” International Journal of Innovati ve Research in Science, Engineering and Technology, Vol. 4, Issue 9 pp8220-8225.
[6] Mosleh-Shirazi S, Akhlaghi F, Li D.Y (2016) “Effect of graphite content on the wear behavior of Al/2SiC/Gr hybrid nano-composites respectively in the ambient environment and an acidic solution” Tribology International 103 pp620–628.
[7] Balasivanandha Prabu S, Karunamoorthy L, Kathiresan S, Mohan B (2006) “Influence of stirring speed and stirring time on distribution of particles in cast metal matrix composite” Journal of Materials Processing Technology 171 pp268–273.
[8] Dunia Abdul Saheb (2011) “Aluminum silicon carbide and Aluminum graphite particulate composites” ARPN Journal of Engineering and Applied Sciences, vol. 6, no. 10 ISSN: 1819-6608 pp41-46.
[9] Maleki, B. Niroumand & A. Shafyei. “Effects of squeeze casting parameters on density, macrostructure and hardness of LM13 alloy” Materials Science and Engineering, A 428 (2006) 135–140.
[10] Adem Onat. “Mechanical and dry sliding wear properties of silicon carbide particulate reinforced aluminium–copper alloy matrix composites produced by direct squeeze casting method” Journal of Alloys and Compounds, 489 (2010) 119–124.
[11] P. Vijian & V.P. Arunachalam. “Optimization of squeeze cast parameters of LM6 aluminium alloy for surface roughness using Taguchi method” Journal of Materials Processing Technology, 180 (2006) 161–166.
[12] D.J. Britnell & K. Neailey. “Macrosegregation in thin walled castings produced via the direct squeeze casting process” Journal of Materials Processing Technology, 138 (2003) 306–310.
[13] L.J. Yang. “The effect of casting temperature on the properties of squeeze cast aluminium and zinc alloys” Journal of Materials Processing Technology, 140 (2003) 391–396.
[14] Hashem F. El-Labban, M. Abdelaziz & Essam R.I. Mahmoud. “Preparation and characterization of squeeze cast-Al–Si piston alloy reinforced by Ni and nano-Al2O3 particles” Journal of King Saud University - Engineering Sciences, Volume 28, Issue 2, July 2016, Pages 230-239.
[15] Stefanos M. Skolianos, Grigoris Kiourtsidis & Thomas Xatzifotiou. “Effect of applied pressure on the microstructure and mechanical properties of squeeze-cast aluminum AA6061 alloy” Materials Science and Engineering, A231 (1997) 17–24.
[16] Yuan Lu, Jianfeng Yang, Weizhong Lu, Rongzhen Liu, Guanjun Qiao & Chonggao Bao. “The mechanical properties of co-continuous Si3N4/Al composites manufactured by squeeze casting” Materials Science and Engineering, A 527 (2010) 6289–6299.
[17] M.T. Abou El-khair. “Microstructure characterization and tensile properties of squeeze-cast AlSiMg alloys” Materials Letters, 59 (2005) 894– 900.
[18] C.H. Fan, Z.H. Chen∗, W.Q. He, J.H. Chen & D. Chen. “Effects of the casting temperature on microstructure and mechanical properties of the squeeze-cast Al–Zn–Mg–Cu alloy” Journal of Alloys and Compounds, 504 (2010) L42–L45.
[19] Shuangjie Chu & Renjie Wu. “The structure and bending properties of squeeze-cast composites of A356 aluminium alloy reinforced with alumina particles” Composites Science and Technology. 59 (1999) 157-162.
[20] Jianbin Zhu & Hong Yan. “Fabrication of an A356/fly-ash-mullite interpenetrating composite and its wear properties” Ceramics International, Volume 43, Issue 15, 15 October 2017, Pages 12996-13003.
[21] O. P. Modi, B. K. Prasad, A. H. Yegneswaran & M. L. Vaidya. “Dry sliding wear behaviour of squeeze cast aluminium alloy-silicon carbide composites” Materials Science and Engineering, A 151 (1992) 235-245.
[22] K. Sukumaran, K.K. Ravikumar, S.G.K. Pillai, T.P.D. Rajan, M. Ravi, R.M. Pillai & B.C. Pai. “Studies on squeeze casting of Al 2124 alloy and 2124-10% SiCp metal matrix composite” Materials Science and Engineering, A 490 (2008) 235–241.
[23] E. Hajjari, M. Divandari & A.R. Mirhabibi. “The effect of applied pressure on fracture surface and tensile properties of nickel coated continuous carbon fiber reinforced aluminum composites fabricated by squeeze casting” Materials and Design, 31 (2010) 2381–2386.
[24] P. Vijian & V.P. Arunachalam. “Modelling and multi objective optimization of LM24 aluminium alloy squeeze cast process parameters using genetic algorithm” Journal of Materials Processing Technology, 186 (2007) 82–86.
[25] Guoping Liu, Qudong Wang, Teng Liu, Bing Ye, Haiyan Jiang & Wenjiang Ding. “Effect of T6 heat treatment on microstructure and mechanical property of 6101/A356 bimetal fabricated by squeeze casting” Materials Science & Engineering A, S0921-5093(2017)30542-30547.
[26] Adem Onat. “Mechanical and dry sliding wear properties of silicon carbide particulate reinforced aluminium–copper alloy matrix composites produced by direct squeeze casting method” Journal of Alloys and Compounds, 489 (2010) 119–124.
[27] R.G. Guana,b, Z.Y. Zhaoa, Y.D. Lib, T.J. Chenb, S.X. Xuc & P.X. Qica. “Microstructure and properties of squeeze cast A356 alloy processed with a vibrating slope” Journal of Materials Processing Technology, 229 (2016) 514–519.

N. Nandakumar, "Experimental Investigation on mechanical properties of Al6061 Hybrid Metal Matrix Composite Reinforced with Silicon Carbide and Graphite," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.397-402, 2018.

This paper describes an ongoing effort in developing a Multi-agent System (MAS) for supply chain management for Multi-brand retail. In our framework, several suppliers, dealers and retailers are involved in transportation of raw materials and goods from dealers to various stores. Agents must have the knowledge of various warehouses where goods are stored. Warehouses must be selected close to the store so that logistic and transports involved in the process get reduced. The ontology defines the domain area and set of rules to understand the mechanism and get the knowledge of information flow between agents.

Multi-agent system, supply chain, agents, ontology

[1]. http://www.techopedia.com/definition/24595/software-agent
[2]. Y. Chen Y. Peng, T. Finin, Y. Labrou, R. Cost, B. Chu, R. Sun and R. Willhelm, “A Negotiation-based Multi-agent System for Supply Chain Management’’, ACM Autonomous Agents workshop on Agent-based Decision-support for Managing the Internet-enabled Supply-chain, Seattle, May, 1999.
[3]. http://wiki.answers.com/Q/What_is_mean_by_multibrand_retail?#slide=1
[4]. Scientific Journal of Riga Technical University. Computer Sciences. Volume 45, Issue -1, Pages 128–132, ISSN (Print) 1407-7493, DOI: 10.2478/v10143-011-0054-x, February 2012
[5]. http://en.wikipedia.org/wiki/Inventory
[6]. Fox et al. “Agent-Oriented Supply-Chain Management”, International Journal of Flexible Manufacturing Systems. 2000, Vol. 12, No 2-3, P. 165-188.
[7]. Vivek kumar , Amit Kumar Goel , Prof. S.Srinivisan, “A Multiagent Conceptulization For Supply-Chain Management”, Ubiquitous Computing and Communication Journal, Vol. 3 No 4, P. 45-49.

Ritu Sindhu, "Manage Supply Chain Management Using Multi-Agent System for Multi-brand Retail," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.403-407, 2018.

This paper shows how to input two numbers using a keyboard component available in Logisim. The addition is performed using a special data structure called “Stack”. We have used the concept of ASCII input. Addition is done in a digit by digit fashion using the ideology of BCD addition. Also, we have applied this logic to design a circuit for maintaining a parking register of a housing society. A special emphasis has been done to allocate parking for the guest members.

Clock cycle, BCD adder, Stack

[1] Shrivastava, Aseem, and Vipul Agarwal. "Design of Reversible 32-Bit and 64-Bit BCD Add-Subtract using DKG Gate." (2017).
[2] Kiruthika, S., and M. Pajany. "Survey of Smart Parking System Enhanced with Current Technology." (2017).
[3] Mano, M. Morris. "Computer System Architecture". New Delhi: Prentice-Hall of India, 2008.
[4] Dal, Mehmet. "Teaching electric drives control course: Incorporation of active learning into the classroom." IEEE Transactions on Education 56.4 (2013): 459-469.
[5] Burch, Carl. "Logisim: a graphical system for logic circuit design and simulation." Journal on Educational Resources in Computing (JERIC) 2.1 (2002): 5-16.
[6] Canfield, Stephen, Sheikh Ghafoor, and Mohamed Abdelrahman. "Enhancing the programming experience for first-year engineering students through hands-on integrated computer experiences." Journal of STEM Education: Innovations and Research 13.4 (2012): 43.
[7] Wolffe, Gregory S., et al. "Teaching computer organization/architecture with limited resources using simulators." ACM SIGCSE Bulletin. Vol. 34. No. 1. ACM, 2002
[8] Salter, Richard M., and John L. Donaldson. "Abstraction and extensibility in digital logic simulation software." ACM SIGCSE Bulletin. Vol. 41. No. 1. ACM, 2009.

Priyam S Shah, Shivani D Desai, Dwisha R Kulkarni, "Addition of two large numbers entered from keyboard using Stack and its application in maintaining a Parking Register," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.408-411, 2018.

Plant pathology is the scientific analysis of plant diseases caused by pathogens and different environmental conditions. The leaf is one of the significant plant parts which highlight the presence of diseases. Existing methods use spectroscopic techniques to detect the diseases present in plants. These techniques are very expensive and can only be utilized by trained persons only. The method mentioned in this paper is an easy and cost-effective way which utilizes the leaf image of the plant. This input image is subjected to segmentation of disease part, feature extraction and classification in order to identify the disease. The main objective of this paper is to compare the clustering approaches FCM, Artificial Bee Colony and K-Means which are useful in disease part segmentation and to identify the best approach yields accurate results for identifying the plant disease. This work utilizes the GLCM, Run Length, Color Moment and Color Histogram features for feature extraction. Once these features are extracted from the segmented disease part, the disease present in the leaf is identified using the KNN (K- Nearest Neighbor) technique. The experimental result shows that the Artificial Bee Colony approach segments the diseased part of the leaf in a better way than the other two approaches.

Leaf disease, FCM, ABC, k-means clustering, GLCM, Support Vector Machine, K-Nearest Neighbor Approach

[1] Shiddalingappa Kadakol, Jyothi B Maned, “Intelligence System for leaf Extraction and disease Diagnostic”, International Journal of computer sciences and Engineering “ Vol.4, Issue 3, May 2016, PP.62-66.
[2] Aakanksha Rastogi, Ritika Arora, and Shanu Sharma,” Leaf Disease Detection and Grading using Computer Vision Technology &Fuzzy Logic” (SPIN) 2015, pp 500-505.
[3] N. Swetha and N. Sasirehka,“Prediction of Leaf Disease uses Segmentation with Hierarchical Clustering”, International Journal of Engineering Technology, Science and Research, Vol. 3, Issue.6, June 2016, pp. 37-42.
[4] Dubey S R., Dixit P., Singh N. And Gupta, J P, “Infected Fruit Part Detection uses K-Means Clustering Segmentation Technique”, International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Issue.2, 2013, pp. 65-72.
[5] Lange P S., Patil S A., Khot D S., Otari O D And Malakar U G, “Automatic Detection and Classification of Plant Disease through Image Processing”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.3, Issue.7, July 2013, pp. 798-801.
[6] Sungkur R K., Baichoo S. And Poligadu A, 2013, “An Automated System to Recognize Fungi-caused Diseases on Sugarcane Leaves”, Proceedings of Global Engineering, Science and Technology Conference, Singapore, 3-4 Oct. 2013, pp. 1-11.
[7] Jagadeesh D. Pujari, Rajesh Yakkundimath and Abdulmunaf S. Byadgi, “ International journal of signal Processing, Image Processing and Pattern Recognition, Vol.6, Issue.6 (2013).
Pp. 321-330.
[8] Al-Bashish, D., M. Braik, and S. Bani-Ahmad. “Detection and classification of leaf diseases using K-means-based segmentation and neural networks based classification”. Information Tech. Journal, Vol 10, Issue 2, 2011, pp 267-275.
[9] Bauer, S. D., F. Koch, W. Forstner. “The potential of automatic methods of classification to identify leaf diseases from multispectral images”. Precision Agriculture, Vol 12, Issue 3, 2011, pp 361-377.
[10] Giuliano Armano and Mohammad Reza Farmani,” Clustering Analysis with Combination of Artificial Bee Colony Algorithm and k-Means Technique”, International Journal of Computer Theory And Engineering”, Vol.6.No.2, April 2014, pp. 141-145.
[11] P. Mohanaiah*, P. Sathyanarayana**, L. GuruKumar,” Image Texture Feature Extraction Using GLCM Approach”, International Journal of Scientific and Research Publications, Volume 3, Issue 5, May 2013, pp. 1-5.

S. Vijayalakshmi, D. Murugan, "Comparative Analysis on Segmentation Approaches for Plant Leaf Disease Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.412-418, 2018.

Remote systems are a developing innovation that will enable clients to get data and from anywhere. The introduce of multi-bounce transmission in remote systems is the arrangement of moderate hubs to hand-off parcels from the source to the goal, in situations where the coordinate correspondence isn`t conceivable because of energy or obstruction confinements. In remote correspondence mastermind, it is basic to find the elevated utility course in multi-hop remote frameworks. For this there are immense traditions has proposed for multi ricochet remote frameworks. However, a key issue with existing remote controlling traditions is that restricting the general number of transmissions to pass on a singular package from a source center point to an objective center point that does not have any stretch of the imagination intensifies in the conclusion to-end throughput. We have proposed a new technique for cluster head replacement, which uses improved k-means algorithm. The experimental result shows that the proposed technique gives us better results than existing techniques

Wireless Sensor Networks, K-means, Routing Protocols, Base Station

[1] I.F. Akyildiz Yi-Bing Lin , Wei-Ru Lai, Rong-Jaye Chen, “A new random walk model for PCS networks”, IEEE Journal on Selected Areas in Communications, Volume: 18, Issue: 7, July 2000, pp. 1254 – 1260.
[1] JenniferYick, Biswanath Mukherjee, Dipak Ghosal, “Wireless sensor network survey”, Computer Networks
Volume 52, Issue 12, 22 August 2008, Pages 2292-2330
[3] Acharya, T, Chattopadhyay, S & Roy, R 2007, ‘Energy-Aware Virtual Backbone Tree for Efficient Routing in Wireless Sensor Networks’, in Proc. IEEE ICNS. pp. 96- 96.
[4] Chandrakasan, R, Min, Bhardwaj, M, Cho, SH & Wang, A 2002, ‘Power Aware Wireless Microsensor Systems’, In Proceedings of the ESSCIRC, Florence, Italy, pp. 47-54
[5] Al-Karaki, JN & Kamal, AE 2004, ‘Routing techniques in wireless sensor networks: a survey’, IEEE Wireless Communications, pp. 6-28.
[6] Frank Comeau & Nauman Aslam 2011, ‘Analysis of LEACH Energy Parameters’ ELSEVIER Procedia Computer Science 5, vol. 5, pp. 933-938.
[7] Schurgers, C & Srivastava, MB 2001, ‘Energy efficient routing in wireless sensor networks’, in The MILCOM Proceedings on Communications for Network-Centric Operations: Creating the Information Force, McLean, VA, pp. 551-591.
[8] Heinzelman, W, Chandrakasan, AP & Balakrishnan, H 2002, ‘An application-specific protocol architecture for wireless microsensor networks,’ IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660-670
[9] Manjeshwar & Agarwal, DP 2002, ‘APTEEN: A hybrid Protocol for efficient routing and comprehensive Information retrieval in wireless sensor networks’, In 2nd International Workshop on Parallel and Distributed Processing Symposium, Proceedings International, IPDPS, pp. 48.
[10] Xu, Y, Heidemann, J & Estrin, D 2002, ‘Geography-informed energy conservation for ad hoc routing’, in Proceedings of the 7th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom_01), Rome, Italy, July 2001, pp. 70-84.
[11] Tao, L, Zhu, QX & Zhang, L 2010, ‘An Improvement for LEACH Algorithm in Wireless Sensor Network’, Proc. 5th IEEE Conference Industrial Electronics Applications, pp. 1811- 1814
[12] Katiyar, V, Chand, N, Gautam, GC & Kumar, A 2011, ‘Improvement in LEACH protocol for large-scale wireless sensor networks’, Proc. 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETEC), India, pp.1070-1075.

S. Anusha, P. Dileep Kumar Reddy, "Energy Consumption in Wireless Sensor Networks using Improved K-Means," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.419-423, 2018.

Increasing complexities in the requirements set on programming solution due to change in the business activities has lead to need of modern approaches, also known as agile methodologies also known as lightweight, which claim to provide solution to above said problem. Knowledge Management (KM) can be easily accepted into agile software development (ASD) environments. Agile software development processes include some practices that support Knowledge Management (KM). KM is about learning, and ASD set up an environment that supports learning processes. In this paper, attempt is made to study the role of Agile methodology in software development, find barriers in applying agile methodologies in software development and to propose the role of agile methodologies for enhancing software development.

Agile Methodology, Software Development, Knowledge Management

[1] Kittlaus, H.-B. 2003. Software Engineering und Software Fabrik - vom Nutzen und Schaden der Metapher in der Informatik, In: Informatik-Spektrum 26: p. 8 - 12, and p. 291 – 292
[2] Davis, M. 2011. “Will Software Engineering Ever Be Engineering?”, in: CACM 54 (11), p. 32-34
[3] White, J., Simons, B. 2002. ACM’s Position on the Licencing of Software Engineers, CACM 45 (11), p. 91
[4] Spek, R. Kruizinga, E Annelies Kleijsen, 2009 Strengthening lateral relations in organisations through knowledge management, Journal of Knowledge Management, Vol. 13 Iss: 3, pp.3 – 12
[5] Awad, M. A. 2005 Comparison between Agile and Traditional Software Development Methodologies, Honours Programme Thesis, University of Western Australia.
[6] Highsmith, J. Orr, K. and Cockburn, A. 2000 Extreme Programming, E- Business Application Delivery, pp. 417
[7] Job Trends www.indeed.com/jobtrends?q=agile%2C+scrum%2C+% 22extreme+programming%22%2C+%22test+driven%22 &l=,
[8] AmitojSingh ,Kawaljeet Singh , Neeraj Sharma “Managing Knowledge in Agile Software Development” International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT2012) Proceedings published in International Journal of Computer Applications® (IJCA)
[9] Williams, L. (2010) Agile Software Development Methodologies and Practices. Advances in Computers, 80, 1-44. https://doi.org/10.1016/S0065-2458(10)80001-4
[10] Tudor, D.2006 An update on Agile methods, ITadviser, Issue 56.
[11] El-Haik, B.S. and Shaout, A. (2010) Software Design for Six Sigma: A Roadmap for Excellence. Wiley, Hoboken. https://file.scirp.org/pdf/JCC_2017033115471602.pdf
[12] Highsmith, J. and Cockburn, A. (2001) Agile Software Development: The Business of Innovation. Computer, 34, 120-127. https://doi.org/10.1109/2.947100
[13] Highsmith, J and Cockburn, A. 2001 Agile Software Development: The Business of Innovation, Computer, Vol. 34,No. 9, pp. 120-122.
[14] Version one, 2008 3rd Annual Survey: The State of Agile Development, www.versionone.com/pdf/3rdAnnualStateOfAgile_FullD ataReport.pdf.
[15] Version one, 2007 Agile development: Result Delivered, http://www.versionone.net/pdf/AgileDevelopment_Resul tsDelivered.pdf.
[16] Versiion One, 2009 4th Annual Survey 2009, www.versionone.com/agilesurvey
[17] Bennet, D. and Bennet, A. 2003 The Rise of the Knowledge Organisation, chapter 1 in Holsapple, C.W. (ed.), Handbook on Knowledge Management,Vol 1, Springer, Berlin, pp. 5–20.
[18] Reifer, D. J. 2002 How to Get the Most out of Extreme Programming/Agile Methods , In proceeding Proc. Extreme Programming and Agile Methods - XP/Agile Universe
[19] Jeffry S. Babb1 , RashinaHoda 2, and Jacob Nørbjerg3 “Barriers to Learning in Agile Software Development Projects” Department of Computer Information and Decision Management, West Texas A&M University, 2403 Russell Long Blvd. Canyon, Texas USA, 79016 jbabb@mail.wtamu.edu
[20] Martin McHugh, Fergal McCaffery, and Valentine Casey “Barriers to Adopting Agile Practices when Developing Medical Device Software” Regulated Software Research Group, Department of Computing and Mathematics, Dundalk Institute of Technology &Lero, Dundalk Co. Louth, Ireland {martin.mchugh, fergal.mccaffery, val.casey}@dkit.ie
[21] Erande, Ameya S., and Alok K. Verma. "Measuring agility of organizations-a comprehensive agility measurement tool (CAMT)." International Journal of Applied Management and Technology 6.3 (2008).
[22] Pressman R. S. ―Software Engineering: a Practitioner`s Approach‖ (2010) 7th ed.(McGraw-Hill, New York).
[23] Arun Kumar Kamepally ,TejaswiniNalamothu “Agile Methodologies in Software Engineering and Web Engineering” Department of Computer Science, Kennesaw State University, Kennesaw, Georgia, USA
[24] PekkaAbrahamsson, OutiSalo, JussiRonkainen and JuhaniWarsta “Agile Software Development Methods: Review and Analysis“ https://arxiv.org/ftp/arxiv/papers/1709/1709.08439.pdf
[25] Agile Manifesto, 2001. Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R., Mellor, S., Schwaber, K., Sutherland, J. & Thomas, D. Manifesto for Agile Software Development. http://AgileManifesto.org.
[26] Abrahamsson, P., Salo, O., Ronkainen, J. &Warsta, J. 2002. Agile Software Development Methods: Review and Analysis. Espoo. 408. VTT Publications 478. 107 p. http://www.vtt.fi/inf/pdf/publications/2002/P478.pdf

Jyoti Kharade, Sneha Prajapati, Dakshata Narkar, Dhanaji S. Kharade, "Role of agile methodology for software product development," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.424-427, 2018.

In this paper, Survey of various topic modeling algorithms is presented. Introduced classification differs from earlier efforts, providing a complementary view of the field. This survey provides a brief overview of the existing probabilistic topic models and gives motivation for future research.

Topic modeling, pLSI, LDA, Dynamical Topic Model, Supervised LDA.

[1] A. Daud, J. Li, L. Zhou, and F. Muhammad, “Knowledge discovery through directed probabilistic topic models: a survey,” Frontiers of Computer Science in China, vol. 4, no. 2, pp. 280–301, Jun. 2010.
[2] David M. Blei. Introduction to Probabilistic Topic Models. Communications of the ACM, 2011
[3] Steyvers, M. and Griffiths, T., Probabilistic Topic Models. In T. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), handbook of Latent Semantic Analysis. Hillsdale, NJ: Erlbaum, 2007
[4] Jelisavcic, V., Furlan, B., Protic, J., & Milutinovic, V. M., “Topic Models and Advanced Algorithms for Profiling of Knowledge in Scientific Papers”, 35th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO’2012), 1030–1035.
[5] Hofmann, T., Probabilistic Latent Semantic Indexing. In Proceedings of the 22nd ACM SIGIR Conference on Research & Development on Information Retrieval, Berkeley, CA, USA, 1999.
[6] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by latent semantic analysis”, Journal of the American Society for Information Science, vol. 41, pp. 391–407, 1990.
[7] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, vol. 3, pp. 993–1022, Jan. 2003.
[8] T. L. Griffiths and M. Steyvers, “Finding scientific topics”, Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. Suppl 1, pp. 5228–5235, Apr. 2004.
[9] D. Blei, T. Gri, M. Jordan, and J. Tenenbaum, “Hierarchical topic models and the nested chinese restaurant process”, 2003.
[10] D. M. Blei and J. D. Lafferty, “Dynamic topic models”, in Proceedings of the 23rd international conference on Machine learning, ser. ICML ’06. New York, NY, USA: ACM, 2006, pp. 113–120.
[11] W. Li and A. McCallum, “Pachinko allocation: DAG-structured mixture models of topic correlations”, in Proceedings of the 23rd international conference on Machine learning, ser. ICML ’06. New York, NY, USA: ACM, 2006, pp. 577–58.
[12] X. Wang and A. McCallum, “Topics over time: a non-Markov continuous-time model of topical trends”, in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ser. KDD ’06. New York, NY, USA: ACM, 2006, pp. 424–433.
[13] D. M. Blei and J. D. Lafferty, “Dynamic topic models”, in Proceedings of the 23rd international conference on Machine learning, ser. ICML ’06. New York, NY, USA: ACM, 2006, pp. 113–120.
[14] M. R. Zvi, C. Chemudugunta, T. Griffiths, P. Smyth, and M. Steyvers, “Learning author-topic models from text corpora”, ACM Trans. Inf. Syst., vol. 28, no. 1, pp. 1–38, Jan. 2010.
[15] D. Mimno and A. McCallum, “Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression”, in Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI ’08), 2008.
[16] J.P. Yamron, I. Carp, L. Gillick, S. Lowe, and P. van Mulbregt, “A Hidden Markov Model Approach to Text Segmentation and Event Tracking”, Proceedings ICASSP-98, Seattle, May 1998.
[17] D. M. Blei and J. D. Mcauliffe, “Supervised topic models”, in Proceedings of th Neural Information Processing Systems – NIPS, 2007.
[18] Steyvers, Mark, Padhraic Smyth, and Chaitanya Chemuduganta. "Combining background knowledge and learned topics", Topics in Cognitive Science 3, no. 1 (2011): 18-47.
[19] Zhu, J., Xing, E.P., “Conditional topic random fields”, Proc. 27th Int. Conf. Mach. Learn. 2010, 1239–1246.
[20] Wang, Xuerui, Andrew McCallum, and Xing Wei. "Topical n-grams: Phrase and topic discovery, with an application to information retrieval" In Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on, pp. 697-702. IEEE, 2007.
[21] Blei, David M., and Pedro J. Moreno. "Topic segmentation with an aspect hidden Markov model" In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 343-348. ACM, 2001.
[22] Bisgin, Halil, Zhichao Liu, Hong Fang, Xiaowei Xu, and Weida Tong. "Mining FDA drug labels using an unsupervised learning technique-topic modeling" BMC bioinformatics 12, no. 10 (2011): S11.

Padmaja Ch V R, Lakshmi Narayana S, Divakar Ch, "A Survey on Advanced Algorithms in Topic Modeling," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.428-436, 2018.

IoT (Internet of things) means to connect heterogeneous devices with internet. When devices are medical then it involves human is at stake. It isimportant to provide security as well as privacy to a human. It should not be like a person using such devices, can be controlled from anybody and from anywhere. In this paper various security threats are discussed which can be raised while using smart health monitoring.

IoT, BAN, RFID

[1] Gubbi Jayavardhana et al.,”Internet of Things(IoT): a vision, architectural elements and future directions “, Journal of Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 1645-1660
[2] NguH. Anne et al., “ IoT Middleware: A Survey on Issues and Enabling Technologies”, IEEE Internet of Things journal, volume 4, issue 1, February 2017
[3] D Sehrawat et al., “Data Mining in IoT and its Challenges”, International Journal of Computer Science and Engineering, Vol-6, Issue-4, 2018
[4] Lee In, Lee Kyoochun,” The Internet of Things (IoT): Applications, investments, and challenges for enterprises”, Business Horizons, volume 58, issue 4, july-august 2015, pages 431-440
[5] D. Raggett, “ The Web of Things: Challenges and Opportunities,” IEEE Computer, volume 48, May 2015
[6] A. Lymberis,”Smart Wearables for remote health monitoring, from preventation to rehabilitation : current R & D, future challenges”, Information Technology Applications in Biomedicine, 2003.
[7] Alexandros Pantelopoulous and Nikolaos G. Bourbakis, “A Survey on Wearable Sensor Based Systems for Health Monitoring and Prognosis”, IEEE transactions and Systems, Man and Cybernetics, Vol 40, No. 1, January 2010.
[8] Ng H.S. et. al. ,”Security issues of wireless sensor networks in healthcare applications”, Springer, April 2006.
[9] Alexandre Santos et. al.,”Internet of Things and Smart Objects for M-health Monitoring and Control”, ProcediaTechnology,2014.
[10] A. Pandey et. al, “IOT Based Home Automation Using Arduino and ESP8266”, International Journal of Computer Science and Engineering, Vol-6, Issue-4, 2018

Renu Sharma, "Security issues in IoT enabled Health Monitoring," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.437-439, 2018.

Cloud computing system provides a lot of convenient computational and data storage services to the users. Data transfer to the cloud environment is convenient. The cloud computing system generates a large amount of private data on the main cloud. Then, the need of data recovery services are increasing day-by-day and require development of efficient data recovery technique. The aim of the data recovery technique is to collect the information from the backup server, when the server lost the data and unable to provide the data to the user. Various techniques are proposed for efficient recovery of data. This paper focuses on the comprehensive review of the data recovery approaches, issues in data recovery, failures in cloud storage, key factors and their role in data recovery and existing data security technologies in the cloud. The main objective of the review paper is to summarize the prevailing data recovery techniques in the cloud computing domain.

Cloud Computing, Cloud Storage, Data Recovery, Data Security, Data Storage Service, Private Data

[1] Z. Ke, W. Hua, and L. Chunhua, "Cloud storage technology and its applications," ed, 2012.
[2] Z. Jian-Hua and Z. Nan, "Cloud computing-based data storage and disaster recovery," in Future Computer Science and Education (ICFCSE), 2011 International Conference on, 2011, pp. 629-632.
[3] C. Modi, D. Patel, B. Borisaniya, A. Patel, and M. Rajarajan, "A survey on security issues and solutions at different layers of Cloud computing," The journal of supercomputing, vol. 63, pp. 561-592, 2013.
[4] S. Subashini and V. Kavitha, "A survey on security issues in service delivery models of cloud computing," Journal of network and computer applications, vol. 34, pp. 1-11, 2011.
[5] J. Wang, Y. Zhao, S. Jiang, and J. Le, "Providing privacy preserving in cloud computing," in Human System Interactions (HSI), 2010 3rd Conference on, 2010, pp. 472-475.
[6] M. Mowbray and S. Pearson, "A client-based privacy manager for cloud computing," in Proceedings of the fourth international ICST conference on COMmunication system softWAre and middlewaRE, 2009, p. 5.
[7] D. Lin and A. Squicciarini, "Data protection models for service provisioning in the cloud," in Proceedings of the 15th ACM symposium on Access control models and technologies, 2010, pp. 183-192.
[8] M. R. Abbasy and B. Shanmugam, "Enabling data hiding for resource sharing in cloud computing environments based on DNA sequences," in IEEE World Congress on Services (SERVICES), 2011, pp. 385-390.
[9] S. J. Stolfo, M. B. Salem, and A. D. Keromytis, "Fog computing: Mitigating insider data theft attacks in the cloud," in Security and Privacy Workshops (SPW), 2012 IEEE Symposium on, 2012, pp. 125-128.
[10] R. P. Sarang and R. K. Bunkar, "Study of Services and Privacy Usage in Cloud Computing," International Journal of Scientific Research in Computer Science and Engineering, vol. 1, pp. 7-12, 2013.
[11] Vishal Kadam and M. Kumbhkar, "Security in Cloud Environment," International Journal of Scientific Research in Computer Science and Engineering vol. 2, pp. 6-10, 2014.
[12] A. Bala and Y. Osais, "Modelling and simulation of DDOS Attack using SimEvents," International Journal of Scientific Research in Network Security and Communication, vol. 1, pp. 5-14, 2013.
[13] K. Sharma and K. R. Singh, "Seed block algorithm: a remote smart data back-up technique for cloud computing," in International Conference on Communication Systems and Network Technologies (CSNT), 2013, pp. 376-380.
[14] R. Gandhi and M. Seshaiah, "Data back-up and recovery techniques for cloud server using seed block algorithm," International Journal of Engineering Research and Applications, vol. 5, pp. 91-95, 2015.
[15] M. Shaikh, A. Achary, S. Menon, and N. Konar, "Improving cloud data storage using data partitioning and data recovery using seed block algorithm," International Journal of Latest Technology in Engineering, Management & Applied Science, vol. 4, 2015.
[16] K. Pophale, P. Patil, R. Shelake, and S. Sapkal, "Seed Block Algorithm: Remote Smart Data-Backup Technique for Cloud Computing," International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, 2015.
[17] M. Tidke, V. Jadhav, S. Parab, S. Patil, Y. Patil, U. Scholar, et al., "Seed Block Algorithm: A New Approach for Data Back-up and Recovery in Cloud Computing," International Journal of Engineering Science, vol. 4093, 2016.
[18] C.-w. Song, S. Park, D.-w. Kim, and S. Kang, "Parity cloud service: a privacy-protected personal data recovery service," in Trust, Security and Privacy in Computing and Communications (TrustCom), 2011 IEEE 10th International Conference on, 2011, pp. 812-817.
[19] H. Jung, Y. Park, C.-W. Song, and S. Kang, "PCS: a parity-based personal data recovery service in cloud," Cluster Computing, vol. 20, pp. 2655-2668, 2017.
[20] S. Zhang, H. Zhou, Y. Yang, and Z. Wu, "A joint Bloom Filter and cross-encoding for data verification and recovery in cloud," in Computers and Communications (ISCC), 2017 IEEE Symposium on, 2017, pp. 614-619.
[21] A. Singh, S. Garg, S. Batra, N. Kumar, and J. J. Rodrigues, "Bloom filter based optimization scheme for massive data handling in IoT environment," Future Generation Computer Systems, 2017.
[22] Y. Ueno, N. Miyaho, S. Suzuki, and K. Ichihara, "Performance evaluation of a disaster recovery system and practical network system applications," in Systems and Networks Communications (ICSNC), 2010 Fifth International Conference on, 2010, pp. 195-200.
[23] S. Suguna and A. Suhasini, "Overview of data backup and disaster recovery in cloud," in International Conference on Information Communication and Embedded Systems (ICICES), 2014, pp. 1-7.
[24] G. Pirro, P. Trunfio, D. Talia, P. Missier, and C. Goble, "Ergot: A semantic-based system for service discovery in distributed infrastructures," in Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 2010, pp. 263-272.
[25] B. Solanki and J. Jha, "Web Service Discovery using Relational Database and Apache Lucene," 2015.
[26] J. Jayalakshmi and G. Mathuramgbigai, "A SURVEY ON BACKUP RECOVERY ISSUES IN CLOUD COMPUTING," 2017.
[27] V. Javaraiah, "Backup for cloud and disaster recovery for consumers and SMBs," in Advanced Networks and Telecommunication Systems (ANTS), 2011 IEEE 5th International Conference on, 2011, pp. 1-3.
[28] K. Bangale, K. Nadhe, N. Gupta, S. S. Parihar, and G. Mankar, "Smart Remote Health Care Data Collection Server," International Journal of Computer Science and Mobile Computing, vol. 3, pp. 415-422, 2014.
[29] M. Assefi, M. Wittie, and A. Knight, "Impact of network performance on cloud speech recognition," in Computer Communication and Networks (ICCCN), 2015 24th International Conference on, 2015, pp. 1-6.
[30] L. Sun, J. An, Y. Yang, and M. Zeng, "Recovery strategies for service composition in dynamic network," in Cloud and Service Computing (CSC), 2011 International Conference on, 2011, pp. 60-64.
[31] D. Niu, L. Rui, C. Zhong, and X. Qiu, "A composition and recovery strategy for mobile social network service in disaster," The Computer Journal, vol. 58, pp. 700-708, 2015.
[32] M. G. Narke, M. A. Harijan, M. A. Shinde, and H. Sonawane, "A smart data backup technique for cloud computing using seed block algorithm strategy," 2015.
[33] M. Raje and D. Mukhopadhyay, "Algorithm for Back-Up and Recovery of Data Stored on Cloud along with Authentication of the User," in Information Technology (ICIT), 2015 International Conference on, 2015, pp. 175-180.
[34] D. Niu, L. Rui, H. Huang, and X. Qiu, "A service recovery method based on trust evaluation in mobile social network," Multimedia Tools and Applications, vol. 76, pp. 3255-3277, 2017.
[35] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, et al., "Above the clouds: A berkeley view of cloud computing," Technical Report UCB/EECS-2009-28, EECS Department, University of California, Berkeley2009.
[36] S. Vishwakarma and P. D. Soni, "Cloud Mirroring: A Technique of Data Recovery," International Journal of Current Engineering and Technology, vol. 5, 2015.
[37] T. Kulkarni, K. Dhaygude, S. Memane, and O. Nene, "Intelligent Cloud Back-Up System," International Journal of Emerging Engineering Research and Technology, vol. 2, pp. 82-89, 2014.
[38] S. Agalya, S. Bhavithra, and S. S. Benitta, "AN INTELLIGENT DATA BACK-UP AND RETRIEVING TECHNIQUE FOR CLUSTER ENVIRONMENT," Journal of Engineering And Technology Research, vol. 3, pp. 1-9, 2015.
[39] B. Cully, G. Lefebvre, D. Meyer, M. Feeley, N. Hutchinson, and A. Warfield, "Remus: High availability via asynchronous virtual machine replication," in Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, 2008, pp. 161-174.
[40] K. Sharma and K. R. Singh, "Online data back-up and disaster recovery techniques in cloud computing: A review," International Journal of Engineering and Innovative Technology (IJEIT), vol. 2, pp. 249-254, 2012.
[41] K. Keahey, M. Tsugawa, A. Matsunaga, and J. Fortes, "Sky computing," IEEE Internet Computing, vol. 13, pp. 43-51, 2009.
[42] M. Wiboonrat, "An empirical IT contingency planning model for disaster recovery strategy selection," in Engineering Management Conference, 2008. IEMC Europe 2008. IEEE International, 2008, pp. 1-5.
[43] J. Che, Y. Duan, T. Zhang, and J. Fan, "Study on the security models and strategies of cloud computing," Procedia Engineering, vol. 23, pp. 586-593, 2011.
[44] M. Wiboonrat, "System reliability of fault tolerant data center," in The Fifth International Conference on Communication Theory, Reliability, and Quality of Service, Chamonix, France, 2012, pp. 19-25.
[45] R. Singha, "A multi-site disaster recovery solution based on ip storage networking," in International Conference on Information and Computer Networks, 2012, pp. 139-142.
[46] M. Wiboonrat and K. Kosavisutte, "Optimization strategy for disaster recovery," in Management of Innovation and Technology, 2008. ICMIT 2008. 4th IEEE International Conference on, 2008, pp. 675-680.
[47] D. Bermbach, M. Klems, S. Tai, and M. Menzel, "Metastorage: A federated cloud storage system to manage consistency-latency tradeoffs," in IEEE International Conference on Cloud Computing (CLOUD), 2011, pp. 452-459.
[48] O. H. Alhazmi and Y. K. Malaiya, "Evaluating disaster recovery plans using the cloud," in Reliability and Maintainability Symposium (RAMS), 2013 Proceedings-Annual, 2013, pp. 1-6.
[49] F. Xiang, C. Liu, and B. Fang, "Novel “rich cloud” based data disaster recovery strategy," J. Commun, vol. 6, pp. 92-101, 2013.
[50] T. Wood, E. Cecchet, K. K. Ramakrishnan, P. J. Shenoy, J. E. van der Merwe, and A. Venkataramani, "Disaster Recovery as a Cloud Service: Economic Benefits & Deployment Challenges," HotCloud, vol. 10, pp. 8-15, 2010.
[51] G. Aceto, A. Botta, W. De Donato, and A. Pescapè, "Cloud monitoring: A survey," Computer Networks, vol. 57, pp. 2093-2115, 2013.

I. Benjamin Franklin, T.N. Ravi, "A Survey on Data Recovery Approaches in Cloud Computing Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.440-447, 2018.

In the recent years the main concern in computers and internet has been information security. Researchers and developers are mainly concerned with services which provides secure exchange of information over the internet and networks. The focus has been on 3 security triads: Confidentiality, Integrity, and Availability. The one simple way of achieving security is by the use of cryptography. There are number of features a chaotic systems possess and can be utilized within cryptography. Features like sensitivity to initial conditions/system parameters,ergodicity, mixing properties, deterministic dynamics, and structure complexity. Cryptosystems which are chaos based as compare to conventional provide high levels of security and yields betterresults [2].In this paper a hybrid approach using the concept of affine cipher and chaotic neural network (CNN) is proposed. Data is first encrypted using affine cipher and result of this is fed to the CNN. The reverse operation is performed for decryption. The experiment was carried out in MATLAB 2012a. Secrecy of the proposed work comes from the fact that total four keys need to be kept secret: two from affine and two from CNN. Further the chaos as part of CNN also add to the security.

affine, encryption, decryption, chaotic, neural, network

[1] Aihara, K., Takabe, T., & Toyoda, M. (1990). Chaotic neural networks. Physics Letters A, 144(6–7), 333–340. http://doi.org/10.1016/0375-9601(90)90136-C
[2] Singla, P., Sachdeva, P., & Ahmad, M. (2014). A Chaotic Neural Network Based Cryptographic Pseudo-Random Sequence Design. 2014 Fourth International Conference on Advanced Computing & Communication Technologies, 301–306. http://doi.org/10.1109/ACCT.2014.38
[3] Lokesh, S., &Kounte, M. R. (2016). Chaotic neural network based pseudo-random sequence generator for cryptographic applications. Proceedings of the 2015 International Conference on Applied and Theoretical Computing and Communication Technology, ICATccT 2015, 1–5. http://doi.org/10.1109/ICATCCT.2015.7456845
[4] Abdoun, N., El Assad, S., Taha, M. A., Assaf, R., Deforges, O., & Khalil, M. (2016). Secure Hash Algorithm based on Efficient Chaotic Neural Network. 2016 International Conference on Communications (COMM), 405–410. http://doi.org/10.1109/ICComm.2016.7528304
[5] Kaur, H., &Panag, T. S. (2011). Cryptography using chaotic neural network, 4(2), 417–422. http://doi.org/10.5923/j.ajsp.20140401.04
[6] Kaur, H., &Panag, T. S. (2011). Cryptography using chaotic neural network, 4(2), 417–422. http://doi.org/10.5923/j.ajsp.20140401.04
[7] Qin, K., &Oommen, B. J. (n.d.). Cryptanalysis of a cryptographic Algorithm that Utilize Chaotic Neural Network, (61300093), 1–8.
[8] Yu, W., & Cao, J. (2006). Cryptography based on delayed chaotic neural networks. Physics Letters, Section A: General, Atomic and Solid State Physics, 356(4–5), 333–338. http://doi.org/10.1016/j.physleta.2006.03.069
[9] Dalkıran, İ., &Danışman, K. (2010). Artificial neural network based chaotic generator for cryptology. Turk J Elec Eng& Comp Sci, 18(2), 225–240. http://doi.org/10.3906/elk-0907-140
[10] Crook, N., & Scheper, T. O. (2001). A Novel Chaotic Neural Network Architecture. ESANN’2001 Proceedings - European Symposium on Artificial Neural Networks, (April), 295–300. http://doi.org/10.1109/TNN.2009.2015943
[11] He, Z., Zhang, Y., & Yang, L. (1999). The Study of Chaotic Neural Network and its Applications in Associative Memory. Neural Processing Letters, 163–175.
[12] Singh, J., Shyam, P., & Yadav, S. (2014). Implementation of Caesar Cipher and Chaotic Neural network by using MATLAB Simulator, 2(6), 16–20.
[13] Chauhan, M., & Prajapati, R. (2014). Image Encryption Using Chaotic Cryptosystems and Artificial Neural Network Cryptosystems: A Review, 5(5), 52–55.
[14] Mhetras, A., &Charniya, N. (2016). Cryptography based on Artificial Neural Networks and Chaos Theory. International Journal of Computer Applications, 133(4), 25–30. http://doi.org/10.5120/ijca2016907743
[15] Jain, A., & Rajpal, N. (2012). Cryptanalysis of a Chaotic Neural Network Based Chaotic Cipher. Proc. Int. Conf. on Control System and Power Electronics, CSPE, 333, 538–544.

Vikas Thada, Utpal Shrivastava, "Affine Neural Network Cryptography," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.448-453, 2018.