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Volume-5 Issue-1, November 2018, ISSN: 2319-9598 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors:

Viswanath N., Kiran Siddharth, Jaya Krishna, Caroline

Paper Title:

STICKAPP Android Application using Android Studio

Abstract: Developing world arising with technologies find its way better to serve the consumer in every possible way. E-commerce, the great idea which lets consumer purchase products without any hassle from their place itself. The development of E-commerce gave an impact of satisfaction and trust over the products by the consumers which made a great influence in fast moving world. Several existing apps sell different products through application or website but there is no particular designated app for selling stickers. With the development of technologies and idea of E-commerce an application for stickers, number plates will relieve people from hurdles faced by them and enhances comfort from their place. Existing system have several drawbacks such as the consumer have to make a physical contact with retailer, digital money is not used, a single retailer cannot satisfy the desire of the consumer in terms of their interest, no particular app for sticker and customization according to consumer’s keen. The system uses string matching algorithm and quick sort. The delivery module uses bees algorithm to efficiently deliver the product.

Keywords: Sticker and Vehicle Accessories Delivery, E-Commerce, String Matching, Quick Sort, Bees Algorithm

References:

  1. International Journal of Advances in Electronics and Computer Science, ISSN: 2393-2835 Volume-4, Issue-3, Mar.-2017 http://iraj.in.
  2. Jiang B. Design and Implementation of Mobile Library APP Service System Based on WeChat[J]. Journal of Modern Information, 2013.
  3. Priambodo et al., International Journal of Advanced Research in Computer Science and Software Engineering 6(6), June- 2016
  4. Internetworking Indonesia Journal Vol.8/No.1 (2016)
  5. Zhao J, Huang X. The Application of WeChat to the University Laboratory Management Information System[M]// Proceedings of the 4th International Conference on Computer Engineering and Networks. Springer International Publishing, 2015:907-916.
  6. Mayur Kumar Patel, “Online Food Order System for Restaurants”,2015
  7. “Android Developer”. Available: http://developer.android.com/. Accessed on April, 1st, 2015
  8. List of devices with assisted GPS. Available: http://en.wikipedia.org/wiki/List_of_devices_with_Assisted_GPS. Accessed on April, 1st, 2015.
  9. Internet Advertising Bureau United Kingdom. Location based advertising on mobile. 2012:20
  10. V. Gils, K. Ramaekers, C. An, R. B. M. D. Koster, Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review, European Journal of Operational Research, 1-15, 2017.
  11. Bhatia S, Hilal S. A New Approach for Location based Tracking. 2013;10 (3):73-77.

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2.

Authors:

Shivkumar Mishra, R. R. Sedmakar

Paper Title:

A Robust and Reversible Watermarking Technique for Healthcare System

Abstract: Advancement in Information and technology results in use informational system on a large scale. This informational system consisting of the relational databases stored over the network and shared by different owners in a collaborative environment for decision-making and knowledge extraction purpose. Sharing these database results in the security threats such as data tampering and ownership rights. Medical data are also stored and shared by the different health organization over the cloud network. This medical data are also targeted by the attacker to manipulate or misuse the original data for their benefits. Thus, this data which are stored and shared over the cloud network needs to be taken care by implementing some powerful security mechanism. Watermarking technique are used as powerful security mechanism for ownership protection and data tampering from the malicious attackers. However, using the watermarking technique results in the modification of the original data that degrade the data quality and results in data distortation. Thus, reversible watermarking techniques are used for data protection along with data recovery. There are various reversible watermarking techniques available such as Reversible Watermarking Technique (RRW), Difference Expansion Watermarking Technique (DEW), Genetic Algorithm based on Difference Expansion (GADEW), Prediction Error Expansion Watermarking technique (PEW), and Quantization Index Modulation (QIM). The proposed system is the combination of the RRW technique and QIM technique along with distortion control for selecting the appropriate features for watermarking and data recovery. The proposed method will be more secured and robust against the attack with less data distortion and higher data recovery, implemented for relational datasets of medical healthcare system.

Keywords: Data Recovery, Data Quality, Numerical Data, Reversible Watermarking, Security.

References:

  1. kamran iftikar, M. kamran, Zahid Anwar, “ , IEE RRW –“ A Robust and Reversible Watermarking Technique for Relational Data”“,IIEE Transactions on Knowledge and Data Engineering, 201
  2. Javier Franco-Contreras, Member, IEEE, and Gouenou Coatrieux, Senior Member, IEEE “Robust Watermarking of Relational Databases With OntologyGuided Distortion Control” , Information IEEE Tranction on Information forensics and security, VOL. 10, NO. 9, September 2015
  3. “Walmart to start sharing its sales data,” http://www.nypost.com/p/news/business/walmart-opensup, last updated: 09:55 AM on February 4, 2012, last accessed: July, 20 2013.
  4. “Identity theft watch,” http://www.scambook.com/blog/2013/ 04/identity-theft-watch-customer-passwords-stolen-fromwalmart-vudu-video-service/, last updated: April 11, 2013, last accessed: July, 20 2013.
  5. “Securing outsourced consumer data,” http://www.databreaches.net/securing-outsourced-consumerdata/, last updated: February 26, 2013, last accessed: July, 20 2013.
  6. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” Image Processing, IEEE Transactions on, vol. 6, no. 12, pp. 1673–1687, 1997.
  7. Gupta and J. Pieprzyk, “Reversible and blind database watermarking using difference expansion,” in Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2008, p. 24.
  8. Jawad and A. Khan, “Genetic algorithm and difference expansion based reversible watermarking for relational databases,” Journal of Systems and Software, 2013
  9. E. Farfoura and S.-J. Horng, “A novel blind reversible method for watermarking relational databases,” in Parallel and Distributed Processing with Applications (ISPA), 2010 International Symposium on. IEEE, 2010, pp. 563–569.
  10. Pournaghshband, “A new watermarking approach for relational data,” in Proc. 6th Annu. Conf. ACM-SE, 2008, pp. 127–131
  11. Chen and G. W. Wornell, “Quantization index modulation: A class of provably good methods for digital watermarking and information embedding,” IEEE Trans. Inf. Theory, vol. 47, no. 4, pp. 1423–1443, May 2001.
  12. Kamran and M. Farooq, “A formal usability constraints model for watermarking of outsourced datasets,” IEEE Trans. Inf. Forensics Security, vol. 8, no. 6, pp. 1061–1072, Jun. 2013.

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3.

Authors:

E. Saraswathi, Riyaz Ahmed, Vaibhav Kanna, Ashwin Kumar Thachat

Paper Title:

An Approach to the Development of a Game Bot based on Reinforcement Learning

Abstract: The current researches for developing a Game Bot for digital games have been studied thoroughly. The bots created based on those studies play the game exactly as an expert would. The bots decide their next move by overlooking a number of moves ahead of the next move. Our approach of creating a Game Bot requires low computational resources without compromising the difficulty and expertise of play. We have proposed to develop our Game Bot through Reinforcement Learning. Reinforcement Learning is an exciting new field of machine learning, in which Bots learn by playing games. The bots are thrown in a gaming environment, and then trained to learn by observing their actions and rewards. We are going to build a Game Bot that will autonomously play against and beat the Atari game Neon Race Car.

Keywords: Game Bot, Reinforcement Learning, Machine Learnin.

References:

  1. Muhammad Firmansyah Kasim Department of Physics University of Oxford Ox ford OX1 3RH, United Kingdom “Playing the Game of Congklak with Reinforcement Learning”, 8th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia, 2016.
  2. Paulo Bruno S. Serafim, Yuri Lenon B. Nogueira, Creto A. Vidal, Joaquim B. Cavalcante Neto Department of Computing Federal University of Cear´ a Fortaleza, Brazil “OntheDevelopmentofanAutonomousAgentfora3DFirst-PersonShooter Game Using Deep Reinforcement Learning “, Brazilian Symposium on Computer Games and Digital Entertainment, 2017.
  3. Etienne Perot Valeo, Maximilian Jaritz Valeo/ Inria, Marin To roman off Valeo, Raoul de Charette Inria, “End-to-EndDrivinginaRealisticRacingGamewithDeepReinforcementLearning”, IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017.
  4. Keiji Kamei, Yuuki Kakizoe, “An Approach to the Development of a Game Agent based on SOM and Reinforcement Learning”, 5th IIAI International Congress on Advanced Applied Informatics, 2016.
  5. Ian Good fellow and Yoshua Bengio and Aaron Courville “Deep Learning,” An MIT Book Press, 2016.
  6. Michael Nielsen “Neural Networks and Deep Learning”

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4.

Authors:

T.H. Feiroz Khan, Riyaz Ahmed, Vaibhav Kanna, Ashwin Kumar Thachat

Paper Title:

Hydroponics System using IoT

Abstract: Hydroponics is the method of cultivating plants without soil. Water with oxygen and required minerals acts as the cultivation medium. Hydroponics improves the quality of crops and increases crop yield, but it requires skilled labor to monitor and control it. Hydroponics is a method of Controlled Environment Agriculture (CEA), which helps close monitoring of plants for a better and successful yield where the soil may not support its maximum yield. Smart hydroponics system allows automation in the field of hydroponics to monitor and regulate growth conditions for the plant. The proposed system aims at automating the Hydroponics setup using an ESP 8266 through IoT. One main advantage of using the ESP 8266 is the reduced cost of the overall build. Though Hydroponics is efficient when it comes to the usage of resources, it can prove to be expensive with each block of the plants requiring specified electronic equipment and hardware to monitor them. One of the main aim of this project will be to ensure cost effective build that can be affordable by farmers and cultivators in order to make sure they get maximum profit and reduced wastage of resources.

Keywords: IoT, pH Sensor, Temperature and Humidity Sensor, ESP -8266/Node MCU, smart farming, MQTT Protocol.

References:

  1. Omveer Singh, Tushar Singh Sisodia. “Solar LED Street Light System with Automatic Scheme” International Conference on Energy, Communication, Data Analytics and Soft Computing (ICEDS-2017) .
  2. Kudryashov A.V, Kalinina A.S, Yagovkin G.N. “Pulse Width Modulated LED Light Control and Vision Adaptation” 2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).
  3. Thomas Novak, Klaus Pollhammer. “Traffic-Adaptive control of LED-Based Strretlights” IEEE Industrial Electronics magazine 2015.
  4. M Sitbon, S Gadelovits and A Kuperman. “Multi-output portable solar charger for Li-Ion batteries “ 7th IET International Conference on Power Electronics, Machines and Drives (PEMD 2014).
  5. Hidemitsu Sugiyama, Shinichiro Haruyama, and Masao Nakagawa. “Brightness Control Methods for Illumination and Visible-Light Communication Systems” Proceedings of the Third International Conference on Wireless and Mobile Communications (ICWMC'07).
  6. Mashyrian, “Effective management of high-power LEDs”, Modern light engineering, vol 3, pp. 72-75, 2010.
  7. Lehman, “Recommended practices of modulating current in high brightness LEDs for mitigating health risk to viewers”, IEEE ECCE, 2010.
  8. Jack Ganssle and Michael Barr,”Pulse Width Modulation”, Embedded Systems Dictionary, pp. 103-104, CMP Books, 2003.
  9. H. Khan, “Non-Conventional Energy Resources, 2nd edition, Tata McGraw Hill education Pvt. Ltd, pp.83-86, 2010.
  10. Maninder Singh, Vatanjeet Singh,”Efficient Autonomous Solar Energy Harvesting System using Dynamic Offset Feed Mirrored Parabolic Dish Integrated Solar Panel”, IEEE WISPNET conference, 2016.
  11. Yunjun Luo, Zinian He, Changgui Wang, “ Solar energy technology”, Chemical Industry Press, beijing, 2005.

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5.

Authors:

Muetaz Almhdimohammed Alfalah

Paper Title:

Evaluating Skid Resistance of Different Asphalt Concrete Mixes

Abstract: Road traffic accident results in the death of over 1.2 million people, each year and 50 million more are injured. One of the main reasons for increase in traffic accidents is attributed to the low skid resistance of the highway surfaces which is a measure of the resistance of the pavement surface to sliding or skidding of the vehicle. Studying the effect that different asphalt concrete mixes have on skid resistance is very essential in order to provide the general public with safer roads and to help relevant authorities select best materials for pavement surfaces. The objective of the study is to compare the skid resistance of six different asphalt concrete mixes(i) asphalt concrete mix using local aggregate at the optimum Marshall asphalt content, ii) mixes with 0.5% and iii)1.0% asphalt contents higher than Marshall optimum asphalt content, iv) a mix designed using Superpave design procedure, v) a mix with steel slag to replace 30% of limestone aggregate, vi) and a mix with stone matrix aggregate gradation) and recommend the best mix that will provide higher skid resistance. The Skid resistance of the asphalt concrete mixes were determined in terms of their Skid resistance numberusing a modified British Pendulum skid resistance tester. The mixture with high skid number was the mixture with 30% slag followed by superpave. Increasing bitumen content for Marshall mix by 1% has the least skid number with about 12.8% decrease in skid resistance compared to the optimum binder content. Addition of steel slag in asphalt concrete mixes and using SMA mixes can be implemented to improve skidresistance of road surfaces, especially at highway intersections.

Keywords: Skid Resistance; British Pendulum; Skid Number; Superpave; Stone Matrix Aggregate; Slag; Micro Texture.

References:

  1. World health organisation. Global Status Report on Road Safety: Time for Action. 2009.
  2. Persson, B.N.J., Tartaglino, U., Tosatti, E., et al., 2004. Rubber friction on wet rough substrates at low sliding velocity: the sealing effect. Kautschuk Gummi Kunststoffe 57 (10), 532e537.
  3. Ueckermann A, Wang D, Oeser M, Steinauer B. Calculation of skid resistance from texture measurements, Journal of traffic and transportation engineering (English Edition) 2015; 2 (1) pp. 3-16
  4. Zhang, T. Liu, C. Liu, Z. Chen, Research on skid resistance of asphalt pavement based on three-dimensional laser-scanning technology and pressure-sensitive film, Constr. Build. Mater. 69 (2014) 49–59.
  5. A. Ali, R. Al-Mahrooqi, M. Al-Mammari, N. Al-Hinai, R. Taha, Measurement, analysis, evaluation, and restoration of skid resistance on streets of Muscat, Transp. Res. Rec. 1655 (1998) 200–210.
  6. W. Hall, K.L. Smith, L. Titus-Glover, L.D. Evans, J.C. Wambold, T.J. Yager, et al., Guide for Pavement Friction Contractor’s Final Report for National Cooperative Highway Research Program (NCHRP) Project 01-43, Transportation Research Board of the National Academies, Washington, D. C., 2009. Available: <http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w108.pdf>
  7. Mataei, H. Zakeri, M. Zahedi, F.M. Nejad, Pavement friction and skid resistance measurement methods: a literature review, Open J. Civ. Eng. 6 (04) (2016) 537.
  8. -W. Lee, S.-K. Rhee, S.-J. Chon, A study for decreasing rate of skid resistance of tinned concrete pavement based on the LTPP data, J. Korean Soc. Civ. Eng. 25 (1D) (2005) 85–90.
  9. Romanoschi, J. Metcalf, Evaluation of probability distribution function for the life of pavement structures, Transp. Res. Record: J. Transp. Res. Board 1730 (2000) 91–98.
  10. R. Ahadi, K. Nasirahmadi, The effect of asphalt concrete micro & macro texture on skid resistance, J. Rehabil. Civ. Eng. 1 (1) (2013) 15–28.
  11. Luce, E. Mahmoud, E. Masad, A. Chowdhury, Relationship of aggregate micro texture to asphalt pavement skid resistance, J. Test. Eval. 35 (6) (2007) 1–12.
  12. Colwill DM, Mercer J, Nicholls JC. UK design procedure for surface dressing in seal coats and asphalt recycling. Transportation Research Record 1507. Washington, DC: Transportation Research Board; 1995.
  13. Ksaibiti K, Cole M, Farrar M. Evaluation of surface treatment practices in the United States. Transportation Research Record 1545. Washington, DC: TRB, National Research Council; 1996. pp. 26–34.
  14. Luce, E. Mahmoud, E. Masad, A. Chowdhury, Relationship of aggregate micro texture to asphalt pavement skid resistance, J. Test. Eval. 35 (6) (2007) 1– 12.
  15. Iuele, R. Vaiana, V. Gallelli, F. De Masi, The influence of aggregate lithological nature on pavement texture polishing: a comparative investigation on a test site in Southern Italy, Adv. Civ. Eng. Mater. 5 (1) (2016) 337–352.
  16. Rezaei, E. Masad, A. Chowdhury, P. Harris, Predicting asphalt mixture skid resistance by aggregate characteristics and gradation, Transp. Res. Record: J. Transp. Res. Board (2104) (2009) 24–33.
  17. M.R. Shah, M.E. Abdullah, Effect of aggregate shape on skid resistance of compacted hot mix asphalt (HMA), in: 2010 Second International Conference on Computer and Network Technology (ICCNT), IEEE, 2010, pp. 421–425.
  18. Wang, X. Chen, X. Xie, H. Stanjek, M. Oeser, B. Steinauer, A study of the laboratory polishing behavior of granite as road surfacing aggregate, Constr. Build. Mater. 89 (2015) 25–35.
  19. M. Asi, Evaluating skid resistance of different asphalt concrete mixes, Build. Environ. 42 (1) (2007) 325–329.
  20. Li, K. Zhu, S. Noureldin, Evaluation of friction performance of coarse aggregates and hot-mix asphalt pavements, J. Test. Eval. 35 (6) (2007) 571– 577.
  21. Wu,X. Yang, V.L. Das, L.N.Mohammad,Evaluating frictional characteristics of typical wearing course mixtures in Louisiana, J. Test. Eval. 41 (4) (2013) 1–11.
  22. Stroup-Gardiner, J. Studdard, C. Wagner, Evaluation of hot mix asphalt macro-and microtexture, J. Test. Eval. 32 (1) (2004) 7–16.
  23. Dunford, Friction and the texture of aggregate particles used in the road surface course PhD thesis, University of Nottingham, UK, 2013.
  24. AASHTO, Guide for Pavement Friction, first ed., American Association of State Highway and Transportation Officials (AASHTO), Washington DC, 2008.
  25. M.J.G. Erkens, Asphalt Concrete Response – Determination, Modeling and Prediction PhD thesis, Delft University of Technology, The Netherlands, 2002.
  26. W. Flintsch, K.K. McGhee, Izeppi E. de León, S. Najafi, The Little Book of Tire Pavement Friction Surface Properties Consortium, 2012. Available from: https://secure.hosting.vt.edu/www.apps.vtti.vt.edu/1-pagers/CSTI_Flintsch/The%20Little%20Book%20of%20Tire%20Pavement%20Friction.pdf (Accessed October 2015).
  27. J. Henry, Evaluation of Pavement Friction Characteristics. NCHRP Synthesis 291, National Cooperative Highway Research Program (NCHRP), Washington, D.C., 2000.
  28. J. Wilson, The Effect of Rainfall and Contaminants on Road Pavement Skid Resistance New Zealand Transport Agency research report 515, 2013. Available: <https://www.nzta.govt.nz/assets/resources/research/reports/ 515/docs/515.pdf>. Accessed November 2015.
  29. K. Srirangam, Numerical simulation of tire-pavement interaction PhD thesis, Delft University of Technology, The Netherlands, 2015. doi: 10.4233/ uuid:ccf73339-112f-4fff-b846a828a6120a3d.
  30. Villani, Friction in asphalt concrete mixes: experimental and computational issues PhD thesis, Delft University of Technology, The Netherlands, 2015, http://dx.doi.org/10.4233/uuid:2930bb7b-e156-4ac7-b9f2-33ce637938f2.

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6.

Authors:

Ramya M., Nandhini P., Nisha R., Suganya.R

Paper Title:

A Research Based on Medical Image Processing in Various Healthcare Industries

Abstract: The Survey is based on the Medical Image Processing in various Healthcare Industries to overcome the risk of diseases. Some of the healthcare applications are Early Diagnostics, Patient Health Record, Lung Cancer, Alzheimer’s disease, Parkinson’s disease, Fish Disease, Retinal Disease, and Brain Tumor. The impact of medical image processing plays a vital role in healthcare industry for the diagnosis of disease. It will be helpful for the doctor to effectively analyse disease in a short span of time. Medical Image processing is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues. In this research paper, Analysis of various healthcare applications was discussed.

Keywords: Early Diagnostic, Lung Cancer, Alzheimer’s Disease, Parkinson’s Disease, Fish Disease, Retinal Disease, Brain Tumor.

References:

  1. Ashis Kumar Dhara and Chanukya Krishna Chama,” Content-Based Image Retrieval System for Differential Diagnosis of Lung Cancer “, International Research Based Journal, Vol (6)-Issue (1), January 2012.
  2. Pedro Miguel and Maravilha Morgado,” Automated Diagnosis of Alzheimer’s disease using PET Images: A study of alternative procedures for feature extraction and selection”, International Journal of Computer Engineering and Information Technology, VOL. 9, NO. 2, 2012.
  3. Imene Garali and Mouloud Adel, “A Novel Feature Selection in the case of Brain PET Image Classification”, 23rd European Signal Processing Conference (EUSIPCO), Volume 6, Issue 1,
  4. Neeraj Sharma and Lalit M. Aggarwal,” Automated medical image segmentation techniques”, Journal of Medical Physics, Vol. 35, No. 1, 3-14, April 2010.
  5. Valli and Dr.G.Wiselin Jiji,” Parkinson’s Disease Diagnosis using Image Processing Techniques a Survey”, International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014.
  6. Nithya, H.Umamageswari, L.Priya, J.Priya and K.Nithya,” New Feature Selection Method on Lung Lesions”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 5, Issue 3, March 2016.
  7. Jeong-Seon Park, Myung-Joo Oh, and Soonhee Han,” Fish Disease Diagnosis System Based on Image Processing of Pathogens’ Microscopic Images”, Frontiers in the Convergence of Bioscience and Information Technologies 2007, Vol.6, No.3, 03 February 2015.
  8. Liqun Ji, Yun Gu, Kun Sun, Jie Yang and Yu Qiao,”Congenital heart disease (CHD) discrimination in fetal echocardiogram based on3D feature fusion”, International Journal of Innovative Research in Science, Engineering and Technology, 20 August 2018.
  9. Lokesh Gowda J and K V Viswanatha,” Automatic Diabetic Retinopathy Detection Using FCM”, International Journal of Engineering Science Invention (IJESI), Volume 7, Issue 4, Ver. III, April 2018.
  10. Parul and Neetu Sharma,” A Study on Retinal Disease Classification and Filteration Approaches”, International Journal of Computer Science and Mobile Computing,4 Issue.5, pg. 158-165, May- 2015.
  11. Sayali D. Gahukar and Dr. S.S. Salankar,”Segmentation of MRI Brain Image Using Fuzzy C Means for Brain Tumor Diagnosis”, International Journal of Engineering Research and Applications, www.ijera.com, ISSN: 2248-9622, Vol. 4, Issue 4(Version 5), pp.107-111, April 2014.
  12. Fang Yang, Murat Hamit, Chuan B. Yan,Juan Yao,Abdugheni Kutluk, Xi M. Kong and Sui X. Zhang,” Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality”, Hindawi Journal of Healthcare Engineering, Volume 2017, Article ID 4620732, https://doi.org/10.1155/2017/4620732, 11 pages, 4 April 2017.
  13. Norliza Mohd Noor, Omar Mohd Rijal, Joel Than Chia Ming, Faizol Ahmad Roseli, Hossien Ebrahimian, Rosminah M. Kassim, and Ashari Yunus,” Segmentation of the Lung Anatomy for High Resolution Computed Tomography (HRCT) Thorax Images”, Springer International Publishing Switzerland 2013, pp. 165–175, 2013.

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