Volume-7 , Issue-1 , Jan - 2019 Go Back

Rapid depletion of fossil fuel resources on a worldwide basis has necessitated an urgent search for alternative energy sources to cater to the present days’ demand. The electric power generation system, which consists of renewable energy and fossil fuel generators together with an energy storage system and power conditioning system, is known as a hybrid power system. A hybrid power system has the ability to provide 24 hour grid quality electricity to the load. This system offers a better efficiency, flexibility of planning and environmental benefits compared to the diesel generator stand-alone system. This Paper focuses on the combination of solar wind systems for sustainable power generation. The solar energy also varies with the hourly, daily and seasonal variation of solar irradiation. The wind turbine output power varies with the wind speed at different conditions. However, a drawback, common to solar irradiation and wind speed options, is their unpredictable nature and dependence on weather and climatic changes, and the variations of solar and wind energy may not match with the time distribution of load demand. This shortcoming not only affects the system’s energy performance, but also results in batteries being discarded too early.

Smart grid, Solar power, Wind power, Hybrid Power System, Storage

[1] E. M. Natsheh, Member, IEEE, A. Albarbar, Member, IEE, and J. Yazdani, Member, IEEE, “Modeling and Control for Smart Grid Integration of Solar/Wind Energy Conversion System”.
[2] S. Sathish Kumar1 , B.Swapna2 , Dr.C.Nagarajan3, “Modeling and Control for Smart Grid Integration with MPPT of Solar/Wind Energy Conversion System Research Scholar”.
[3] Haider Ali, Syed WajidALi Shah, Usman Khalid, M. Baseer, NajamusSaqib “A Synchronized Wind-Solar Hybrid System for Future Smart Grids” COMSATS Institute of I.T.
[4] Avinash Chougule1, Dr. S. G. Kanade2 “Performance analysis of Smart Grid with super conducting fault current imiter in a Solar and Wnd based microgrid.” S.Ganesh kumaran[1] ,Dr.S.Singaravelu[2] , K.vivekanandan[3] “Hybrid Energy System and its Modelling in Smart Grid”
[5] A. Arestova, M. Khmelik, V. Shipilov, and Y. Nikitin, A. Grobovoy, Member IEEE “Smart Grid Technologies Simulation Experience at Russky Island”.
[6] Lawrence K. Lettinga* , Josiah L. Mundaa , YskandarHamama a Tshwane “Dynamic performance analysis of an integrated windphotovoltaicmicrogrid with storage”
[7] Priya .N1 ,Saranya .C .M2, “Maximum Power Generation by Integrating Solar & Wind System Using Fuzzy for Voltage Regulation in Smart Grid”
[8] D. Mahesh Naik1 , D. Sreenivasulu Reddy2 , Dr. T. Devaraju3 1 “Dynamic Modeling, Control and Simulation of a Wind and PV Hybrid System for Grid Connected Application Using MATLAB”
[9] S.ChandraShekar, “Modeling and control of MPPT Based Grid Connected Wind-PV Hybrid Generation System”
[10] E. Bitar, P. P. Khargonekar, K. Poolla, “Systems and Control Opportunities in the Integration of Renewable Energy into the Smart Grid”

B.J. Patel, M.J. Chauhan, "Modelling and Simulation of Solar-Wind Hybrid System with Smart Grid Integration," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.367-376, 2019.

This paper collects the information about contents available over webpage since the Web is a huge stock of information that requires precise automated classifiers for web pages to manage web directories and increase search engine performance. In the Web page classification problem, each term can be used as a feature of each HTML / XML tag of each web page. This is an efficient way to select the best features to reduce the functional space of the derived Web page classification problem here. Content classification of web pages is essential for many Web information retrieval tasks, such as web directory management and targeted scanning. The uncontrolled nature of web content poses additional problems for the classification of web pages over traditional text classification. However, the interdependent nature of hypertext also provides functions that support the process. As with the work described in the Web page classification, the meaning of these Web-specific functions and algorithms describes leading practices and follows the assumptions underlying the use of adjacent page information.

Algorithm, Assumption, Classification, Directory, Features, Information, Process, XML, Wepage etc

[1] Daya Gupta ; Harsh Tripathi ; Mayukh Maitra, Classifying web hierarchically using multi label tree classifier, 2015 Annual IEEE India Conference (INDICON), 2015
[2] Sumaia Mohammed Al-Ghuribi ; Saleh Alshomrani, A Simple Study of Webpage Text Classification Algorithms for Arabic and English Languages, 2013 International Conference on IT Convergence and Security (ICITCS), 2013
[3] Chinese Web-page Classification Study, Weitong Huang ; LuXiongXu ; Junfeng Duan ; Yuchang Lu, Chinese Web-page Classification Study, 2007 IEEE International Conference on Control and Automation, 2007
[4] Guixian Xu ; Ziheng Yu ; Qi Qi, Efficient Sensitive Information Classification and Topic Tracking Based on Tibetan WebPages,IEEE Access, 2018
[5] Jinbeom Kang ; Joongmin Choi, Block Classification of a Web Page by Using a Combination of Multiple Classifiers, 2008 Fourth International Conference on Networked Computing and Advanced Information Management,2008Sara Chadli,Mohamed Emharraf and Mohammed Saber "The design of an IDS architecture for MANET based on multi-agent" International Colloquium on Information Science and Technology (CiSt),IEEE,2014
[6] Feiyue Ye ; Zhian Yu, Finding the Semantic Relation between Web Pages through Topic Knowledge Repository, 2009 Ninth IEEE International Conference on Computer and Information Technology, 2009
[7] He Youquan ; Xie Jianfang ; Xu Cheng, An improved Naive Bayesian algorithm for Web page text classification, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011
[8] Moonis Javed ; Aly Akhtar ; Akif Khan Yusufzai, Classification of Web Pages as Evergreen Or Ephemeral Based on Content, 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015
[9] Guixian Xu ; Chuncheng Xiang ; Xu Gao ; Xiaobing Zhao ; Guosheng Yang, Automatic Classification of Tibetan Web Pages, International Conference on Computer Science and Electronics Engineering, 2012
[10] Jie Chen, Jian Li, Hao Liao, Qingsheng Yuan, Xiuguo Bao; Study on Meaningful String Extraction Algorithm for Improving Webpage Classification, IEEE, 2011
[11] Prabhjot Kaur ,Web Content Classification: A Survey, IJCTT, 2014
[12] Sankalap Arora,Satvir Singh, The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection, IJCA, 2013
[13] Bundit Manaskasemsak and Arnon Rungsawang, Web Spam Detection using Link-based Ant Colony Optimization Apichat Taweesiriwate, IEEE, 2012
[14] Ontological Based Webpage ClassificationWui Kheun Ong,Jer Lang Hong,Fariza Fauzi,Ee Xion Tan, IEEE, 2012

R Khan, R K Gupta, V. Namdeo, "An Overview of Various Classification Concepts of Web Page Content," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.377-380, 2019.

The rapid development of the internet and web publishing techniques create numerous information sources published as HTML pages on World Wide Web. WWW is now a popular medium by which people all around the world can spread and gather the information of all kinds. The importance of these Web-specific features and algorithms, describe the state-of-the-art practices, and the following hypothesis. This work is for a better description of Web page classification problem. Since Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, which simulates the flash patterns and characteristics of fireflies. Clustering is a popular data analysis technique to identify homogeneous groups of objects based on the values of their attributes. Here is used for clustering on benchmarks which is more suitable than Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and other nine methods used. The webpage optimization using Naïve Bayes classifier is an improved optimized web page classification using firefly algorithm with NB classifier. The inclusion of Naïve Bayes is an expert in the field of firefighting. Current classification techniques use word consistency and grouping techniques for classifying web pages. These Techniques use an ad hoc approach to review and reconcile whole keywords on a website for classification. These methods are effective, but not without problems like slow Processing, word meaning differences, poor identification of sentences also disregard the homonymy of the words. Hence this work is better, in the accuracy, precision, etc. parameters with respect to existing concepts.

Accuracy, Artificial Bee, Classification, Clustering, Colony, Firefly, Features, Homogeneous, HTML, Information, Optimization, Precision, Web, etc

[1] Guixian Xu ; Ziheng Yu ; Qi Qi, Efficient Sensitive Information Classification and Topic Tracking Based on Tibetan WebPages,IEEE Access, 2018
[2] Ankit Dilip Patel ; Vimal N. Pandya, Web page classification based on context to the content extraction of articles 2nd International Conference for Convergence in Technology (I2CT), 2017
[3] Eldhose P Sim, Classification & detection of near duplicate web pages using five stage algorithm, IEEE, 2015
[4] Guixian Xu ; Chuncheng Xiang ; Xu Gao ; Xiaobing Zhao ; Guosheng Yang, Automatic Classification of Tibetan Web Pages, International Conference on Computer Science and Electronics Engineering, 2012
[5] Jonáš Krutil ; Miloš Kudělka ; Václav Snášel, Web page classification based on Schema.org collection,2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN), 2012
[6] He Youquan ; Xie Jianfang ; Xu Cheng, An improved Naive Bayesian algorithm for Web page text classification, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011
[7] Boyi Xu ; Jing Wang ; Hongming Cai, A Web page classification algorithm and its application in E-government system, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010
[8] Weitong Huang ; Luxiong Xu ; Yanmin Liu, Preprocessing and Feature Preparation in Chinese Web Page Classification, 2009 International Conference on Computer Engineering and Technology, 2009
[9] Jinbeom Kang ; Joongmin Choi, Block Classification of a Web Page by Using a Combination of Multiple Classifiers, 2008 Fourth International Conference on Networked Computing and Advanced Information Management,2008
[10] Yong Zhang ; Bin Fan ; Long-bin Xiao, Web Page Classification Based on a Least Square Support Vector Machine with Latent Semantic Analysis, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008
[11] Moonis Javed ; Aly Akhtar ; Akif Khan Yusufzai, Classification of Web Pages as Evergreen Or Ephemeral Based on Content, 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015
[12] Feiyue Ye ; Zhian Yu, Finding the Semantic Relation between Web Pages through Topic Knowledge Repository, 2009 Ninth IEEE International Conference on Computer and Information Technology, 2009
[13] Chinese Web-page Classification Study, Weitong Huang ; LuXiongXu ; Junfeng Duan ; Yuchang Lu, Chinese Web-page Classification Study, 2007 IEEE International Conference on Control and Automation, 2007
[14] Sumaia Mohammed Al-Ghuribi ; Saleh Alshomrani, A Simple Study of Webpage Text Classification Algorithms for Arabic and English Languages, 2013 International Conference on IT Convergence and Security (ICITCS), 2013
[15] Daya Gupta ; Harsh Tripathi ; Mayukh Maitra, Classifying web hierarchically using multi label tree classifier, 2015 Annual IEEE India Conference (INDICON), 2015
[16] Prabhu, Yashoteja, Manik Varma, FastXML: a fast accurate and stable tree-classifier for extreme multilabel learning, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014
[17] E. Lee, J. Kang, J. Choi, and J. Yang., Topic-specific web content adaptation to mobile devices,e 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pages 845-848. IEEE Computer Society, 2006

R Khan, R K Gupta, V. Namdeo, "A New Technique of Web Page Classification and Optimization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.381-385, 2019.

Mobile Phones, now-a-days have become the primary source of evidence in any type of investigation for providing initial leads for framing the future investigation as well as primary evidence. The retrieval and analysis of SMS messages, including deleted ones from the memory of smart mobiles is one of the main process of mobile phone forensics. With the evolution of technology, criminals can manipulate the SMS database and tamper the messages to prove their virtue and mis-guide the investigation. In the current paper we have discussed the method adopted for proving the authenticity of SMS messages retrieved during the analysis of an iPhone.

SMS Messages, iPhone, Tampering, Authenticity, iOS

[1] Practical Investigations of Digital Forensics Tools for Mobile Devices by Maynard Yates II, M.S. Florida Agricultural and Mechanical University Department of Computer and Information Sciences Technical Building A, Room 211 Tallahassee, FL 32307-5100 Maynard1.yates@famu.edu retrieved December 2018.
[2] Barrios, Rita M. and Lehrfeld, Michael R., "Ios Mobile Device Forensics: Initial Analysis" (2011). Annual ADFSL Conference on Digital Forensics, Security and Law. 4. http://commons.erau.edu/adfsl/2011/friday/4 December 2018
[3] Hoene, Thomas & Creutzburg, Reiner. (2011). iPhone forensics: a practical overview with certain commercial software. 10.1117/12.884589. December 2018.
[4] Cheema, Ahmad & Iqbal, Waseem & Ali, Waqas. (2014). An Open Source Toolkit for iOS Filesystem Forensics. 10.1007/978-3-662-44952-3_15. December 2018.
[5] Forensic Analysis on iOS Devices Author: Tim Proffitt, tim@timproffitt.com https://www.sans.org/reading-room/whitepapers/forensics/forensic-analysis-ios-devices-34092 December 2018.
[6] iPhone and iOS Forensics: Investigation, Analysis and Mobile Security for Apple iPhone, iPad and iOS Devices by Andrew HoogKatie Strzempka.
[7] Bader, M., & Baggili, I. (2010). iPhone 3GS forensics: logical analysis using apple itunes backup utility. Small scale digital device forensics journal, 4(1), 1-15 January 2019.
[8] SQLite Wikipedia article. URL https://en.wikipedia.org/wiki/SQLite January 2019.
[9] How to back up your iPhone, iPad, and iPod touch URL https://support.apple.com/en-in/HT203977 January 2019.
[10] Parsing the iPhone SMS Database article. URL https://linuxsleuthing.blogspot.com/2011/02/parsing-iphone-sms-database.html January 2019.
[11] Wikipedia article. URL https://www.theiphonewiki.com/wiki/Messages#msg_group January 2019.
[12] https://smarterforensics.com/2014/09/sqlite-parser-theres-a-new-gui/ January 2019.
[13] https://smarterforensics.com/2017/09/time-is-not-on-our-side-when-it-comes-to-messages-in-ios-11/ visited on 19/01/2019.
[14] https://linuxsleuthing.blogspot.com/2012/10/whos-texting-ios6-smsdb.html visited on 19/01/2019.
[15] https://www.codeproject.com/Articles/833535/Accessing-Backed-Up-iPhone-SMS-Messages visited on 19/01/2019.
[16] https://blog.jverkamp.com/2015/01/27/ios-backups-in-racket-messages/ visited on 19/01/2019.
[17] http://mrdreigon.com/ios6-sms-database-investigation/ visited on 19/01/2019.
[18] https://www.vivekmchawla.com/2013/04/erd-crows-foot-relationship-symbols-quick-reference.html/ visited on 19/01/2019.
[19] Forensic investigations of Apple’s iPhone by Mats Engman, 2013. https://www.diva-portal.org/smash/get/diva2:651693/fulltext01.pdf retreived January 2019.
[20] Smartphone Forensics Analysis: A Case Study by Mubarak Al-Hadadi and Ali AlShidhani International Journal of Computer and Electrical Engineering, Vol. 5, No. 6, December 2013. January 2019.

Eswara Sai Prasad Chunduru, Krishna Mangarai, Subrahmani Babu, Manohar B Pathak, "Identification of Tampered SMS Messages in iPhone – A Case Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.386-395, 2019.

The artificial intelligence is one of the most emerging area of research in the field of advance computing, which emphases a new direction of thought known as nature based computing or bio-inspired artificial system. The concept is based on the different organisms such as insect colonies, flock of birds or school of fish. This manuscript is presenting different algorithms which are useful to develop artificial systems by deploying different nature based ideas.

Evolutionary Algorithm, Behavioral System, Artificial Life, Swarm Intelligence, ACO, PSO

[1] Arkin, R. C. (1998). An Behavior-based Robotics (1st ed.). Cambridge, MA, USA: MIT Press.
[2] Blum, C. (2015). Springer Handbook of Computational Intelligence. Springer.
[3] Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence From Natural to Artificial System. Oxford University Press.
[4] Brooks, R. A. (1986). Achieving Artificial Intelligence through building robots. MIT AI Memo 899. Tech. rep., Massachusetts Institute of Technology, Cambridge, MA.
[5] Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence , 47, 139-159.
[6] Dario Floreano, C. M. (2008). Bio-Inspired Artificial Intelligence Theories, Methods and Technologies. The MIT Press.
[7] Farooq, M. (2009). Bee-Inspired Protocol Engineering From Nature to Network. Springer.
[8] J Kennedy, R. C. (1995). Particle Swarm Optimization. In Proceedings of the 1995 IEEE International Conference on Natural Networks. IEEE Press.
[9] Merkle, C. B. (Ed.). (2008). Swarm Intelligence Introduction and Applications (1 ed.). Springer-Verlag Berlin Heidelberg.

Amit Das, R.K.Singh, "Epistemology of Nature inspired Artificial System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.396-403, 2019.

The paper describes about the research work carried out for design and development of a robotic glove that will be able to find out the finger movement in a mathematical form. Further, this data can be used for treating semi-paralysed patients. In Stroke patients where body parts movement cannot take place entirely or partially can take the benefit of proposed system. Flex Sensors have been used for each finger in order to design the proposed system. Arduino-UNO software is being used for the coding of sensors. Collected mathematical data can be stored for exercise selection criteria or to develop a passive exercise model.

Physiotherabot, Flex sensors, Arduino IDE

[1] Laver K, George S, Ratcliffe J, Crotty M. Virtual reality stroke rehabilitation— hype or hope? Australian Occupational Therapy Journal 2011;58: 215–9.
[2] P. Joshi, S. Gupta, "A Innovative Approch for Robotic Hand for Object Tracking and Grasping Methods", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.2, pp.10-14, 2016
[3] Donnan GA, Fisher M, Macleod M, Davis SM. Stroke. The Lancet 2008; 371:1612–23.
[4] M. Bergamasco, B. Allotta, L. Bosio, L. Ferretti, G. Parrini, G. Prisco, F. Salsedo, and G. Sartini, “An arm exoskeleton system for teleoperation and virtual environments applications,” in Proc. IEEE Int. Conf. Robot. Autom., vol. 2, 1994, pp. 1449–1454.
[5] R. Zade, N. Khadgi, M. Kasbe, T. Mujawar, "Online Garbage Monitoring System Using Arduino and LabVIEW", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.6, pp.5-9, 2018
[6] M. Mihelj, T. Nef, and R. Reiner, “ARMin II - 7 DoF rehabilitation robot: mechanics and kinematics,” in Proc. IEEE Int. Conf. on Robotics and Automat., Roma, Italy, 2007, pp. 4120-4125.
[7] Y. Yong, W. Lan, T. Jie, and Z. Lixun, “Arm Rehabilitation Robot Impedance Control and Experimentation,” in Proc. IEEE Int. Conf. on Robotics and Biomimetics, 2006, pp. 914-918.
[8] https://learn.sparkfun.com/tutorials/flex-sensor-hookup-guide/all
[9] https://www.youtube.com/watch?v=Gc1wVdbVI0E
[10] https://learn.sparkfun.com/tutorials/pcb-basics/all
[11] https://store.arduino.cc/usa/arduino-starter-kit
[12] https://www.arduino.cc/en/main/software
[13] http://www.stroke.org.uk/information/index.html], 07 2007.
[14] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4688009/
[15] https://synergypublishers.com/journal-of-rehabilitation-robotics/
[16]https://www.choosebetterwindows.com/windows/advancement-in-glass-technology/

Vimalkumar A. Parmar, Kartik D. Kothari, "Design and Development of Active Physiotherabot for Stroke Patients," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.404-408, 2019.

A genetic algorithm based multi objective optimization technique for very large-scale integration (VLSI) circuit partitioning has been proposed. An efficient fitness function that simultaneously optimizes minimum net cut size and delay time and maximum sleep time has been worked out along with minimum power consumption. Use of bipartition has balanced the circuit perfectly. Circuit partitioning is a non-polynomial (NP) hard problem. I have used Genetic algorithm (GA)-based optimization as it shows a global optimum solution. This is a hyper graph - based solution. Since it is a part of a physical design, all the computational part including input-output (IO) pads are converted into a hyper graph. Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural selection. Fitness value has been evaluated and solution with low fitness value has been discarded. The method has been applied on the net list files used in ISPD’98 circuit benchmark suite where each file contained 20-30 nodes. MATLAB18a was used to code all the algorithms. The improvement of net cut size, delay and sleep time was 40.62%, 41.54% and 95.42% respectively compared to initial bipartition of circuit. Thus, the proposed methodology might be promising for current trends in VLSI circuit partitioning.

Partitioning, Genetic Algorithm, NP-hard, Net list, Sleep time, Delay Crossover, Mutation, Cut size

[1] B.W. Kernighan, S. Lin, An efficient heuristic procedure for partitioning graphs, The Bell System Technical Journal, 1970, 49: 291-372. DOI: 10.1002/j.1538-7305.1970.tb01770.x
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[3] L.D.E. Goldberg, Genetic algorithms in search, optimization and machine learning. Pearson Education, 2004.
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[5] C. Ababei, S. Navaratnasothie, K. Bazargan, G. Karypis, Multi-objective circuit partitioning for cut-size and path-base delay minimization. IEEE International Conference on Computer aided Design, 2002. DOI: 10.1109/ICCAD.2002.1167532
[6] M. Palesi, T. Givargis, Multi-objective design space exploration using genetic algorithms, Proceedings of the 10th international symposium on Hardware/software codesign, ACM Press, Estes Park, Colorado, 2002, 67-72. DOI: 10.1145/774789.774804
[7] Z.Q. Chen, Y.F. Yin, A new crossover operator for real-coded genetic algorithm with selecting breeding based on difference between individuals, Natural Computation (ICNC), 2012 Eighth International Conference, 2012, 644-648. DOI: 10.1109/ICNC.2012.6234556
[8] S.Y. Yuen, C.K. Chow, A genetic algorithm that adaptively mutates and never revisits, Evolutionary Computation, 2009, 13: 454-472. DOI: 10.1109/TEVC.2008.2003008
[9] W. Jigang, T. Srikanthan, Efficient algorithms for hardware/software partitioning to minimize hardware area, Circuits and System, 2006, IEEE Asia Pacific Conference, 1875-1878. DOI: 10.1109/APCCAS.2006.342205
[10] S.S. Gill, R. Chandel, A. Chandel, Genetic algorithm-based approach to circuit partitioning, International Journal of Computer and Electrical Engineering, 2010, 2: 1793-1863. DOI: 10.7763/IJCEE.2010.V2.136
[11] P. Arato, S. Juhasz, Z.A. Mann, D. Papp, Hardware-Software partitioning in embedded system design, International Conference on Complex, Intelligent and Software Intensive Systems, 2003, 197-202. DOI:10.1109/ISP.2003.1275838
[12] A. Prakkash, R.K. Lal, PSO: An approach to multi-objective VLSI Partitioning. Proceedings of the 2nd International Conference on Innovations in Information, Embedded and Communication systems. IEEE, 2015. DOI: 10.1109/ICIIECS.2015.7192971
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ISBN:02620821

Sharadindu Roy, "Genetic Algorithm Based Multiobjective Optimization for Very Large-Scale Integration (Vlsi) Circuit Partitioning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.409-417, 2019.

Additive manufacturing or 3D printing technology has been developing rapidly since the last 30 years, and it indicates excellent potential for future development. The promising future of this technology causes its significant impact on medical science. Currently, this technology is expensive; however, it is slowly becoming more affordable. 3-D printing has a huge potential to change the future of medical model production and prototyping, due to the beneficial products it can produce. 3-D printing is a revolutionary technology that can positively impact the work of medical professionals while enhancing the lives of others. The findings from the research reveal that digital and rapid prototyping are viable in reducing development time and improving prototype accuracy. Current research work describes about constructing dental implants, bridge, and crowns for damaged tooth using Additive manufacturing. Researchers have obtained a 50% reduction in the manufacturing time of the tooth implants as compared to the conventional manufacturing method. The selected material is biocompatible, and it can perform the desired operation without being affected by the chemical reaction. In addition to the reactiveness, authors have also taken into consideration the patient-specific design and pattern manufacturing. With the help of the digital X-ray one can obtain the precise dimensions of the tooth and the CAD software enables the transformation, further castable resin pattern developed through additive manufacturing aids in the final stage of casting.

Biocompatible materials, Digital Dentistry, Castable resin

[1] Vaka Vamshi Krishna Reddy, Devavarapu Sreenivasarao, Shaik Khasim Saheb, "Process, Types and Applications of 3D Printing Technologies", International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.201-205, 2018.
[2] Yang, L., Zhang, S., Oliveira, G., & Stucker, B. Development of a 3D printing method for production of a dental application. 24th International SFF Symposium - An Additive Manufacturing Conference, pp. 346–353, 2013.
[3] Sun, J., & Zhang, F. (2012). The Application of Rapid Prototyping in Prosthodontics, Journal of Prosthodontics, Vol 21, Issue 8, pp 1–4, 2012.
[4] Shruti S. Bammani, Pranav R. Birajdar and Shriniwas S Metan. Dental Crown Manufacturing using Stereolithography Method, Proceeding of Int. Conf. on Advances in Industrial and Production Engineering, AMAE DOI: 02.AIPE.2012
[5] Klim, J., & Corrales, E. B. (n.d.). Innovation in Dentistry : CAD / CAM Restorative procedures.
[6] Dr M. Taruna, Dr Ch. Vyshnavi, Dr G. Kalpana and Dr Aditya Sai Jagini. The concept of rapid prototyping and its uses in dentistry. pp. 953-957, Int. J. Adv. Res. Vol. 4 Issue. 9. 2016.
[7] Bammani, S. S., Birajdar, P. R., & Metan, S. S. Dental Crown Manufacturing using Stereolithography Method, In Proceeding of Int. Conf. on an advance in industrial and production engineering. 2012
[8] P. Umorya, R. Singh, "A Comparative Based Review on Image Segmentation of Medical Image and its Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.71-76, 2017
[9] Punam Mahesh Ingale, "The importance of Digital Image Processing and its applications", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.31-32, 2018
[10] http://dentalimplantsolutionz.com/dental-implants/custom- crowns-and-bridges-for-your-dental-implants/
[11] https://www.for.org/en/learn/patient-cases/six-maxillary-anterior-all-ceramic-crowns
[12] https://www.youtube.com/watch?v=eTD6D8ZcW7Q
[13] https://www.youtube.com/watch?v=duDiAyPfATk
[14] http://www.dhsmilecenter.com/technology/itero-digital-impression-
toronto/
[15] https://www.mynewsmile.com/blog/get-radiolucent-crown-radiopaque-crown/
[16] https://www.infodentis.com/fixed-prosthodontics/impression-techniques.php
[17] http://www.westbowmanvilledental.com/technology/itero-digital
impression
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Marmik M Dave, Kartik D Kothari, "Design of Process Flow for Dental Prosthesis Using The Concept of Additive Manufacturing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.418-423, 2019.

The exponential growth of text documents over the internet has paved the way for systematic document organization. It is widely accepted that the document clustering has augmented the information retrieval process to a greater extend. Basically all the text clustering algorithms tend to establish more appropriate clusters of text documents, and the accuracy of text clustering algorithms are measured based on cluster cohesion and separation. Keeping to the basic principle of clustering to minimize cohesion and maximize separation, all the algorithms deploy different strategies to generate better quality clusters. It is observed from the detailed literature survey that Classification, Categorization, Plagiarism Detection and Clustering are correlated. All these text mining tasks are performed based on indexing, searching or relating the key terms present in the documents. Moreover, all the text mining methods focuses on establishing the similarity or difference among the text documents, by which they perform their intended tasks. Hence, they tend to limit the application of clustering only to complement information retrieval task. This paper tries to present an algorithm to establish the partial order among the text documents and thus to extend the applications of clustering.

clustering, partial ordering, classification, categorization, indexing

[1] 1.www.wikipdia.com/ Hierarchy
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A. George Louis Raja, F. Sagayaraj Francis, P. Sugumar, "Deriving the Partial Order of Documents to Extend Clustering Applications," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.424-430, 2019.

Network operators anticipate the offering of an increasing variety of cloud-based services with stringent Service Level Agreements. Technologies currently supporting IP networks however lack the edibility and scalability properties to realize such evolution. In this article, we present Segment Routing (SR), anew network architecture aimed at sling this gap, driven by use cases denied by network operators. SR implements the source routing and tunneling paradigms, letting nodes steer packets over paths using a sequence of instructions (segments) placed in the packet header. As such, SR allows the implementation of routing policies without entries at intermediate routers. This paper introduces the SR architecture, describes its related ongoing standardization efforts, and reviews the main use-cases envisioned by network operators. The criteria for protecting ad hoc networks encompass both physical entity security and data security (authentication, integrity, confidentiality, non-repudiation). Availability is another very significant concern. For example, a robust network should not lose connectivity when a small number of nodes leave the network or become unresponsive. Access control must also be considered to prevent unauthorized access.

Segment Routing, Service Level Agreement, Authentication , Integrity, confidentiality, Non- Repudiation

[1] Ina Minei et al. MPLS-enabled applications: emerging developments and new technologies. John Wiley & Sons, 2010.
[2] R. Shakir. Spring Forward(ing) - Evolving IP/MPLS Networks with Segment Routing. In UKNOF27. 2014.
[3] Seisho Yasukawa, et al. An Analysis of Scaling Issues in MPLS-TECore Networks. IETF RFC 5439, 2009.
[4] C. Filsfils. Segment Routing: Update and Future Evolution. In MPLSSDN World 2014. 2014.
[5] IETF. Source Packet Routing in networking (spring) workinggroup.https://datatracker.ietf.org/wg/spring/charter/, 2013.
[6] Thomas D Nadeau et al. SDN: Software Defined Networks. O’ReillyMedia, Inc., 2013.
[7] Clarence Filsfils, et al. Segment Routing Architecture. draft-ietf-spring-segment-routing-01. IETF Draft, 2014.
[8] Clarence Filsfils, et al. Segment Routing with MPLS data plane. draft-ietf-spring-segment-routing-mpls-01. IETF Draft, 2015.
[9] Stefano Previdi, et al. IPv6 Segment Routing Header. draft-previdi-6man-segment-routing-header-07. IETF Draft, 2015.
[10] Peter Psenak, et al. OSPF Extensions for Segment Routing. draft-psenak-ospf-segment-routing-extensions-05. IETF Draft, 2014.
[11] C. Filsfils, N. K. Nainar, and Pignataro, “The Segment Routing Architecture,” in Proc. IEEE Globecom, 2015.
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[13] George Trimponias, Yan Xiao, Hong Xu, Xiaorui Wu, and Yanhui Geng “On Traffic Engineering with Segment Routing in SDN based WANs,” .
[14] Antonio Cianfrani ; Marco Listanti ; Marco Polverini “Incremental Deployment of Segment Routing Into an ISP Network: a Traffic Engineering Perspective”.
[15] Radu Carpa ; Olivier Glück ; Laurent Lefevre” Segment routing based traffic engineering for energy efficient backbone networks”

Kumar Parasuraman, A. Anbarasa Kumar, "Traffic Valuation using Routing Segmentation for Neural Network Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.431-437, 2019.