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Semantic Web allows machine to understand the data, for that machine-readable semantic metadata is needed.Intelligence is necessary for the creation and processing of semantic metadata. Ontologies play an important role to implement the idea of the semantic web. Ontology is about the exact description of things and their relationships to represent the knowledge. Nowadays Automatic annotation based on artificial intelligence is required for gathering such knowledge. Manual ontology construction is labour-intensive, error-prone process, inflexible, expensive, time consuming and complex task. Ontology Generation or ontology learning includes the automatic extraction of domain’s terms and the relationships between the concepts from a corpus of text, and encoding them with an ontology language for easy information retrieval. Automatic Ontology generation and sharing it through the web make the web content more accessible to machine. Ontologies can be automatically extracted using various techniques. This paper describes the survey about automatic ontology extraction techniques and various methods used to extract the ontology.

Ontology, Resource description Framework, ontology learning, Ontology Acquisition

[1] Alani, Harith; Kim, Sanghee; Millard, David E.; Weal, Mark J.; Hall, Wendy; Lewis, Paul H. and N.R. Shadbolt, “Automatic ontology-based knowledge extraction from web documents”. IEEE Intelligent Systems, 18(1) pp. 14–21. Jan-Feb 2003.
[2] Alexandra Moreira, Jugurta Lisboa Filho, and Alcione de Paiva Oliveira ,”Automatic creation of ontology using a lexical database: an application for the energy sector”, International Conference on Applications of Natural Language to Information Systems NLDB 2016: Natural Language Processing and Information Systems pp 415-420, 17 June 2016.
[3] Amel grissa touzi1, hela ben massoud and alaya ayadi,“Automatic ontology generation for data mining using FCA and clustering”, 7 Nov 2013.
[4]Andreia Dal Ponte Novelli,José Maria Parente de Oliveira, “Simple Method for Ontology Automatic Extraction from Documents” , (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 12, 2012.
[5] Caden Howell,” Machine Learning Methods of Mapping Semantic Web Ontologies”,Published in November 22, 2008.
[6] D. Heyer, M. Läuter, U. Quasthoff, T. Wittig and C. Wolff, “Learning relations using collocations”, Proceedings of the IJCAI 2001 Workshop on Ontology Learning, 2001.
[7] Mohammad Syafrullah , Naomie Salim ,” A Framework for Ontology Learning from Textual Data “,published in 2009.
[8] J. I. Toledo-Alvarado, A. Guzmán-Arenas, G. L. Martínez-Luna,” Automatic Building of an Ontology from a Corpus of Text Documents Using Data Mining Tools”, Journal of Applied Research and Technology, Vol. 10 No.3, June 2012.
[9] Jone Correia, Rosario Girardi, Carla Faria,” Extracting Ontology Hierarchies From Text”,
Conference: Proceedings of the 23rd International Conference on Software Engineering & Knowledge Engineering (SEKE`2011), Eden Roc Renaissance, Miami Beach, USA, January 2011.
[10] M. Doerr, The CIDOC Conceptual Reference Module: An Ontological Approach to Semantic Interoperability of Metadata, AI Magazine,24(3) (2003).
[11]M. Shamsfard and A. Barforoush. “The state of the art in ontology learning: A framework for comparison. The Knowledge Engineering Review”, Vol. 18 No.4, pp. 293-316, 2003.
[12]Mazen Alobaidi, Khalid Mahmood Malik and Susan Sabra. Alobaidi, “Linked open data-based framework for automatic biomedical ontology generation “, BMC Bioinformatics (2018) 19:319.
[13] MA. Hearst, “Automatic acquisition of hyponyms from large text corpora”, Proceedings of the 14th International Conference on Computational Linguistics, 539–545, 1992.
[14]Mithun Balakrishna, Munirathnam Srikanth, “Automatic Ontology Creation from Text for National Intelligence Priorities Framework (NIPF)”, Published in OIC 2008.
[15] Pease, A., Niles, I., and Li, J.,”The Suggested Upper Merged Ontology: A Large Ontology for the Semantic Web and its Applications”, in Working Notes of the AAAI-2002 Workshop on Ontologies and the Semantic Web, Edmonton, Canada, July 28-August 1, (2002).
[16]Philipp Cimiano, Andreas Hotho, Steffen Staab,” ”Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis”, Journal of Artificial Intelligence Research (JAIR) pages 305–339, 2005.
[17]Rakesh Agrawal and Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB `94), 1994, pp 487-499, San Francisco, CA, USA.
[18]R.Girardi. “Analyzing the Problem and Main Approaches for Ontology Population”, Proceedings of 10th International Conference on Information Technology: New Generations, 2013.
[19]Roberto navigli and Paola velardia,”From Glossaries to Ontologies: Extracting Semantic Structure from Textual Definitions”, Published in Ontology Learning and Population 2008.
[20]Sanju Mishra, Sarika Jain,” Automatic Ontology Acquisition and Learning”, IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308, Volume: 03 Special Issue: 14 | Nov-2014 | SMART-2014.
[21] Sidi Benslimane, Mimoun Malki, Mustapha Rahmouni2, and Adellatif Rahmoun3 ,” Towards Ontology Extraction from Data-Intensive Web Sites: An HTML Forms-Based Reverse Engineering Approach” , The International Arab Journal of Information Technology, Vol. 5, No. 1, January 2008 .
[22] Ting Wang12, Yaoyong Li1 , Kalina Bontcheva1 , Hamish Cunningham1 , and Ji Wang, “Automatic Extraction of Hierarchical Relations from Text”, Proceedings of the 3rd European conference on The Semantic Web: research and applications Pages 215-229, June 11 - 14, 2006.
[23] Trent Apted, Judy Kay,” Automatic Construction of Learning Ontologies”, Published in ICCE 2002.
[24] W. Wilson, W. Liu and M. Bennamoun, “Ontology learning from text: A look back and into the future”, ACM Comput. Surv. 44, 4, Article 20, 2012.

R. Manimala, G. MuthuLakshmi, "Recent Survey on Automatic Ontology Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.143-147, 2019.

Human-Computer Interaction explored the outline and use of computer technology, engaged on interface between people and the computers. This interface begins with the interaction between computers and human intends to show the powerful benefits of an user oriented approach to design the modern computer system. When the concept of interface begins to come into sight, it is universally understood as the hardware and software through which a human and computer can communicate. The interface can be taken as distinct or tangible, that developer can plan describe design, implement and append to the existing functions. Furthermore it balances, the technical and cognitive issues required for understanding the delicate interplay between people and computers, specifically in emerging fields like multimedia, virtual environment and computer supported cooperative work. Recent advancements in various signal processing technology such as speech, vision based gesture recognition, eye tracking, etc. are nowadays, successfully embedded to the systems. Still researches are going on to and needed for interpreting and fusing multiple sensing modalities in the context of HCI. Human-Computer Interaction (HCI) is a discipline concerned with the design, analysis, and executes of interactive computing system, along with appropriate theoretical methods and models is described here. The basic design and its principles of HCI are discussed.

Human Computer Interaction,Cognitive psychology

[1] Changchun Pan, Human-Computer Interaction System Design and Implementation in Network, V7-104, 2010 2nd International Conference on Computer Engineering and Technology
[2] Fox, E.A., Akscyn, R., Furuta, R.,&Leggett, J. Digital libraries: Introduction. Communications of the ACM.1995, 38(4): 22-28.
[3] S. Dusan, G.J. Gadbois, J. Flanagan, Multimodal interaction on PDA’s integrating speech and pen inputs, Eurospeech (2003)
[4] A.M. Malkawi, R.S. Srinivasan, Multimodal human–computer interaction for immersive visualization: integrating speech-gesture recognition and augmented reality for indoor environments, International Association of Science and Technology for Development Conference on Computer Graphics and Imaging (2004).
[5] Preece Jenny, Rogers Yvonne, Sharp Helen, Interaction Design, John Wiley & Sons 2002.
[6] Q. Yuan, S. Sclaroff, V. Athitsos, Automatic 2D hand tracking in video sequences, IEEE Workshop on Applications of Computer Vision (2005).
[7] William P.Birmingham, An Agent-Based Architecture for Digital Libraries, The university of Michigan, D-Lib Magazine, July 1995.
[8]Murray R. Little, Engineering for human-computer interaction, Germany: Springer, 2001.
[9]Murray R. Little, Human-Computer Interaction (3rd edition), Englewood Cliffs: Prentice Hall, 2003.
[10] Ben Shneiderman, Catherine Plaisant, Designing the User Interface, Reading, Mass: Addison Wesley, 2005

M. Usha, "Human Computer Interaction: Simple Design and Principles", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.148-150, 2019.

In Health Sector real time decision making can save a person’s life. To treat the patient who has disease the doctor needs to know the history [both family and medical] of the patient. Timely decision making is very much important to increase the quality of health care.The patient details are scattered among various stake holders [doctor, scan center, Insurance, medical store] of health industry. Availability of patient data at one place can help the doctors to make diagnosis of disease easily. At the time of emergency doctors need not repeat lab test. Accumulation of patient details at one place which is collected from various resources must be stored in the format for analysis and the data extractedfor effective decision making. The accumulated data can be dealt with big data tools and techniques.The application of Big Data Analytics in Health Care helps to lower health service costs while improving patient care process. This paper deals with the dimensions of Big Data, Tools and Techniques of big data and a Model for Health Care using Big Data.

Big Data, Health Care, Big Data Analytical Tools

[1]http://hadoop.apache.org/last access 17.01.2014
[2]“EHR incentive programs,” 2014. Available: https://www.cms.gov/Regulation-and-Guidance/ Legislation/HER Incentive Programs/index.html.
[3]”Public Law 111-148-Patient Protection and Affordable Care Act,” U.S. Government Printing Office (GPO), 2013.
[4] W.Raghupathi and V.Raghupathi,”Big Data Analytics in Healthcare:promise and potential,” Health Information Science and Systems,Vol.2,pp.1-10,2014.
[5]H.Chen,R.H.L.Chisng and V.C. Story,” Business intelligence and analytics: from big data to big impact,” MIS Quarterly,Vol.36,pp.1165-1188,2012.
[6]A.Belle,R.Thiagarajan,S.M.R.Soroushmehr,F.Navadi, D.A.Beard and K.Najarian,”Big Data Analytics in Healthcare,”BioMed Research International,vol.2015, pp.1-16,2015.
[7] Transforming health care through Big Data:”strategies for leveraging big data in health care industry,” Institute for health technology information,2013.
[8] J.Bain,U.Topalogun and F.YU,”Towards Large-Scale Twitter Mining for Drug related Adverse Events,”SHB,vol.12,pp.25-32,2012.
[9] W.Raghupathi and V.Raghupathi,”An Overview of Health Analytics,”J.Health Med,Informat.,vol.4, pp.1-11,2013.
[10]Data Driven Healthcare Organizations Use Big Data Analytics for Big Gains, IBM Software,pp.1-8,(2013). http://www03.ibm.com/industries/ca/en/healthcare/documents/data_driven_healthcare_organizations_use_big_data_analytics_for_big_gains.pdf
[11]Health IT Outcomes,”The Power of Real Time Analytics at the point of Care,”December 2015.
[12]Andreu-Perez,J., Poon, C.C., Merrifield, R.D., Wong S.T. and Yang,G.Z.,2015.Big data for health. IEEE journal of biomedical and health informatics, 19(4), pp.1193-1208.
[13]Gantz J.Reinsel D,Extracting value from Chaos IDC iview,2011,pp.1-12.
[14]Mayer –Schonberger V, and Cukier K, Big data: a revolution that will transform how we live, work and think. Boston: Houghtan,Miffin Harcourt;2013.
[15]A.Katal, M.Wazid, and R.Goudar,”Big data issues,challenges,tools and good practices,” in the Sixth international conference on contemporary computing,Aug.2013.pp.404-409.

M. Vijayalakshmi, S. Kanagasankari, "Big Data Analytics in Health Care System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.151-153, 2019.

Cloud Computing is a technology, which involves sharing the resources of both hardware and software to the customers through the Internet with the reduced cost and time. But still it has many security and time related issues. To overcome the difficulties faced by the cloud many new technologies are found. In that the new technology named Edge computing most over satisfy the customers need with secured manner and within the time constraint given by the customer. In Edge the data are not accessed in the main server of the cloud instead they process it in the nearby data centres, so the time saved and security is also maintain only in the prescribed range resources. This paper includes the architecture and functions of the Edge computing, which gives new ideas to the researchers in the Edge filed

Cloud Computing, Edge Computing, Data centre

[1]. Garcia Lopez, Pedro; Montresor, Alberto; Epema, Dick; Datta, Anwitaman; Higashino, Teruo; Iamnitchi, Adriana; Barcellos, Marinho; Felber, Pascal; Riviere, Etienne (2015-09-30). "Edge-centric Computing: Vision and Challenges". ACM SIGCOMM Computer Communication Review. 45 (5): 37–42. doi:10.1145/2831347.2831354. ISSN 0146-4833.
[2] M. Jammal, T. Singh, A. Shami, R. Asal, and Y. Li, “Softwaredefined networking: State of the art and research challenges,” ComputerNetworks, vol. 72, pp. 74–98, 2014.
[3] V. R. Tadinada, “Software defined networking: Redefining the futureof internet in iot and cloud era,” in Future Internet of Things andCloud (FiCloud), 2014 International Conference on. IEEE, 2014, pp.296–301.
[4] R. Jain and S. Paul, “Network virtualization and software definednetworking for cloud computing: a survey,” Communications Magazine,IEEE, vol. 51, no. 11, pp. 24–31, 2013.
[5] S. Tomovic, M. Pejanovic-Djurisic, and I. Radusinovic, “Sdn basedmobile networks: Concepts and benefits,” Wireless Personal Communications,vol. 78, no. 3, pp. 1629–1644, 2014.
[6] N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner, “Openflow: enabling innovationin campus networks,” ACM SIGCOMM Computer CommunicationReview, vol. 38, no. 2, pp. 69–74, 2008.
[7] X.-N. Nguyen, D. Saucez, C. Barakat, and T. Turletti, “Rules placementproblem in openflow networks: a survey,” IEEE CommunicationsSurveys & Tutorials, vol. 18, no. 2, pp. 1273–1286, 2016.
[8] F. Hu, Q. Hao, and K. Bao, “A survey on software-defined network andopenflow: from concept to implementation,” Communications Surveys& Tutorials, IEEE, vol. 16, no. 4, pp. 2181–2206, 2014.
[9] “Open Networking Foundation, OpenFlow,” https://www.opennetworking.org/sdn-resources/openflow, Accessed: January,2017.
[10] A. Lara, A. Kolasani, and B. Ramamurthy, “Network innovation using openflow: A survey,” Communications Surveys & Tutorials, IEEE,vol. 16, no. 1, pp. 493–512, 2014.
[11] J. Bailey and S. Stuart, “Faucet: deploying sdn in the enterprise,”Queue, vol. 14, no. 5, p. 30, 2016
[12] D. Amendola, N. Cordeschi, and E. Baccarelli, “Bandwidth managementvms live migration in wireless fog computing for 5g networks,” inCloud Networking (Cloudnet), 2016 5th IEEE International Conferenceon. IEEE, 2016, pp. 21–26.
[13] W.-C. Lin, C.-H. Liao, K.-T. Kuo, and C. H.-P. Wen, “Flow-and-vmmigration for optimizing throughput and energy in sdn-based clouddatacenter,” in Cloud Computing Technology and Science (CloudCom),2013 IEEE 5th International Conference on, vol. 1. IEEE, 2013, pp. 206–211.
[14] “Open Networking Foundation, SDN Definition,” https://www.Opennetworking.org/sdn-resources/sdn-definition, Accessed: January,2017.
[15] H. Li, G. Shou, Y. Hu, and Z. Guo, “Mobile edge computing: Progressand challenges,” in 2016 4th IEEE International Conference on MobileCloud Computing, Services, and Engineering (MobileCloud). IEEE,2016, pp. 83–84.
[16] A. Mendiola, J. Astorga, E. Jacob, and M. Higuero, “A survey on thecontributions of software-defined networking to traffic engineering,”IEEE Communications Surveys & Tutorials, 2016.
[17] Mobile Edge Computing: A Survey-Nasir Abbas, Yan Zhang, Senior Member, IEEE, Amir Taherkordi, Member, IEEE, and Tor Skeie Member, IEEE

B. Parvathi Devi, V. Vallinayagi, "Overview of Edge Computing With Software Defined Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.154-157, 2019.