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Priyanka, R.
- Integrating Dynamic Architecture with Distributed Mobility Management to Optimize Route in Next Generation Internet Protocol Mobility
Abstract Views :196 |
PDF Views:0
Authors
Affiliations
1 Amrita School of Engineering, Amrita Vishwa Vidyapeetham (University), Coimbatore, Tamilnadu, IN
1 Amrita School of Engineering, Amrita Vishwa Vidyapeetham (University), Coimbatore, Tamilnadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 10 (2015), Pagination: 963-974Abstract
Increasing population of mobile users has lead to the demand of higher mobility support. Many protocols have been standardized for mobility management such as Mobile Internet Protocol version 6, hierarchical mobile IPv6 and proxy mobile IPv6 and so on. The predominantly used approach in the existing mobile networks is the centralized mobility management. In this, the messages transferred between mobile node and correspondent node must pass through each level due to the hierarchical architecture. When a mobile network is implemented with the centralized architecture, the messages are routed to the MN irrespective of its location using mobile IP for continuing services during the handover. But this approach is susceptible to issues such as single point of failure, non-optimized routes, latency issues, wastage of resources and security threats which affect the performance and scalability, demanding a flatter architecture with an efficient mechanism to face the traffic overload from the mobile users. Hence, the paper proposes a new scheme to form a flatter architecture by distributing the mobility management functionalities as distributed access point at the access level. The resistance against security threat such as man-in-the-middle attack, replay attack and false binding update attack has been achieved. Finally, numerical results show that the proposed scheme provides significant reduction in signaling cost and improves efficiency in route optimization.Keywords
Binding Acknowledgement, Binding Cache, Binding Update, Distributed Access Point, Distributed Mobility ManagementFull Text
- PTMIBSS:Profiling Top Most Influential Blogger Using Synonym Substitution Approach
Abstract Views :289 |
PDF Views:6
Authors
G. U. Vasanthakumar
1,
R. Priyanka
1,
K. C. Vanitha Raj
1,
S. Bhavani
1,
B. R. Asha Rani
1,
P. Deepa Shenoy
1,
K. R. Venugopal
1
Affiliations
1 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, IN
1 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 2 (2017), Pagination: 1408-1420Abstract
Users of Online Social Network (OSN) communicate with each other, exchange information and spread rapidly influencing others in the network for taking various decisions. Blog sites allow their users to create and publish thoughts on various topics of their interest in the form of blogs/blog documents, catching the attention and letting readers to perform various activities on them. Based on the content of the blog documents posted by the user, they become popular. In this work, a novel method to profile Top Most Influential Blogger (TMIB) is proposed based on content analysis. Content of blog documents of bloggers under consideration in the blog network are compared and analyzed. Term Frequency and Inverse Document Frequency (TF-IDF) of blog documents under consideration are obtained and their Cosine Similarity score is computed. Synonyms are substituted against those unmatched keywords if the Cosine Similarity score so computed is below the threshold and an improved Cosine Similarity score of those documents under consideration is obtained. Computing the Influence Score after Synonym substitution (ISaS) of those bloggers under conflict, the top most influential blogger is profiled. The simulation results demonstrate that the proposed Profiling Top Most Influential Blogger using Synonym Substitution (PTMIBSS) algorithm is adequately accurate in determining the top most influential blogger at any instant of time considered.Keywords
Blog Document, Content Analysis, Cosine Similarity Score, Influential Blogger, Profiling.
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- Hong-Jun Yoon and Georgia Tourassi, “Analysis of Online Social Networks to Understand Information Sharing Behaviors through Social Cognitive Theory”, Proceedings of Annual Oak Ridge National Laboratory Biomedical Science and Engineering Center Conference, pp. 1-4, 2014.
- Noor Izzati Ariff and Zaidatun Tasir, “Meta-analysis of Content Analysis Models for Analysing Online Problem Solving Discussion”, Proceedings of IEEE Conference on e-Learning, e-Management and e-Services, pp. 148-152, 2015.
- Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung and Svetha Venkatesh, “Overcoming Data Scarcity of Twitter: Using Tweets as Bootstrap with Application to Autism-related Topic Content Analysis”, Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1354-1361, 2015.
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- Faiza Belbachir, Khadidja Henni and Lynda Zaoui, “Automatic Detection of Gender on the Blogs”, Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, pp. 1-4, 2013.
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- G.U. Vasanthakumar, Bagul Prajakta, P. Deepa Shenoy, K.R. Venugopal and Lalit M. Patnaik, “PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach”, Proceedings of Eleventh International Multi-Conference on Information Processing, Vol. 54, pp. 362-370, 2015.
- G.U. Vasanthakumar, R. Priyanka, K.C. Vanitha Raj, S. Bhavani, B.R. Asha Rani, P. Deepa Shenoy and K.R. Venugopal, “PTMIB: Profiling Top Most Influential Blogger using Content Based Data Mining Approach”, Proceedings of IEEE International Conference on Data Science and Engineering, 2016.
- G.U. Vasanthakumar, P. Deepa Shenoy and K.R. Venugopal, “PTIB: Profiling Top Influential Blogger in Online Social Networks”, International Journal of Information Processing, Vol. 10, No. 1, pp. 77-91, 2016.
- Antifungal Activity of Bacillus subtilis Subsp. spizizenii (MM19) for the Management of Alternaria Leaf Blight of Marigold
Abstract Views :280 |
PDF Views:246
Authors
Affiliations
1 Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore – 641003, Tamil Nadu, IN
2 Department of Agricultural Microbiology, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, IN
1 Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore – 641003, Tamil Nadu, IN
2 Department of Agricultural Microbiology, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, IN
Source
Journal of Biological Control, Vol 32, No 2 (2018), Pagination: 95-102Abstract
Biological control with bioagents is a cost effective alternate method for the management of crop diseases. The antagonistic bacterial strains were explored for the management of leaf blight of marigold which is caused by Alternaria alternata. The present study clearly proved that the mycelial growth of A. alternata was inhibited up to 83% by Bacillus subtilis subsp. spizizenii (MM19) in vitro. GC/MS analysis of partially purified extracts of B. subtilis subsp. spizizenii (MM19) revealed the presence of antifungal Phthalic acid esters which might be responsible for the inhibition of the pathogen. Foliar application of B. subtilis subsp. spizizenii (MM19) under field conditions suppressed leaf blight by 77%. This study highlighted the potential of B. subtilis subsp. spizizenii (MM19) for the management of Alternaria leaf blight.Keywords
Alternaria, Bacillus subtilis Subsp. spizizenii, GCMS, PCR.Full Text
References
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- Air Quality Monitoring System Based on IOT
Abstract Views :315 |
PDF Views:1
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, TamilNadu, IN
1 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, TamilNadu, IN
Source
Programmable Device Circuits and Systems, Vol 11, No 3 (2019), Pagination: 37-39Abstract
The change of ecology and the environmental atmosphere is due to different forms of chemical and industrial waste results in air pollution. Pollution affects the climate most. In the proposed work, the main aim is to identify the pollutants in atmosphere with the assistance of the Internet of Things (IoT). IoT based system will examine the air quality to identify whether it exceeds past a certain limit. MQ135 gas sensor is efficiently used for air quality analysis to detect most hazardous gases in the atmosphere. This proposed IOT based mechanism helps in continuously monitoring harmful pollution level in web page which can be viewed from anywhere. Arduino controller is used for performing the entire controller operation. Air quality measured and monitored using IoT overcome drawbacks of existing mechanism by continuous graphical variation using mobile application.
Keywords
IoT, Pollution, Air Quality, Arduino.References
- Phala, Kgoputjo Simon Elvis, Anuj Kumar, and Gerhard P. Hancke. "Air quality monitoring system based on ISO/IEC/IEEE 21451 standards." IEEE Sensors Journal 16, no. 12, pp. 5037-5045, 2016.
- Palaghat Yaswanth Sai: An IoT Based Automated Noise and Air Pollution Monitoring System Vol. 6, Issue 3, March 2017.
- 1 L. Ezhilarasi, 2 K. Sripriya, 3 A. Suganya, 4 K. Vinodhini.: A System for Monitoring Air and Sound Pollution using Arduino Controller with IOT Technology Vol. 3 Issue 2 (2017) Pages 1781 – 1785.
- Zheng, Kan, Shaohang Zhao, Zhe Yang, Xiong Xiong, and Wei Xiang. "Design and implementation of LPWA-based air quality monitoring system." IEEE Access 4, pp. 3238-3245, 2016.
- Marinov, Marin B., Ivan Topalov, Elitsa Gieva, and Georgi Nikolov, "Air quality monitoring in urban environments", 39th IEEE International Spring Seminar in Electronics Technology (ISSE), pp. 443-448, 2016.
- Liu, X., & Baiocchi, O. (2016, October) "A comparison of the definitions for smart sensors, smart objects and Things in IoT”. 7th IEEE Conference in Information Technology, Electronics and Mobile Communication (IEMCON), pp. 1-4, 2016.
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- Transforming Relational Database to Ontology Using Multiple Tables
Abstract Views :429 |
PDF Views:1
Authors
R. Priyanka
1,
V. Sujatha
1
Affiliations
1 Department of Computer Applications, S. A. Engineering College, IN
1 Department of Computer Applications, S. A. Engineering College, IN
Source
Data Mining and Knowledge Engineering, Vol 11, No 4 (2019), Pagination: 57-60Abstract
Ontology is a data model for sharing knowledge and the general information on the concept or word. Data are sharable, repeatedly used in ontology. Ontology is a programming language using the database component for triggering and transforming relational database to ontology model to increase the power of data. Ontology is using OWL (WEB ONTOLOGY LANGUAGE) it is like SQL (STRUCTURE QUERY LANGUAGE). Reason for moving relational database to ontology is to map each table to class, each column mapping to the database property and each row are mapping to an instance. It can be used to only move the data. Transforming table is only possible in Relational database to ontology and not use in reverse direction. ONTOLOGY is used for developing the set of data and their structure of data for other programs. Program is solved by the method of application software agent using ontology. Ontology files are stored in relational database for continuing storage of data, so it can avoid the lacking of data. Reason for moving relational database to ontology is mapping of each table to class.
Keywords
Ontology, Relational Database, Tables to Table, Table To Table, Rows and Column, Multiple Tables.References
- Mona Dadjoo, EsmaeilKherikhah, “An Approach for Transforming of Relational database to OWL Ontology", International Journal of Web & Semantic Technology (IJWest) Vol.6, No.1, January 2015.
- Irina Astrova, NahumKorda, and AhtoKalja, “Storing OWL Ontologies in SQL Relational Database”, International Journal of Electrical, Computer and Systems Engineering Vol.1ISSN 1307-5179.
- Su-Cheng Haw,JiaweiMay,Samini Subramanian, “Mapping Relational Database to Ontology Representation A Review”,Permissions@acm.org,ICDTE 17,Aug 6-8,2017Taipei, Taiwan 2017.
- Aman Achpal, Vinayshekhar Bannihatti Kumar and Kavi Mahesh, “Modelling Ontology Semantic Constraints in Relational Database Management System”, Vol .1, March 2016.
- DiptikalyanSaha, Avriliaa Floratou, Karthik Sankaranarayanan, UmarFarooq Minhas, Ashish R. Mittal, Fatma Ozcan, “ATHENA: An Ontology-Driven System for Natural Language Querying Over Relational Data Stores”, Vol.9 2016.
- LeilaZemmouchi- Ghomari, “ Cohabitation of Relational Database and Ontologies in the Semantic Web Context”, Journal of System Integration 2018
- F.Song, G.Zacharewicz, D.Chen, “An ontology-driven framework towards building enterprise semantic information layer”, Elsevier 2013.
- J. Bakkas, M.orBahaj, “Generating of RDF graph from a relational database using Jena API”, International Journal of Engineering and Technology, 2013.
- J. Bakkas, M. Bahaj, A. Marzouk, “Direct Migration Method of RDB to Ontology while Keeping Semantics”. International Journal of Computer Applications (0975 – 8887) Volume 65– No.3, March 2013.
- Diego Calvanese, Marco Montali, Alifah Syamsiyah, Wil M.P. van der Aalst“Ontology-Driven Extraction of Event Logsfrom Relational Databases”.
- Irina Astrova, Nahum Korda, and Ahto Kalja, "Rule-Based. Transformation of SQL Relational Databases to OWL Ontologies".
- Shalom Tsurt and Carlo Zanrolo, "An implementation of GEM, supporting a semantic model on a relational backend".
- Monika Rani*, Amit Kumar Dhar and O. P. Vyas “Semi-Automatic Terminology Ontology Learning Based on Topic Modeling”.
- Chun-hua Liao,” Research on Learning OWL Ontology from Relational Database”.
- Ricardo Giuliani Martini, Giovani Rubert Librelotto, Pedro Rangel Henriques,” Formal Description and Automatic Generation of Learning Spaces based on Ontologies”