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Gunasekar, M.
- Performance Analysis of AODV, DSR and OLSR under Different Traffic Loads in OPNET
Abstract Views :205 |
PDF Views:2
Authors
Affiliations
1 Department of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
1 Department of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
Source
International Journal of Emerging Trends in Science & Technology, Vol 3, No 1 (2017), Pagination: 15-18Abstract
A Mobile Ad-Hoc Network is a special type of dynamic network where no infrastructure is required for communication. The mobile node uses multiple hops to communicate with one another across the network. In recent years, the development of routing protocols for MANET has been increased, but in literature only few realistic performance comparison among the protocols are available. This study compares the performance of three routing protocols (AODV, DSR and OLSR) over different traffic loads. The QoS metrics End-to-End delay, Throughput and Network load are considered for the analysis of the routing protocols. OPNET tool has been used for simulation.Keywords
AODV, DSDV, Mobile ad hoc Network, Performance Analysis, Routing Protocols.
References
- J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva, “A performance comparison of multi-hop wireless ad hoc network routing protocols,” Proceedings of the 4th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Texas, USA, 2530 October 1998.
- L. Layuan, L. Chunlin, and Y. Peiyan, “Performance evaluation and simulations of routing protocols in ad hoc networks,” Computer Communications, vol. 30, no. 8, pp. 1890-1898, June 2007.
- A. Verma, “A study of performance comparisons of simulated ad hoc network routing protocols,” International Journal of Computer Technology and Applications, vol. 2, no. 3, pp. 565-569, May-June 2011.
- N. Adam, M. Y. Ismail, and J. Abdullah, “Effect of node density on performances of three MANET routing protocols,” IEEE International Conference on Electronic Devices, Systems and Applications (ICEDSA), Kuala Lumpur, Malaysia, 1-14 April 2010.
- P. Rohal, R. Dahiya, and P. Dahiya, “Study and analysis of throughput, delay and packet delivery ratio in MANET for topology based routing protocols (AODV, DSR and DSDV),” International Journal for Advance Research in Engineering and Technology, vol. 1, no. 2, pp. 54-58, March 2013.
- U. Chezhian, and R. A. Ahmed, “Average delay and throughput analysis on ad hoc network protocols,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 2, pp. 99-102, February 2013.
- V. P. Patil, “Impact of mobility and network load on the performance of reactive and proactive routing protocol in MANET,” International Journal of Computer Engineering and Science, vol. 2, no. 1, pp. 8-16, September 2012.
- Adaptive Word Embedding to Reduce the Dimensionality of the Document to Vector Representation
Abstract Views :134 |
PDF Views:0
Authors
Affiliations
1 Assistant Professor, Department of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 UG Scholar, Department of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
1 Assistant Professor, Department of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 UG Scholar, Department of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
Source
International Journal of Emerging Trends in Science & Technology, Vol 6, No 2 (2020), Pagination: 38-41Abstract
Sentiment Analysis is a methodology of detecting the emotions from the text. It is an application of Natural Language Processing (NLP) methodology. The NLP enables us to know the common day to day language of the people. This will helps to decipher the sentiments of the users and hence explain liking and disliking of the people. The traditional bag-of-words models lack the accuracy of sentiment classifications. The intention of this project is to improve the accuracy of the sentiment classification by employing the concept of dimensionality reduction. Reducing the dimensionality of a large document helps to reduce the computational cost and increase efficiency. Word embedding methods capture the context of a word in a document which helps to reduce the dimensionality of text data. Vector representation of the words using a technique like Word2Vector proves to be very effective in interpreting the meaning and hence the sentiments. The words in the document will be converted into vectors. Each word is assigned a unique value (vectors) such that these vectors represent its context, meaning, and semantics. The resulting word vectors are wont to train machine learning algorithms within the sort of classifiers for sentiment classification. We use the Machine Learning classifier Naive Bayes to analyze the sentiment from the given pre-processed dataset (word vectors). Our experiments on real-world datasets show the improvement in the accuracy of sentiment classification using the word embedding techniques.Keywords
Dimensionality Reduction, Sentiment Analysis, Vector Representation, Word EmbeddingReferences
- B. Pang, and L. Lee, “Opinion mining and sentiment analysis,” Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.
- A. Pak, and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, Valletta, Malta, May 17-23, 2010.
- J. Khimar, and M. Kinikar, “Machine learning algorithms for opinion mining and sentiment classification,” International Journal of Scientific and Research Publications, vol. 3, no. 6, pp. 1-6, Jun. 2013.
- R. Mehra, M. K. Bedi, G. Singh, R. Arora, T. Bala, and S. Saxena, “Sentimental analysis using fuzzy and Naïve Bayes,” 2017 International Conference on Computing Methodologies and Communication (ICCMC), Jul. 2017.
- B. Liu, E. Blasch, Y. Chen, D. Shen, and G. Chen, “Scalable sentiment classification for big data analysis using Naïve Bayes classifier,” 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA, Oct. 6-9, 2013.
- S. Rana, and A. Singh, “Comparative analysis of sentiment orientation using SVM and Naïve Bayes techniques,” 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, India, Oct. 14-16, 2016.
- A. Goel, J. Gautam, and S. Kumar, “Real-time sentiment analysis of tweets using Naïve Bayes,” 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, India, Oct. 14-16, 2016.
- H. Parveen, and S. Pandey “Sentiment analysis on twitter data-set using Naïve Bayes algorithm,” 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Bangalore, India, Jul. 21-23, 2016.
- V. Vryniotis, “Machine learning tutorial: The multinomial logistic regression (Softmax Regression),” 2013.
- N. Zainuddin, and A. Selamat, “Sentiment analysis using support vector machine,” 2014 International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, Malaysia, Sept. 2-4, 2014.
- T. Gunasekhar, and K. T. Rao, “EBCM: Single encryption, multiple decryptions,” International Journal of Applied Engineering Research, vol. 9, no. 19, pp. 5885-5893, 2014.
- K. T. Rao, P. S. Kiran, and L. S. S. Reddy, “High-level architecture to provide cloud services using green data center,” Advances in Wireless and Mobile Communications (AWMC), 2014.
- K. T. Rao, P. S. Kiran, D. L. S. S. Reddy, V. K. Reddy, and B. T. Rao, “Genetic algorithm for energy placement of virtual machines in cloud environment,” Proceedings of the IEEE International Conference on Future Information Technology, 2012.
- W. P. Ramadhan, S. T. M. T. Astri Novianty, and S. T. M. T. Casi Setianingsih, “Sentiment analysis using multinomial logistic regression,” 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), Yogyakarta, Indonesia, Sept. 26-28, 2017.
- V. A. Kharde, and S. Sonawane, “Sentiment analysis of twitter data: A survey of techniques,” International Journal of Computer Applications, vol. 139, no. 11, pp. 5-15, Apr. 2016.
- P. V. V. Kishore, S. R. C. Kishore, and M. V. D. Prasad, “Conglomeration of hand shapes and texture information for recognizing gestures of Indian sign language using feed forward neural networks,” International Journal of Engineering and Technology, vol. 5, no. 5, pp. 3742-3756, 2013.