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Karthik, S.
- Prediction of Transmitted Wave Height of Tandem Breakwater Using PSO-SVM
Abstract Views :180 |
PDF Views:2
Authors
S. Karthik
1,
Subba Rao
1
Affiliations
1 Department of Applied Mechanics and Hydraulics, National Institute of Technology, Karnataka, IN
1 Department of Applied Mechanics and Hydraulics, National Institute of Technology, Karnataka, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 1 (2017), Pagination: 1544-1548Abstract
Economic development of a country is directly dependent on the functioning of the ports and transportation facilities available. It is of vital importance to protect the ports and harbors and thereby providing safe and effective loading and unloading facilities. Proper protective measures like breakwaters have to be constructed to protect and maintain tranquility conditions inside the harbor. Apart from conventional rubble mound breakwater, newly developed hybrid type breakwaters are also used as protective structures. Final layout of the structure is determined only after prior physical model studies. Soft computing techniques, widely used in the field of prediction, are recently used in the field of breakwater studies for the prediction of transmitted wave height, damage analysis etc. Present work deals with the transmitted wave height prediction of a tandem breakwater using hybrid PSO-SVM model. Effectiveness of the models developed were measured using various statistical parameters such as RMSE, MAE, CC and SI. Results showed that, from among various kernels used, model developed with polynomial kernel showed better correlation.Keywords
Tandem Breakwater, PSO-SVM, Polynomial Kernel, Soft Computing.
References
- V. Vapnik, S. Golowich and Alex Smola, “Support Vector method for Function Approximation Regression Estimation and Signal Processing”, Advance in Neural Information Processing Systems, pp. 281-287, 1996.
- S. Gunn, “Support Vector Machines for Classification and Regression”, Technical Report, School of Electronics and Computer Science, University of Southampton, 1997.
- Yong-Sik Cho, Jong-In Lee and Young-Taek Kim, “Experimental Study of Strong Reflection of Regular Water Waves over Submerged Breakwaters in Tandem”, Ocean Engineering, Vol. 31, No. 10, pp. 1325-1335, 2004.
- Sukomal Mandal, Subba Rao, N. Harish and Lokesha “Damage Level Prediction of Non-Reshaped Berm Breakwater using ANN, SVM and ANFIS Models”, International Journal Naval Architecture Ocean Engineering, Vol. 4, No. 2, pp. 112-122,2012.
- Subba Rao and Kiran G Shirlal, “Studies on tandem breakwater” R&D project, Technical Report, Ministry of Water Resources through ISH Journal of Hydraulic Engineering, pp. 36-45, 2003.
- K.G. Shirlal and Subba Rao “Laboratory Studies on the Stability of Tandem Breakwater”, ISH Journal of Hydraulic Engineering, Vol. 9, No. 1, pp. 36-45, 2003.
- K. G. Shirlal, Subba Rao, Venkata Ganesh and Manu, “Stability of Breakwater Defenced by a Submerged Reef”, Ocean Engineering, Vol. 33, No. 5-6, pp. 829-846, 2006.
- Eldr Thesnaar, “Efficiency of Tandem Breakwater in Reducing Wave Heights and Damage Level: A Mossel Bay Case Study”, Master of Engineering Thesis, Stellenbosch University, 2015.
- J. S. Jithin, “Prediction of Transmitted Wave Height and Damage Level of Tandem Breakwater using SVM”, Master of Engineering Thesis, National Institute of Technology, 2016.
- S.G. Patil, S. Mandal and A.V. Hegde, “Genetic Algorithm based Support Vector Machine Regression in Predicting Wave Transmission of Horizontally Interlaced Multi-Layer Moored Floating Pipe Breakwater”, Advances in Engineering Software, Vol. 45, No. 1, pp. 203-212, 2012.
- Chandrabhushan Roy, Shervin Motamedi, Roslan Hashim, Shahaboddin Shamshirb and Dalibor Petkovic, “A Comparative Study for Estimation of Wave Height using Traditional and Hybrid Soft-Computing Methods”, Environmental Earth Science, Vol. 75, pp. 590-595, 2016.
- Iman Malekmohamadi, Mohammad Reza Bazargan-Lari, Reza Kerachian, Mohammad Reza Nikoo and Mahsa Fallahnia, “Evaluating the Efficacy of SVMs, BNs, ANNs and ANFIS in Wave Height Prediction”, Ocean Engineering, Vol. 38, No. 2-3, pp. 487-497, 2011.
- O. Yagci, D.E. Mercan, H.K. Cigizoglu and M.S. Kabdasli, “Artificial Intelligence Methods in Breakwater Damage Ratio Estimation”, Ocean Engineering, Vol. 32, No. 17-18, pp. 2088-2106, 2005.
- Pasquale Contestabile, Claudio Iuppa, Enrico Di Lauro, Luca Cavallaro, Thomas Lykke Andersen and Diego Vicinanz, “Wave Loadings Acting on Innovative Rubble Mound Breakwater for Overtopping Wave Energy Conversion”, Coastal Engineering, Vol. 122, pp. 60-74, 2017.
- Aji Ali Akbar, Junun Sartohadi, Tjut Sugandawaty Djohan and Su Ritohardoyo, “The Role of Breakwaters on the Rehabilitation of Coastal and Mangrove Forests in West Kalimantan, Indonesia”, Ocean and Coastal Management, Vol. 138, pp. 50-59, 2017.
- Dookie Kim, Dong Hyawn Kim and Seongkyu Chang, “Application of Probabilistic Neural Network to Design Breakwater Armor Blocks”, Ocean Engineering, Vol. 35, pp. 294-300, 2008.
- Can Elmar Balas, M. Levent Koc and Rifat Tur, “Artificial Neural Networks based on Principal Component Analysis, Fuzzy Systems and Fuzzy Neural Networks for Preliminary Design of Rubble Mound Breakwaters”, Applied Ocean Research, Vol. 32, No. 4, pp. 425-433, 2010.
- An Integrated Deep Learning Approach for Sentiment Analysis on Twitter Data
Abstract Views :33 |
PDF Views:2
Authors
Affiliations
1 TATA Consultancy Services, Bengaluru, IN
2 Department of Computer Science and Engineering, SNS College of Technology, IN
1 TATA Consultancy Services, Bengaluru, IN
2 Department of Computer Science and Engineering, SNS College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 2 (2023), Pagination: 3188-3194Abstract
Analysis of Sentiment (SA) is a computational technique that seeks to extract subjective evaluations, attitudes, and emotional states from online platforms, specifically social media sites like Twitter. The subject has gained significant traction within the research community. The predominant emphasis of traditional sentiment analysis lies in the analysis of textual data. Twitter is widely recognized as a prominent online social networking platform that facilitates microblogging, wherein users share updates pertaining to various subjects through concise messages known as tweets. Twitter is a widely utilized platform that enables individuals to articulate their perspectives and emotions through the medium of tweets. Sentiment analysis refers to the computational methods of classification of given data in text format, categorizing it as either positive, negative, or neutral. The main goal of this study is to use deep learning methods for SA. The purpose is to guess sentiment and then evaluate the results based on accuracy, recall, and f-score. In this paper, a hybrid approach combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques, specifically referred to as PSOGA, is proposed to optimize features for a modified neural network (MNN). The final step is to use the K-fold cross-validation method to assess the results. The dataset was obtained through the utilization of the Ruby Twitter API. The ultimate outcome is juxtaposed with the preceding Cuckoo Search (CS) algorithm that had been terminated.Keywords
Tweeter, Deep Learning, K-Fold Cross Validation, HDFS, Modified Neural Network.References
- Saurabh Singh, “Twitter Sentiments Analysis using Machine Learning”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 6, No. 4, pp. 1-9, 2020.
- Senthil Murugan Nagarajan and Usha Devi Gandhi, “Classifying Streaming of Twitter Data based on Sentiment Analysis using Hybridization”, Neural Computing and Applications, Vol. 31, pp. 1425-1433, 2018.
- R.B. Shamantha and P. Rai, “Sentiment Analysis using Machine Learning Classifiers: Evaluation of Performance”, Proceedings of International Conference on Computer and Communication Systems, pp. 21-25, 2019.
- P. Tyagi and R.C. Tripathi, “A Review Towards the Sentiment Analysis Techniques for the Analysis of Twitter Data”, Proceedings of International Conference on Advanced Computing and Software Engineering, pp. 1-6, 2019.
- Łukasz Augustyniak, Piotr Szymanski, Tomasz Kajdanowicz and Włodzimierz Tuliglowicz, “Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis”, Entropy, Vol. 18, No. 1, pp. 1-7, 2015.
- B.M. Bandgar and Binod Kumar, “Real Time Extraction and Processing of Social Tweets”, International Journal of Computer Sciences and Engineering, Vol. 3, No. 3, pp. 1-13, 2015.
- Vedurumudi Priyanka, “Twitter Sentiment Analysis using Deep Learning”, SSRN, Vol. 89, pp. 1-17, 2021.
- Vishal A. Kharde and S.S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications, Vol. 139, No. 11, pp. 1-14, 2016.
- Bandaru Mounika and M.S.V.S. Bhadri Raju, “Sentiment Analysis of Twitter Data using Machine Learning Approaches”, International Journal of Research, Vol. 5, No. 4, pp. 1-13, 2018.
- Vishu Tyagi, Ashwini Kumar and Sanjoy Das, “Sentiment Analysis on Twitter Data using Deep Learning approach”, Proceedings of International Conference on Advances in Computing, Communication Control and Networking, pp. 1-5, 2020.
- S. Zhai and Z.M. Zhang, “Semi Supervised Auto Encoder for Sentiment Analysis”, Proceedings of International Conference on Artificial Intelligence, pp. 14-18, 2016.
- Nikhil Yadav and Ajitkumar Shitole, “Twitter Sentiment Analysis using Supervised Machine Learning”, Springer, 2020.