A Comprehensive review of Conversational Agent and its prediction algorithm
Aditya M. Pujari, Rahul M. Dalvi, Kaustubh S. Gawde, Tatwadarshi P. N., "A Comprehensive review of Conversational Agent and its prediction algorithm", VIVA-IJRI Volume 1, Issue 2, Article 7, pp. 1-6, 2019. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.
There is an exponential increase in the use of conversational bots. Conversational bots can be described as a platform that can chat with people using artificial intelligence. The recent advancement has made A.I capable of learning from data and produce an output. This learning of data can be performed by using various machine learning algorithm. Machine learning techniques involves construction of algorithms that can learn for data and can predict the outcome. This paper reviews the efficiency of different machine learning algorithm that are used in conversational bot.
Machine learning, Random Forest, Linear Regression, K-means, Chatbot.
- A. Ghosh, R. Sharma, P.K. Joshi, “Random forest classification of urban landscape using Landsat archive and ancillary data: Combining seasonal maps with decision level fusion”, Applied Geography Journal, 2014, pp. 31-41.
- Y. Liu, H. Wu, “Prediction of Road Traffic Congestion Based on Random Forest”, 10th International Symposium on Computational Intelligence and Design, 2017, pp. 361-364.
- M. Lehsaini, M.B. Benmahdi, “An improved K-means Cluster-based Routing Scheme for Wireless Sensor Networks”, IEEE, 2018.
- H. Zhang, Z. Zhou, “A Novel clustering algorithm combining Niche genetic algorithm with canopy and K-means”, International Conference on artificial Intelligence and Big Data, 2018, pp. 26-32.
- T.R.V. Anandharajan, G.A. Hariharan, K. K. Vignajeth, R. Jitendiran, “Weather Monitoring Using Artificial Intelligence”, International Conference on Computational Intelligence and Networks, 2016.
- H. L. Siew, M.J, Nordin, “Regression Techniques for the Prediction of Stock Price Trend”, International Conference on Statistics in Science, Business and Engineering (ICSSBE), 2012, pp. 1-5.
- S. Prabakaran, P. N. Kumar, P. S. M. Tarun, “Rainfall Prediction Using Modified Linear Regression”, ARPN Journal of Engineering and Applied Sciences, 2017, pp. 3715-3718
- S. Kumar, M. Anamika Upadhyay, C. Gola, “Rainfall prediction based on 100 years of Meteorological data”, IEEE, 2017, pp. 162-166.
- X. Xun, L. Mo, Y. Yu, “Discovery and Prediction of the Unused Land for Construction Based on Random Forest”, Fifth International Conference on Agro-Geoinformatics, 2016.
- Y. C. Shiao, L. Liu, Q. Zhao, R. C. Chen, “Predicting Passenger Flow using Different Influence Factors for Taipei MRT System”, IEEE 8th International Conference on Awareness Science and Technology (iCAST), 2017.
- S. Ye, X. Huang, Y. Teng, Y. Li, “K-Means Clustering Algorithm Based on Improved Cuckoo Search Algorithm and Its Application”, IEEE 8th International Conference on Awareness Science and Technology, 2018, pp. 447-451.
- Z. Ya-Ling, W. Ya-nan, Y. Lil, “An Improved Sampling K-means Clustering Algorithm Based on MapReduce”, IEEE 3rd International Conference on Big Data Analysis,2017.
- https://en.wikipedia.org/wiki/Machine_learning , Last Accessed on 05th Sept. 2018.
- https://en.wikipedia.org/wiki/Artificial_intelligence , Last Accessed on 05th.Sept. 2018.
- https://en.wikipedia.org/wiki/Linear_regression , Last Accessed on 04th Sept. 2018.
- https://en.wikipedia.org/wiki/Random_forest , Last Accessed on 05th Sept. 2018.
- https://en.wikipedia.org/wiki/K-means_clustering , Last Accessed on 05th Sept. 2018.
- B. R. Ranoliya, N. Raghuwanshi, S. Singh, “Chatbot for University Related FAQs”, International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.
- R. Ravi, “Intelligent Chatbot for Easy Web-Analytics Insights”, International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.