The major aim of this project is to study the usage of glycerin in various industries. The researcher also focuses and explores the demand percentage for glycerin in the present market and studies the future market position. It reveals which company leads the market in the present scenario. The researcher has used primary data through questionnaire and secondary data through previous research studies, books and browsed the Internet. The data analysis used is the percentage analysis which perfectly fits in this research work. The researcher has concluded with interesting finding and given scope for the future research work.

Keywords: Glycerin, Market, Industry.

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Selecting an appropriate career is one of the most important decisions and with the increase in the number of career paths an d opportunities, making this decision have become quite difficult for the students. According to the survey conducted by the Council of Scientific and Industrial Research's (CSIR), about 40% of students are confused about their career options. This may lead to wrong career selection and then working in a field which was not meant for them, thus reducing the productivity of human resource. Therefore, it is quite important to take a right decision regarding the career at an appropriate age to prevent the consequences that results due to wrong career selection. This system is a web application that would help students studying in high schools to select a course for their career. The system would recommend the student, a career option based on their personality trait, interest a nd their capacity to take up the course.

Keywords: Career prediction; Data mining; Personality traits; C5.0; Adaptive boosting.

[1]. Y. Zheng, L. Capra, O. Wolfson, and H. Yang, "Urban Computing: Concepts, Methodologies, and Applications," ACM Transaction on Intelligent Systems and Technology, vol. 5, no. 3, pp. 38:1–38:55, 2014.
[2]. "World Urbanization Prospects, the 2014 Revision: Highlights," United Nations, Department of Economic and Social Affairs, Population Division, New-York, Tech. Rep., 2014.
[3]. J. Eisenstein, A. Ahmed, and E. P. Xing, "Sparse Additive Genera- tive Models of Text," in ICML, Seattle, WA, 2011, pp. 1041–1048.
[4]. J. Cranshaw, J. I. Hong, and N. Sadeh, "The livehoods project: Utilizing social media to understand the dynamics of a city," in ICWSM, 2012, pp. 58–65.
[5]. A. X. Zhang, A. Noulas, S. Scellato, and C. Mascolo, "Hoodsquare: Modeling and recommending neighborhoods in location-based social networks," in ASE/IEEE SocialCom, 2013, pp. 69–74.

Water scarcity is the most prevailing problem that is existing in day to day life. The main reason for water scarcity and parts which were affected by acute water scarcity is discussed. The alarming water scarcity especially fresh water scarcity is also discussed here. The percentage of people who are suffering from severe water scarcity is also discussed. This research made to analysis the water scarcity in various areas in Chennai. The main objective of this research is to understand the problems of water scarcity with people. To analyse the relationship between public and the prevailing water scarcity. In this study we will discuss about the water scarcity among the public. The statistical tools used are ANOVA, independent t test and chi-square. The sample size is 1480 samples. This research concludes that within few years there will be no water for our future generations.

Keywords: water scarcity, acute, suffrage, problems, public, severe.

[1]. Mekonnen, Mesfin M., and Arjen Y. Hoekstra. 2016. "Four Billion People Facing Severe Water Scarcity." Science Advances 2 (2): e1500323.
[2]. Moore, Scott M. 2017. "The Dilemma of Autonomy: Decentralization and Water Politics at the Subnational Level." Water International 42(2): 222–39.
[3]. Seckler, David, Randolph Barker, and Upali Amarasinghe. 1999. "Water Scarcity in the Twenty-First Century." International Journal of Water Resources Development15 (1-2): 29–42.
[4]. Petersen-Perlman, Jacob D., Jennifer C. Veilleux, and Aaron T. Wolf. 2018. "International Water Conflict and Cooperation: Challenges and Opportunities." Water International 42 (2): 105–20
[5]. Swain, Ashok. 2011. "Challenges for Water Sharing in the Nile Basin: Changing Geo-Politics and Changing Climate." Hydrological Sciences Journal 56 (4): 687–702.

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, this now have a huge volume of opinionated data recorded in digital form for analysis.

[1]. Akay A. Dragomir A and Erlandsson B. E. (2015), 'A novel data-mining approach leveraging social media to monitor consumer opinion of sitagliptin',
[2]. J. Biomed Health Inform, Vol: PP, Issue: 99. Angulakshmi1 G. and Dr.ManickaChezian R. (2014),'An Analysis on Opinion Mining: Techniques and Tools',Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 7.

[3]. Danushka Bollegala and David Weir. (2013), 'Carroll Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus', IEEE Transactions On Knowledge And Data Engineering, Vol. 25, No. 8.
[4]. Das S. R. and Chen M. Y. (2007), 'Yahoo! for Amazon: Sentiment extraction from small talk on the Web', Manage. Sci., vol. 53, pp. 1375–1388.
[5]. Hsinchun Chen S. (2010), 'AI and Opinion Mining', Part 2.Published by the IEEE Computer Society.

A dynamic stall model to predict the unsteady airloads on wind turbine airfoils where presents in this paper.The proposed model which is based on the Beddoes-Leishman (B-L) model and wind turbine applications are modified.S809 airfoil oscillating of the lift, drag and pitch moment in stall-development and deep-stall regimes are predicted. Overall good agreement for validation against available experimental data.

Keywords: wind turbine; aeronyamics; airfoil; dynamic stall

[1]. H. J.G., d.V. J.B., v.Z. A.H., B. H., Comparing different dynamic stall models, Wind Energy, 16 (2013) 139-158.
[2]. X. Liu, X. Zhang, G. Li, Y. Chen, Z. Ye, Dynamic response analysis of the rotating blade of horizontal axis wind turbine, Wind Engineering, 34 (2010) 543-560.
[3]. F.J. Tarzanin, Prediction of control loads due to blade stall, Journal of American Helicopter Society, 17 (1972) 33-46.
[4]. S. Gupta, J.G. Leishman, Dynamic stall modelling of the S809 aerofoil and comparison with experiments, Wind Energy, 9 (2006) 521-547.
[5]. J.G. Leishman, T.S. Beddoes, A semi-empirical model for dynamic stall, Journal of American Helicopter Society, 34 (1989) 3-17.

MapReduce may be a programming model and an associated implementation for processing and generating large data sets. The uses dynamic decomposition based distributed algorithm is used which increases the performance of data. The health of the data nodes are verified using health care algorithm. Also the aggregator which is used to group the data from the data node is to be placed correctly by using aggregator placement problem. In this paper, we study to scale back network traffic cost for a MapReduce job by designing a completely unique intermediate data partition scheme. Index Terms: MapReduce, Distributed Algorithm

[1]. J. Dean and S. Ghemawat,(2008) ―Mapreduce: simplified data processing on large clusters,‖ Communications of the ACM, vol. 51, no. 1, pp. 107–113.
[2]. W. Wang, K. Zhu, L. Ying, J. Tan, and L. Zhang, ( 2013) ―Map task scheduling in mapreduce with data locality: Throughput and heavy-traffic optimality,‖ in INFOCOM, Proceedings IEEE. IEEE, pp. 1609–1617.
[3]. F. Chen, M. Kodialam, and T. Lakshman,( 2012) ―Joint scheduling of processing and shuffle phases in mapreduce systems,‖ in INFOCOM,Proceedings IEEE, pp. 1143–1151.
[4]. Y. Wang, W. Wang, C. Ma, and D. Meng,(2013) ―Zput: A speedy data uploading approach for the hadoop distributed file system,‖ in Cluster Computing (CLUSTER), IEEE International Conference on. IEEE, pp. 1–5
[5]. L. Fan, B. Gao, X. Sun, F. Zhang, and Z. Liu,( 2014) ―Improving the load balance of mapreduce operations based on the key distribution of pairs,‖ arXiv preprint arXiv:1401.0355,.

In this paper we discuss about the water which we face problem in more rainfall like flood and in dry we face water shortage. To overcome this we use Geospatial Technology which sends the water detail in the world and how we can save and wastage will be reduced. Some effect due to water can be overcome by this will be calculated by deep learning technique algorithms. Also some software like arcgis learn module in ArcGIS API for Python enables GIS analysts and data scientists to train deep learning models with a simple, intuitive API. ArcGIS Notebooks provides a ready-to-use environment for training deep learning models.

[1]. Building city dashboards (BCD) project (http://dashboards.maynoothuniversity.ie) (accessed on September 18, 2019)
[2]. Operations Dashboard for ArcGIS – https://www.esri.com/enus/arcgis/products/operations-dashboard/overview (accessed on September 18, 2019)
[3]. Dameri, R.P. Urban Smart Dashboard. Measuring Smart City Performance. In Smart City Implementation; 2017; pp. 67–84 ISBN 978-3-319-45765-9.
[4]. Sjöbergh, J., & Tanaka, Y., "Geospatial Digital Dashboard for Exploratory Visual Analytics", Communications in Computer and Information Science Information Search, Integration, and Personalization, 2014, 3–17. DOI: 10.1007/978-3-319-08732-0_1
[5]. Lwin, K.K., Zettsu, K. and Sugiura. K., "Geovisualization and Correlation Analysis between Geotagged Twitter and JMA Rainfall Data: Case of Heavy Rain Disaster in Hiroshima", 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, July 8-10, 2015, Fuzhou, P.R. China.