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Krishna Anand, S.
- Paleo Earthquake Analysis for Sumatra Region in Order to Forewarn the Possible Occurrence of Future Earthquake
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Authors
Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
2 School of Electrical and Electronics Engineering, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
2 School of Electrical and Electronics Engineering, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 16 (2015), Pagination:Abstract
By analysing the previous earthquakes in Sumatra region the long term earthquake prediction may help the Government and NGOs for necessary emergency preparedness. In order to predict chaotic behaviour of nature K-Means clustering algorithm is employed over the previous earthquake data for clustering the regions based on the coordinates of the earthquakes occurred places and Laws of Haversine is used further to compute the distance between the main earthquakes and secondary shocks. The 14 years of earthquake data of 8400 samples were collected from various sources. Each cluster results in different radius of the cluster. Based on the laws of Haversine results the data were loaded and analysed. The analysis results show the pattern of earthquakes and its changes. It gives the information about next larger earthquake pattern and place of occurrence.Keywords
Aftershock, Cluster, Earthquake, Foreshock, Laws of Haversine, Seismic Event- Incorporating Supervisory Learning through Type–2 Fuzzy Expert System for Increasing Productivity of a Boiler
Abstract Views :163 |
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Authors
Affiliations
1 SKCET, Coimbatore, IN
2 Seshasayee Paper & Boards Ltd, IN
1 SKCET, Coimbatore, IN
2 Seshasayee Paper & Boards Ltd, IN
Source
Fuzzy Systems, Vol 3, No 8 (2011), Pagination: 348-354Abstract
The ways of maximization of net steam output from Chemical Recovery Boilers has been a puzzle over the years. To lend support to the same, a type 2 fuzzy expert system has been designed which takes into account a large number of parameters. The composition of Black liquor solids ( fuel) is dependent on a large number of factors right from raw materials like wood and bagasse down through chemicals used for Paper manufacture and pulping since it is the source of heat and power for producing paper of quality at high productivity level. The choice of parameters playing a significant role in the same has to be determined. Pruning is carried out by performing sensitivity analysis. It has been observed that Boiler Liquor solids flow, Moisture content in fuel and Gross Calorific value are found to be more sensitive while parameters like flue gas outlet temperature hardly makes an impact in the process. These apart, apportioning of combustion air at three levels does play a part in productivity. The parameters with a larger impact have been grouped together using c-means clustering. It has been observed in the real world that some measurements are being omitted owing to carelessness of the operators. C-Means clustering could deal with missing data The existing system gives emphasis to operator’s experience and specialists expertise. A temporary lack of focus or the absence of a specialist can lead to major consequences. There arises a need for designing a Type 2 Fuzzy logic system to ensure better performance at all times of operation.Keywords
Fuzzy Expert System, Backpropogation Neural Network, C-Means Clustering, Superheater, Type 2 Fuzzy Logic, Supervisory Learning, Sensitivity Analysis.- Development of a Predictive System for Anticipating Earthquakes using Data Mining Techniques
Abstract Views :144 |
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Authors
Affiliations
1 School of Computing, SASTRA University, Thirumalaisamudram, Thanjavur − 613401, Tamil Nadu, IN
1 School of Computing, SASTRA University, Thirumalaisamudram, Thanjavur − 613401, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 48 (2016), Pagination:Abstract
Objectives: A wide variety of disasters occur across the globe, prediction of such disaster is the requisite for early precaution and evacuation process. Prediction of earthquake could be achieved using precursors or seismographic data but all such methods can be carried out only by the domain experts (seismologist). Methods: Data mining methods have been used in a wide variety of applications and in various domains. It allows prediction of performance and expected progression which enables derivation of rational decisions. Anticipating earthquake using previous earthquake history data can be achieved using data mining concepts. In this paper, a prediction model is proposed for anticipating earthquakes by applying clustering and association rule mining on earthquake history data. Initially, the data is collected and they are clustered, this clustered data is passed to next phase where frequent patterns are obtained by applying association rule mining, finally by using the obtained pattern, the upcoming earthquakes are predicted by performing rule matching. Findings: This paper describes a predictive model employing significant earthquake data and mining techniques which predicts the fore coming earthquake. Applications: This prediction model can be used to predict various seismic events and also they can be used for making prediction in other fields by employing appropriate dataset.Keywords
Association Rule Mining, Clustering, Data Mining, Earthquake Prediction, Frequent Pattern.- Feature Selection for Microarray Data using WGCNA Based Fuzzy Forest in Map Reduce Paradigm
Abstract Views :157 |
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Authors
Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN