Use of soft computing techniques in disaster prediction and management has interested measures at different levels. Soft computing techniques have given the humans the power and capability to identify events preluding the occurrence of major disasters. Even though it’s quite impossible to forecast all natural calamities, it is imperative to identify methods that can forewarn the possible occurrence of an event. Soft computing techniques have been used by researchers to provide these fore warning capabilities. This research work is such an attempt to extensively investigate and exploit the power of soft computing techniques to identify the location of volcano hot spots. In this proposed research work different soft computing techniques have been studied for their suitability in identifying volcano hot spots in satellite images. Multi spectral satellite data have been employed for image processing and analysis. Suitable modification and improvements have been suggested for existing techniques like KNN, SVM, ANN, to increase the prediction performance and accuracy. As a part of this research work an ANFIS based system classifier has also been developed and tested for its performance.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Use of soft computing techniques in disaster prediction and management has interested measures at different levels. Soft computing techniques have given the humans the power and capability to identify events preluding the occurrence of major disasters. Even though it's quite impossible to forecast all natural calamities, it is imperative to identify methods that can forewarn the possible occurrence of an event. Soft computing techniques have been used by researchers to provide these fore warning capabilities. This research work is such an attempt to extensively investigate and exploit the power of soft computing techniques to identify the location of volcano hot spots. In this proposed research work different soft computing techniques have been studied for their suitability in identifying volcano hot spots in satellite images. Multi spectral satellite data have been employed for image processing and analysis. Suitable modification and improvements have been suggested for existing techniques like KNN, SVM, ANN, to increase the prediction performance and accuracy. As a part of this research work an ANFIS based system classifier has also been developed and tested for its performance. 180 pp. Englisch. N° de réf. du vendeur 9786138920687
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Taschenbuch. Etat : Neu. Soft Computing Techniques Based Identification of Hotspots in Images | Identification of Volcano Hotpsots in Satellite Images Using Computing Techniques | S. Munirtathnam (u. a.) | Taschenbuch | 180 S. | Englisch | 2020 | Scholars' Press | EAN 9786138920687 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 118001023
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Use of soft computing techniques in disaster prediction and management has interested measures at different levels. Soft computing techniques have given the humans the power and capability to identify events preluding the occurrence of major disasters. Even though it's quite impossible to forecast all natural calamities, it is imperative to identify methods that can forewarn the possible occurrence of an event. Soft computing techniques have been used by researchers to provide these fore warning capabilities. This research work is such an attempt to extensively investigate and exploit the power of soft computing techniques to identify the location of volcano hot spots. In this proposed research work different soft computing techniques have been studied for their suitability in identifying volcano hot spots in satellite images. Multi spectral satellite data have been employed for image processing and analysis. Suitable modification and improvements have been suggested for existing techniques like KNN, SVM, ANN, to increase the prediction performance and accuracy. As a part of this research work an ANFIS based system classifier has also been developed and tested for its performance.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 180 pp. Englisch. N° de réf. du vendeur 9786138920687
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