This item is printed on demand - Print on Demand Titel. Neuware -This book aims to flood detection to recognize flooding-related photographs from web-based media. These photographs, related with their geological areas, can give free, ideal, and dependable visual data about flood occasions to the judgment producer. This screening framework, intended for utilization to web-based media pictures, incorporates a few key modules: tweet/picture computerize, flooding photograph discovery, and Web GIS utilization for human check. In this investigation, a preparation dataset of 4800 flooding photographs was built dependent on an fine-tuning mechanism utilizing deep leaning (DL) created and prepared to recognize flooding photographs. The framework was planned such that the DL can be re-prepared by a bigger preparing dataset when extra expert confirmed flooding photographs are being added to the preparation set in an insistent way.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 76 pp. Englisch.

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Bibliographic Details

Title: Fine-Tuning Method for Deep Learning
Publisher: LAP LAMBERT Academic Publishing Apr 2024
Publication Date: 2024
Language: English
ISBN 10: 6207477464
ISBN 13: 9786207477463
Binding: Taschenbuch
Condition: Neu
Dimensions: 220 millimeters width by 150 millimeters height by 5 millimeters depth
Weight: 131 grams

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This book aims to flood detection to recognize flooding-related photographs from web-based media. These photographs, related with their geological areas, can give free, ideal, and dependable visual data about flood occasions to the judgment producer. This screening framework, intended for utilization to web-based media pictures, incorporates a few key modules: tweet/picture computerize, flooding photograph discovery, and Web GIS utilization for human check. In this investigation, a preparation dataset of 4800 flooding photographs was built dependent on an fine-tuning mechanism utilizing deep leaning (DL) created and prepared to recognize flooding photographs. The framework was planned such that the DL can be re-prepared by a bigger preparing dataset when extra expert confirmed flooding photographs are being added to the preparation set in an insistent way.

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