SVM-Based Detection of Miniature Area of LCLU: A False-Damage Assessment Index for Disaster Management Application
Darala Siva1, Polaiah Bojja2
1Darala Siva, Research Scholar,ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India.
2Polaiah Bojja, Professor, ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 10957-10962 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9539118419/2019©BEIESP | DOI: 10.35940/ijrte.D9539.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we reflect on changing the detection environment for addressing the difficulty of detecting miniature area of Land Cover Land Use (LCLU) with a technique using Support Vector Machines (SVMs).We then become accustomed and sophisticatedly changing the Support Vector Machine for designing a supervised learning basis detection that enfolds the False Damage Assessment Index(FDAI). Primarily our proposed detection technique is controls easily the FDAI by simply adjusting two parameters() where it can be facilitate to control sensitivity of detection to the binary classifier and numerical supervised learning algorithm. The experimental results demonstrating about ours proposing detector noticeably improving the detection probability on many existing classifiers in both DAI and FDAI cases .
Keywords: Machine Learning, False Damage Assessment Index (FDAI), Miniature area.
Scope of the Article: Machine Learning.