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Genomic sequence analysis of lung infections using artificial intelligence technique
R. Kumar, F. Al-Turjman, L. Anand, A. Kumar, S. Magesh, K. Vengatesan, , M. Rajesh
Published in Springer Science and Business Media Deutschland GmbH
PMID: 33558984
Volume: 13
Issue: 2
Pages: 192 - 200
Attributable to the modernization of Artificial Intelligence (AI) procedures in healthcare services, various developments including Support Vector Machine (SVM), and profound learning. For example, Convolutional Neural systems (CNN) have prevalently engaged in a significant job of various classificational investigation in lung malignant growth, and different infections. In this paper, Parallel based SVM (P-SVM) and IoT has been utilized to examine the ideal order of lung infections caused by genomic sequence. The proposed method develops a new methodology to locate the ideal characterization of lung sicknesses and determine its growth in its early stages, to control the growth and prevent lung sickness. Further, in the investigation, the P-SVM calculation has been created for arranging high-dimensional distinctive lung ailment datasets. The data used in the assessment has been fetched from real-time data through cloud and IoT. The acquired outcome demonstrates that the developed P-SVM calculation has 83% higher accuracy and 88% precision in characterization with ideal informational collections when contrasted with other learning methods. Graphical abstract: [Figure not available: see fulltext.]. © 2021, International Association of Scientists in the Interdisciplinary Areas.
About the journal
JournalData powered by TypesetInterdisciplinary Sciences: Computational Life Sciences
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH