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Genomic sequence analysis of lung infections using artificial intelligence technique

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Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

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.

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, National Institute of Technology, Chumkedima, Dimapur, Nagaland, 797103, India

    R. Kumar

  2. Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey

    Fadi Al-Turjman

  3. School Computing Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India

    L. Anand

  4. School of Computer science and IT, JAIN (Deemed to be University), Banglore, Karnataka, India

    Abhishek Kumar

  5. Maruthi Technocrat E Services, Chennai, India

    S. Magesh

  6. Department of Computer Science, Sanjivani College of Engineering, Kopargaon, India

    K. Vengatesan & M. Rajesh

  7. Department of Electrical Engineering, School of Electrical Engineering, Vellore Institute of Technology and Science, Vellore, 632014, India

    R. Sitharthan

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  1. R. Kumar
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Correspondence to R. Sitharthan.

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The authors declare that they did not get any financial support or influential support to be reported in this paper.

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Kumar, R., Al-Turjman, F., Anand, L. et al. Genomic sequence analysis of lung infections using artificial intelligence technique. Interdiscip Sci Comput Life Sci 13, 192–200 (2021). https://doi.org/10.1007/s12539-020-00414-3

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  • DOI: https://doi.org/10.1007/s12539-020-00414-3

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