ArticleInterdisciplinary sciences, computational life sciences2021
Genomic sequence analysis of lung infections using artificial intelligence technique.
Article in Interdisciplinary sciences, computational life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India.Neural computing & applications · 2023Article
- Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence.Measurement : journal of the International Measurement Confederation · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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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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