Optimization of Fault Learning in Medical Devices

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V. Kakulapati 1,*

1. Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana-501301

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2022.06.04

Received: 7 Jul. 2022 / Revised: 26 Aug. 2022 / Accepted: 24 Sep. 2022 / Published: 8 Dec. 2022

Index Terms

Physician, Predicting, Genetic Algorithm, Machine Learning, Fault, Diagnosis, Knowledge, Discovery, Relative


A relatively effective training system and advancements in data science demonstrate their evolutionary algorithm power to discover defects and abnormalities in the specified learning process. This work employs a fast and precise fault modelling environment to enhance genetic input implantable devices defect diagnostics. We offer a genetic data technique that incorporates phylogenetic analysis operations and faulty efficiency analysis. This study contributes to fault training in three different ways: 1) it exposes communicative training categories of information formulating adhesion, 2) it introduces a hierarchical system dissemination processing principles to design the fault aggregative, and 3) it indicates forecasting the genetic data sector that corresponds to complicated fault training. The proposed algorithm analyses methods that combine automatically generated fault detection development with massive data testing by non-repetitive fault instances. Analyzing data from validation challenges, infrastructure blowouts, and failure uncertainty make our algorithm more productive in the health sector.

Cite This Paper

V. Kakulapati, "Optimization of Fault Learning in Medical Devices", International Journal of Intelligent Systems and Applications(IJISA), Vol.14, No.6, pp.38-49, 2022. DOI:10.5815/ijisa.2022.06.04


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