A comparative recognition research on excretory organism in medical applications using artificial neural networks

Frontiers in Bioengineering and Biotechnology · 5 authors, 4 centres

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TL;DR

The proposed approach achieved a data loss rate of 1.39% and an effectiveness rate of approximately 68% compared to control cases.

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The system achieved an optimized data loss rate of 1.39% and reported an effectiveness rate of approximately 68% versus control conditions. The authors note that only simulated models were tested, as integration of healthcare components necessitates extensive testing.

STUDY TYPE

other

SPECIALTY

gastroenterology

SUMMARISED BY

AI pipeline

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100% · A

ORIGINAL PAPER

PMC10312079

A comparative recognition research on excretory organism in medical applications using artificial neural networks

Frontiers in Bioengineering and Biotechnology

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ABSTRACT

Purpose: In the contemporary era, a significant number of individuals encounter various health issues, including digestive system ailments, even during their advanced years. The major purpose of this study is based on certain observations that are made in internal digestive systems in order to prevent severe cause that usually occurs in elderly people. Approach: To solve the purpose of the proposed method the proposed system is introduced with advanced features and parametric monitoring system that are based on wireless sensor setups. The parametric monitoring system is integrated with neural network where certain control actions are taken to prevent gastrointestinal activities at reduced data loss. Results: The outcome of the combined process is examined based on four different cases that is designed based on analytical model where control parameters and weight establishments are also determined. As the internal digestive system is monitored the data loss that is present with wireless sensor network must be reduced and proposed approach prevents such data loss with an optimized value of 1.39%. Conclusion: Parametric cases were conducted to evaluate the efficacy of neural networks. The findings indicate a significantly higher effectiveness rate of approximately 68% when compared to the control cases.

CITATION EXPORT

Shitharth Selvarajan, Hariprasath Manoharan, Celestine Iwendi et al.. A comparative recognition research on excretory organism in medical applications using artificial neural networks. Frontiers in Bioengineering and Biotechnology. (2023). https://doi.org/10.3389/fbioe.2023.1211143

AUTHORS

5 · 4 CENTRES

Shitharth Selvarajan, Hariprasath Manoharan, Celestine Iwendi, Rakan A. Alsowail, Saravanan Pandiaraj

1 Department of Computer Science, Kebri Dehar University, Kebri Dehar, Ethiopia
2 Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, India
3 School of Creative Technologies, University of Bolton, Bolton, United Kingdom
4 Computer Skills, Self-Development Skills Department, Deanship of Common First Year, King Saud University, Riyadh, Saudi Arabia