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

Shitharth Selvarajan; Hariprasath Manoharan; Celestine Iwendi; Rakan A Alsowail; Saravanan Pandiaraj

doi:10.3389/fbioe.2023.1211143

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

Front Bioeng Biotechnol. 2023 Jun 16:11:1211143. doi: 10.3389/fbioe.2023.1211143. eCollection 2023.

Authors

Shitharth Selvarajan¹, Hariprasath Manoharan², Celestine Iwendi³, Rakan A Alsowail⁴, Saravanan Pandiaraj⁴

Affiliations

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.

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.

Keywords: attacks; control parameters; digestive systems; loss; neural network.

Grants and funding

The authors of this study extend their appreciation to the Researchers Supporting Project number (RSPD2023R544), King Saud University, Riyadh, Saudi Arabia.