Selvarajan Shitharth, Manoharan Hariprasath, Iwendi Celestine, Alsowail Rakan A, Pandiaraj Saravanan
Department of Computer Science, Kebri Dehar University, Kebri Dehar, Ethiopia.
Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, India.
Front Bioeng Biotechnol. 2023 Jun 16;11:1211143. doi: 10.3389/fbioe.2023.1211143. eCollection 2023.
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. 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. 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%. 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.
在当代，相当多的人即使在老年时也会遇到各种健康问题，包括消化系统疾病。本研究的主要目的基于对内部消化系统的某些观察，以预防通常在老年人中发生的严重病因。为实现所提出方法的目标，引入了具有先进功能和基于无线传感器设置的参数监测系统的系统。参数监测系统与神经网络集成，采取某些控制行动以减少数据丢失的情况下预防胃肠道活动。基于四个不同案例对组合过程的结果进行检查，这些案例基于分析模型设计，其中还确定了控制参数和权重设定。在监测内部消化系统时，必须减少无线传感器网络存在的数据丢失，所提出的方法以1.39%的优化值防止此类数据丢失。进行参数案例以评估神经网络的功效。结果表明，与对照案例相比，有效率显著更高，约为68%。
