Optimization: Molecular Communication Networks for Viral Disease Analysis Using Deep Leaning Autoencoder

Abstract

Developing new treatments for emerging infectious diseases in infectious and noninfectious diseases has attracted a particular attention. The emergence of viral diseases is expected to accelerate; these data indicate the need for a proactive approach to develop widely active family specific and cross family therapies for future disease outbreaks. Viral disease such as pneumonia, severe acute respiratory syndrome type 2, HIV infection, and Hepatitis-C virus can cause directly and indirectly cardiovascular disease (CVD). Emphasis should be placed not only on the development of broad-spectrum molecules and antibodies but also on host factor therapy, including the reutilization of previously approved or developing drugs. Another new class of therapeutics with great antiviral therapeutic potential is molecular communication networks using deep learning autoencoder (DL-AEs). The use of DL-AEs for diagnosis and prognosis prediction of infectious and noninfectious diseases has attracted a particular attention. MCN is map to molecular signaling and communication that are found inside and outside the human body where the goal is to develop a new black box mechanism that can serve the future robust healthcare industry (HCI). MCN has the ability to characterize the signaling process between cells and infectious disease locations at various levels of the human body called point-to-point MCN through DL-AE and provide targeted drug delivery (TDD) environment. Through MCN, and DL-AE healthcare provider can remotely measure biological signals and control certain processes in the required organism for the maintenance of the patient’s health state. We use biomicrodevices to promote the real-time monitoring of human health and storage of the gathered data in the cloud. In this paper, we use the DL-based AE approach to design and implement a new drug source and target for the MCN under white Gaussian noise. Simulation results show that transceiver executions for a given medium model that reduces the bit error rate which can be learned. Then, next development of molecular diagnosis such as heart sounds is classified. Furthermore, biohealth interface for the inside and outside human body mechanism is presented, comparative perspective with up-to-date current situation about MCN.

Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model

“…To address the challenge of substantial, varying attenuation affecting optical signal transmission through water bodies, a modified CNN model is proposed for data recovery in underwater optical wireless communication [20]. Lastly, the paper discusses a deep learning-based autoencoder approach for designing a new drug source and target for molecular communication networks (MCN) under white Gaussian noise [21]. This approach aims to develop a robust mechanism for future healthcare applications, mapping MCN to molecular signaling and communication found in and around the human body.…”

“…In the second phase, the input layer, which will determine the new information, comes into play and first updates the information with the sigmoid function by employing Eq. (21). Next, the candidate data, which will contribute to creating new information, are identified using Eq.…”

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

“…For a critical introduction to the application of interpretable genomics, see [48]. Another subfield, the Deep Learning in drug response prediction is discussed in [49][50][51][52].…”