Molecular communication via diffusion with spherical receiver &amp; transmitter and trapezoidal container

Mohamed, Soha; Dong, Jian; Liu, Hongwei; Zuo, Decheng

doi:10.1016/j.micpro.2020.103017

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Regression approaches have been used to extract the relationship between several independent variables and one dependent variable. Therefore, they have supported the solution of a large variety of problems that range from the area of nano- materials and nano-structure design to data-driven applications in biomedical engineering [112], [123]. Moreover, they usually require no input features or tuning for scaling and they are easy to regularize.…”

Section: Discussion and The Road Aheadmentioning

confidence: 99%

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Machine Learning in Nano-Scale Biomedical Engineering

Boulogeorgos

Trevlakis

Tegos

et al. 2021

IEEE Trans. Mol. Biol. Multi-Scale Commun.

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Machine learning (ML) empowers biomedical systems with the capability to optimize their performance through modeling of the available data extremely well, without using strong assumptions about the modeled system. Especially in nanoscale biosystems, where the generated data sets are too vast and complex to mentally parse without computational assist, ML is instrumental in analyzing and extracting new insights, accelerating material and structure discoveries and designing experience as well as supporting nano-scale communications and networks. However, despite these efforts, the use of ML in nano-scale biomedical engineering remains still under-explored in certain areas and research challenges are still open in fields such as structure and material design and simulations, communications and signal processing, and bio-medicine applications. In this article, we review the existing research regarding the use of ML in nano-scale biomedical engineering. In more detail, we first identify and discuss the main challenges that can be formulated as ML problems. These challenges are classified in the three aforementioned main categories. Next, we discuss the state of the art ML methodologies that are used to countermeasure the aforementioned challenges. For each of the presented methodologies, special emphasis is given to its principles, applications and limitations. Finally, we conclude the article with insightful discussions, that reveals research gaps and highlights possible future research directions.

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Section: Discussion and The Road Aheadmentioning

confidence: 99%

“…In [123] a diffused molecular communication system model was proposed with the use a spherical transceiver and a trapezoidal container. The model was developed through SVM-Regression and other ML techniques, and it was shown that it performs with high accuracy, especially if long distance is assumed.…”

Section: Classmentioning

confidence: 99%