@article { author = {Nemati, Nazafarin and Sohooli, Maryam and Fereydouni, Mehrnoush and Nemati, Mahnaz}, title = {Contribution of the computational tools in understanding the blood rheology}, journal = {Journal of Bioengineering Research}, volume = {3}, number = {3}, pages = {1-7}, year = {2021}, publisher = {Tissues and Biomaterial Research Group-(TBRG)}, issn = {2645-5633}, eissn = {}, doi = {10.22034/jbr.2021.294370.1041}, abstract = {This article presents a literature review to evaluate the capabilities and limitations of available computational tools in studying the behavioral properties of blood flow in capillaries. PubMed, Embase, and Web of Science databases are explored for articles published on topics such as red blood cells, blood flow, non-Newtonian fluid field, and finite element analysis of biomedical devices. Recent advancements in the modeling of complex fluid fields help researchers better understand RBCs' different mechanisms and interactions in micro-capillaries. Hence, the characteristics of a single red blood cell in micro-capillaries is reviewed and discussed in this article. Such understanding could help us predict, manipulate and control the blood flow by changing the viscosity or interactions between different components. Moreover, computational tools provide a quantitative assessment of interactions between various components. While more research is required to fully understand the blood flow in veins & arteries, the presence of experimental studies is of paramount importance to verify and validate the current models.}, keywords = {Non-Newtonian flow,Blood flow,computational tools,Rheology}, url = {https://www.journalbe.com/article_133379.html}, eprint = {https://www.journalbe.com/article_133379_d891d2ea2bb7a85df30bf82ba7044ab2.pdf} } @article { author = {Izadi, Vahid and Morovati, Mehdi and Ranjbaran, Golshid and Homayounmajd, Shahabedin}, title = {An Empirical Study of Nail Fungus Classification Using Deep and Traditional Machine Learning Methods}, journal = {Journal of Bioengineering Research}, volume = {3}, number = {3}, pages = {16-23}, year = {2021}, publisher = {Tissues and Biomaterial Research Group-(TBRG)}, issn = {2645-5633}, eissn = {}, doi = {10.22034/jbr.2021.295687.1043}, abstract = {Some of the main human health hazards occur through the consumption of fungus. It is detrimental to food and threatens human health. Fungus’s symptom is not clear, making them extremely harmful to human and food resources. Over the past few decades, many studies have been conducted on the early detection of fungus existence in biological systems. Fungus’s symptom is not clear, making them extremely harmful to human and food resources. Over the past few decades, many studies have been conducted on the early detection of fungus existence in biological systems. In this paper, we study and implement machine learning methods that can detect and classify fungus using nail finger fungus images. Our study shows that the neural network-based method and more importantly deep learning method using state-of-the-art CNN models have higher predictive performance compared to that of other machine learning approaches.Keywords: Deep Learning, Feature Extraction, Conventional Machine Learning, Nail Fungus}, keywords = {Deep Learning,Feature Extraction,Conventional Machine Learning,Nail Fungus}, url = {https://www.journalbe.com/article_137356.html}, eprint = {https://www.journalbe.com/article_137356_86044f08c1415fc7334979a50a49b45c.pdf} }