Alloys selection based on the supervised
learning technique for design of
biocompatible medical materials

Tepla, Tetiana; Izonin, Ivan; Duriagina, Zoia; Tkachenko, Roman; Trostianchyn, А.М.; Lemishka, I.A.; Kulyk, Volodymyr

doi:10.5604/01.3001.0012.6944

Cited by 14 publications

(9 citation statements)

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“…It is well-known that data on the phase composition, structure, texture and structural defects of the applied layers is extremely necessary to develop advanced technologies for producing electrodeposited coatings having improved physical [1][2][3], mechanical [4][5][6] or chemical [7][8][9] properties. These data can be predictably controlled using the recently established phenomenon of electrochemical phase formation in metals and alloys via a supercooled liquid state-stage [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Electrochemical Polymorphic Phase Formation in Metals

Girin

2021

Acta Metall Slovaca

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The phenomenon of electrochemical phase formation in metals and alloys via a supercooled liquid state stage was discussed. Assuming the electrodeposited metal to be a product of formation and ultrarapid solidification of supercooled metallic liquid, a possibility of metastable phase formation during electrodeposition of polymorphous metals was suggested. It was anticipated that the polymorphic transition of the metal’s metastable form to the stable one occurs by shear, as does the martensitic transformation. To enable revealing an orientation relationship between grains of the two phases, a method for X-ray texture analysis of metals was developed using a combination of direct pole figures. It was established that the phase formation during electrodeposition of polymorphous metals produces metastable modifications typical of entities that crystallized from a liquid state at extremely high rates. In regards polymorphic transitions in metal electrodeposition, certain orientation relationships were observed between grains of the stable and the metastable phase, which is typical of phase transformations proceeding at extremely high rates. The results obtained provided additional arguments in favor of the phenomenon under discussion.

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Section: Introductionmentioning

confidence: 99%

Electrochemical Polymorphic Phase Formation in Metals

Girin

2021

Acta Metall Slovaca

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“…Among all applications of DL, the medical material might not be anything special or significant due to its small data quantity. More likely, the medical material becomes an extension of biomaterials [95,96] , biocompatible studies [97] , or biomedical oriented study sourced from general datasets. Such a situation is not likely to weaken the study of medical material, rather push the medical material field to be fused with other subjects and form some interdisciplinary ideas.…”

Section: Deep Learning and Medical Materialsmentioning

confidence: 99%

Deep Learning for Medical Materials: Review and Perspective

Qi¹,

Liu²

2021

ES Mater. Manuf.

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Deep learning and similar computational research approaches have cooperated with materials research for years. Especially in the last few years, with the fast evolution of machine learning and deep learning algorithms, a novel branch for material research is presented to be recognized, learned, practiced, adapted and perfected. Different conventional computational modeling methods, the deep learning approach assists material science and engineering from the aspect of data processing and analysis, rather than simulate the reality. However, mistakes always exist in all less explored concepts and developments. Here, in order to offer a better instruction of deep learning for materials scientists, several recent works on deep learning based material research were reviewed. The application of deep learning in material research is introduced and discussed. As an example of both general material and advanced material, medical material was discussed for the impact and future of deep learning methods. At last, we provided several insights on future work for both deep learning scientists, data scientists and materials scientists.

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“…In [18], T.L. Tepla et al develop a classification method based on the application of multiclass logistic regression for the design of biocompatible materials in medical products in order to reduce the probability of incorrect alloy identification.…”

Section: Related Workmentioning

confidence: 99%

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

et al. 2020

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Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.

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Alloys selection based on the supervised learning technique for design of biocompatible medical materials

Cited by 14 publications

References 0 publications

Electrochemical Polymorphic Phase Formation in Metals

Electrochemical Polymorphic Phase Formation in Metals

Deep Learning for Medical Materials: Review and Perspective

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

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