Complexity visualization, dataset acquisition, and machine-learning perspectives for low-temperature plasma: a review

Sakai, Osamu; Kawaguchi, Satoru; Murakami, Tomoyuki

doi:10.35848/1347-4065/ac76fa

Cited by 9 publications

(8 citation statements)

References 85 publications

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“…The definition of "plasma dose" will be aided by standardization, which is both a crucial problem and a necessity in the field of plasma medicine. The issue of determining the "plasma dose" using Al is growing in significance despite the fact that there is numerous research for plasma dose assessment in the literature [82,83]. In this study, it is expected that the widespread use of Al in the medical and biomedical fields today will enable the targeted standardization for plasma medicine.…”

Section: Discussionmentioning

confidence: 98%

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids

Özdemir¹,

Özdemir²,

Gül³

et al. 2022

Preprint

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Plasma is defined as the fourth state of matter and non-thermal plasma can be produced at atmospheric pressure under a high electrical field. The strong and broadspectrum antimicrobial effect of plasma-activated liquids (PALs) is now well known. The antimicrobial effects of PALs depend on many different variables, which complicates the comparison of different studies and determines the most dominant parameters of the antimicrobial effect. The proven applicability of machine learning (ML) in the medical field is encouraging for its application in the field of plasma medicine as well. Thus, ML applications on PALs could present a new perspective to better understand the influences of various parameters on their antimicrobial effects. In this paper, comparative supervised ML models are presented by using previously obtained data to qualitatively predict the in vitro antimicrobial activity of PALs. A literature search was performed and data is collected from 33 relevant articles. After the required normalization, feature encoding, and resampling steps, two supervised ML methods, namely classification, and regression are applied to data to obtain microbial inactivation (MI) predictions. For classification, MI is labeled in four categories and for regression, MI is used as a continuous variable. 16 different classifiers and 14 different regressors are implemented to predict MI value. Two different robust cross-validation strategies are conducted for classification and regression models to evaluate the proposed method; repeated stratified k-fold cross-validation and k-fold cross-validation, respectively. We also investigate the effect of different features on models. The results demonstrated that the hyperparameter-optimized Random Forest Classifier (oRFC) and Random Forest Regressor (oRFR) provided better results than other models for the classification and regression, respectively. Finally, the best test accuracy of 82.68% for oRFC and R 2 of 0.75 for the oRFR are obtained. ML techniques could contribute to a better understanding of plasma parameters that have a dominant * Corresponding author.

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Section: Discussionmentioning

confidence: 98%

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids

Özdemir¹,

Özdemir²,

Gül³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The definition of 'plasma dose' will be aided by standardization, which is both a crucial problem and a necessity in the field of plasma medicine. The issue of defining the 'plasma dose' using Al is growing in significance despite the fact that there is numerous research for plasma dose assessment in the literature [85,86]. In this study, it is expected that the widespread use of AI in the medical and biomedical fields today will enable the targeted standardization of plasma medicine.…”

Section: Discussionmentioning

confidence: 99%

Machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids

Özdemir

Gül

et al. 2023

Mach. Learn.: Sci. Technol.

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Plasma is defined as the fourth state of matter, and non-thermal plasma can be produced at atmospheric pressure under a high electrical field. The strong and broad-spectrum antimicrobial effect of plasma-activated liquids (PALs) is now well known. The antimicrobial effects of PALs depend on many different variables, which complicates the comparison of different studies and determining the most dominant parameters for the antimicrobial effect. The proven applicability of machine learning (ML) in the medical field is encouraging for its application in the field of plasma medicine as well. Thus, ML applications on PALs could present a new perspective to better understand the influences of various parameters on their antimicrobial effects. In this paper, comparative supervised ML models are presented by using previously obtained data to predict the in vitro antimicrobial activity of PALs. A comprehensive literature search was performed, and 12 distinct features related to PAL-microorganism interactions were collected from 33 relevant articles to automatically predict the antimicrobial activity of PALs. After the required normalization, feature encoding, and resampling steps, two supervised ML methods, namely classification and regression, are applied to the data to obtain microbial inactivation (MI) predictions. For classification, MI is labeled in four categories, and for regression, MI is used as a continuous variable. 16 different classifiers and 14 regressors are implemented to predict the MI value. Two different robust cross-validation strategies are conducted for classification and regression models to evaluate the proposed method: repeated stratified k-fold cross-validation and k-fold cross-validation, respectively. We also investigate the effect of different features on models. The results demonstrated that the hyperparameter-optimized Random Forest Classifier (oRFC) and Random Forest Regressor (oRFR) provided superior performance compared to other models for classification and regression. Finally, the best test accuracy of 82.68% for oRFC and R2 of 0.75 for the oRFR are obtained. Furthermore, the determined most important features of predictive models are in line with the outcomes of PALs reported in the literature. An ML framework can accurately predict the antimicrobial activity of PALs without the need for any experimental studies. To the best of our knowledge, this is the first study that investigates the antimicrobial efficacy of PALs with ML. Furthermore, ML techniques could contribute to a better understanding of plasma parameters that have a dominant role in the desired antimicrobial effect. Moreover, such findings may contribute to the definition of a plasma dose in the future.

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“…Then, the work was extended with betweenness and centrality indices to highlight not only the influentiality of each species on/to the others but also global structures in chemical networks. 83 In this respect, these works have opened up possibilities for simpler prediction of chemical reactions that one would like to understand and/or control. A similar approach for graph construction has been adopted by Sun et al, 84 with a different choice of undirected edges weighted based on steady-state reaction rates.…”

Section: Derivation Of Reduced Reaction Mechanismsmentioning

confidence: 99%

“…In their work, they have constructed a network structure for methane and silane plasmas by representing the species as nodes and reactions as directed unweighted edges and they demonstrated that this network analysis allows one to estimate substantial effects of given species on other ones existing in the network. Then, the work was extended with betweenness and centrality indices to highlight not only the influentiality of each species on/to the others but also global structures in chemical networks 83 . In this respect, these works have opened up possibilities for simpler prediction of chemical reactions that one would like to understand and/or control.…”

Section: Reviewmentioning

confidence: 99%

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

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Machine learning has had an enormous impact in many scientific disciplines. It has also attracted significant interest in the field of low-temperature plasma (LTP) modeling and simulation in past years. Its application should be carefully assessed in general, but many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and datadriven modeling. In this survey, we approach two main objectives: (a) we review the state-of-the-art, focusing on approaches to LTP modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data.

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Complexity visualization, dataset acquisition, and machine-learning perspectives for low-temperature plasma: a review

Cited by 9 publications

References 85 publications

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids

Machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids

Review: Machine learning for advancing low-temperature plasma modeling and simulation

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