Navigating micro- and nano-motors/swimmers with machine learning: Challenges and future directions

Xue, Jueyi; Alinejad-Rokny, Hamid; Liang, Kang

doi:10.1016/j.chphma.2024.06.001

Cited by 3 publications

References 77 publications

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Advancements in Machine Learning for Microrobotics in Biomedicine

Salehi,

Hosseinpour,

Tabatabaei

et al. 2024

Advanced Intelligent Systems

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Microrobotics, particularly in the field of biomedicine, has garnered considerable attention due to its potential for noninvasive medical interventions enabled by the small size of microrobots. However, controlling and imaging them present unique challenges compared to their macroscale counterparts, primarily due to the intricate anatomical spaces and dynamic environments within the human body. Existing imaging modalities also face limitations, hindering real‐time visualization and control of microrobots in deep tissue. Machine learning (ML) algorithms offer promising solutions to these challenges by enabling adaptive motion control and enhancing image resolution through robust data analysis and decision‐making capabilities. In this review, a comprehensive overview of recent advancements in ML‐based techniques for microrobotic research is provided, emphasizing their applications in imaging and control in biomedical contexts. Additionally, current obstacles and potential future directions for ML algorithms in microrobotics, particularly regarding their translation to clinical settings, are discussed.

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Advancements in Machine Learning for Microrobotics in Biomedicine

Salehi,

Hosseinpour,

Tabatabaei

et al. 2024

Advanced Intelligent Systems

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A Comparative Study of Machine Learning Algorithms for Predicting Project Outcomes

B. Pacot,

Daguil,

Pitogo

2024

2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches

Aytaç,

Khanzada,

Ibrahim

et al. 2024

Membranes

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Membrane engineering is a complex field involving the development of the most suitable membrane process for specific purposes and dealing with the design and operation of membrane technologies. This study analyzed 1424 articles on reverse osmosis (RO) membrane engineering from the Scopus database to provide guidance for future studies. The results show that since the first article was published in 1964, the domain has gained popularity, especially since 2009. Thin-film composite (TFC) polymeric material has been the primary focus of RO membrane experts, with 550 articles published on this topic. The use of nanomaterials and polymers in membrane engineering is also high, with 821 articles. Common problems such as fouling, biofouling, and scaling have been the center of work dedication, with 324 articles published on these issues. Wang J. is the leader in the number of published articles (73), while Gao C. is the leader in other metrics. Journal of Membrane Science is the most preferred source for the publication of RO membrane engineering and related technologies. Author social networks analysis shows that there are five core clusters, and the dominant cluster have 4 researchers. The analysis of sentiment, subjectivity, and emotion indicates that abstracts are positively perceived, objectively written, and emotionally neutral.

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Navigating micro- and nano-motors/swimmers with machine learning: Challenges and future directions

Cited by 3 publications

References 77 publications

Advancements in Machine Learning for Microrobotics in Biomedicine

Advancements in Machine Learning for Microrobotics in Biomedicine

A Comparative Study of Machine Learning Algorithms for Predicting Project Outcomes

Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches

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