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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.14413 |
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| _version_ | 1866929224890187776 |
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| author | Araújo, Carlos Eduardo do Egito Sgobbi, Lívia F. Sene Jr, Iwens Gervasio de Carvalho, Sergio Teixeira |
| author_facet | Araújo, Carlos Eduardo do Egito Sgobbi, Lívia F. Sene Jr, Iwens Gervasio de Carvalho, Sergio Teixeira |
| contents | This systematic review focuses on analyzing the use of machine learning techniques for identifying and quantifying analytes in various electrochemical applications, presenting the available applications in the literature. Machine learning is a tool that can facilitate the analysis and enhance the understanding of processes involving various analytes. In electrochemical biosensors, it increases the precision of medical diagnostics, improving the identification of biomarkers and pathogens with high reliability. It can be effectively used for the classification of complex chemical products; in environmental monitoring, using low-cost sensors; in portable devices and wearable systems; among others. Currently, the analysis of some analytes is still performed manually, requiring the expertise of a specialist in the field and thus hindering the generalization of results. In light of the advancements in artificial intelligence today, this work proposes to carry out a systematic review of the literature on the applications of artificial intelligence techniques. A set of articles has been identified that address electrochemical problems using machine learning techniques, more specifically, supervised learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14413 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aprendizado de máquina aplicado na eletroquímica Araújo, Carlos Eduardo do Egito Sgobbi, Lívia F. Sene Jr, Iwens Gervasio de Carvalho, Sergio Teixeira Machine Learning Artificial Intelligence This systematic review focuses on analyzing the use of machine learning techniques for identifying and quantifying analytes in various electrochemical applications, presenting the available applications in the literature. Machine learning is a tool that can facilitate the analysis and enhance the understanding of processes involving various analytes. In electrochemical biosensors, it increases the precision of medical diagnostics, improving the identification of biomarkers and pathogens with high reliability. It can be effectively used for the classification of complex chemical products; in environmental monitoring, using low-cost sensors; in portable devices and wearable systems; among others. Currently, the analysis of some analytes is still performed manually, requiring the expertise of a specialist in the field and thus hindering the generalization of results. In light of the advancements in artificial intelligence today, this work proposes to carry out a systematic review of the literature on the applications of artificial intelligence techniques. A set of articles has been identified that address electrochemical problems using machine learning techniques, more specifically, supervised learning. |
| title | Aprendizado de máquina aplicado na eletroquímica |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.14413 |