Intelligent ECG Signal Filtering Method Based on SVM Algorithm

Assali, Ines; Nouira, Ibtihel; Abidi, Afef; Bedoui, Sammy

doi:10.1007/s00034-022-02196-z

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Finally, based on the evaluation results of the model, the risk of corrosion of automotive materials was managed and controlled. The specific details are as follows: the data collection and preprocessing module is responsible for collecting relevant data on the automotive usage environment, body and components, and preprocessing these data to provide a reliable data foundation for subsequent modules [15][16]. The feature extraction and selection module utilizes data mining technology to extract corrosion related features through data analysis and modeling, and screens these features to select the features that have the greatest impact on corrosion as model inputs [17][18].…”

Section: Risk Assessment Model Designmentioning

confidence: 99%

Intelligent Algorithms for Air Pollution Corrosion Risk Assessment and Management of Automotive Materials

2023

AJMC

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The evaluation of corrosion risk of automotive materials refers to the assessment of the potential risk of corrosion of automotive materials during use, in order to determine their impact on automotive performance and safety. At present, the methods for assessing and managing the corrosion risk of automotive materials are mainly based on expert experience and manual measurement. These methods have problems such as inaccurate evaluation results and low efficiency. With the development of artificial intelligence technology, intelligent algorithms are gradually being applied in the automotive field. Therefore, this article proposed a risk assessment model for automotive material corrosion based on intelligent algorithms, aiming to predict risks in advance and reduce unnecessary losses. This article mainly applied experimental analysis and algorithm comparison to analyze the influencing factors of automotive material corrosion, and compared the performance of different intelligent algorithms. The experimental results showed that the minimum error rate of the support vector machine algorithm was 9.8%, and further improvement of the intelligent algorithm was needed.

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Section: Risk Assessment Model Designmentioning

confidence: 99%