Recognition of the Effect of Vocal Exercises by Fuzzy Triangular Naive Bayes, a Machine Learning Classifier: A Preliminary Analysis

Santana, Émile Rocha; Lopes, Leonardo Wanderley; Moraes, Ronei Marcos de

doi:10.1016/j.jvoice.2022.10.001

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…It can be calculated by knowing the data is classified correctly. To calculate accuracy, the following formula is used [10]:…”

Section: Classifier Accuracy Testmentioning

confidence: 99%

Determination of Bank Indonesia Scholarship Recipients Using Naïve Bayes Classifier

Febri,

Sari

2023

BAREKENG: J. Math. & App.

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A scholarship is a grant given to students as financial aid for education. One of the most sought-after scholarships is the scholarship from Bank Indonesia. Currently, the selection process for Bank Indonesia scholarship recipients still involves verifying the completeness of the administrative documents of the prospective recipients. Manual administrative verification requires a long time for data processing and re-verification. Therefore, there is a need for a data classification system to assist in the decision-making process for Bank Indonesia scholarship recipients. This study aims to implement the naïve Bayes classifier method to classify Bank Indonesia scholarships accurately. The variables used include gender, semester, parental income, grade point average (GPA), achievement, organizational activity, and the number of dependents. This research found that the naïve Bayes classifier method for classifying Bank Indonesia scholarship recipients can be done accurately with an accuracy rate of 86,84%.

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“…It can be calculated by knowing the data is classified correctly. To calculate accuracy, the following formula is used [10]:…”

Section: Classifier Accuracy Testmentioning

confidence: 99%

Determination of Bank Indonesia Scholarship Recipients Using Naïve Bayes Classifier

Febri,

Sari

2023

BAREKENG: J. Math. & App.

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Prediction of Heart Disease Using Fuzzy Rough Set Based Instance Selection and Machine Learning Algorithms

Torkul,

Turgay,

Şişci

et al. 2023

Lecture Notes in Mechanical Engineering

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A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method

Nobel,

Swapno,

Islam

et al. 2024

Sci Rep

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In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system’s ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system’s classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field’s capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease.

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Recognition of the Effect of Vocal Exercises by Fuzzy Triangular Naive Bayes, a Machine Learning Classifier: A Preliminary Analysis

Cited by 6 publications

References 38 publications

Determination of Bank Indonesia Scholarship Recipients Using Naïve Bayes Classifier

Determination of Bank Indonesia Scholarship Recipients Using Naïve Bayes Classifier

Prediction of Heart Disease Using Fuzzy Rough Set Based Instance Selection and Machine Learning Algorithms

A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method

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