Classification of Noni Fruit Ripeness Using Support Vector Machine (SVM) Method DOI Creative Commons

Yudha Islami Sulistya,

Maie Istighosah,

Maryona Septiara

и другие.

Indonesian Journal of Data and Science, Год журнала: 2024, Номер 5(3), С. 206 - 215

Опубликована: Дек. 31, 2024

The classification of Noni fruit (Morinda citrifolia) ripeness is essential for maximizing its medicinal benefits and ensuring product quality. This research aimed to classify using the Support Vector Machine (SVM) method, comparing three kernel functions: linear, Radial Basis Function (RBF), polynomial. A dataset consisting images ripe unripe fruits was utilized, with preprocessing steps including extraction color texture features. Performance evaluation revealed that RBF achieved highest accuracy at 86.18%, followed by polynomial 84.55%, linear 81.30%. These results suggest most effective this task, showing superior capability in capturing non-linear patterns complexities within dataset.

Язык: Английский

Spectral-Based Fault Diagnosis Methodology for Industrial Shot Blast Machinery Leveraging XGBoost and Feature Importance DOI Open Access
Joon-Hyuk Lee, Chibuzo Nwabufo Okwuosa,

Beak Cheon Shin

и другие.

Опубликована: Авг. 13, 2024

The optimal functionality and dependability of mechanical systems are important for the sustained productivity operational reliability industrial machinery which has direct impact on it’s longevity profitability. Therefore, failure a system or any it component would be detrimental to production continuity availability. Consequently,this study proposes robust diagnostic framework analyzing blade conditions shot blast machinery. involves spectral characteristics vibration signals generated by Industrial Shot Blast. Additionally, peak detection algorithms is introduced identify extract unique features present in magnitudes each signal spectrum. A feature importance algorithm then deployed as selection tool, these selected fed into 10 machine learning classifier, with Extreme gradient boosting (XGB) core classifier. Results show that XGB classifier achieved best accuracy 98.05%, cost-efficient computational cost 0.83 seconds. Other global assessment metrics were also implemented further validate model.

Язык: Английский

Процитировано

0
Machine Learning Approach with Multiple Feature Selection Techniques to Diagnose the Inter-Turn Winding Faults in Induction Motor DOI
Rajeev Kumar, R. S. Anand

Arabian Journal for Science and Engineering, Год журнала: 2024, Номер unknown

Опубликована: Окт. 24, 2024

Язык: Английский

Процитировано

0
Classification of Noni Fruit Ripeness Using Support Vector Machine (SVM) Method DOI Creative Commons

Yudha Islami Sulistya,

Maie Istighosah,

Maryona Septiara

и другие.

Indonesian Journal of Data and Science, Год журнала: 2024, Номер 5(3), С. 206 - 215

Опубликована: Дек. 31, 2024

The classification of Noni fruit (Morinda citrifolia) ripeness is essential for maximizing its medicinal benefits and ensuring product quality. This research aimed to classify using the Support Vector Machine (SVM) method, comparing three kernel functions: linear, Radial Basis Function (RBF), polynomial. A dataset consisting images ripe unripe fruits was utilized, with preprocessing steps including extraction color texture features. Performance evaluation revealed that RBF achieved highest accuracy at 86.18%, followed by polynomial 84.55%, linear 81.30%. These results suggest most effective this task, showing superior capability in capturing non-linear patterns complexities within dataset.

Язык: Английский

Процитировано

0