AdaBoost Support Vector Machine Method for Human Activity Recognition

Yulita, Intan Nurma; Paulus, Erick; Sholahuddin, Asep; Novita, Dessy

doi:10.1109/icaibda53487.2021.9689769

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…SVM is a machine learning technique commonly employed for the purposes of classification and regression [21]. The algorithm was initially devised for classification.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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Combining inception-V3 and support vector machine for garbage classification

Yulita,

Ardiansyah,

Ramdhani

et al. 2023

J.INFOTEL

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The global volume of trash has increased due to population growth and consumption, with a growing variety of materials and materials being generated. Inadequate garbage disposal practices, particularly in plastics, have led to environmental contamination and pollution in various regions. Artificial Intelligence (AI) technologies, particularly in machine learning, have demonstrated significant potential in trash sorting, particularly in the realm of machine learning. The Inception-V3 model and Support Vector Machines (SVM) are used in this study to extract relevant features and classify garbage categories. The Inception-V3 and SVM combination exhibits superior performance, with greater accuracy and F1 score compared to other methods. The radial basis function (RBF) kernel is the most optimal model of SVM, but it faces challenges in accurately categorizing the "trash" category due to limited data and resemblance to the "paper" class. The system developed in this study has a high level of effectiveness, with superior accuracy and F1 scores of 0.876 and 0.874, respectively.

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“…SVM is a machine learning technique commonly employed for the purposes of classification and regression [21]. The algorithm was initially devised for classification.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…AdaBoost is an ensemble technique that aggregates many weak models, which are characterized by their somewhat inferior performance and random guessing [21], to build a robust model. The AdaBoost algorithm gave varying weights to individual samples within the dataset.…”

Section: Data Splittingmentioning

confidence: 99%