Image Classification of Autism Spectrum Disorder Children Using NaÃ¯ve Bayes Method With Hog Feature Extraction

Muhathir, Muhathir; Muliono, Rizki; Hafni, Merri

doi:10.31289/jite.v5i2.6365

Cited by 7 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the previous study conducted by [15], the findings showed that the autistic classification model based on facial data using the Naive Bayes classification method with the assistance of HOG feature extraction achieved highly satisfying performance. In the study, the model was built using a total of 200 data, where 80% was used as training data and 20% as testing data.…”

Section: Discussionmentioning

confidence: 92%

Facial-Based Autism Classification Using Support Vector Machine Method

Muhathir,

DR,

El Akmal

et al. 2024

IJCDS

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is a complex neural developmental condition characterized by difficulties in communication, social interaction, and delayed brain development. Despite previous studies, there is a need to explore and enhance autism classification techniques using facial data. This research aims to classify individuals with autism based on facial images using the Support Vector Machine (SVM) method. It also evaluates the performance of SVM-based classification with HOG and SURF feature extraction, contributing to the identification of autism through facial features. A dataset of 200 facial images of students, including individuals with and without autism, was analyzed. The data was divided into 80:20 and 70:30 splits for training and testing purposes. SVM models with HOG and SURF feature extractions were evaluated using accuracy, precision, recall, and F1-Score metrics. The HOG-SVM and SURF-SVM models showed consistent performance in both data splitting scenarios. Accuracy values exceeded 0.88, and precision, recall, and F1-Score values were above 0.9. The 80:20 data split demonstrated improved performance, especially for the HOG-SVM model. Both HOG and SURF feature extraction methods showed good performance in classifying autism data. The SVM model with HOG achieved an accuracy of 0.95 in the 80:20 data split, while the SURF model achieved 0.9. Early autism detection based on facial data holds potential for use in student selection in elementary schools. However, the study has limitations due to limited data and the focus on accuracy alone. Future research can expand the data size, explore other feature extraction methods, and implement advanced deep learning techniques to improve classification performance and contribute further to autism detection based on facial data .

show abstract

Section: Discussionmentioning

confidence: 92%

Facial-Based Autism Classification Using Support Vector Machine Method

Muhathir,

DR,

El Akmal

et al. 2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Skor f1 menghitung hasil rata-rata yang seimbang dengan menggabungkan presisi dan daya ingat. Persamaan di bawah ini mengilustrasikan cara menghitung nilainilai ini, dengan TP, TN, FP, dan FN masing-masing mewakili true positive, true negative, false positive, dan false negative (Muhathir et al, 2022)…”

Section: E Evaluationunclassified

The Nutritional Classification of Pregnant Women Using Support Vector Machine (SVM)

Sahputra,

Bustami,

Cut Farida Aryani

2023

JITE

View full text Add to dashboard Cite

Determining the nutritional status of pregnant women is one of the efforts to control the condition of pregnant women so that they can adjust their health conditions properly. The health condition of pregnant women can affect the condition of the baby who will be born. This study aims to apply the SVM method to a web-based application to classify the nutritional status of pregnant women based on data obtained from several health centers in the city of Lhokseumawe. SVM functions as the core of the application in charge of classifying the nutritional status of pregnant women based on several features including: age, weight, height, lila, hemoglobin and BMI. While the data class consists of 3 categories, namely: undernourished, normal nutrition and normal nutrition + overweight. Primary data obtained from the field amounted to 355 data which were then divided into two parts with a ratio of 70% training data and 30% testing data. Based on the research conducted, it was found that the application of different kernels in the Support Vector Machine (SVM) will have a different performance impact in classifying data. In this study, the linear kernel has the best performance with an accuracy value of 0.84, the RBF kernel has an accuracy value of 0.83, the polynomial kernel has an accuracy value of 0.72, and the sigmoid kernel has the worst performance with an accuracy value of 0.58

show abstract

“…HOG feature extraction has also been used to identify plant species from leaf patterns using HOG feature extraction and the KNN method (Bao et al, 2020) (Krairina, N,. et al 2022) (Muhathir et al, 2022).…”

mentioning

confidence: 96%

Performance Comparison of Boosting Algorithms in Spices Classification Using Histogram of Oriented Gradient Feature Extraction

2023

jcositte

View full text Add to dashboard Cite

Spice classification is an important task in the food industry to ensure food safety and quality. This study focuses on the classification of spices using the HoG feature extraction method and boosting algorithms. The objective of this research is to compare the performance of four different models of boosting algorithms, namely Adaboost Classifier, Gradient Boosting Classifier, XGB Classifier, and Light GBM Classifier, in classifying spices. The evaluation metrics used in this research are Precision, Recall, F1-Score, F2-Score, Jaccard Score, and Accuracy. The results show that the XGB Classifier model achieved the best performance, with a precision of 0.811, recall of 0.809, and F1-score of 0.809, while the Adaboost Classifier model had the lowest performance, with a precision of 0.709, recall of 0.689, and F1-score of 0.682. Overall, the results indicate a fairly good success rate in classifying spices using the HoG feature extraction method and boosting algorithms. However, further evaluation is needed to improve the accuracy of the classification results, such as increasing the number of training data or considering the use of other feature extraction methods.

show abstract

Image Classification of Autism Spectrum Disorder Children Using NaÃ¯ve Bayes Method With Hog Feature Extraction

Cited by 7 publications

References 2 publications

Facial-Based Autism Classification Using Support Vector Machine Method

Facial-Based Autism Classification Using Support Vector Machine Method

The Nutritional Classification of Pregnant Women Using Support Vector Machine (SVM)

Performance Comparison of Boosting Algorithms in Spices Classification Using Histogram of Oriented Gradient Feature Extraction

Contact Info

Product

Resources

About