Abstract Classification Using Support Vector Machine Algorithm (Case Study: Abstract in a Computer Science Journal)

Lumbanraja, Favorisen Rosyking; Fitri, Eliza; Ardiansyah, Ardiansyah; Junaidi, Apri; Prabowo, Rizky

doi:10.1088/1742-6596/1751/1/012042

Cited by 7 publications

(8 citation statements)

References 5 publications

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“…K-Fold CV divides data into 𝐾 folds. At each iteration, one-fold (𝐾) is used as test data set while training data set is resided folds (K1) in 𝐾 experiments [51], [52]. In this work, the value of 𝐾 = 10 folds, nine data sets for training and one for testing, then repeat this process ten times until all data has been evaluated.…”

Section: Resultsmentioning

confidence: 99%

A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine

Bahnam

Dawwod

2022

IJ-AI

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One of the most dangerous health diseases affecting the world's population is diabetes mellitus (DM), and its diagnosis is the key to its treatment. Several methods have been implemented to diagnose diabetes patients. In this work, a hybrid model which combines of coyote optimization algorithm (COA) and least squares support vector machine (LS-SVM) is proposed to classify of Type-II-DM patients. LS-SVM classifier is applied for classification process but it's very sensitive when its parameter values are changed. To overcome this problem, COA algorithm is implemented to optimize parameters of the LS-SVM classifier. This is the goal of the proposed model called the COA-LS-SVM. The proposed model is implemented and evaluated using pima Indians diabetes dataset (PIDD). Also, it's compared with several classification algorithms that were implemented on the same PIDD. The experimental results demonstrated the effectiveness of the proposed model and its superiority over other algorithms, as it could accomplish an average classification accuracy of 98.811%.

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Section: Resultsmentioning

confidence: 99%

A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine

Bahnam

Dawwod

2022

IJ-AI

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“…In this study, a confusion matrix and receiver operating characteristic (ROC) curve were used to assess algorithm performance using various metrics such as accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC). The confusion matrix is a condensed table that is used to assess a classifier’s performance using data about actual and expected values [ 20 , 21 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques

NDAGIJIMANA¹,

Ntaganda²,

Masabo³

et al. 2023

J Prev Med Public Health

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Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children.Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen.Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model’s ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother’s height, television, the child’s age, province, mother’s education, birth weight, and childbirth size were the most important predictors of stunting status.Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

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“…Quality of performance evaluation of ten machine learning algorithms … (Nashaat M. Hussien Hassan) 105 2.5. Support vector machine SVM is an universal learning machine parameterized by set weights and support vectors to make the decision, also characterized by a kernel function [26], [27]. The maximum margin classifier produces a D(x), that is maximize the stop between the border and the data [26], [27].…”

Section: Gradient Boosting Machinementioning

confidence: 99%

Quality of performance evaluation of ten machine learning algorithms in classifying thirteen types of apple fruits

Hassan

Elshoky²,

Mabrouk

2023

IJEECS

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Recently, computer vision technology has become essential for the automatic, accurate, and fast classification of fruits. Actually, there are many challenges in separating the types of fruits that are somewhat similar, such as apples, pears, and peaches. However, the challenges become more difficult if the separation is on different varieties of the same fruit. While the difficulty doubles if the classification takes place with a large number of different varieties of the same fruit. Most of the literature which is presented in this regard, and which is relied on the use of machine learning techniques lacked the following: first; the focus was on certain technologies such as k-nearest neighbor (KNN), support vector machine (SVM) without looking at many other machine learning techniques. Second; the literature was concerned only with measuring the accuracy of the techniques that are used, without looking at the relationship between the accuracy and processing speed (computation times). This manuscript aims to study and analyze the results of measuring accuracy and computation times for ten machine-learning techniques in order to identify and classify thirteen types of apples. After studying and analyzing the results, many observations were made, which will be referred to in the results section.

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Abstract Classification Using Support Vector Machine Algorithm (Case Study: Abstract in a Computer Science Journal)

Cited by 7 publications

References 5 publications

A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine

A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine

Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques

Quality of performance evaluation of ten machine learning algorithms in classifying thirteen types of apple fruits

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