<span lang="EN-US">This study aims to validate self-portraits using one-class support vector machine (OCSVM). To validate accurately, we build a model by combining texture feature extraction methods, Haralick and local binary pattern (LBP). We also reduce irrelevant features using forward selection (FS). OCSVM was selected because it can solve the problem caused by the inadequate variation of the negative class population. In OCSVM, we only need to feed the algorithm using the true class data, and the data with pattern that does not match will be classified as false. However, combining the two feature extractions produces many features, leading to the curse of dimensionality. The FS method is used to overcome this problem by selecting the best features. From the experiments carried out, the Haralick+LBP+FS+OCSVM model outperformed other models with an accuracy of 95.25% on validation data and 91.75% on test data.</span>

The research aimed to compare the classification performance of arrhythmia classification from the ECG signal dataset from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) database. Shallow learning methods that were used in this study are Support Vector Machine,  Naïve Bayes, and Random Forest. 1D Convolutional Neural Network (1D CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were deep learning methods that were used for the study. The models were tested on a dataset with 140 samples that were grouped into four class labels, and each sample has 2160 features. Those models were tested for classification performance. This research shows Random Forest and 1D CNN have the best performance.

Purpose: The purpose of this study was to determine how the variables of supply chain performance, performance of medical personnel, and occupancy overload affect operating performance moderated by dynamic capability variables. In a case study of public health center in Indonesia.Design/methodology/approach: This study uses the SEM-PLS quantitative method to analyze the questionnaire data obtained from 112 respondents consisting of medical administrators, nurses, and doctors. Validity and reliability tests were also used to ensure that the data were normally distributed and reliable.Findings: This study found that the supply chain performance variable and the performance of medical personnel had a positive effect on the operational performance of the public health center either through moderating the dynamic capability variable or not. Meanwhile, occupancy overload was found to have a negative effect on the operational performance of the public health center. And the moderating of the dynamic capability variable is only able to reduce its negative impact.Research limitations/implications: This study covers only a small number of public health center in Indonesia, so it is quite difficult to produce generalizable findings. This study also did not involve other internal and external variables that could potentially affect the operational performance of the public health center.Practical implications: The findings of this study can be a suggestion for the government and the management of the public health center to pay more attention to the variables that affect the operational performance of the public health center. Variables that have a positive impact should be increased and variables that have a negative impact should be mitigated.Social implications: Health centers that have effective and efficient operating management will be able to maximize the performance of patient services armed with available resources. The findings of this study can help the public health center to anticipate a surge in patient visits which can reduce the operating performance of the public health center.Originality/value: This study combines the variables of supply chain performance, medical personnel performance, occupancy overload, dynamic capability, and operating performance in one causality model framework. In contrast to other studies that did it separately.