Designing Hybrid CNN-SVM Model for COVID-19 Classification Based on X-ray Images Using LGBM Feature Selection

COVID-19 still exists at an alarming level;hence, early diagnosis is important for treating and controlling this disease due to its rapid spread. The use of X-rays in medical image analysis can play an essential role in fast and affordable diagnosis. This study used a two-level feature selection in hybrid deep convolutional features obtained from the extraction of X-ray images. The transfer learning-based approach was implemented using five convolutional neural networks (CNNs) named VGG16, VGG19, ResNet50, InceptionV3, and Xception. The combination of two or three CNNs' performance as a feature extractor was then carefully analyzed. We selected the features obtained from multiple CNNs in a particular layer with a specified percentage of features in the first level for getting relevant features from various models. Then, we combined those features and did the second level of feature selection to select the most informative features. Both levels of feature selection were carried out using the light gradient boosting machine (LightGBM) algorithm. The final feature set has been used to classify COVID-19 and non-COVID-19 chest X-ray images using the support vector machines (SVM) classifier. The proposed model's performance was evaluated and analyzed on the open-access dataset. The highest accuracy was 99.80% using only 5% of the features extracted from ResNet50 and Xception. The other way of combining the ensemble of deep features and a few recent works for the classification of COVID-19 were also compared with the proposed model. As a result, our proposed model has achieved the best success rate for this dataset and may be deployed to support decision systems for radiologists © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License

Legal Protection Of Health Workers In Indonesia And Indonesian Workers (Tki) Abroad According To Positive Law

That the whole world has experienced Covid 19, as well as Indonesia, the occurrence of the virus outbreak in Indonesia so that it experiences social restrictions that result in a reduction in various activities, especially those experienced by workers/workers. And at the time of the Inauguration of the President, precisely on October 20, 2019, in his speech stipulated the omnibus law, which among other things stipulated the Undang-Unadang Job Creation, the law changed the articles contained in Law N0.13 of 2003, resulting in workers/workers feeling that there was no legal protection. The law should be made for the benefit of workers/workers and employers as well as the governmenth, research methods used with qualitative methods, research approaches, namely normative juridical, As for the problem of how to legally protect Indonesian workers working abroad according to positive law, and how to protect the law against workers/workers after the enactment of the Job Creation Law.

Student independence in overcoming the impact of covid-19: classical online guidance based on character values

Independence is an individual's ability to face, accept, and find solutions to solve problems without harming/incriminating other parties. The purpose of the study is to increase student independence in solving the problem of the impact of COVID-19 through online classical guidance based on character values. This research is experimental with a one-group pre-test-post-test experimental design. The research population was 190 FKIP students in Solo Raya. 48 samples were determined using purposive sampling, 20 students were selected from samples with low levels of independence. Data collection using self-reliance instrument questionnaires, observation, interviews, and documentation. Data analysis used non-parametric statistics with the Wilcoxon Signed Rank Test. The results showed that there was a significant difference in the pre-test and post-test scores of students' independence in overcoming the problem of the impact of COVID-19 obtained from the results of the Wilcoxon Signed Rank test, namely the value (p = 0.000) < 0.05, which means online classical guidance is based on character values. can increase student independence by overcoming problems with the impact of COVID-19. © 2022, author.

Performance Analysis of Deep Convolutional Features using Support Vector Machines for COVID-19 Diagnosis on X-ray Images

Since the first case of COVID-19 appeared in Wuhan city, China, in December 2019, the disease has affected more than millions of people worldwide. Therefore, early detection of COVID-19 is important to prevent transmission to more people. One method widely used to detect COVID-19 through X-ray images is Convolutional Neural Networks (CNN). However, CNN needs large amounts of image data to build models with high accuracy, while the medical image has limited amounts of data. To overcome this problem, transfer learning technique where CNN is used as a feature extraction method is usually be chosen as an alternative. However, most studies use the extraction results of the final layers such as fully connected layer or the last convolutional layer. In this study, all layers will be used by turns to analyze how the extraction results affect the performance of classification method. The CNN models used are pre-trained models VGG16 and VGG19, while the classification method used is Support Vector Machines (SVM). Based on the results of the study, the extraction results by the initial layer gave a better performance on SVM compared to the layers that are deeper in the selected CNN architecture. Several layers in CNN model did not analyze due to limited source capability in doing computation. Therefore, as the future work, the rest layers of CNN in this study can be analyzed as well as the other CNN models and the classification method. © 2021 IEEE.

An approach for COVID-19 detection using deep convolutional features on chest X-ray images

First screening of COVID-19 becomes very crucial because of its fast spread. There are several ways to diagnose someone who has COVID-19, but chest X-ray is one of the efficient tools that can be used. Deep learning, especially Convolutional Neural Network (CNN), is commonly utilized in medical images due to its superiority in extracting high-level features of images. However, in order to train CNN, we need enormous data to avoid overfitting. Meanwhile, there is a limit of chest X-ray availability that can be access publicly. Considering this problem, we propose pre-trained CNN model as a feature extractor, and the feature vector obtained as the output of CNN that is used as the input of machine learning classifier, namely Support Vector Machines (SVM), Random Forest (RF), and k-Nearest Neighbors (kNN). Using the data from Kaggle COVID-19 Radiography Database, our proposed method with SVM as a classifier succeeded in delivering accuracy of 99.73% in the testing data. Moreover, the performance of CNN-SVM held on training data provides the average accuracy of 99.77%. Thus, our proposed approach can be used as an alternative on screening COVID-19. © 2021 Little Lion Scientific.

Purchaseintention of elderly in a pandemic covid-19 situation based on analysis ofcustomer-based brand equity

Background: During an unpredictable situation (uncertain environment), high purchase intentions for better and quality services results providers need an extraordinary level of expertise in the healthcare market. Purpose: To determine the effect of customer-based brand equity on purchase intention. Method: observational study with cross-sectional design. The population was all elderly in the East Lombok Regency, samples that fulfilled the criteria;not experience chronic diseases such as stroke, cataracts, chronic kidney disease (CKD), severe diabetes mellitus, and coronary heart disease. Measurement of independence used the ADL Bartle Index instrument. The cognitive level used the MMSE instrument and respondents were willing to fill in the questionnaire. Results: the characteristics of respondents and customer-based brand equity on purchase intention used the chi-square test and fisher's exact test. Risk estimates and 95% confidence intervals of customer-based brand equity, namely brand awareness 1,243 (1,000 to 1,546), brand association 1,245 (1,014 to 1,527), perceived quality 1,229 (1,014 to 1,488), and brand loyalty 1,240 (1,039 to 1,479), the test results showed a significant relationship and had a prevalence ratio (PR)> 1 to the purchase intention of outpatient health centers. Conclusion: the majority of respondents who have an elderly purchase intention for outpatient services at the public health center. © 2020 Ubiquity Press. All rights reserved.