The COVID-19 pandemic has become the focus of world problems that need to be resolved. This is because the rate of spread is speedy and able to take down the world's health system. Therefore, many researchers are focusing their research on solving this problem by doing an initial screening on the X-Ray image of the subject's lungs. One of them is by using Deep Learning. Several articles that talk about implemented Deep Learning for classifying X-Ray images have been published. But most of them are comparing different architecture CNN (Convolutional Neural Network). In this study, the authors try to create a multi-classifier Deep Learning system that consists of nine different CNN architectures and combined with three different Majority Vote techniques. The target of this research is to maximize the performance of classification and to minimize errors because the final decision is a compilation of decisions contained in each CNN architecture. Several models of CNN are tested in this study, both the model which used Majority Vote and Conventional CNN. The results show that the proposed model achieves an accuracy value average F1-Score 0.992 and Accuracy 0.993, according to 5 K-Fold test. The best model is CNN, which used Soft Majority Vote.
Media sosial memudahkan interaksi antar pengguna sebagai alat penyebaran informasi. Sehingga semua orang mencoba untuk memperoleh seluruh atensi yang ada pada suatu platform seperti Instagram. Hal ini menimbulkan dampak negatif seperti penipuan akun, pencurian data pribadi, dan penjualan akun yang telah diretas. Jenis kejahatan yang sering terjadi adalah Phishing yaitu penipuan yang menampilkan hal yang sama persis dengan platform yang asli. Digital forensik dapat memudahkan pencarian barang bukti digital. Dalam penelitian ini peneliti akan melakukan analisis digital forensik terkait tindak kejahatan spear phishing dengan menggunakan metode yang telah diusulkan oleh National Institute of Justice (NIJ). Dalam metode ini tahapan yang akan dilakukan antara lain Preparation, Collection, Examination, Analysis, dan Reporting. Berdasarkan penelitian didapatkan laman phishing yang digunakan oleh pelaku dengan domain laman instagram-page-login.herokuapp.com dan IP Address yang digunakan yaitu 18.208.60.216 dan 54.165.58.209.
Background: Neuropathic pain is one of the quality of life problems after spinal cord injury (SCI) that is caused by the lack of neurotrophic agents that modulate the regeneration process. The neural stem cell (NSC)-secretome has the potential as a neurotrophic agent for palliative treatment after SCI. The effect of NSC-secretome is still unproven. The aim of this study is to investigate the effect of NSC-secretome on neuropathic pain.
Methods: In this experimental study, ten male rats were divided into two groups. The first group did not receive NSC-secretome. The second group was injected with NSC-secretome (30 µL) intrathecally into the injury site. The neuropathic pain was measured using a real-time rat grimace scale in the 3rd and 4th weeks after injury.
Result: The result of the Statistical Package for the Social Sciences (SPSS) independent sample t-test analysis shows a significant difference between a group without and with NSC-secretome administration in the 3rd week (p<0.001) and 4th week (p=0.004). NSC-secretome group significantly decreased the neuropathic pain compared to the control group.
Conclusion: NSC-secretome injection is able to reduce the neuropathic pain expression on subacute SCI rats model.
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