No abstract

<em>Tanda tangan mempunya pola yang unik berdasarkan fitur yang ditinjau. Penelitian ini mengindentifikasi tanda tangan secara otomatis dengan menggunakan fitur biner dari hasil tanda tangan scanner. Identifikasi tanda tangan penting dilakukan otentifikasi dokumen administrasi dan resmi dimana nilai akurasi hal yang diperlukan. Dalam pendekatan yang dilakukan, fitur tanda tangan diekstrak dengan menggunakan dua descriptor yaitu binary statistical image features (BSIF) dan </em><em>local binary patterns (LBP). Penilaian menggunakan metode ini dengan melakukan percobaan dengan dua dataset yang sudah tersedia untuk umum yaitu database MCYT-75 dan GPDS-100. Dengan menggunakan metode klasifikasi KNN, mendapatkan nilai tertinggi masing-masing 96,7% dan 93,9%. Dalam verifikasi identifikasi tanda tangan akurasi klasifikasi diukur berdasarkan equal error rate (EER)yaitu 4.2% dan 5.33% pada GPDS-200 dan GPSD-150. Sehingga EER untuk database MCYT-75 sudah mencapau 7,78%. Nilai akurasi tersebut sudah dapat diketegorikan unggul.</em>

Aims After the COVID-19 pandemic, the risk of thrombosis and bleeding has been an important issue. It is reported that higher D-dimer levels are associated with poor prognosis. Cardiovascular disease risk has also been reported to poor prognosis of COVID-19. Both cardiovascular disease risk and high D-dimer level can be a lethal combination leading to death in COVID-19 patients. We are aiming this research to analyse the capability ten-year risk of fatal cardiovascular disease obtained from SCORE-Risk chart to predict the D-dimer level in hospitalised COVID-19 patients. Methods and Results This is a cross-sectional study including 85 moderate-severe hospitalised COVID-19 patients without previous cardiovascular disease. We assessed the patient’s cardiovascular risk by using SCORE-Risk chart and separate samples into two D-dimer groups. Comparison between patient’s clinical variables and D-Dimer level is done by using Mann-Whitney analysis. Variables which show p value &lt;0.2 will then be analysed by Logistic Regression. There are 27 patients (31.8%) with a high cardiovascular risk. Median D-dimer is 560 ng/mL. Mann-Whitney analysis comparing patient’s clinical variables with D-dimer shows CRP, blood glucose, lactic acid and SCORE-Risk chart give p-value with value &lt; 0.2. The logistic regression analysis shows that SCORE-Risk chart is the strongest predictor of higher D-dimer level with OR 5.647 (1.670-19.092; p value 0.005) with 88.2% specificity and 45.1% sensitivity. Conclusions Ten-year risk of fatal cardiovascular disease measured by SCORE-risk chart can be used as accurate predictor of higher D-dimer level on moderate-severe COVID-19 patients.

A neural network is a data processing system consisting of a large number of simple and highly interconnected processing elements in an architecture inspired by the structure of the cortical regions of the brain. Therefore, neural networks can often do things that humans or animals can do, but traditional computers are often lousy. This research discusses brain tumors that can be detected by artificial intelligence. Stroke includes the sudden death of brain cells due to lack of oxygen, blockage of the circulatory system, or severance of flexible pathways to the brain. Therefore the need for action that must be faster to be able to detect this deadly disease. The method used is a Neural Network which can collect knowledge by detecting patterns and relationships between data and learning experiences. So that the detection process is carried out more quickly and the patient can be given medical action as soon as possible. In the study I conducted brain stroke from the number of strokes with a value of 0 4733 and 1 out of 248. This research has a test conducted by conducting epoch training from 1 to 300, the highest score accuracy is in epoch 1 and 2 with more high scores.