Hamdani

Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images

Objectives Glaucoma is an incurable eye disease and the second leading cause of blindness in the world. Until 2020, the number of patients of this disease is estimated to increase. This paper proposes a glaucoma detection method using statistical features and the k-nearest neighbor algorithm as the classifier. Methods We propose three statistical features, namely, the mean, smoothness and 3rd moment, which are extracted from images of the optic nerve head. These three features are obtained through feature extraction followed by feature selection using the correlation feature selection method. To classify those features, we apply the k-nearest neighbor algorithm as a classifier to perform glaucoma detection on fundus images. Results To evaluate the performance of the proposed method, 84 fundus images were used as experimental data consisting of 41 glaucoma image and 43 normal images. The performance of our proposed method was measured in terms of accuracy, and the overall result achieved in this work was 95.24%, respectively. Conclusions This research showed that the proposed method using three statistics features achieves good performance for glaucoma detection.

The Proposed Development of Prototype with Secret Messages Model in Whatsapp Chat

Basis test paths is a method that uses a graph contains nodes as a representation of codes and the lines as a sequence of code execution steps. Determination of basis test paths can be generated using a Genetic Algorithm, but the drawback was the number of iterations affect the possibility of visibility of the appropriate basis path. When the iteration is less, there is a possibility the paths do not appear all. Conversely, if the iteration is too much, all the paths have appeared in the middle of iteration. This research aims to optimize the performance of Genetic Algorithms for the generation of Basis Test Paths by determining how many iterations level corresponding to the characteristics of the code. Code metrics Node, Edge, VG, NBD, LOC were used as features to determine the number of iterations. J48 classifier was employed as a method to predict the number of iterations. There were 17 methods have selected as a data training, and 16 methods as a data test. The system was able to predict 84.5% of 58 basis paths. Efficiency test results also show that our system was able to seek Basis Paths 35% faster than the old system.A single-fed linearly polarized 2x2 microstrip bow tie array antenna is proposed. The feed network has microstrip line and slot line where microstrip-slot branch circuit is connected in parallel. The feed network of the array is designed using both-sided MIC Technology to overcome the impedance matching problem of conventional feed networks. The 2x2 half bow tie array antenna is also truncated with spur lines for optimization of antenna performance. The array antenna unit can be realized in very simple and compact structure, as all the antenna elements and the feeding circuit is arranged on a Teflon glass fiber substrate without requiring any external network. The design frequency of the proposed antenna is 5 to 8 GHz (C-Band) and the obtained peak gain is 12.41 dBi. The resultant axial ratio indicates that linear polarization is achieved.This paper presents a novel method for QRS detection. To accomplish this task ECG signal was first filtered by using a third order Savitzky Golay filter. The filtered ECG signal was then preprocessed by a Wavelet based denoising in a real-time fashion to minimize the undefined noise level. R-peak was then detected from denoised signal after wavelet denoising. Windowing mechanism was also applied for finding any missing R-peaks. All the 48 records have been used to test the proposed method. During this testing, 99.97% sensitivity and 99.99% positive predictivity is obtained for QRS complex detection.