Ulcerative Colitis Diagnosis Based on Artificial Intelligence System

مرض التهاب القولون التقرحي هو تهيج في القولون الذي يرتبط في كثير من الأحيان مع العدوى ونقص المناعة. يكون جدار القولون للشخص مصاب بالالتهاب دائمًا أكثر سماكة من المعتاد. قد يكون مرض التهاب القولون التقرحي مهدد للحياة ويؤدي إلى الموت إذا لم يتم اكتشافه مبكرًا. الاكتشاف المبكر لهذا المرض مهم للغاية لبدء العلاج المناسب. في هذا البحث، تم تقديم شبكة العصبية الاصطناعية للكشف عن مرض التهاب القولون التقرحي وفقًا لمجموعة البيانات النظرية التي تم إنشاؤها بواسطة المعايير. تم تدريب الشبكة باستخدام خوارزمية Levenberg-Marquardt. أفضل اداء للشبكة كان حيث نسبة الخطأ تساوي 1.9947×10-24   للنظام الذي عدد خلاياه العصبية = 4.Ulcerative colitis (UC) disease is irritation of the colon that is frequently related to infection and immune compromise. The wall of the colon with inflammation is always thicker than normal. UC may be life-threatening and lead to death if not detected early. Early detection of this disease is very important to initiate appropriate treatment. In this paper, the Artificial Neural Network (ANN) applied to detect the UC according to a theoretical dataset generated by the criteria of UC. The Levenberg-Marquardt (LM) algorithm has trained the single hidden layer ANN. The best behaviour is equal to 1.9947×10-24for the system which the number of neurons =4

AN APPROACH FOR SINGLE-TONE FREQUENCY ESTIMATION USING DFT INTERPOLATION WITH PARZEN WINDOWING

In many applications, including radar, radio and television, medical, industrial, and others, frequency estimate of a periodic sinusoidal signal is a crucial step in the signal processing process. Due to its simplicity of usage in digital systems, the interpolation-based signal frequency estimation algorithm is now frequently employed. Because of its performance speed, interpolation methods in the analysis rely on the fast Fourier transform (FFT). This paper proposes an approach for single-tone frequency estimate utilizing DFT interpolation with Parzen windowing in order to increase the accuracy of frequency estimation. In addition, compared to Li and Dian algorithm, the proposed method has a lower computing complexity and more steady performance. To minimize undesirable effects brought on by spectrum leakage from the FFT procedure, suitable windows have been investigated. To investigate the viability of the suggested method, three windows Flattop, Parzen, and Bohman, were applied to the simulation signal. When compared to the other windows, the Parzen window with the proposed algorithm outperformed them with a maximum frequency estimation error of 0.00003 compared to 0.0001 and 0.0002 for the Dian and Li algorithms, respectively, when the Sample Size was 8192