The Using of PCA, Wavelet and GLCM In Face Recognition System, A Comparative Study

         تلعب عملية التقليل البعدي للبيانات دوراً هاماً في اي نظام لتمييز الوجه، لان العديد من هذه البيانات متكررة و غير ذي صلة وهذا يسبب مشكلة في تطبيقات التعلم الآلي والتنقيب عن البيانات. الغرض الرئيسي من التقليل البعدي للبيانات هو لتحسين اداء التمييز عن طريق ازالة الميزات المتكررة.          تم في هذا البحث استخدام عدد من تقنيات تقليل البيانات مثل تقنية تحليل المركبات الاساسية(PCA) و تحويل المويجة المتقطع(DWT) ومصفوفة الحدوث المشترك(GLCM). هدف البحث هو استخلاص الميزات الاكثر اهمية من الصور، تم استخدام اعداد مختلفة من الصور في التدريب والاختبار لغرض مقارنة الاداء لكل من التقنيات اعلاه في عملية التمييز، كما تم استخدام مقياس المسافة الاقليدية للحصول على النتائج.The process of data dimension reduction plays an important role in any  face recognition system because many of these data are repetitive and irrelevant and this cause a problem in applications of data mining and learning the machine. The main purpose is to improve the performance of recognition by eliminating repetitive features.           In this research, a number of data reduction techniques were used like: Principal Component Analysis, Gray-Level Co-occurrence Matrix and Discrete Wavelet Transform for extracting the most important features from the images of persons. A different number of training and testing images were used to compare the performance of each of the techniques above in the recognition process. Euclidean distance scale was used to get results. &nbsp

Haar Transformation for Compressed Speech Hiding

علم الكتابة المغطاة هو واحد من أكثر العلوم شيوعا في مجال امنية المعلوم.  في هذا البحث ، سيتم تعديل خوارزمية لتضمين صوتك مكبوس داخل صورة رمادية باستخدام تحويل المويجات المتقطعة (Haar) . في البداية تم كبس بيانات الصوت  الى نصف حجمها الأصلي ومن  ثم تحويل البيانات المكبوسة من الترميز العشري إلى الترميز الثنائي وتضمينه داخل معاملات  الحزم الاتجاهية الاربعة (cA :Low Low ,cH :High Low ,cV:Low High,cD:High High)   الناتجة من تحليل صورة الغطاء Cover_Image باستخدام تحويل المويجة المتقطع Haar حيث ان cA   تمثل حزمة الترددات الواطئة و cH ,cV ,cD تمثل حزم الترددات العالية .         تم اختبار كفاءة الخوارزمية بقياس معاملات كفاءة الاخفاء (MSE,PSNR,SNR,Correlation) واظهرت النتائج صعوبة اكتشاف المراقب لصورة الغطاء الحاوية على البيانات السرية المطمورة.          تظهر نتائج هذا البحث أنه يمكننا بنجاح إخفاء بيانات الكلام (الصوت) في صورة رمادية ثم استخراجها مع معدل سعة  خزن  (1) خلية ثنائية (bit) لكل نقطة ضوئية   اي ان سعة الخزن باستخدام  الطريقة المقدمة يعتمد على حجم صورة الغطاء  وكذلك تبين انه معاملات الترددات العالية تكون افضل للاخفاء من حيث عدم ادراك  المتطفلين بانه يوجد بيانات سرية  داخل الوسط الحامل لها  stego_imag. Steganography  science is one of the most popular field in security direction. In this paper an algorithm will be adopted to embed a compressed speech inside a gray image using discrete wavelet (Haar transformation). In the beginning the speech was compressed up to its half original size by applying (Daubechies) then convert the speech data from decimal code to binary code and embed it inside Haar coefficients of the cover _image using the Four sub bands (cA : Low Low,cH: High Low,cV:Low High,cD: High High) which got by applying the wavelet on the cover_ image. Measuring Peak Signal to Noise Ratio (PSNR) to determine the accuracy of the stego_image with respect to the original image, MSE and the correlation factors were checked show that the proposed algorithm has positive effect in field of speech hiding.The proposed  technique in this research  turned out to be able to hide  speech data (audio) in the cover image and then extract the hidden data  with  storage rate (1) bits per pixel. Hiding capacity can be achieved using this method proportionally depends on cover_image size. High frequency coefficients have also been shown to be better for data hiding in terms of perceptibility and intruders' cannot be able to recognize the cover medium (stego_image) which included secret data

Patient Satisfaction and Its Predictors in the General Hospitals of Southwest Saudi Arabia: A Cross-sectional Survey

Background: Patient satisfaction occupies a central position in measuring the quality of care as it provides information on the provider's success, meeting the patient’s values and expectations. Hence, it is an essential tool for assessing health services outcomes. This study aimed to assess patients' satisfaction level and factors influencing healthcare quality of general hospitals in the Jazan region, Saudi Arabia (SA). Methods: This observational cross-sectional study was conducted on a sample of 423 patients selected through stratified random sampling from general hospitals of the Jazan region. Results: The overall satisfaction rate among the study participants was 80.9%. Satisfaction with food services was the highest (91.15%) followed by doctor services (81.0%), reception and entry procedures (80%), and nursing services (78.15%). The various aspects of satisfaction with doctors and nurses included the treatment prescribed by physicians, clarity in communication with patients, compassion and providing clear explanation of what they were doing. However, about 27.3% of the patients were dissatisfied with the length of waiting period before seeing a doctor. Binary logistic regression analysis suggested that uneducated patients and patients with secondary school education were more likely to have higher satisfaction level than university-educated patients (OR = 3.40, 95% C.I. [1.56–7.45], p = 0.002), (OR = 2.66, 95% C.I. [1.28–5.55], p = 0.009), and (OR = 2.29, 95% C.I. [1.40–3.73], p = 0.001), respectively. Conclusion: The health services satisfaction level was high in the Jazan population. However, some aspects of dissatisfaction were reported, such as the long waiting period before seeing a doctor. These aspects are recommended to be improved to ensure that the services provided by general hospitals are of high quality