Background: For various reasons, inguinal hernia repair under local anaesthesia is not well accepted to both patients and surgeons. The patients fear from pain and surgeons need full relaxation and co-operation to do successful hernia repairMethods: purpose of this study is to evaluate the effectiveness of local anaesthesia in inguinal hernia repair.prospective study was made from January 2011-0ctober 2013 , on a total of 50 patients with inguinal hernia operated on under local anaesthesia. Patients were selected primarily on the basis of their willingness to accept the procedure after the technique was described to them.Results: In this study 50 patient and 58 herniorrhaphies done for them during a period of about 34months were evaluated .A questionnaire was sent to each patient postoperatively to aid in evaluating the subjective reactionto the local anaesthesia. all patients were males ,age range between 31-83 year, median age 51 .types of inguinal hernia were sliding 1case ,pantaloon 5cases ,indirect 28cases ,direct 24 cases ,Rt. Side 27cases, Lt. side 15 cases ,bilateral hernia 8 cases .we give local anesthesia by 2 methodsConclusions: Local anesthesia is without question the safest available technique of anesthesia.Local anesthesia is an extension of the surgical procedure (indeed, often an integral part of that procedure), and as such its administration should be the concern of the surgeon
Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d
... Show More
In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations so that we can obtain estimators with characteristics close to the properties of real parameters, and based on medical data for patients with chro
... Show More