Carbamazepine (CBZ) is a narrow therapeutic index drug used in the treatment of trigeminal neuralgia and psychiatric disorders. Valerian (VAL) is a popular herbal product which should be prescribed to treat insomnia and anxiety. The study was designed to investigate the presence of significant pharmacokinetic (PK) interaction between Valerian (VAL) at different concentrations on Carbamazepine (CBZ) pharmacokinetic parameters in healthy male rabbits. In an in vivo, parallel-randomized controlled trial, the rabbits in three groups "first (control), second and third" were given oral doses of CBZ (50 mg/kg), for "second and third" groups (as test groups) rabbits were given (20 and 40 mg/kg/day) of the VAL respectively, as  suspensi

Distributed Denial of Service (DDoS) attacks on Web-based services have grown in both number and sophistication with the rise of advanced wireless technology and modern computing paradigms. Detecting these attacks in the sea of communication packets is very important. There were a lot of DDoS attacks that were directed at the network and transport layers at first. During the past few years, attackers have changed their strategies to try to get into the application layer. The application layer attacks could be more harmful and stealthier because the attack traffic and the normal traffic flows cannot be told apart. Distributed attacks are hard to fight because they can affect real computing resources as well as network bandwidth. DDoS attacks

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

MR Younus, Al-A'DAB, 2011

All over the world and in different societies and cultures , people are always looking for different ways of favourable , gentle smooth and soft communication .The 'powerful linguistic devices ' that are embedded in the daily English language is called "euphemism". It plays an important and vital role in daily communication. It represents a significant part of English . It is used to express any aspect of everyday realities such as the business world , mass media , etc. The comprehension of such realities can not be verified unless a reasonable command of euphemism is achieved.
Euphemism is defined according to various perspectives and views. It is " that figure of speech which consists in the substitution of a word or expression of c

Objective: Since the vaccination rate is largely affected by low knowledge and negative attitudes ofhealthcare professionals, so this study aimed to weigh up the vaccination knowledge and attitudes ofpharmacy students.Method: A pilot study using a survey to investigate demographic data, knowledge (20 questions), andattitudes (5 questions) of 156 fifth year and 121 third year pharmacy students from College of Pharmacy/University of Baghdad.Results: The mean score of knowledge and attitudes was intermediate (16.654 and 14.917 out of 25 for thefifth and the third grades, respectively) with a significant difference between the two groups, the studentsshown to have favorable attitudes about vaccination. The score of the students is not i