This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Objective: Zerumbone (ZER) is a well-known natural compound that has been reported to have anti-cancer effect. Thus, this study investigated the ZER potential to inhibit Thymidine Phosphorylase (TP) and the ability to trigger Reactive oxygen species (ROS)-mediated cytotoxicity in non-small cell lung cancer, NCI-H460, cell line. Material and Method: The antiangiogenic activity for ZER was evaluated by using the thymidine phosphorylase inhibitory test. Reactive oxygen species (ROS) production was determined via DCFDA dye by using flow cytometry. Result and Discussion: ZER was found to be potent TP inhibitory with the IC50 value of 50.3± 0.31 μg/ml or 230±1.42 µM. NCI-H460 cells upon treatment with ZER produced sign

In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

Symptoms of posttraumatic stress disorder are associated with various variables such as the exposure to traumatic events, sex and age. Such events could lead to negative cognitions towards self and the world. These cognitions, in turn, may lead to traumatic related disorders.

The present study aims to identify the percentage of traumatised individuals according to sex and age category variables. It also aims to assess the average of spreading symptoms of posttraumatic stress disorder of traumatised individuals according to sex and age category variables. Likewise, it aims to test variables significance in cognitions towards the world and the self according to the level of the spread of posttraumatic stre