Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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

A new Schiff base ligand Bis-1,4-di[N-3-(2-hydroxy-1-amino)- acetophenonylidene] benzylidene [L] and its complexes with (Mn(II) ,Co(II) ,Ni(II and Cu(II)) were synthesized . The ligand was prepared in two steps. In the first step a solution of (terphthalaldehyde) in methanol reacts under reflux with (p-aminoacetophenone) to give an intermediate compound [1-[3-({4-[(3-Acetyl-phenylimino)-methyl]-benzylidene}-amino)-phenyl]- ethanone which reacts in the second step with (2-Amino-phenol) giving the mentioned ligand. The complexes were synthesized by addition the corresponding metal salt solution to the solution of the ligand in methanol under reflux in (1:1) metal to ligand ratio. On the basis of, molar conductance, I.R., UV-Vis, HPLC, chlorid

Until recently, researchers have utilized and applied various techniques for intrusion detection system (IDS), including DNA encoding and clustering that are widely used for this purpose. In addition to the other two major techniques for detection are anomaly and misuse detection, where anomaly detection is done based on user behavior, while misuse detection is done based on known attacks signatures. However, both techniques have some drawbacks, such as a high false alarm rate. Therefore, hybrid IDS takes advantage of combining the strength of both techniques to overcome their limitations. In this paper, a hybrid IDS is proposed based on the DNA encoding and clustering method. The proposed DNA encoding is done based on the UNSW-NB15

Abstract:

                In light of the development in the banking environment and the increasing reliance on electronic systems in providing banking services and due to the intense competition witnessed by the banking sector, the need has emerged to apply the comprehensive electronic banking system, which works on the Internet in providing new and diverse banking services regardless of time and place by linking all branches to one central database, and despite the advantages achieved from the application of the comprehensive system, there is a set of risks that accompany the use of that system, What requires the auditors to develop the audit method in line with the size of the development in the