Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the use of Gray Level Co-occurrence Matrix (GLCM) features and DBN classifier provides 98.26% accuracy with the two specific classes were tested. Improvements/Applications: AD is a neurological condition affecting the brain and causing dementia that may affect the mind and memory. The disease indirectly impacts more than 15 million relatives, companions and guardians. The results of the present research are expected to help the specialist in decision making process.
Abstract
Objective(s): To assess the job satisfaction during of covid-19 among the nurses in respiratory isolation units of coronavirus disease.
Methodology: A descriptive cross-sectional design was carried out in four hospitals at isolation units of coronavirus disease from the period (21th December, 2021 to 27th January, 2022). A non-probability (convenience) sampling method consists of (300) nurse was selected convenience based on the study criteria. The tool used to measure the job satisfaction is Job satisfaction scale for clinical nursing (JSS-CN). This tool consists of two parts, the first part is for demographic information and consists of 8 items, and the second
... Show MoreImage 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
... Show MoreSkull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor
... Show MoreSecure information transmission over the internet is becoming an important requirement in data communication. These days, authenticity, secrecy, and confidentiality are the most important concerns in securing data communication. For that reason, information hiding methods are used, such as Cryptography, Steganography and Watermarking methods, to secure data transmission, where cryptography method is used to encrypt the information in an unreadable form. At the same time, steganography covers the information within images, audio or video. Finally, watermarking is used to protect information from intruders. This paper proposed a new cryptography method by using thre
... Show MoreThis study aims to enhance the RC5 algorithm to improve encryption and decryption speeds in devices with limited power and memory resources. These resource-constrained applications, which range in size from wearables and smart cards to microscopic sensors, frequently function in settings where traditional cryptographic techniques because of their high computational overhead and memory requirements are impracticable. The Enhanced RC5 (ERC5) algorithm integrates the PKCS#7 padding method to effectively adapt to various data sizes. Empirical investigation reveals significant improvements in encryption speed with ERC5, ranging from 50.90% to 64.18% for audio files and 46.97% to 56.84% for image files, depending on file size. A substanti
... Show MoreIn cyber security, the most crucial subject in information security is user authentication. Robust text-based password methods may offer a certain level of protection. Strong passwords are hard to remember, though, so people who use them frequently write them on paper or store them in file for computer .Numerous of computer systems, networks, and Internet-based environments have experimented with using graphical authentication techniques for user authentication in recent years. The two main characteristics of all graphical passwords are their security and usability. Regretfully, none of these methods could adequately address both of these factors concurrently. The ISO usability standards and associated characteristics for graphical
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