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

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

Attacking a transferred data over a network is frequently happened millions time a day. To address this problem, a secure scheme is proposed which is securing a transferred data over a network. The proposed scheme uses two techniques to guarantee a secure transferring for a message. The message is encrypted as a first step, and then it is hided in a video cover.  The proposed encrypting technique is RC4 stream cipher algorithm in order to increase the message's confidentiality, as well as improving the least significant bit embedding algorithm (LSB) by adding an additional layer of security. The improvement of the LSB method comes by replacing the adopted sequential selection by a random selection manner of the frames and the pixels wit

     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

The aim of this paper is to design suitable neural network (ANN) as an alternative accurate tool to evaluate concentration of Copper in contaminated soils. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Copper. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this study show that the ANN technique trained on experimental measurements can be successfully applied to the rapid est