Different ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)xL, & two hidden layers (M-H1-H2-1)xL). Classification accuracy up to 90% has been achieved using one-against-all strategy with two hidden layers architecture. The performance of one-against-all approach is slightly better than the ordinary approach

Blockchain technology relies on cryptographic techniques that provide various advantages, such as trustworthiness, collaboration, organization, identification, integrity, and transparency. Meanwhile, data analytics refers to the process of utilizing techniques to analyze big data and comprehend the relationships between data points to draw meaningful conclusions. The field of data analytics in Blockchain is relatively new, and few studies have been conducted to examine the challenges involved in Blockchain data analytics. This article presents a systematic analysis of how data analytics affects Blockchain performance, with the aim of investigating the current state of Blockchain-based data analytics techniques in research fields and

Data hiding is the process of encoding extra information in an image by making small modification to its pixels. To be practical, the hidden data must be perceptually invisible yet robust to common signal processing operations. This paper introduces a scheme for hiding a signature image that could be as much as 25% of the host image data and hence could be used both in digital watermarking as well as image/data hiding. The proposed algorithm uses orthogonal discrete wavelet transforms with two zero moments and with improved time localization called discrete slantlet transform for both host and signature image. A scaling factor ? in frequency domain control the quality of the watermarked images. Experimental results of signature image

Data compression offers an attractive approach to reducing communication costs using available bandwidth effectively. It makes sense to pursue research on developing algorithms that can most effectively use available network. It is also important to consider the security aspect of the data being transmitted is vulnerable to attacks. The basic aim of this work is to develop a module for combining the operation of compression and encryption on the same set of data to perform these two operations simultaneously. This is achieved through embedding encryption into compression algorithms since both cryptographic ciphers and entropy coders bear certain resemblance in the sense of secrecy. First in the secure compression module, the given text is p

The absorption spectrum for three types of metal ions in different concentrations has been studying experimentally and theoretically. The examination model is by Gaius model in order to find the best fitting curve and the equation controlled with this behavior. The three metal ions are (Copper chloride Cu⁺², Iron chloride Fe^+3, and Cobalt chloride Co⁺²) with different concentrations (10^-4, 10^-5, 10^-6, 10^-7) gm/m³. The spectroscopic study included UV-visible and fluorescence spectrum for all different concentrations sample. The results refer to several peaks that appear from the absorption spectrum in the high concentration of all metal ions solution.

Introduction: Nitrofurantoin (NFT) is abroad spectrum bactericidal antibiotic. The bioavailability of NFT is affected by many factors. Samafurantin® tablets containing 50 mg NFT were manufactured by Samarra drug industry. Urinary excretion studies were employed since; the urinary tract is the main site of NFT action and excretion. Objective: The objective of the study was to investigate the effect of Uricol® and food on secondary pharmacokinetic parameters of Samafurantin® tablets with different doses by applying urinary data. Methods: Twelve healthy male volunteers participated in this study. Urine samples were collected from each volunteer after overnight fasting at a specified time intervals which considered as a blank sample for meas

Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re