Steganography involves concealing information by embedding data within cover media and it can be categorized into two main domains: spatial and frequency. This paper presents two distinct methods. The first is operating in the spatial domain which utilizes the least significant bits (LSBs) to conceal a secret message. The second method is the functioning in the frequency domain which hides the secret message within the LSBs of the middle-frequency band of the discrete cosine transform (DCT) coefficients. These methods enhance obfuscation by utilizing two layers of randomness: random pixel embedding and random bit embedding within each pixel. Unlike other available methods that embed data in sequential order with a fixed amount. These methods embed the data in a random location with a random amount, further enhancing the level of obfuscation. A pseudo-random binary key that is generated through a nonlinear combination of eight Linear Feedback Shift Registers (LFSRs) controls this randomness. The experimentation involves various 512x512 cover images. The first method achieves an average PSNR of 43.5292 with a payload capacity of up to 16% of the cover image. In contrast, the second method yields an average PSNR of 38.4092 with a payload capacity of up to 8%. The performance analysis demonstrates that the LSB-based method can conceal more data with less visibility, however, it is vulnerable to simple image manipulation. On the other hand, the DCT-based method offers lower capacity with increased visibility, but it is more robust.
In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending
In recent years, Elliptic Curve Cryptography (ECC) has attracted the attention of
researchers and product developers due to its robust mathematical structure and
highest security compared to other existing algorithms like RSA. It is found to give
an increased security compared to RSA for the same key-size or same security as
RSA with less key size. In this paper a new approach is proposed for encrypting
digital image using the arithmetic of elliptic curve algebra. The proposed approach
produced a new mask for encrypt the digital image by use a new convolution
processes based on ECC algebra operations and work as symmetric cryptographic
system instead of asymmetric system. A new approach combined both compression
This work presents an approach for the applying Triple DES (TRIPLE DES) based on using genetic algorithm by adding intelligent feature for TRIPLE DES with N round for genetic algorithm. Encapsulated cipher file with special program which send an acknowledgment to a sender to know who decipher or broken to crash it , Thus it is considered as the initial step to improve privacy. The outcome for proposed system gives a good indication that it is a promising system compared with other type of cipher system.
Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that
... Show MoreThis paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi
The current research aims to build a training program for chemistry teachers based on the knowledge economy and its impact on the productive thinking of their students. To achieve the objectives of the research, the following hypothesis was formulated:
There is no statistically significant difference at (0.05) level of significance between the average grades of the students participating in the training program according to the knowledge economy and the average grades of the students who did not participate in the training program in the test of productive thinking. The study sample consisted of (288) second intermediate grade students divided into (152) for the control group
... Show MoreIn this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showe
... Show MoreFusidic acid (FA) is a well-known pharmaceutical antibiotic used to treat dermal infections. This experiment aimed for developing a standardized HPLC protocol to determine the accurate concentration of fusidic acid in both non-ionic and cationic nano-emulsion based gels. For this purpose, a simple, precise, accurate approach was developed. A column with reversed-phase C18 (250 mm x 4.6 mm ID x 5 m) was utilized for the separation process. The main constituents of the HPLC mobile phase were composed of water: acetonitrile (1: 4); adjusted at pH 3.3. The flow rate was 1.0 mL/minute. The optimized wavelength was selected at 235 nm. This approach achieved strong linearity for alcoholic solutions of FA when loaded at a serial concentrati
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