Using watermarking techniques and digital signatures can better solve the problems of digital images transmitted on the Internet like forgery, tampering, altering, etc. In this paper we proposed invisible fragile watermark and MD-5 based algorithm for digital image authenticating and tampers detecting in the Discrete Wavelet Transform DWT domain. The digital image is decomposed using 2-level DWT and the middle and high frequency sub-bands are used for watermark and digital signature embedding. The authentication data are embedded in number of the coefficients of these sub-bands according to the adaptive threshold based on the watermark length and the coefficients of each DWT level. These sub-bands are used because they a

Information security in data storage and transmission is increasingly important. On the other hand, images are used in many procedures. Therefore, preventing unauthorized access to image data is crucial by encrypting images to protect sensitive data or privacy. The methods and algorithms for masking or encoding images vary from simple spatial-domain methods to frequency-domain methods, which are the most complex and reliable. In this paper, a new cryptographic system based on the random key generator hybridization methodology by taking advantage of the properties of Discrete Cosine Transform (DCT) to generate an indefinite set of random keys and taking advantage of the low-frequency region coefficients after the DCT stage to pass them to

The research aims to shed light on the obstacles that hinder the use of taxpayers' commercial books covered by provisions of the amended commercial book-keeping system No. 2 of 1985 and ways to address these obstacles. On this basis, the main null hypothesis was formulated that there is no statistically significant relationship between the obstacles to the adoption of the commercial books and taxable income.

The research data were collected on the base of three applied case studies of registered taxpayers in the General Commission of Taxes, GCT, together with the use of a questionnaire distributed to a sample of taxpayers ( companies ). The collected data were analyzed, the result  were presented  and the hypothesis was

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

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

The impact of undergraduate research experiences on students' academic development and retention in STEM fields is significant. Students' success in STEM fields is based on developing strong research and critical thinking skills that make it essential for students to engage in research activities throughout their academic programs. This work evaluates the effectiveness of undergraduate research experiences with respect to its influence on student retention and academic development. The cases presented are based on years of experience implementing undergraduate research programs in various STEM fields at Colorado State University Pueblo (CSU Pueblo) funded by HSI STEM Grants. The study seeks to establish a correlation between students' reten

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n