Image Hiding Using QR Factorization And Discrete Wavelet Transform Techniques

Steganography is one of the most important tools in the data security field as there is a huge amount of data transferred each moment over the internet. Hiding secret messages in an image has been widely used because the images are mostly used in social media applications. The proposed algorithm is a simple algorithm for hiding an image in another image. The proposed technique uses QR factorization to conceal the secret image. The technique successfully hid a gray and color image in another one and the performance of the algorithm was measured by PSNR, SSIM and NCC. The PSNR for the cover image was in the range of 41 to 51 dB. DWT was added to increase the security of the method and this enhanced technique increased the cover PSNR to 48 t0 56 dB. The SSIM is 100% and the NCC is 1 for both implementations. Which improves that the imperceptibility of the algorithm is very high. The comparative analysis showed that the performance of the algorithm is better than other state-of-the-art algorithms

The Development Of QMMS: A Case Study for Reliable Online Quiz Maker and Management System

The e-learning and assessment systems became a dominant technology nowadays and distribute across the globe. With severe consequences of COVID19-like crises, the key importance of such technology appeared in which courses, quizzes and questionnaires have to be conducted remotely. Moreover, the use of Learning Management Systems (LMSs), such as blackboard, eCollege, and Moodle, has been sanctioned in all respects of education. This paper presents an open-source interactive Quiz Maker and Management System (QMMS) that suits the research, education (under-grad, grad, or post-grad), and industrial organizations to perform distant quizzes, training and questionnaires with an integration facility with other LMS tools such as Moodle. The proposed system supports three basic levels: 1) administration, 2) instructors, and 3) learners at the micro-level teaching. The proposed system is adopted using .Net framework integrated with SQL-Server database engine that compromise between performance, security and stability. The proposed QMMS is described through different phases of Software Development Life Cycle (SDLC) including detailed analysis, design, implementation, testing, verification, and maintenance in order to exploit the importance of the analysis and design of LMS from the software engineering point of view. A comparative analysis, among the proposed system and a recent list of challenging ones, is presented in different aspects that shows the effectiveness, reliability and validity of proposed tool. Moreover, the proposed QMMS shows an enhancement ratio of up to 42.19% in response time perspective as compared to Moodle system in the case of massive concurrent transactions.

A Statistical-Mining Techniques’ Collaboration for Minimizing Dimensionality in Ovarian Cancer Data

A feature is a single measurable criterion to an observation of a process. While knowledge discovery techniques successfully contribute to many fields, however, the extensive required data processing could hinder the performance of these techniques. One of the main issues in processing data is the dimensionality of the data. Therefore, focusing on reducing the data dimensionality through eliminating the insignificant attributes could be considered one of the successful steps for raising the applied techniques’ performance. On the other hand, focusing on the applied field, ovarian cancer patients continuously suffer from the extensive analysis requirements for detecting the disease as well as monitoring the treatment progress. Therefore, identifying the most significant required analysis could be a positive step to reduce the emotional and financial suffering. This research aims to reduce the data dimensionality of the ovarian cancer disease and highlight the most significant analysis using the collaboration of clustering techniques and statistical techniques. The research succeeded to identify twelve significant analysis out of forty-four with a total of fourteen significant attributes for ovarian cancer data.

The Strategic Goals of -Using Service Oriented Architecture Principles

Organizations use heterogonous systems and legacy systems, they are implemented at different platforms, different databases, and different language programs. These systems need to exchange information and reuse the same functionality to achieve integration between these systems. Many Software companies failed to achieve information follow and reuse the same functionality. This paper introduces guidelines introduces Service-Oriented Architecture Principles guidelines and rules to help Applications developers to achieve information integrations and reuse the same functionality, SOA principles providing rules and guidelines that specify exactly how solution logic should be decomposed and molded into technology solutions

Enhancing Academic Advising In Credit Hours System Using Dss

Academic advising plays a vital role in achieving higher educational institution’s purposes. Academic advising is a process where an academic advisor decides to select a certain number of courses for a student to register in each semester to fulfil the graduation requirements. This paper presents an Academic Advising Decision Support System (AADSS) to enhance advisors make better decisions regarding their students’ cases. AADSS framework divided into four layers, data preparation layer, data layer, processing layer and decision layer. The testing results from those participating academic advisors and students considered are that AADSS beneficial in enhancing their decision for selecting courses.

The Factors Influencing the Acceptance of Web-Based E-Learning System Among Academic Staffs of Saudi Arabia

It is possible to learn more quickly and effectively with e-learning software development because it provides learners with convenient and flexible learning environments. This allows them to progress further in their careers. Reports on web-based e-learning systems for inservice education have frequently neglected to include the viewpoint of the instructor. In order to conduct quantitative research, a sample of 50 academic staff members was selected. The purpose of this study was to investigate various factors that influence the intention to use webbased e-learning, with the theoretical foundation being provided by university lecturers. According to the findings of the study, the intention to use web-based e-learning for in-service training is positively correlated with the motivation to use the Internet and the belief in one's own ability to use the Internet. In terms of intentions to use web-based e-learning in-service training, a statistically significant increase in computer anxiety had an impact. University lecturers embraced Web-based e-learning systems because they believed they would be beneficial and because they were eager to put them to use

A CONFIGURABLE MINING APPROACH FOR LEARNING SERVICES CUSTOMIZATION

There is no doubt that this age is the age of data and technology. Moreover, there is tremendous development in all fields. The personalized material is a good approach in the different fields. It provides a fit material that matches the styles of readers. It supports readers in various reading domains. This research paper aims to support students in the educational system. Additionally, the research paper designs to increase education values for students. Furthermore, the research paper builds the smart appropriate materials through Egyptian Knowledge Banking (EKB) based on the learner question. The Egyptian Knowledge Bank (EKB) is a rich platform for data. The research paper is implemented in the faculty of Commerce and Business Administration, Business Information System program (BIS) at Helwan University, Egypt.

DATA QUALITY DIMENSIONS, METRICS, AND IMPROVEMENT TECHNIQUES

Achieving high level of data quality is considered one of the most important assets for any small, medium and large size organizations. Data quality is the main hype for both practitioners and researchers who deal with traditional or big data. The level of data quality is measured through several quality dimensions. High percentage of the current studies focus on assessing and applying data quality on traditional data. As we are in the era of big data, the attention should be paid to the tremendous volume of generated and processed data in which 80% of all the generated data is unstructured. However, the initiatives for creating big data quality evaluation models are still under development. This paper investigates the data quality dimensions that are mostly used in both traditional and big data to figure out the metrics and techniques that are used to measure and handle each dimension. A complete definition for each traditional and big data quality dimension, metrics and handling techniques are presented in this paper. Many data quality dimensions can be applied to both traditional and big data, while few number of quality dimensions are either applied to traditional data or big data. Few number of data quality metrics and barely handling techniques are presented in the current works.

Review of Data Mining Techniques for Detecting Churners in the Telecommunication Industry

The telecommunication sector has been developed rapidly and with large amounts of data obtained as a result of increasing in the number of subscribers, modern techniques, data-based applications, and services. As well as better awareness of customer requirements and excellent quality that meets their satisfaction. This satisfaction raises rivalry between firms to maintain the quality of their services and upgrade them. These data can be helpfully extracted for analysis and used for predicting churners. Researchers around the world have conducted important research to understand the uses of Data mining (DM) that can be used to predict customers' churn. This paper supplies a review of nearly 73 recent journalistic articles starting in 2003 to introduce the different DM techniques used in many customerbased churning models. It epitomizes the present literature in the field of communications by highlighting the impact of service quality on customer satisfaction, detecting churners in the telecoms industry, in addition to the sample size used, the churn variables used and the results of various DM technologies. Eventually, the most common techniques for predicting telecommunication churning such as classification, regression analysis, and clustering are included, thus presenting a roadmap for new researchers to build new churn management models.

A DEEP LEARNING APPROACH FOR FORECASTING GLOBAL COMMODITIES PRICES

Forecasting future values of time-series data is a critical task in many disciplines including financial planning and decision-making. Researchers and practitioners in statistics apply traditional statistical methods (such as ARMA, ARIMA, ES, and GARCH) for a long time with varying. accuracies. Deep learning provides more sophisticated and non-linear approximation that supersede traditional statistical methods in most cases. Deep learning methods require minimal features engineering compared to other methods; it adopts an end-to-end learning methodology. In addition, it can handle a huge amount of data and variables. Financial time series forecasting poses a challenge due to its high volatility and non-stationarity nature. This work presents a hybrid deep learning model based on recurrent neural network and Autoencoders techniques to forecast commodity materials' global prices. Results showbetter accuracy compared to traditional regression methods for short-term forecast horizons (1,2,3 and 7days).