The information technology educational programs at most universities in Egypt face many obstacles that can be overcome using technology enhanced learning. An open source Moodle e-learning platform has been implemented at many universities in Egypt, as an aid to deliver e-content and to provide the institution with various possibilities for implementing asynchronous e-learning web-based modules. This paper shows that the use of interactive features of e-learning increases the motivation of the undergraduate students for the learning process.

In the current business environment, where the customer is the primary focus, effective communication between marketing and senior management is vital for success. Effective customer profiling is a cornerstone of strategic decision-making for digital start-ups seeking sustainable growth and customer satisfaction. This research investigates the clustering of customers based on recency, frequency, and monetary (RFM) analysis and employs validation metrics to derive optimal clusters. The K-means clustering algorithm, coupled with the Elbow method, Silhouette coefficient, and Gap Statistics method, facilitates the identification of distinct customer segments. The study unveils three primary clusters with unique characteristics: new customers (Cluster A), best customers (Cluster B), and intermittent customers (Cluster C). For platform-based Edutech start-ups, Cluster A underscores the importance of tailored learning content and support, Cluster B emphasizes personalized incentives, and Cluster C suggests re-engagement strategies. By understanding and addressing the diverse needs of these clusters, digital start-ups can forge enduring connections, optimize customer engagement, and fuel sustainable business growth.

With the increasing prevalence of diabetes in Saudi Arabia, there is a critical need for early detection and prediction of the disease to prevent long-term health complications. This study addresses this need by using machine learning (ML) techniques applied to the Pima Indians dataset and private diabetes datasets through the implementation of a computerized system for predicting diabetes. In contrast to prior research, this study employs a semisupervised model combined with strong gradient boosting, effectively predicting diabetes-related features of the dataset. Additionally, the researchers employ the SMOTE technique to deal with the problem of imbalanced classes. Ten ML classification techniques, including logistic regression, random forest, KNN, decision tree, bagging, AdaBoost, XGBoost, voting, SVM, and Naive Bayes, are evaluated to determine the algorithm that produces the most accurate diabetes prediction. The proposed approach has achieved impressive performance. For the private dataset, the XGBoost algorithm with SMOTE achieved an accuracy of 97.4%, an F1 coefficient of 0.95, and an AUC of 0.87. For the combined datasets, it achieved an accuracy of 83.1%, an F1 coefficient of 0.76, and an AUC of 0.85. To understand how the model predicts the final results, an explainable AI technique using SHAP methods is implemented. Furthermore, the study demonstrates the adaptability of the proposed system by applying a domain adaptation method. To further enhance accessibility, a mobile app has been developed for instant diabetes prediction based on user-entered features. This study contributes novel insights and techniques to the field of ML-based diabetic prediction, potentially aiding in the early detection and management of diabetes in Saudi Arabia.

Tables are everywhere, from scientific journals, articles, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches. Furthermore, we provide an analysis of both classic and new applications in the field. Lastly, the datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature. Finally, we go over the architecture of utilizing various object detection and table structure recognition methods to create an effective and efficient system, as well as a set of development trends to keep up with state-of-the-art algorithms and future research. We have also set up a public GitHub repository where we will be updating the most recent publications, open data, and source code. The GitHub repository is available at https://github.com/abdoelsayed2016/table-detection-structure-recognition.

This report will exhibit an outline of the basic features of Mobile Augmented Reality (MAR) and the key concepts of this technology in the Fashion Retail Industry. Several obstacles such as the necessity of putting on the garments to experience its actual view have been tackled by customers throughout the process of clothes shopping. The goal is to constringe these obstacles by creating an Augmented Reality based application that will directly solve these problems. The methodology was conducted by questioning shoppers about the different holdbacks faced during shopping. Solutions for the issues are expected to be achieved by developing an easily utilizable MAR application for targeted customers. The design of this paper involved the utilization of handed questionnaire and semi structured interviews with several store managers to gather sufficient about their sales strategy and proposing the solution of Augmented Reality utilization within the market. Augmented Reality Technology have a great potential to be utilized efficiently inside the fashion market as it will meritoriously improve the shopping experience of consumers across multiple fashion related industry channels.

The information technology educational programs at most universities in Egypt face many obstacles that can be overcome using technology enhanced learning. An open source Moodle eLearning platform has been implemented at many universities in Egypt, as an aid to deliver e-content and to provide the institution with various possibilities for implementing asynchronous e-learning web-based modules. This paper shows that the use of interactive features of e-learning increases the motivation of the undergraduate students for the learning process.