This paper develops two tempered fractional matrices that are computationally accurate, efficient, and stable to treat myriad tempered fractional differential problems. The suggested approaches are versatile in handling both spatial and temporal dimensions and treating integer- and fractional-order derivatives as well as non-tempered scenarios via utilizing pseudospectral techniques. We depend on Lagrange basis functions, which are derived from the tempered Jacobi-Müntz functions based on the left- and right-definitions of Erdélyi-Kober fractional derivatives. We aim to obtain the pseudospectral-tempered fractional differentiation matrices in two distinct ways. The study involves a numerical measurement of the condition number of tempered fractional differentiation matrices and the time spent to create the collocation matrices and find the numerical solutions. The suggested matrices' accuracy and efficiency are investigated from the point of view of the , -norms errors, the maximum absolute error matrix  of the two-dimensional problems, and the fast rate of spectral convergence. Finally, numerical experiments are carried out to show the exponential convergence, applicability, effectiveness, speed, and potential of the suggested matrices.

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images’ quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image.

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/tabledetection-structure-recognition.

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. *is 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 Egypt and Saudi Arabia.
 

Emotion prediction is a subset of sentiment analysis that aims to extract emotions from text, speech, or images. The researchers posit that emotions determine human behavior, making the development of a method to recognize emotions automatically crucial for use during global crises, such as the COVID-19 pandemic. In this paper, a real-time system is developed that identifes and predicts emotions conveyed by users in Arabic tweets regarding COVID-19 into standard six emotions based on the big data platform, Apache Spark. The system consists of two main stages: (1) Developing an ofine model and (2) Online emotion prediction pipeline. For the frst stage, two diferent approaches: The deep Learning (DL) approach and the Transfer Learning-based (TL) approach to fnd the optimal classifer for identifying and predicting emotion. For DL, three classifers are applied: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). For TL, fve models are applied: AraBERT, ArabicBERT, ARBERT, MARBERT, and QARiB. For the second stage, create a Transmission Control Protocol (TCP) socket between Twitter’s API and Spark used to receive streaming tweets and Apache Spark to predict the label of tweets in real-time. The experimental results show that the QARiB model achieved the highest Jaccard accuracy (65.73%), multi-accuracy (78.71%), precision-micro (78.71%), recall-micro (78.71%), f-micro (78.71%), and f-macro (78.55%). The system is available as a web-based application that aims to provide a real-time visualization of people’s emotions during a crisis.

Social networks are popular for advertising, idea sharing, and opinion formation. Due to COVID-19, coronavirus information disseminated on social media affects people’s lives directly. Individuals sometimes managed it well, but it often hampered daily activities. As a result, analyzing people’s feelings is important. Sentiment analysis identifies opinions or sentiments from text. In this paper, we present an effective model that leverages the benefits of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to categorize Arabic tweets using a stacked ensemble learning model. First, the tweets are represented as vectors using a word embedding model, then the text feature is extracted by CNN, and finally the context information of the text is acquired by BiLSTM. Aravec, FastText, and ArWordVec are employed separately to assess the impact of the word embedding on the our model …