Blockchain is currently one of the most widely discussed inventions in the information and communication technology industry. It is a pillar of the fourth industrial revolution and it is a cryptographically demanding technology that is regarded as one of the most influential topics in academia. Many blockchain platforms currently utilize third-party cryptographic libraries that offer many cryptographic primitives in order to ensure users' protection against cyber-attacks. The Networking and Cryptography library (NaCl) is an open-source library for cryptographic primitives. NaCl is known to be

Sequence analysis is a major area in bioinformatics encompassing the methods and techniques for studying the biological sequences, DNA, RNA, and proteins, on the linear structure level. The focus of this area is generally on the identification of intra-and inter-molecular similarities. Identifying intra-molecular similarities boils down to detecting repeated segments within a given sequence, while identifying inter-molecular similarities amounts to spotting common segments among two or multiple sequences. From a data mining point of view, sequence analysis is nothing but string-or pattern

Customer churn poses a formidable challenge within the Telecom industry, as it can result in significant revenue losses. In this research, we conducted an extensive study aimed at developing a viable customer churn prediction method. Our method utilizes the Apriori algorithm's strength to identify the key causes of customer churn. In the pursuit of this goal, we utilized multiple machine learning predictive models. All of which were developed from the insights gleaned from the Apriori algorithm's feature extraction for churning customers. This extensive analysis encompassed a spectrum of

Irony and Sarcasm Detection (ISD) is a crucial task for many NLP applications, especially sentiment and opinion mining. It is also considered a challenging task even for humans. Several studies have focused on employing Deep Learning (DL) approaches, including building Deep Neural Networks (DNN) to detect irony and sarcasm content. However, most of them concentrated on detecting sarcasm in English rather than Arabic content. Especially studies concerning deep neural networks, including convolutional neural networks (CNN) and recurrent neural network (RNN) architectures. This paper investigates

Nowadays Mobile Applications are a necessity as everyone is depending on them in their everyday tasks. We use them for communication, entertainment, and utilities. Every day new devices are introduced to the market. The diversity in these devices resulted in many platforms like Android and iOS. These different mobile platforms used by different companies and manufacturers made it challenging for mobile developersto build an application or a system that can be deployed on different platforms. Developers will be required to go through multiple development cycles to develop the application for

Pseudo-Random Number Generator (PRNG) is required for various applications, especially cryptography. PRNGs are employed in symmetric-key algorithms, where a single key is used as a seed to the PRNG to generate a sequence of random numbers that are employed to encrypt and decrypt certain data. This work proposes a PRN G system that employs the time series generated from the numerical solution of systems of chaotic-generators Differential Equations (DEs) utilizing three different DEs solvers; Euler, Runge-Kutta 4th order, and Runge-Kutta 5th order. Various systems were solved using each of the

Mobile applications are very popular nowadays. The two most common platforms depend on different languages to have native performance. Most trans-compiler-based converters between those languages didn't take webservices conversion into consideration. However, in recent years, mobile applications developers have used web services for lighter and more dynamic mobile applications. Therefore, it becomes crucial to include web services mapping to the trans-compiler-based code converters for mobile applications code. In this paper, we present a model for web service trans-compiler code mapper from

Diabetic patients need a management tool that combines multiple features and tracks and views detailed data time-efficiently. Effective food logging is an important element of health monitoring. In this paper, we propose 'Suger.ly', a lightweight mobile application with artificial intelligence food recognition for diabetes management. The system has been trained to recognize 101 distinct types of food, with a focus on Egyptian cuisine. The app can then get nutritional value and insulin calculations. The results obtained from the Single-Shot multibox Detection (SSD) MobileNet-V1 food detection

Computer vision is a broad field of study that encompasses different tasks (e.g., object detection, semantic segmentation, 3D reconstruction). Although computer vision is relevant to the African communities in various applications, yet computer vision research is under-explored in the continent and constructs only 0.06% of top-tier publications in the last ten years. In this paper, our goal is to have a better understanding of the computer vision research conducted in Africa and provide pointers on whether there is equity in research or not. We do this through an empirical analysis of the

Cross-platform mobile application development frameworks are now widely used among software companies and developers. Despite their time and cost-effectiveness, they still lack the performance and experience of natively developed applications. Many research tools have been proposed to solve this problem by converting a natively developed application from one platform to another. The Trans-Compiler Based Android to iOS Converter (TCAIOSC) was proposed to convert the front-end and back-end code of Android Java applications to iOS applications. Since databases are essential for mobile