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

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

Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an epileptic patient due to sudden seizure onset that may cause loss of consciousness. Seizures are periods of aberrant brain activity patterns. Early prediction of an epileptic seizure is critical for those who suffer from it as it will give them time to prepare for an incoming seizure and alert anyone in their close circle of contacts to aid them. This has been an active field of study, powered by the decreasing cost of non-invasive electroencephalogram (EEG) collecting equipment and the rapid evolution of

Background: Remarkable work has been recently introduced to enhance the usage of Electromyography (EMG) signals in operating prosthetic arms. Despite the rapid advancements in this field, providing a reliable, naturalistic myoelectric prosthesis remains a significant challenge. Other challenges include the limited number of allowed movements, lack of simultaneous, continuous control and the high computational power that could be needed for accurate decoding. In this study, we propose an EMG-based multi-Kalman filter approach to decode arm kinematics; specifically, the elbow angle (θ), wrist

Gold is one of the valuable materials that is used for funding trading purchases. Nowadays, more investors are interested in gold investments due to the sudden increase in gold prices. However, transactions involving gold are risky, the price of gold fluctuates wildly due to the unpredictability of the gold market. Hence, there is a need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper analyzes the correlation between gold price movements and sentiments of

The explosive growth of mobile internet services and demand for data connectivity boosts the innovation and development in Radio Access Network (RAN) to define how next generation mobile networks will look like. Continuous improvement in existing RAN is crucial to meet very strict speed and latency requirements by different mobile applications with minimum investments. Exploiting the advancement in Machine Learning and AI-driven algorithms is essential to tackle these challenges in different functions within the RAN domain. In this paper we surveyed how to leverage different clustering

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA)

Food is an integral aspect of daily life in all cultures. It highly affects people's diets, eating behaviors, and overall health. People with poor eating habits are usually overweight or obese, which leads to chronic diseases such as diabetes and cardiovascular disease. Today, the classification of food images has several uses in managing medical conditions and dieting. Deep convolutional neural network (DCNN) architectures provide the foundation for the most recent food recognition models. However, DCNNs are computationally expensive due to high computation time and memory requirements. In

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

Internet of Vehicles is considered one of the most unprecedented outputs of the Internet of Things. No one has realized or even expected the rapidly-growing revolution regarding autonomous connected vehicles. Nowadays, Internet of Vehicles is massively progressing from Vehicular Ad-Hoc Networks as a huge futuristic research and development discipline. This paper proposes a novel reliable and secure architecture for ubiquitously controlling remote connected cars' internal systems, such as engine, doors' locks, sunroof, horn, windows' and lights' control systems. The main contribution is that