Text classification has been one of the most common natural language processing (NLP) objectives in recent years. Compared to other languages, this mission with Arabic is relatively restricted and in its early stages, and this combination in the medical application area is rare. This paper builds an Arabic health care assistant, specifically a pediatrician that supports Arabic dialects, especially Egyptian accents. The proposed application is a chatbot based on Artificial Intelligence (AI) models after experimenting with Two Bidirectional Encoder Representations from Transformers (BERT) models

Wireless video sensor networks (WVSNs) are opening the door for many applications, such as industrial surveillance, environmental tracking, border security, and infrastructure health monitoring. In WVSN, energy conservation is very essential because: 1) sensors are usually battery-operated and 2) each sensor node needs to compress the video prior to transmission, which consumes more power than conventional wireless sensor networks. In this paper, we study the problem of minimizing the total power consumption in a cluster-based WVSN, leveraging cross-layer design to optimize the encoding power

We consider a time-slotted primary system where both the primary channel and primary activity are modeled as two independent two-state Markov chains. The primary transmitter can be idle or busy, whereas the channel can be in erasure or not. Moreover, the sensing channel between the primary transmitter and secondary transmitter is modeled as a two-state Markov chain to represent two levels of sensing reliability. At the beginning of each time slot, the secondary transmitter may remain idle, transmit directly, or probe the channel and access the channel only if it is sensed to be free. At the

The aim of this paper is to introduce an improved strategy for controlling COVID-19 and other pandemic episodes as an environmental disinfection culture for public places. The scheduling aims at achieving the best utilization of the available working day-time hours, which is calculated as the total consumed disinfection times minus the total loosed transportation times. The proposed problem in network optimization identifies a disinfection group who is likely to select a route to reach a subset of predetermined public places to be regularly disinfected with the most utilization of the

Determining the optimum location of facilities is critical in many fields, particularly in healthcare. This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019 (COVID-19) pandemic. The used model is the most appropriate among the threemost common locationmodels utilized to solve healthcare problems (the set covering model, the maximal covering model, and the P-median model). The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints. The model is used to

Many application problems are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. One of these practical applications is to optimally distribute protective materials for the newly emerged COVID-19. It is defined for a decision-maker who wants to choose a subset of candidate hospitals comprising the maximization of the distributed quantities of protective materials to a set of chosen hospitals within a specific time shift. A nonlinear binary mathematical programming model for the problem is introduced with a real

Epileptic seizures occur as a result of functional brain dysfunction and can affect the health of the patient. Prediction of epileptic seizures before the onset is beneficial for the prevention of seizures through medication. Electroencephalograms (EEG) signals are used to predict epileptic seizures using machine learning techniques and feature extractions. Nevertheless, the pre-processing of EEG signals for noise removal and extraction of features are two significant problems that have an adverse effect on both anticipation time and true positive prediction performance. Considering this, the