Over the last 10 years, Arabic language have attracted researchers in the area of Natural Language Processing (NLP). A lot of research papers suddenly emerged in which the main work was the processing of Arabic language and its dialects too. Arabic language processing has been given a special name ANLP (Arabic Natural Language Processing). A lot of ANLP work can be found in literature including

Shortest Path Algorithms are an important set of algorithms in today's world. It has many applications like Traffic Consultation, Route Finding, and Network Design. It is essential for these applications to be fast and efficient as they mostly require real-Time execution. Sequential execution of shortest path algorithms for large graphs with many nodes is time-consuming. On the other hand

Alzheimer’s Disease (AD) is a dangerous disease that is known for its characteristics of eroding memory and destroying the brain. The classification of Alzheimer's disease is an important topic that has recently been addressed by many studies using Machine Learning (ML) and Deep Learning (DL) methods. Most research papers tackling early diagnosis of AD use these methods as a feature extractor for

There is an increasing interest in energy harvesting for wearable biomedical devices. This requires power conservation and management to ensure long-term and steady operation. Hence, task scheduling algorithms will be used throughout this work to provide a reliable solution to minimize energy consumption while considering the system operation constraints. This study proposes a novel power-aware

Intelligent management plays a vital role in the preservation and success of cultural heritage, particularly in the context of museums. To ensure the long-term safeguarding of valuable and significant artifacts housed in museums, regular inspections are necessary. In this specific research study, the National Museum of Egyptian Civilization (NMEC) serves as a case study. To effectively monitor the

Deep neural networks (DNNs) are memory and computationally intensive; hence they are difficult to apply to real-time systems with limited resources. Therefore, the DNN models need to be carefully optimized. The solution was a model based on a convolutional neural network (CNN) called MobileNet that decreases the computational and space complexities with classification precision loss by utilizing

The popularity of wearable devices has grown as they improve the quality of life in many applications. In particular, for medical devices, energy harvesters are the dominating source of energy for wearable devices. However, their power budget is limited. Thus, power-saving techniques are essential components in the whole technology stack of those devices. That is, choosing the optimal schedule for

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

People's lives have been severely disrupted recently due to the COVID-19 outbreak's fast worldwide proliferation and transmission. An option for controlling the epidemic is to make individuals wear face masks in public. For such regulation, automatic and effective face detection systems are required. A facial mask recognition model for real-time video-recorded streaming is provided in this

This chapter proposes a novel binary version of recently developed Gaining-Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. Binary version of GSK named novel binary Gaining-Sharing knowledge-based optimization algorithm (BGSK) depends on mainly two