Los lingüistas no árabes suelen quedar perplejos a la hora de  interpretar el  fenómeno de Al-ḍdad, −normalmente traducido en español como palabras  autoantónimas (1)− en la lengua árabe, en el que los arabistas, y entre ellos los españoles, han quedado sombrosos ante este fenómeno. El jefe del tribunal de la defensa de mi tesis me pidió que le explicara este fenómeno según mi teoría “El esquema básico de la referencia”; pues me dijo que no podía  imaginar que una palabra tuviera dos significados opuestos, y en ese momento le expliqué la palabra albayn  البين que significa la separación,  o la relación o  el enlace que comentar

Philology is a science concerned with issues raised about language. It is a research into the mental aspects of its origin in its various sciences. The term (philology) appeared in the writings of Arab scholars in the fourth century AH. One of the most prominent topics of philology is research on the origin and development of language; Because it is one of the topics that preoccupied thinkers and many scholars, philosophers, and theologians, as well as linguists, have tackled research in it.                

التطور الدلالي لمعاني الكلمات في اللغة العبرية

The research aimed at designing teaching program using jigsaw in learning spiking in volleyball as well as identifying the effect of these exercises on learning spring in volleyball. The researchers used the experimental method on (25) students as experimental group and (27) students as controlling group and (15) students as pilot study group. The researchers conducted spiking tests then the data was collected and treated using proper statistical operations to conclude that the strategy have a positive effect in experimental group. Finally, the researchers recommended using the strategy in making similar studies on other subjects and skills.

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach