Named Entity Recognition (NER) is an important task in many natural language processing applications. There are several studies that have focused on NER for the English language. However, there are some limitations when applying the current methodologies directly on the Arabic language text. Recent studies have shown the effectiveness of pooled contextual embedding representations and significant

In different approaches, the temperature-baryon density plane of QCD matter is studied for deconfinement and chemical freezeout boundaries. Results from various heavy-ion experiments are compared with the recent lattice simulations, the effective QCD-like Polyakov linear-sigma model, and the equilibrium thermal models. Along the entire freezeout boundary, there is an excellent agreement between

The optimized perturbation theory (OPT) is confronted to first-principle lattice simulations. We compare results from the Polyakov linear-sigma model (PLSM) in OPT with the conventional mean-field approximation (MFA). At finite temperatures and chemical potentials, the chiral condensates and the decofinement order parameters, the thermodynamic pressure, the pseudocritical temperatures, the

This study attempts to show the effectiveness of using coconut fibre as a loss circulation material in drillingoperation. The research incorporated a practical approach. Laboratory experiments was conducted to designdrilling muds with the desired loss circulation materials. Their rheological properties and permeabilityplugging ability was ascertained and compared to determine the most effective

Using generic (non)extensive statistics, in which the underlying system likely autonomously manifests its extensive and nonextensive statistical nature, we extract various fit parameters from the CMS experiment and compare these to the corresponding results obtained from Tsallis and Boltzmann statistics. The present study is designed to indicate the possible variations between the three types of

Being sarcastic is to say something and to mean something else. Detecting sarcasm is key for social media analysis to differentiate between the two opposite polarities that an utterance may convey. Different techniques for detecting sarcasm are varying from rule-based models to Machine Learning and Deep Learning models. However, researchers tend to leverage Deep Learning in detecting sarcasm

Neural networks have become the main building block on revolutionizing the field of artificial intelligence aided applications. With the wide availability of data and the increasing capacity of computing resources, they triggered a new era of state-of-the-art results in diverse directions. However, building neural network models is domain-specific, and figuring out the best architecture and hyper

Federated learning (FL) has recently been proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are

With the web-scale data volumes and high velocity of generation rates, it has become crucial that the training process for recommender systems be a continuous process which is performed on live data, i.e., on data streams. In practice, such systems have to address three main requirements including the ability to adapt their trained model with each incoming data element, the ability to handle

Emotional expressions are an important part of daily communication between people. Emotions are commonly transferred non verbally through facial expressions, eye contact and tone of voice. With the rise in social media usage, textual communication in which emotions are expressed has also witnessed a great increase. In this paper automatic emotion intensity prediction from text is addressed