Over the past decade, deep learning (DL) has been applied in a large number of optical sensors applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as a promising technology for modern intelligent sensing platforms. These sensors are widely used in process monitoring, quality prediction, pollution, defence, security, and many other applications. However, they suffer major challenges such as the large generated datasets and low processing speeds for these data, including the high cost of these sensors. These challenges can be mitigated by integrating DL systems with optical sensor technologies. This paper presents recent studies integrating DL algorithms with optical sensor applications. This paper also highlights several directions for DL algorithms that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future development of related research.

Over the past decade, Deep Learning (DL) had been applied in a large number of optical sensors’ applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as promise technology for the modern intelligent sensing platforms. These sensors are widely used to process monitoring, quality prediction, pollution, defense, security and many other applications. However, they suffer major challenges such as the large generated data and low processing speed for that data and moreover the much cost of these sensor. These challenges can be mitigated by integrating deep learning system with the optical sensor technologies. This paper presents recent studies that integrate DL algorithms with optical sensors applications. This paper also highlights several of DL algorithms directions that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future related research development.

Massive multiple‐input multiple‐output (MIMO) is a critical component of 5G cellular networks, which utilizes large numbers of antennas at both the transmitter and receiver to enhance throughput and radiated energy efficiency. Various linear detection techniques are employed with massive MIMO to counteract path loss and interference, and maximize throughput. The first aim of this paper is to analyse the performance of uplink massive MIMO system for different linear detection techniques including: Maximum ratio combining (MRC), zero‐forcing (ZF), regularized ZF (RZF) and minimum mean squared error ( MMSE ) over Rayleigh channel model. The second aim is to jointly investigate the optimal values of signal‐to‐noise ratio ( SNR ), the number of antennas M and the number of users K for maximizing the spectral efficiency ( SE ) and energy efficiency ( EE ) through simulation using MATLAB and 3D plotting patterns. The obtained results show that the best SE and EE are achieved by uplink massive MIMO setup while using optimal values of SNR , M and K . It is observed that MMSE achieved the best performance. However, it requires estimation of average SNR at BS. Therefore, the best choice is ZF or RZF without any need for SNR estimation.

This comprehensive literature review is dedicated to the field of Arabic Automatic Question Generation (AQG), which focuses on the development of computational models and algorithms for the automatic generation of questions. The review systematically covers key concepts in AQG, including question types and evaluation metrics. Additionally, it delves into the specific challenges associated with applying AQG techniques to the Arabic language, considering factors like complex morphology and dialectal variations. The review introduces a taxonomy of Arabic AQG approaches, classifying them into rule-based, template-based, and machine learning-based methods. It examines the pivotal role of datasets, resources, and evaluation methodologies in the training and assessment of AQG systems. Advancements in Arabic AQG are highlighted, and the review identifies emerging research directions, such as domain-specific question generation and integration into educational platforms. In conclusion, the review provides valuable insights for researchers, developers, and educators interested in Arabic AQG. It addresses current advancements, challenges, and outlines potential future research directions, including the scarcity of labeled data and the necessity for domain-specific approaches. Overall, this review serves as a comprehensive resource in the realm of Arabic AQG.