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

Recent years witness a great deal of interest in artificial intelligence (AI) tools in the area of optimization. AI has developed a large number of tools to solve the most difficult search-and-optimization problems in computer science and operations research. Indeed, metaheuristic-based algorithms are a sub-field of AI. This study presents the use of the metaheuristic algorithm, that is, water

Automatic lane detection is a classical task in autonomous vehicles that traditional computer vision techniques can perform. However, such techniques lack reliability for achieving high accuracy while maintaining adequate time complexity in the context of real-time detection in complex and dynamic road scenes. Deep neural networks have proved their ability to achieve competing accuracy and time

In a vehicular communications environment, the need for information sharing, entertainment, and multimedia will increase, leading to congestion of backhaul networks. The major challenge of this network is latency and resource limitations. Proactive caching can be obtained from local caches rather than from remote servers, which can avoid long delays resulting from limited backhaul capacity and

In this paper, we propose a Reinforcement Learning-based MAC layer protocol for cognitive radio networks, based on exploiting the feedback of the Primary User (PU). Our proposed model relies on two pillars, namely an infinite-state Partially Observable Markov Decision Process (POMDP) to model the system dynamics besides a queuing-theoretic model for the PU queue, where the states represent whether

In this paper, we propose a reinforcement learning (RL) approach to design an access scheme for secondary users (SUs) in a cognitive radio (CR) network. In the proposed scheme, we introduce a deep Q-network to enable SUs to access the primary user (PU) channel based on their past experience and the history of the PU network's automatic repeat request (ARQ) feedback. In essence, SUs cooperate to

The gaining sharing knowledge based optimization algorithm (GSK) is recently developed metaheuristic algorithm, which is based on how humans acquire and share knowledge during their life-time. This paper investigates a modified version of the GSK algorithm to find the best feature subsets. Firstly, it represents a binary variant of GSK algorithm by employing a probability estimation operator (Bi

Filter bank multi-carrier (FBMC) is considered a promising alternative to the Orthogonal Frequency Division Multiplexing (OFDM) scheme. It improves spectral efficiency by eliminating the need for cyclic prefix while attenuating interference due to the robustness of the out-of-band emission. In this work, we present a framework, and the performance evaluation of FBMC is a multi-carrier modulation

During last two decades, Differential Evolution (DE) proved to be one of the most popular and successful evolutionary algorithms for solving global optimization problems over continuous space. Proposing new mutation strategies to improve the optimization performance of (DE) is considered a significant research study. In DE, mutation operation plays a vital role in the performance of the algorithm

Creating a model to detect freely moving fish underwater in real-time is a challenging process for two main reasons. First, the available datasets suffer from some limitations that severely affect the results of the detection models operating in challenging and blurry environments. These models should be able to capture all of the fish movement given different types of surroundings. Second