This paper suggests a new nature inspired metaheuristic optimization algorithm which is called Sea Lion Optimization (SLnO) algorithm. The SLnO algorithm imitates the hunting behavior of sea lions in nature. Moreover, it is inspired by sea lions' whiskers that are used in order to detect the prey. SLnO algorithm is tested with 23 well-known test functions (Benchmarks). Optimization results show that the SLnO algorithm is very competitive compared to Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA) and Dragonfly Algorithm (DA).

In software projects, the requirement engineering phase is the base of any project and this phase is concerned in software requirements processes. Stakeholders are the main source of these requirements with respect of constraints and regulations. Requirements prioritization is considered as one of the most significant approach in the requirement engineering process because it is used to prioritize the execution order of requirements with respect to stakeholders' viewpoints. On the other hand, whale optimization algorithm is recently used in optimization problems since it mimics the Humpback whale hunting behavior by employing bubble net hunting technique. In this paper the Whale Optimization Algorithm (WOA) is employed in order to prioritize the software requirements by assuming the requirements in the search space and the hunting behavior is implemented to prioritize these requirements, the proposed technique is evaluated in term of running time with analytical hierarchy Process (AHP) to prioritize different size of requirement's sets, the results shows the RP_WOA outperforms the AHP technique by approximately (40%).

The Internet of Things (IoT) is a technology that allows machines to communicate with each other without the need for human interaction. Usually, IoT devices are connected via a network. A wide range of network technologies are required to make the IoT concept operate successfully; as a result, protocols at various network layers are used. One of the most extensively used network layer routing protocols is the Routing Protocol for Low Power and Lossy Networks (RPL). One of the primary components of RPL is the trickle timer method. The trickle algorithm directly impacts the time it takes for control messages to arrive. It has a listen-only period, which causes load imbalance and delays for nodes in the trickle algorithm. By making the trickle timer method run dynamically based on hop count, this research proposed a novel way of dealing with the difficulties of the traditional algorithm, which is called the Elastic Hop Count Trickle Timer Algorithm. Simulation experiments have been implemented using the Contiki Cooja 3.0 simulator to study the performance of RPL employing the dynamic trickle timer approach. Simulation results proved that the proposed algorithm outperforms the results of the traditional trickle algorithm, dynamic algorithm, and e-trickle algorithm in terms of consumed power, convergence time, and packet delivery ratio.

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.