Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.
International border security operations are diverse and include tasks to facilitate the legitimate movement of goods, thwart crime, maintain safety around borders and safeguard natural resources. All these operations are vital and enduring; however, three operations are currently of exceptional concern to countries around the world: counterterrorism, illegal drug control and illegal migration. The usage of flying ad hoc networks promises new ways for both military and civilian applications, such as border surveillance and remote sensing. Many systems were developed to assist border authorities with more effective surveillance and reliable decision-making support. Such systems vary in terms of the used technology, accuracy, types of events that can be detected and monitoring continuity. This article investigates the technical capabilities of existing and emerging surveillance technologies used for international border monitoring applications. It describes the effectiveness of these systems along with the technological infrastructure required for their implementation. Particular attention has been given to identifying the strengths and weaknesses of these systems and their ability to meet current and future challenges. Our analysis shows that flying ad hoc networks can be used to deliver a rapidly deployable, self-configurable, flexible and relatively small operating cost network for border surveillance.
Breast ailments have affected women since the time of the pharaohs, and can be traced back to the beginning of recorded medical history. It still poses a significant threat to the population. Statistics from the National Cancer Institute place breast cancer at the top of the list of all forms of cancers afflicting women and treated there. In the Kingdom of Saudi Arabia (KSA), breast cancer constitutes 18% of all cancers in Saudi women. In our work we have proposed a prototype (website) which allows the interaction between the patient and the expert system through system's interface. Our approach includes collaborative information provisioning by the community members in additions to other sources of information like online organizations and the experts in the field [3].
Connected autonomous vehicles (CAVs) currently promise cooperation between vehicles, providing abundant and real-time information through wireless communication technologies. In this paper, a two-level fusion of classifiers (TLFC) approach is proposed by using deep learning classifiers to perform accurate road detection (RD). The proposed TLFC-RD approach improves the classification by considering four key strategies such as cross fold operation at input and pre-processing using superpixel generation, adequate features, multi-classifier feature fusion and a deep learning classifier. Specifically, the road is classified as drivable and non-drivable areas by designing the TLFC using the deep learning classifiers, and the detected information using the TLFC-RD is exchanged between the autonomous vehicles for the ease of driving on the road. The TLFC-RD is analyzed in terms of its accuracy, sensitivity or recall, specificity, precision, F1-measure and max F measure. The TLFC- RD method is also evaluated compared to three existing methods: U-Net with the Domain Adaptation Model (DAM), Two-Scale Fully Convolutional Network (TFCN) and a cooperative machine learning approach (i.e., TAAUWN). Experimental results show that the accuracy of the TLFC-RD method for the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset is 99.12% higher than its competitors.
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