The rapid and enormous growth of the Internet of Things, as well as its widespread adoption, has resulted in the production of massive quantities of data that must be processed and sent to the cloud, but the delay in processing the data and the time it takes to send it to the cloud has resulted in the emergence of fog, a new generation of cloud in which the fog serves as an extension of cloud services at the edge of the network, reducing latency and traffic. The distribution of computational resources to minimize makespan and running costs is one of the disadvantages of fog computing. This paper provides a new approach for improving the task scheduling problem in a Cloud-Fog environment in terms of execution time(makespan) and operating costs for Bag-of-Tasks applications. A task scheduling evolutionary algorithm has been proposed. A single custom representation of the problem and a uniform intersection are built for the proposed algorithm. Furthermore, the individual initialization and perturbation operators (crossover and mutation) were created to resolve the inapplicability of any solution found or reached by the proposed evolutionary algorithm. The proposed ETS (Evolutionary Task Scheduling algorithm) algorithm was evaluated on 11 datasets of varying size in a number of tasks. The ETS outperformed the Bee Life (BLA), Modified Particle Swarm (MPSO), and RR algorithms in terms of Makespan and operating costs, according to the results of the experiments.
The major goal of this research was to use the Euler method to determine the best starting value for eccentricity. Various heights were chosen for satellites that were affected by atmospheric drag. It was explained how to turn the position and velocity components into orbital elements. Also, Euler integration method was explained. The results indicated that the drag is deviated the satellite trajectory from a keplerian orbit. As a result, the Keplerian orbital elements alter throughout time. Additionally, the current analysis showed that Euler method could only be used for low Earth orbits between (100 and 500) km and very small eccentricity (e = 0.001).
The Al-Kindy College Medical Journal (KCMJ) is an Iraqi scholarly journal published by the Al-Kindy College of Medicine, University of Baghdad. It was officially founded in 2004. It is a peer-reviewed journal, published in both online and printed forms. It has a mission to offer a publication platform that mirrors recent knowledge and findings in the field of medicine and medical sciences. It publishes various types of articles, including editorial, review article, research article, brief report, case report, and letter to editor. It accepts articles in the English language. It was biannually published till 2021 when it started to launch three issues per year. The journal is registered with numerous partners, including Iraqi Academi
... Show MoreThe evaluation of banks plays an important role in maintaining the interests of customers with the bank as well as providing continuous supervision and control by the Central Bank. The Central Bank of Iraq conducted an assessment of the Iraqi banks through the implementation of the CAMEL model during a certain period. This evaluation did not continue. The research provides continuity to the Central Bank's assessment and as a step to continue the evaluation process for all banks through the use of the CAMEL model. ROA and ROE by using the regression model for four Iraqi banks registered in the Iraqi market for securities during the period 2010-2016. The results showed that the capital and profitability indicators have a significan
... Show MoreA new modified differential evolution algorithm DE-BEA, is proposed to improve the reliability of the standard DE/current-to-rand/1/bin by implementing a new mutation scheme inspired by the bacterial evolutionary algorithm (BEA). The crossover and the selection schemes of the DE method are also modified to fit the new DE-BEA mechanism. The new scheme diversifies the population by applying to all the individuals a segment based scheme that generates multiple copies (clones) from each individual one-by-one and applies the BEA segment-wise mechanism. These new steps are embedded in the DE/current-to-rand/bin scheme. The performance of the new algorithm has been compared with several DE variants over eighteen benchmark functions including sever
... Show More This paper describes the application of consensus optimization for Wireless Sensor Network (WSN) system. Consensus algorithm is usually conducted within a certain number of iterations for a given graph topology. Nevertheless, the best Number of Iterations (NOI) to reach consensus is varied in accordance with any change in number of nodes or other parameters of . graph topology. As a result, a time consuming trial and error procedure will necessary be applied
to obtain best NOI. The implementation of an intellig ent optimization can effectively help to get the optimal NOI. The performance of the consensus algorithm has considerably been improved by the inclusion of Particle Swarm Optimization (PSO). As a case s
Cryptography is the process of transforming message to avoid an unauthorized access of data. One of the main problems and an important part in cryptography with secret key algorithms is key. For higher level of secure communication key plays an important role. For increasing the level of security in any communication, both parties must have a copy of the secret key which, unfortunately, is not that easy to achieve. Triple Data Encryption Standard algorithm is weak due to its weak key generation, so that key must be reconfigured to make this algorithm more secure, effective, and strong. Encryption key enhances the Triple Data Encryption Standard algorithm securities. This paper proposed a combination of two efficient encryption algorithms to
... Show MoreThis paper presents an improved technique on Ant Colony Optimization (ACO) algorithm. The procedure is applied on Single Machine with Infinite Bus (SMIB) system with power system stabilizer (PSS) at three different loading regimes. The simulations are made by using MATLAB software. The results show that by using Improved Ant Colony Optimization (IACO) the system will give better performance with less number of iterations as it compared with a previous modification on ACO. In addition, the probability of selecting the arc depends on the best ant performance and the evaporation rate.
The need for quick airborne transportation is critical, especially in emergencies. Drones with suspended payloads might be used to accomplish quick airborne transportation. Due to the environment or the drone's motion, the slung load may oscillate and lead the drone to fall. The altitude and attitude controls are the backbones of the drone's stability, and they must be adequately designed. Because of their symmetrical and simple structure, quadrotor helicopters are one of the most popular drone classes. In this work, a genetic algorithm with two weighted terms fitness function is used to adjust a Proportional-Integral-Derivative (PID) controller to compensate for the altitude and attitude controllers in a quadrotor drone
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