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(a). Sequential minimal optimization regression error (X: Availability vs. Y: Predicted Ranking). (b). Sequential minimal optimization regression error (X: Security vs. Y: Predicted Ranking). (c). Sequential minimal optimization regression error (X: Reliability vs. Y: Predicted Ranking. (d). Sequential minimal optimization regression error (X: Cost vs. Y: Predicted Ranking). (e). Sequential minimal optimization regression error (X: Ranking vs. Y: Predicted Ranking).
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Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the...
Citations
... Cloud computing (CC) paradigm is a cutting edge approach to computing that offers the consumers access to resources and applications as a service over the Internet, meeting their computing needs. 1 It offers Infrastructure as a Service (IaaS) and Software as a Service (SaaS), Platform as a Service (PaaS) influences the availability, confidentiality and integrity of cloud resources, major cloud providers such as Windows Azure, Rack Space, Eucalyptus, Open Nebula, and Amazonetc.Implements a firewall to guard against the incursions on cloud services. 2 Comparing network security in cloud computing and self-defense depends on the intrusion detection systems. ...
Cloud computing (CC) offers on demand, flexible resources and services over the internet, to secure cloud assets and resources, privacy and security remains a difficult challenge. To overcome this problem, we proposed a Modified Dove Swarm Optimization Based Enhanced Feed Forward Neural Network (MDSO-EFNN) to examine the network traffics flow that targets a cloud environment.NetworkIntrusion detection systems (NIDSs) are crucial in identifying assaults in the cloud environment, which helps to reduce theproblem. In this study, we gather a NSL-KDD network traffic dataset.Secondly, collected data is preprocessed using Z score normalization to clean the data. Thirdly, Continuous wavelet transform (CWT) is employed to extract the unwanted data. Ant colony optimization (ACO) is used to choose the appropriate data. The selected appropriate data is used to test the process using MDSO-EFNN. The simulation findings of the result use a Python tool. As a result, our proposed method achieves significant outcomes with classification of accuracy (95%), precision rate (97%), sensitivity (98%), and specificity (96%)
... Cloud computing (CC) paradigm is a cutting edge approach to computing that offers the consumers access to resources and applications as a service over the Internet, meeting their computing needs. 1 It offers Infrastructure as a Service (IaaS) and Software as a Service (SaaS), Platform as a Service (PaaS) influences the availability, confidentiality and integrity of cloud resources, major cloud providers such as Windows Azure, Rack Space, Eucalyptus, Open Nebula, and Amazonetc.Implements a firewall to guard against the incursions on cloud services. 2 Comparing network security in cloud computing and self-defense depends on the intrusion detection systems. ...
Cloud computing (CC) offers on-demand, flexible resources and services over the internet, to secure cloud assets and resources, privacy and security remain a difficult challenge. To overcome this problem, we proposed a Modified Dove Swarm Optimization Based Enhanced Feed Forward Neural Network (MDSO-EFNN) to examine the network traffic flow that targets a cloud environment. Network Intrusion detection systems (NIDSs) are crucial in identifying assaults in the cloud environment, which helps to reduce the problem. In this study, we gather an NSL-KDD network traffic dataset. Secondly, collected data is preprocessed using Z-Score normalization to clean the data. Thirdly, Continuous wavelet transform (CWT) is employed to extract the unwanted data. Ant colony optimization (ACO) is used to choose the appropriate data. The selected appropriate data is used to test the process using MDSO-EFNN. The simulation findings of the result use a Python tool. As a result, our proposed method achieves significant outcomes with classification of accuracy (95%), precision rate (97%), sensitivity (98%), and specificity (96%).
... The working principle of a linear motor is simple: the magnetic flux of the moving part is synchronized with the stationary section, which converts electromagnetic energy to linear motion, and the load is directly fixed with the mover. The invention of linear machines eliminated many tools such as ball and screw, belt and pulley, and other turning arrangements which were used for the conversion of rotary motion to linear motion, as well as losses involved in this conversion process [8][9][10][11]. Additionally, linear machines provide speed precision with improved efficiency and are easy to construct as the idea is simple: cut the rotary motor radially and set it flat. The working rule for linear and rotary induction motors is the same, but the air gap is more extensive in LIM, and the mover is shorter for a track, resulting in end effects [12]. ...
... (6)- (8): (6) λ qs = L ls .i qs + L m i qs + i qr = L s i qs + L m i qr (7) Total flux linkages of stator side = (λ ds ) 2 + λ qs 2 (8) (9) λ qr = L lr i qr + L m i qs + i qr = L r i qr + L m i qs (10) Total flux linkages of rotor side = (λ dr ) 2 + λ qr 2 (11) where, R r and L r are rotor resistance and inductance, respectively, d represents the primary length in meters and v represents the primary linear speed in m/s. From Figs. 4 and 5, q-and d-axis air gap flux linkages are given in Eqs. ...
Vector control schemes have recently been used to drive linear induction motors (LIM) in high-performance applications. This trend promotes the development of precise and efficient control schemes for individual motors. This research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation (SVPWM) inverters. The framework under consideration is developed in four stages. To begin, MATLAB Simulink was used to develop a detailed mathematical and electromechanical dynamic model. The research presents a modified SVPWM inverter control scheme. By tuning the proportional-integral (PI) controller with a transfer function, optimized values for the PI controller are derived. All the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control scheme. The reference speed values were chosen to evaluate the performance of the respective system, and the developed system’s response was verified using various data sets. For the low-speed range, a reference value of 10 m/s is used, while a reference value of 100 m/s is used for the high-speed range. The speed output response indicates that the motor reached reference speed in a matter of seconds, as the delay time is between 8 and 10 s. The maximum amplitude of thrust achieved is less than 400 N, demonstrating the controller’s capability to control a high-speed LIM with minimal thrust ripple. Due to the controlled speed range, the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.
... On 24 November 2021, the World Health Organization (WHO) announced Omicron as a COVID-19 variant of concern, leading to travel restrictions, a scramble to speed up booster immunization programs, and new attempts to address vaccine inequity [1]. On bibliometrics, it is important to consider how ML approaches can be used to estimate the number of citations, offer helpful advice for creating new bibliometric indexes, and uncover relationships between various variables [21,22]. ...
Human respiratory infections caused by coronaviruses can range from mild to deadly. Although there are numerous studies on coronavirus disease 2019 (COVID-19), few have been published on its Omicron variant. In order to remedy this deficiency, this study undertook a bibliometric analysis of the publishing patterns of studies on the Omicron variant and identified hotspots. Automated transportation, environmental protection, improved healthcare, innovation in banking, and smart homes are just a few areas where machine learning has found use in tackling complicated problems. The sophisticated Scopus database was queried for papers with the term “Omicron” in the title published between January 2020 and June 2022. Microsoft Excel 365, VOSviewer, Bibliometrix, and Biblioshiny from R were used for a statistical analysis of the publications. Over the study period, 1917 relevant publications were found in the Scopus database. Viruses was the most popular in publications for Omicron variant research, with 150 papers published, while Cell was the most cited source. The bibliometric analysis determined the most productive nations, with USA leading the list with the highest number of publications (344) and the highest level of international collaboration on the Omicron variant. This study highlights scientific advances and scholarly collaboration trends and serves as a model for demonstrating global trends in Omicron variant research. It can aid policymakers and medical researchers to fully grasp the current status of research on the Omicron variant. It also provides normative data on the Omicron variant for visualization, study, and application.
... Machine learning (ML) methods can be broadly divided into two categories: supervised learning, in which the learning data is presented and provided by the user, and unsupervised learning, in which the learning data is learned as a clustering approach by taking into account the vastness of the dataset [24][25][26][27][28]. Evolutionary algorithms are crucial in this regard; they have been used in a range of optimization tasks, including picture classification, global optimization, text classification, and parallel machine scheduling, to name a few. ...
Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public’s mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public’s mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions.
