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Izzat ISMAIL,Asyraf AFTHANORHAN,Noor Aina Amirah MOHAMAD NOOR,Nurul Aisyah Awanis A RAHIM,Sheikh Ahmad Faiz Sheikh Ahmad TAJUDDIN,Muhammad Takiyuddin Abdul GHANI 한국유통과학회 2024 유통과학연구 Vol.22 No.4
Purpose: This study is aimed to introduce the application of phantom approach with structural equation modelling method for online learning. By integrating these innovative methodologies, the research seeks to advance the understanding of how the phantom approach can effectively complement and augment structural equation modeling techniques. Research design, data and methodology: A theoretical framework of Technology Acceptance Model (TAM) was modified and updated. A questionnaire was developed and used to extract information from 189 instructors who used online learning as their primary medium. The Covariance Based Structural Equation Modelling (CBSEM) was applied to test the direct effects and the phantom approach is used to handle the 2 mediators in the model. Results: social influence, perceived usefulness, and perceived ease of use exerted discernible impacts on instructors' intentions to engage in online learning. These findings illuminate the intricate dynamics influencing instructor behavior within the realm of online education, underscoring the significance of various factors in shaping their intentions. Conclusions: In additions, the perceived usefulness and perceived ease of use had mediated the effect of social influence and instructor intention using phantom approach. Therefore, one can have concluded that this modified model was also confirmed, thereby reinforcing distribution strategies to online learning and overall education presence.
Scheduling Jobs on Cloud Computing using Firefly Algorithm
Demyana Izzat Esa,Adil Yousif 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.7
Cloud computing is a new technology, instead of all computer hardware and software that used on desktop, or somewhere within company's network, it's presented as a service by cloud service providers and accessed via the Internet. Exactly where hardware and software are located and how everything works does not matter. In cloud computing there are many jobs that requires to be executed on the available resources to achieve best minimal execution time. Several optimization methods are available for cloud job scheduling. However, the job scheduling process is still need to be optimized. This paper proposes a new job scheduling mechanism using Firefly Algorithm to minimize the execution time of jobs. The proposed mechanism based on information of jobs and resources such as length of job speed of resource and identifiers. The scheduling function in the proposed job scheduling mechanism firstly creates a set of jobs and resources to generate the population by assigning the jobs to resources randomly and evaluates the population using a fitness value which represents the execution time of jobs. Secondly the function used iterations to regenerate populations based on firefly behavior to produce the best job schedule that gives the minimum execution time of jobs. Several scenarios are implemented using Java Language and CloudSim simulator. Different settings have been considered in the evaluation and experimentation phase to examine the proposed mechanism in different workloads. The first phase of the evaluation process describes how the proposed mechanism can be used to minimize the execution time of jobs. The second phase of the evaluation process compares the proposed mechanism with First Come First Serves (FCFS) algorithm. The results revealed that the proposed mechanism minimizes the execution time significantly. Furthermore, the proposed mechanism outperformed the FCFS algorithm.