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Title |
Computer Architecture Execution Time Optimization Using Swarm in Machine Learning |
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Author |
Sarah AlBarakati, Sally AlQarni, Rehab K. Qarout, and Kaouther Laabidi |
| Citation |
Vol. 23 No. 10 pp. 49-56 |
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Abstract |
Computer architecture serves as a link between application requirements and underlying technology capabilities such as technical, mathematical, medical, and business applications' computational and storage demands are constantly increasing. Machine learning these days grown and used in many fields and it performed better than traditional computing in applications that need to be implemented by using mathematical algorithms. A mathematical algorithm requires more extensive and quicker calculations, higher computer architecture specification, and takes longer execution time. Therefore, there is a need to improve the use of computer hardware such as CPU, memory, etc. optimization has a main role to reduce the execution time and improve the utilization of computer recourses. And for the importance of execution time in implementing machine learning supervised module linear regression, in this paper we focus on optimizing machine learning algorithms, for this purpose we write a (Diabetes prediction program) and applying on it a Practical Swarm Optimization (PSO) to reduce the execution time and improve the utilization of computer resources. Finally, a massive improvement in execution time were observed. |
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Keywords |
computer architecture, Machine learning, optimization, execution time, PSO, medical. |
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