DOI : https://doi.org/10.18517/ijaseit.14.5.11220

A Comparative Study on the Performance of Algorithms on Different AI Platforms

Youngseok Lee (1), Jungwon Cho (2)
(1) Department of Computer Education, Seoul National University of Education, 96 Seochojungang-ro, Seocho-gu, Seoul, Republic of Korea
(2) Department of Computer Education, Jeju National University, 102 Jejudaehakno, Jeju-si, Jeju-do, Republic of Korea
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How to cite (IJASEIT) :
Lee , Youngseok, and Jungwon Cho. “A Comparative Study on the Performance of Algorithms on Different AI Platforms”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 5, Oct. 2024, pp. 1809-14, doi:10.18517/ijaseit.14.5.11220.
Citation Format :
To understand the basic concepts and principles of artificial intelligence (AI) and to solve problems using AI, it is necessary to use various platforms. Among AI machine-learning (ML) models, the prediction algorithm is a basic AI model that can be used in various fields, such as for predicting weather, grades, product prices, and population, and is likely to be used to gain a basic understanding of AI. Many educational AI platforms implement prediction algorithms to help understand these AI models. In this study, prediction algorithms were implemented using the following AI platforms: Orange3, Entry, and Python to learn the temperature data in the Seoul area of Korea using a linear regression model, predict the value of temperature change, and evaluate the performance of the prediction algorithm for each platform. Additionally, to understand machine learning classification models and develop effective teaching methods, we conducted a prototype test to compare and analyze each platform's photo classification methods and performance. As a result of the comparison, Python exhibited the best performance, followed by Orange3 and Entry, with differences in accuracy and predicted values. To understand AI, it is necessary to understand the reliability of AI models and use an appropriate platform that considers the development level of the learner. In the future, we aim to research different ways to efficiently understand AI by comparing and analyzing its performance using various AI models.

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