Classification of different types of DDR RAM using transfer learning in convolutional learning networks
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Author |
Jessica S. Velasco, Jonathan Michael A. Manalo, Marc Anthony Christopher L. Abana, Kenji Gabriel B. Ebron, Jovencio V. Merin and Jomer V. Catipon
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e-ISSN |
1819-6608 |
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On Pages
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210-215
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Volume No. |
19
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Issue No. |
4
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Issue Date |
April 15, 2024
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DOI |
https://doi.org/10.59018/022434
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Keywords |
RAM classification (Random Access Memory), deep learning, convolutional neural networks, transfer learning, python.
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Abstract
Technology, specifically computers play an important role in modern society. People who are new to computers can determine what type of RAM they have, which can be used to avoid confusion on what type of RAM their computer needs with the help of an Android device. For this study, a powerful computer with a Graphics Processing Unit (GPU) needed to be used to shorten the amount of time that the deep learning process takes. The study gathered images of 4 types of Random Access Memory for a RAM classification system. There were 1000 images in total for DDR1, DDR2, DDR3, and DDR4 RAM. The study utilized transfer learning to RAM type classification with pre-trained models such as VGG16, VGG19, Inception V3, and Xception. The data that was gathered showed that Xception is the best classifier with an initial average accuracy of 85.034% and a 100% Val_Accuracy even though the model had the longest loading time with 12 seconds.
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