Biological constraint in digital data encoding: A DNA based approach for image representation
Digital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. T...
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| Format: | Article |
| Language: | English |
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Universitas Ahmad Dahlan
2025-08-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
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| Online Access: | https://ijain.org/index.php/IJAIN/article/view/1747 |
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| author | Muhammad Rafi Muttaqin Yeni Herdiyeni Agus Buono Karlisa Priandana Iskandar Zulkarnaen Siregar Wisnu Ananta Kusuma |
| author_facet | Muhammad Rafi Muttaqin Yeni Herdiyeni Agus Buono Karlisa Priandana Iskandar Zulkarnaen Siregar Wisnu Ananta Kusuma |
| author_sort | Muhammad Rafi Muttaqin |
| collection | DOAJ |
| description | Digital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. The goal of this research is to develop a digital data encoding technique based on DNA, while considering biological constraints such as homopolymer and GC-content. The process involves converting image pixel values into binary format, followed by encoding into DNA sequences, ensuring they meet biological constraints. The validity of the resulting DNA sequences is assessed through transcription and translation processes. Additionally, Multiple Sequence Alignment analysis is conducted to compare the similarities between the encoded DNA sequences. The results indicate that the DNA sequences from MNIST images share similar characteristics, reflected in the phylogenetic tree's close clustering. Multiple Sequence Alignment analysis shows that biological constraints successfully preserved the core visual features, allowing accurate clustering. However, this method also faces drawbacks, particularly in the reduction of visual information and sensitivity to changes in image intensity. Despite these challenges, DNA-based encoding shows potential for digital image representation. Further development, particularly the integration of deep learning, could lead to more efficient, secure, and sustainable data storage systems, especially for image data. |
| format | Article |
| id | doaj-art-c41c23f218584e65b10a38c72bd15bc0 |
| institution | Kabale University |
| issn | 2442-6571 2548-3161 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj-art-c41c23f218584e65b10a38c72bd15bc02025-08-20T03:58:49ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612025-08-0111337939510.26555/ijain.v11i3.1747348Biological constraint in digital data encoding: A DNA based approach for image representationMuhammad Rafi Muttaqin0Yeni Herdiyeni1Agus Buono2Karlisa Priandana3Iskandar Zulkarnaen Siregar4Wisnu Ananta Kusuma5IPB UniversityIPB UniversityIPB UniversityIPB UniversityIPB UniversityIPB UniversityDigital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. The goal of this research is to develop a digital data encoding technique based on DNA, while considering biological constraints such as homopolymer and GC-content. The process involves converting image pixel values into binary format, followed by encoding into DNA sequences, ensuring they meet biological constraints. The validity of the resulting DNA sequences is assessed through transcription and translation processes. Additionally, Multiple Sequence Alignment analysis is conducted to compare the similarities between the encoded DNA sequences. The results indicate that the DNA sequences from MNIST images share similar characteristics, reflected in the phylogenetic tree's close clustering. Multiple Sequence Alignment analysis shows that biological constraints successfully preserved the core visual features, allowing accurate clustering. However, this method also faces drawbacks, particularly in the reduction of visual information and sensitivity to changes in image intensity. Despite these challenges, DNA-based encoding shows potential for digital image representation. Further development, particularly the integration of deep learning, could lead to more efficient, secure, and sustainable data storage systems, especially for image data.https://ijain.org/index.php/IJAIN/article/view/1747biological constraintdigital image encodingdna data storagemnist datasetmultiple sequence allignment |
| spellingShingle | Muhammad Rafi Muttaqin Yeni Herdiyeni Agus Buono Karlisa Priandana Iskandar Zulkarnaen Siregar Wisnu Ananta Kusuma Biological constraint in digital data encoding: A DNA based approach for image representation IJAIN (International Journal of Advances in Intelligent Informatics) biological constraint digital image encoding dna data storage mnist dataset multiple sequence allignment |
| title | Biological constraint in digital data encoding: A DNA based approach for image representation |
| title_full | Biological constraint in digital data encoding: A DNA based approach for image representation |
| title_fullStr | Biological constraint in digital data encoding: A DNA based approach for image representation |
| title_full_unstemmed | Biological constraint in digital data encoding: A DNA based approach for image representation |
| title_short | Biological constraint in digital data encoding: A DNA based approach for image representation |
| title_sort | biological constraint in digital data encoding a dna based approach for image representation |
| topic | biological constraint digital image encoding dna data storage mnist dataset multiple sequence allignment |
| url | https://ijain.org/index.php/IJAIN/article/view/1747 |
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