Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/5646
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dyubele, Sithembiso | en_US |
dc.contributor.author | Cele, Noxolo Pretty | en_US |
dc.contributor.author | Mbangata, Lubabalo | en_US |
dc.date.accessioned | 2024-10-28T16:05:22Z | - |
dc.date.available | 2024-10-28T16:05:22Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | Dyubele, S., Cele, N.P. and Mbangata, L. 2024. Integration of an autoencoder model with an actor-oriented system. Advances in Artificial Intelligence and Machine Learning. 04(03): 2629-2647. doi:10.54364/aaiml.2024.43153 | en_US |
dc.identifier.issn | 2582-9793 (Online) | - |
dc.identifier.uri | https://hdl.handle.net/10321/5646 | - |
dc.description.abstract | Traditional machine learning frameworks often struggle with scalability, modularity, and efficient resource management, especially when dealing with vast data. Actor-Oriented Systems offer a robust framework for building such scalable systems, allowing concurrent processing and efficient handling of large datasets. This study investigated the integration of Autoencoders (AE), which are pivotal in unsupervised learning, with Actor-Oriented Systems to enhance the modularity, scalability, and maintainability of the model training process. The study seeks to leverage the capabilities of AE and Actor-Oriented Systems to achieve high-quality image reconstruction and efficient processing. The study also attempted to understand the underlying patterns in the data, assess the performance of the model, and demonstrate the benefits of modular and scalable systems. Key findings from the results showed significant improvements in training efficiency and performance of the model, especially when using Actor-Oriented Systems. The training time was reduced from 16.96 seconds to 14.21 seconds, and the validation loss improved from 0.2768 to 0.2100, indicating better generalisation and learning. Data augmentation techniques further enhanced the robustness of the model, leading to more accurate reconstructions of the test images. Actor-Oriented Systems facilitated concurrent processing, improved modularity, and enabled the system to scale efficiently with increasing data volume. This study also highlighted the practical benefits of integrating AE with Actor-Oriented Systems, providing valuable insights into building more robust, maintainable, and scalable machine learning workflows. | en_US |
dc.format.extent | 19 p | en_US |
dc.language.iso | en | en_US |
dc.publisher | Advances in Artificial Intelligence and Machine Learning | en_US |
dc.relation.ispartof | Advances in Artificial Intelligence and Machine Learning; Vol. 04, Issue 03 | en_US |
dc.subject | Autoencoder | en_US |
dc.subject | Actor-oriented system | en_US |
dc.subject | Machine learning | en_US |
dc.title | Integration of an autoencoder model with an actor-oriented system | en_US |
dc.type | Article | en_US |
dc.date.updated | 2024-10-25T16:29:29Z | - |
dc.publisher.uri | http://dx.doi.org/10.54364/aaiml.2024.43153 | en_US |
dc.identifier.doi | 10.54364/aaiml.2024.43153 | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
Appears in Collections: | Research Publications (Accounting and Informatics) |
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AAIML Copyright Clearance.docx | 132.56 kB | Microsoft Word XML | View/Open | |
Dyubele_Cele_Mbangata_2024.pdf | 696.45 kB | Adobe PDF | View/Open |
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