Preserving the Topology of Self-Organizing Maps for Data Analysis: A Review

Yusob, Bariah; Mustaffa, Zuriani; Shamsuddin, Siti Mariyam

doi:10.1088/1757-899x/769/1/012004

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Thus far, we have not found any references in research on this type of source code analysis with a focus on the extraction of the programming concepts from the content. SOMs are typically used for visualization and exploratory data analysis [25][26][27], but they are rarely, if ever, used on the source code.…”

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confidence: 99%

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

2023

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Due to the everchanging and evergrowing nature of programming technologies, the gap between the programming industry’s needs and the educational capabilities of both formal and informal educational environments has never been wider. However, the need to learn computer programming has never been greater, regardless of the motivation behind it. The number of programming concepts to be taught is increasing over time, while the amount of time available for education and training usually remains the same. The objective of this research was to analyze the source codes used in many educational systems to teach fundamental programming concepts to learners, regardless of their prior experience in programming. A total of 25 repositories containing 3882 Python modules were collected for the analysis. Through self-organization of the collected content, we obtained very compelling results about code structure, distribution, and differences. Based on those results, we concluded that Self-Organizing Maps are a powerful tool for both content and knowledge management, because they can highlight problems in the curriculum’s density as well as transparently indicate which programming concepts it has successfully observed and learned to recognize. Based on the level of transparency exhibited by Self-Organizing Maps, it is safe to say that they could be used in future research to enhance both human and machine learning of computer programming. By achieving this level of transparency, such an Artificial Intelligence system would be able to assist in overall computer programming education by communicating what should be taught, what needs to be learned, and what is known.

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confidence: 99%

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

2023

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Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions

Chanhemo

Mohsini

Mjahidi

et al. 2023

IJICC

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PurposeThis study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.Design/methodology/approachThe study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.FindingsFollowing the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.Originality/valueThis study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.

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Performance Evaluation of Self-Organising Map Model in Organising the Unstructured Data

You

Tan

Lim

et al. 2022

2022 IEEE International Conference on Computing (ICOCO)

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Preserving the Topology of Self-Organizing Maps for Data Analysis: A Review

Cited by 3 publications

References 7 publications

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions

Performance Evaluation of Self-Organising Map Model in Organising the Unstructured Data

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