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All Journal The Indonesian Journal of Computer Science
Chibaya, Colin
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Computer Science & IT Electrical & Electronics Engineering Engineering

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An Assessment of the Visibility of Particular Swarm Intelligence Technologies in the Resolution of the Object Classification Problem Tsedura, Nyaradzo Alice; Bhero, Ernest; Chibaya, Colin
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4275

Abstract

This article assesses the visibility of five swarm intelligence algorithms in resolving the object classification problem explicitly particle swarm optimization, artificial bee colonies, ant colony optimization, bacterial foraging optimization, and the Social Spider Optimization. 58 articles in total were reviewed and used as the ground on which this assessment was based on. Primarily articles were grouped into two categories namely the articles which directly resolve the object classification problem and those which in directly resolve the object classification problem followed by a further grouping to indicate articles that were directly linked to the object classification problem through swarm technology and finally grouped by the aim. Three aims were observable which are to modify, to improve and to investigate. More than 70% of the articles aimed at either modifying or improving already existing swarm intelligence algorithms. PSO was the most dominant algorithm of the five technologies assessed. Interesting to note was that although all these algorithms were applied there is no formal representation of knowledge in this domain.
Text Prediction Standards for Modelling Under-Resourced Languages: A Shona Case Study Chibaya, Colin
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4369

Abstract

Text prediction is critical in natural language processing aiding as writing assistance. However, under-resourced languages lack support in current text prediction models. We examine research on text prediction standards towards modelling under-resourced languages. To achieve this, 806 studies were scrutinized, out of which 59 remained relevant. Key findings indicate the prevalence of N-gram, BERT, and LSTM models. A gap in the literature was noted, explaining why under-resourced languages are lagging. Precisely, data scarcity and domain specificity are the obstacles to progress in under-resourced language modeling. A potential solution to this challenge is visible, linked to the leverage of transfer learning techniques such as cross-lingual model pre-training. This way, data scarcity issues can be mitigated. We observed that N-gram models stand out in this respect. These are the most used text prediction approaches yielding outstanding perplexity scores. Our Shona text prediction prototype, RNN model achieved 83.69% accuracy with a perplexity score of 4.825. Notably, the N-gram based prototype outperformed the RNN model in all the measured categories. The development of text prediction standards will likely impact text prediction accuracy in under-resourced languages. Hopefully, research on under-resourced languages can draw insights from these standards and explore the development of tailored solutions.
Co-Authors Bhero, Ernest Tsedura, Nyaradzo Alice