Development of Expert System for the Diagnosis of Computer System Startup Problems.

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Olayemi Olasehinde
Olayemi Olasehinde
2
Kayode Tolulope Miracle
Kayode Tolulope Miracle

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In the rapidly evolving realm of computer technology, seamless system start-up is crucial for maintaining operational efficiency and minimizing downtime. This study introduces an expert system develoed to diagnose and resolve computer system startup problems effectively. Using a combination of artificial intelligence (AI) techniques and a detailed knowledge base, the system aims to replicate human expert decision-making in troubleshooting. Initial testing involved a dataset of 96 cases, with the system achieving an accuracy and precision of 92.71%, and a recall of 93.68%. Subsequent refinement of the system was evaluated on an expanded dataset of 246 cases, resulting in improved metrics: an accuracy of 98.78%, precision of 99.17%, and a perfect recall of 100%.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Olayemi Olasehinde. 2026. \u201cDevelopment of Expert System for the Diagnosis of Computer System Startup Problems.\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 24 (GJCST Volume 24 Issue D1): .

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AI diagnostic computer startup system for diagnosing computer issues. Innovative system boosts efficiency and accuracy.
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GJCST Volume 24 Issue D1
Pg. 27- 35
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

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August 28, 2024

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English

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In the rapidly evolving realm of computer technology, seamless system start-up is crucial for maintaining operational efficiency and minimizing downtime. This study introduces an expert system develoed to diagnose and resolve computer system startup problems effectively. Using a combination of artificial intelligence (AI) techniques and a detailed knowledge base, the system aims to replicate human expert decision-making in troubleshooting. Initial testing involved a dataset of 96 cases, with the system achieving an accuracy and precision of 92.71%, and a recall of 93.68%. Subsequent refinement of the system was evaluated on an expanded dataset of 246 cases, resulting in improved metrics: an accuracy of 98.78%, precision of 99.17%, and a perfect recall of 100%.

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Development of Expert System for the Diagnosis of Computer System Startup Problems.

Olayemi Olasehinde
Olayemi Olasehinde
Kayode Tolulope Miracle
Kayode Tolulope Miracle

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