Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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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%.
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): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 132
Country: United Kingdom
Subject: Global Journal of Computer Science and Technology - D: Neural & AI
Authors: Olayemi Olasehinde, Kayode Tolulope Miracle (PhD/Dr. count: 0)
View Count (all-time): 287
Total Views (Real + Logic): 1306
Total Downloads (simulated): 22
Publish Date: 2026 01, Fri
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Neural Networks and Rules-based Systems used to Find Rational and
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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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