Comparison of CPU Damage Prediction Accuracy Between Certainty Factor and Forward Chaining Techniques
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Abstract
The CPU plays a vital role in determining the performance of a computer system in contemporary computing. If the CPU sustains damage, it may result in significant interruption to the computer's functioning. This study presents a computational technique that aims to enhance the accuracy of CPU damage predictions. The system utilises fundamental knowledge of damage diagnosis and is validated via evaluating 11 early damage symptoms that are often seen. The Certainty Factor and Forward Chaining approaches ascertain CPU damage by quantifying the degree of truth in the expert's opinion conclusions via a comparison of the symptoms of harm. The second algorithm assesses the confidence level in a development by considering the weight assigned to the system by two parties: the user and the expert. The suggested algorithm yields the mean accuracy of the certainty factor approach in diagnosing computer damage utilising the constructed system. The diagnostic system has a precision rate of 84.9%, indicating that 9 out of 10 diagnoses made by the system align with those made by an expert. Next, the outcomes of the forward chaining algorithm test. All questions about symptoms were answered affirmatively, except for one test which had a negative response. A total of 39 diagnoses were obtained, with an average value of 82.9%. The study findings indicate that the suggested confidence factor method is more suited for use in embedded systems or web-based applications, however it is constrained by low processing.
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