AI Powered Server Log Management System Using Random Forest Model

As technology continues to advance, system errors have become more frequent, and users often struggle to find the right solutions. Traditionally, users had to search across multiple servers for answers, which often resulted in a flood of responses, making it difficult to identify the correct one. This process can be time-consuming and frustrating. To address this issue, we've created a solution that collects error logs from all servers and utilizes Artificial Intelligence (AI) to build a smart system. By training the AI model on past error questions and their solutions, the system can now analyse a user’s error and predict a suitable solution. This means users no longer have to search through various sources. Instead, they can get a quick and accurate answer from a single, centralized server. This AI-powered system simplifies error resolution by providing more direct, relevant answers. Unlike Google or Bing, which may return too many irrelevant results, our AI ensures users receive the most precise solution to their specific problem. The system is built using a carefully selected dataset containing questions and answers related to operating systems (OS), programming issues, and other technical problems. Over time, with the addition of new questions and answers from users, the AI will learn and improve, solving an even wider variety of errors. A key advantage of this system is that the dataset can be continuously updated with new information from users. This ensures that the AI model remains current and is capable of addressing a broader range of errors. Whether the issue relates to OS problems, software bugs, or network errors, the AI will keep getting better, making it a valuable tool for troubleshooting

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Publication Details

Journal
Global Journal of Engineering Innovations and Interdisciplinary Research
Published
2025-06-27
DOI
https://doi.org/10.33425/3066-1226.1144
Primary Topic
Advanced Technology in Applications
Type
article
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article

AI Powered Server Log Management System Using Random Forest Model

Steffy Sherly P, Yamini Ch, Aditya Ram P, Vinay Kumar T
Global Journal of Engineering Innovations and Interdisciplinary Research
Advanced Technology in Applications
article

AI Powered Server Log Management System Using Random Forest Model

Steffy Sherly P, Yamini Ch, Aditya Ram P, Vinay Kumar T
article en

Abstract

As technology continues to advance, system errors have become more frequent, and users often struggle to find the right solutions. Traditionally, users had to search across multiple servers for answers, which often resulted in a flood of responses, making it difficult to identify the correct one. This process can be time-consuming and frustrating. To address this issue, we've created a solution that collects error logs from all servers and utilizes Artificial Intelligence (AI) to build a smart system. By training the AI model on past error questions and their solutions, the system can now analyse a user’s error and predict a suitable solution. This means users no longer have to search through various sources. Instead, they can get a quick and accurate answer from a single, centralized server. This AI-powered system simplifies error resolution by providing more direct, relevant answers. Unlike Google or Bing, which may return too many irrelevant results, our AI ensures users receive the most precise solution to their specific problem. The system is built using a carefully selected dataset containing questions and answers related to operating systems (OS), programming issues, and other technical problems. Over time, with the addition of new questions and answers from users, the AI will learn and improve, solving an even wider variety of errors. A key advantage of this system is that the dataset can be continuously updated with new information from users. This ensures that the AI model remains current and is capable of addressing a broader range of errors. Whether the issue relates to OS problems, software bugs, or network errors, the AI will keep getting better, making it a valuable tool for troubleshooting

Global Journal of Engineering Innovations and Interdisciplinary ResearchVol. 5(4)
Indian Institute of Technology Hyderabad (IN)
Openalex Percentile: Top 73%
Advanced Technology in Applications
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AI Powered Server Log Management System Using Random Forest Model — Steffy Sherly P, Yamini Ch, et al. · Global Journal of Engineering Innovations and Interdisciplinary Research (2025) | TGRS Research Map | TGRS