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A summary of approaches to identify hard disk failure through the utilization of machine learning algorithms | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 4، دوره 15، شماره 7، مهر 2024، صفحه 28-32 اصل مقاله (256.78 K) | ||
| نوع مقاله: Review articles | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.31039.4551 | ||
| نویسندگان | ||
| Somayeh Askarpour؛ Maryam Saberi Anari* | ||
| Department of Computer Engineering, Technical and Vocational University (TVU),Tehran, Iran | ||
| تاریخ دریافت: 04 فروردین 1402، تاریخ بازنگری: 03 تیر 1402، تاریخ پذیرش: 16 تیر 1402 | ||
| چکیده | ||
| This article delves into the techniques employed for identifying failures in hard disks through the utilization of machine learning algorithms. Hard disks serve as essential components within computer systems, and as they age and undergo repetitive usage, they may manifest indications of failure or inadequate performance, culminating in data loss and system malfunction. Consequently, the early detection and anticipation of hard disk failures are of utmost significance. Recent advancements in machine learning methods have enabled the precise detection of hard disk failures within a short timeframe. Within this investigation, we explore the foundational concepts pertaining to hard disks and their failures. We scrutinize various machine learning algorithms employed for the detection of hard disk failures. Furthermore, we introduce performance evaluation metrics for failure detection models. The challenges and limitations in the detection of hard disk failures are discussed, along with potential strategies for enhancing system performance and accuracy. | ||
| کلیدواژه‌ها | ||
| Hard drives؛ failures؛ identification؛ machine learning؛ deep neural networks | ||
| مراجع | ||
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