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All Journal Asian Journal of Science, Technology, Engineering, and Art Mikailalsys Journal of Advanced Engineering International
Baba, MA.
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Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences

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Development of Hard Drive Failure Prediction Model for Cloud Platform Using Intelligent Techniques Ahmad, I. I.; Jiya, J. D.; Baba, MA.
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 3 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i3.9186

Abstract

Disk failures in cloud platforms remain a critical reliability concern because they can cause severe data loss, service downtime, and financial losses. This study aims to develop an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based hard drive failure prediction model, investigate the impact of selected Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes on predictive performance, and evaluate ANFIS against existing prediction techniques. A quantitative predictive modeling approach was employed using Backblaze SMART telemetry data, with Recursive Feature Elimination (RFE) applied for feature selection. Eight critical SMART attributes were selected, including reallocated sector count (SMART 5), seek-error rate (SMART 7), and temperature (SMART 231). The proposed ANFIS model achieved 89.4% accuracy, 91.2% precision, 87.8% recall, and an area under the curve (AUC) of 0.934. Comparative results show that ANFIS outperformed Random Forest, Gradient Boosting, Neural Networks, and Support Vector Machines (SVMs) in predictive performance. The study concludes that integrating ANFIS with RFE provides an effective and interpretable approach for hard drive failure prediction in cloud computing environments. These findings contribute to intelligent predictive maintenance research by demonstrating the value of neuro-fuzzy modeling for improving disk failure detection, supporting proactive maintenance, reducing downtime, and enhancing operational reliability in large-scale cloud platforms.
Development of ANFIS-Based Hard Drive Failure Prediction Model for Cloud Platforms Using Intelligent Techniques Ahmad, I. I.; Jiya, J. D.; Baba, MA.
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9185

Abstract

Hard drive failures remain a critical reliability concern in large-scale cloud data centres because they can lead to data loss, service downtime, and increased operational costs. Traditional threshold-based monitoring techniques often fail to capture nonlinear relationships among hard drive health indicators and may produce high false-positive rates. This study presents a conceptual framework for developing an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based hard drive failure prediction model using selected Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes. It further examines the potential impact of key SMART indicators on predictive performance. By integrating fuzzy logic reasoning with neural network learning, the proposed framework is designed to improve predictive accuracy while maintaining interpretability. The study concludes that an ANFIS-based prediction framework can support proactive maintenance strategies for cloud service providers by enabling earlier identification of potential hard drive failures. This framework contributes to the development of intelligent predictive maintenance systems in cloud computing environments and offers practical implications for improving system reliability, reducing downtime, and enhancing operational efficiency.
Co-Authors Ahmad, I. I. Jiya, J. D.