Failure Prediction, Lead Time Estimation and Health Degree Assessment for Hard Disk Drives Using Voting based Decision Trees

Kaur, Kamaljit; Kaur, Kuljit

doi:10.32604/cmc.2019.07675

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…However, choosing an optimum value of k is still a research question [144]. -Decision trees (DT) : The statistical model of DT represents the dataset in the form of tree-like structure [75,92]. The model considers all the possibilities and we can track the path to the decision.…”

Section: Artificial Intelligencementioning

confidence: 99%

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

Shah

Tanwar

et al. 2021

Multimedia Systems

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The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.

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Section: Artificial Intelligencementioning

confidence: 99%

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

Shah

Tanwar

et al. 2021

Multimedia Systems

View full text Add to dashboard Cite

show abstract

“…Rincón et al [23] used a decision tree to predict hard disk failures owing to missing SMART values [24]. Kaur and Kaur [12] introduced a voting-based decision tree classifier to predict HDD failures and an R-CNN-based approach for health status estimation. A prediction model using online random forests (ORFs), which evolve as new HDDs health data arrived, was proposed to achieve online failures prediction for HDD [13].…”

Section: Prediction Of Soon-to-failmentioning

confidence: 99%

“…To improve the performance of HDD failures prediction, many machine-learning-based prediction approaches have been proposed, including Bayesian algorithms [6][7][8][9], support vector machine (SVM) [10], classification tree (CT) [11,12], random forest (RF) [13,14], artificial neural network (ANN) [15], convolution neural network (CNN) [16], and recurrent neural network (RNN) [17,18]. RNN-based prediction models achieve the highest FDRs, and RF-based models attain the lowest FARs.…”

Section: Introductionmentioning

confidence: 99%

Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Shen

Ren

Wan

et al. 2021

Mobile Information Systems

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With the increase in intelligence applications and services, like real-time video surveillance systems, mobile edge computing, and Internet of things (IoT), technology is greatly involved in our daily life. However, the reliability of these systems cannot be always guaranteed due to the hard disk drive (HDD) failures of edge nodes. Specifically, a lot of read/write operations and hazard edge environments make the maintenance work even harder. HDD failure prediction is one of the scalable and low-overhead proactive fault tolerant approaches to improve device reliability. In this paper, we propose an LSTM recurrent neural network-based HDD failure prediction model, which leverages the long temporal dependence feature of the drive health data to improve prediction efficiency. In addition, we design a new health degree evaluation method, which stores current health details and deterioration. The comprehensive experiments on two real-world hard drive datasets demonstrate that the proposed approach achieves a good prediction accuracy with low overhead.

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“…e instructor is responsible for the movement guidance, health care [7][8][9] guidance, and health assessment [10,11] of sports activities and various exercises. Due to the rigorous involvement of sports instructors, athletes can master scientific exercise methods and correct techniques, which eventually and effectively improve the level of exercise and achieve sports training.…”

Section: Introductionmentioning

confidence: 99%

“…At present, the standard of movement is judged by naked eye observation. e shortcomings of this judgment method are low efficiency, too subjective, lack of objectivity, and lack of unified quantitative indicators [10,11].…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Decision Support Model of Sports Training Based on Association Rules

Zhang

Wang

et al. 2021

Mobile Information Systems

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In sports or fitness training, nonstandard movements will affect the training effect and even lead to sports injuries. However, the standard movements of various sports activities need professional guidance, so it is difficult to find out whether the movements are standard or not. In recent years, body pose estimation has become a hot topic in computer vision research. A deep learning model can effectively identify the human nodes and movement trajectory in pictures or videos and evaluate the movements of the target human body. However, the movement process is generally covered by others or the situation of nearby personnel, which leads to the deviation of the movement recognition of the human body and affects the evaluation of the movement. Thus, it is unable to effectively correct the wrong movement, but will mislead the training personnel. Therefore, this paper proposes a novel decision support model for sports training based on association rules. We use posterior probability settings to reveal the weights of the discriminative ability of attribute items and set the classification performance to reflect the weights of three measures to evaluate credit contribution. Thus, the learning threshold setting reflects the weight of the decision-making ability of sports training. Furthermore, compared with traditional association rules, attribute items, frequent item sets, and classification rules that can improve the decision-making performance of sports training are discovered, which complement the deficiencies of different measures. Finally, using the weighted voting strategy to fuse the decision-making information of the classification rules, we can effectively assist in sports training so that the coach can work out corresponding countermeasures and realize scientific management.

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Failure Prediction, Lead Time Estimation and Health Degree Assessment for Hard Disk Drives Using Voting based Decision Trees

Cited by 12 publications

References 18 publications

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Auxiliary Decision Support Model of Sports Training Based on Association Rules

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