A COMPARISON OF MACHINE LEARNING ALGORITHMS EFFICIENCY FOR HARD DISK DRIVE FAILURE DETECTION

2016 ◽  
Vol 100 (11) ◽  
pp. 1933-1940
Author(s):  
A. V. Vladimirov ◽  
A. A. Krutikov
Keyword(s):  
Machine Learning ◽  
Hard Disk Drive ◽  
Learning Algorithms ◽  
Hard Disk ◽  
Failure Detection ◽  
Disk Drive ◽  
Machine Learning Algorithms

Fourier-assisted machine learning of hard disk drive access time models

2013 ◽  
Author(s):  
Adam Crume ◽  
Carlos Maltzahn ◽  
Lee Ward ◽  
Thomas Kroeger ◽  
Matthew Curry ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hard Disk Drive ◽  
Hard Disk ◽  
Disk Drive ◽  
Access Time

scholarly journals Heart Failure Detection Using Quantum-Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yogesh Kumar ◽  
Apeksha Koul ◽  
Pushpendra Singh Sisodia ◽  
Jana Shafi ◽  
Verma Kavita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Random Forest ◽  
Learning Algorithms ◽  
Failure Detection ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Research Progress ◽  
Record Management

Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.

scholarly journals Feature Selection to Improve Performance of Yield Prediction in Hard Disk Drive Manufacturing

2020 ◽  
pp. 420-428
Author(s):  
Anusara Hirunyawanakul ◽  
◽  
Nuntawut Kaoungku ◽  
Nittaya Kerdprasop ◽  
Kittisak Kerdprasop
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Problem Solving ◽  
Hard Disk Drive ◽  
Information Gain ◽  
Hard Disk ◽  
Disk Drive ◽  
Absolute Error ◽  
Support Vector ◽  
Yield Prediction

Hard Disk Drive (HDD) manufacturing is one real-world application area that machine learning has been extensively adopted for problem solving. However, most problem solving activities in HDD industry tackle on failure root-cause analysis task. Machine learning is rarely applied in a task of yield prediction. This research presents the application of machine learning and statistical techniques to select appropriate features to be used in yield prediction for the HDD manufacturing process. The seven well-known algorithms are used in the feature selection step. These algorithms are decision tree (C5 and CART), Support Vector Machine (SVM), stepwise regression, Genetic Algorithm (GA), chi-square and information gain. The two prominent learning algorithms, Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), are used in the yield prediction modeling step. Yield prediction performance has been assessed based on the two evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Yield prediction with MLR shows higher accuracy than yield estimation traditionally performed by human engineers. Resulting to conclusion that the proposed novel learning steps can help HDD process engineers to predict yield with the better performance, especially on applying GA as feature selection tool, the MAE is reduced from 0.014 (yield estimated by human engineer) to 0.0059 (yield predicted by MLR). That means error reduction is about 60%.

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