Online Failure Prediction Model for Open-Source Software System Based on CNN

Alazawi, Sundos Abdulameer; Abdullah, Mohammed Najm

doi:10.1007/978-981-16-2094-2_70

Cited by 3 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main features are extracted by repeating the convolutional and the pooling layer. After that, the extracted features are passed to the fully connected layer [19], [20]. Figure 2 presents the CNN structures.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network

Abood

Nasser

Al-Khazraji

2023

IJ-AI

View full text Add to dashboard Cite

<p>Predictive maintenance (PdM) is a cost-cutting method that involves avoiding breakdowns and production losses. Deep learning (DL) algorithms can be used for defect prediction and diagnostics due to the huge amount of data generated by the integration of analog and digital systems in manufacturing operations. To improve the predictive maintenance strategy, this study uses a hybrid of the convolutional neural network (CNN) and conditional generative adversarial neural network (CGAN) model. The proposed CNN-CGAN algorithm improves forecast accuracy while substantially reducing model complexity. A comparison with standalone CGAN utilizing a public dataset is performed to evaluate the proposed model. The results show that the proposed CNN-CGAN model outperforms the conditional GAN (CGAN) in terms of prediction accuracy. The average F-Score is increased from 97.625% for the CGAN to 100% for the CNN-CGAN.</p>

show abstract

Section: Convolutional Neural Networkmentioning

confidence: 99%

Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network

Abood

Nasser

Al-Khazraji

2023

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Computers that have the capability to mimic human thought and reason are considered a part of the larger field of Artificial Intelligence (AI), which is recognized in the computer science world. AI assists machines in mimicking human cognitive abilities by learning to build recommender systems [1], [2], prediction [3], [4], [5], identification [6], recognition [7], [8], [9], and decision-making ability. It is widely agreed that artificial intelligence has greatly boosted efficiency in several industries, and computers utilizing AI are becoming increasingly popular in the problem-solving arena [10].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Predicting Airfare Prices Using Machine Learning

2022

IJCCCE

View full text Add to dashboard Cite

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Trees (DT), K-nearest neighbor (KNN), and Logistic Regression (LR), have been used to identify the parameters that allow for effective price estimation. These approaches were tested on a data set of an extensive Indian airline network. When it came to estimating flight prices, the results demonstrate that the Decision tree method is the best conceivable Algorithm for predicting the price of a flight in our particular situation with 89% accuracy. The SGD method had the lowest accuracy, which was 38 %, while the accuracies of the KNN, NB, ADA, and LR algorithms were 69 %, 45 %, and 43 %, respectively. This study's presented methodologies will allow airline firms to predict flight prices more accurately, enhance air travel, and eliminate delay dispersion. Index Terms— Machine learning, Prediction model, Airline price prediction, Software testing,

show abstract

“…Predictive decision utilizes predictive tools to decide when maintenance steps are important before the failure occurs [8]. It depends on real-time monitoring of machines and letting maintenance be achieved when it is required.…”

Section: Introductionmentioning

confidence: 99%

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

Abood¹,

Nasser²,

Al-Khazraji³

2022

ISI

View full text Add to dashboard Cite

Predictive Maintenance (PM) is a major part of smart manufacturing in the fourth industrial revolution. The classical fault diagnosis approach for complex systems such as an electromechanical system is not effective. Taking the advantage of the successful implementations of PM together with Deep Learning (DL) methods replaces the conventional diagnosis methods with modern diagnosis methods. This study intends to aid experts, engineers, and technicians in different electromechanical systems in comprehending how the PM used DL methods to find the multi-fault diagnosis. In this direction, this paper presents a comprehensive review of recent works of DL techniques that are applied to PM for electromechanical systems by classifying the research according to equipment, fault, parameters, and method. To perform the review, 30 papers that are published in proceedings and journals are reviewed within a time window between the years 2016 to 2022. In the context of the electromechanical system, it is observed that motors are the most equipment selected for PM. Moreover, stator winding faults are found to be less selected than bearing for diagnosis of the unhealthy status of the motor. In terms of DL methods, the study reveals that AE, LSTM, and CNN are mostly used. In addition, poorly mixed models of DL methods are also noticed. Finally, finding the optimal design variable of the DL architecture was not widely explored.

show abstract

Online Failure Prediction Model for Open-Source Software System Based on CNN

Cited by 3 publications

References 14 publications

Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network

Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network

A Framework for Predicting Airfare Prices Using Machine Learning

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

Contact Info

Product

Resources

About