Fault Prediction for Software System in Industrial Internet: A Deep Learning Algorithm via Effective Dimension Reduction

Yang, Siqi; Yang, Shuaipeng; Fang, Zigang; Yu, Xiuzhi; Rui, Lanlan; Ma, Yucheng

doi:10.1007/978-981-15-1922-2_40

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Several studies have explored the effectiveness of different approaches in enhancing fault prediction accuracy, precision, recall, and f-measure. Yang et al (2019) [21] proposed a fault prediction model based on the combination of the locally linear embedding (LLE) algorithm and the long short-term memory (LSTM) algorithm. Their model was trained on datasets obtained from NASA's MDP dataset and demonstrated superior performance compared to other existing algorithms.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

Raju

Ramaswamy

Eid

et al. 2023

Sensors

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Artificial intelligence (AI) systems are increasingly used in corporate security measures to predict the status of assets and suggest appropriate procedures. These programs are also designed to reduce repair time. One way to create an efficient system is to integrate physical repair agents with a computerized management system to develop an intelligent system. To address this, there is a need for a new technique to assist operators in interacting with a predictive system using natural language. The system also uses double neural network convolutional models to analyze device data. For fault prioritization, a technique utilizing fuzzy logic is presented. This strategy ranks the flaws based on the harm or expense they produce. However, the method’s success relies on ongoing improvement in spoken language comprehension through language modification and query processing. To carry out this technique, a conversation-driven design is necessary. This type of learning relies on actual experiences with the assistants to provide efficient learning data for language and interaction models. These models can be trained to have more natural conversations. To improve accuracy, academics should construct and maintain publicly usable training sets to update word vectors. We proposed the model dataset (DS) with the Adam (AD) optimizer, Ridge Regression (RR) and Feature Mapping (FP). Our proposed algorithm has been coined with an appropriate acronym DSADRRFP. The same proposed approach aims to leverage each component’s benefits to enhance the predictive model’s overall performance and precision. This ensures the model is up-to-date and accurate. In conclusion, an AI system integrated with physical repair agents is a useful tool in corporate security measures. However, it needs to be refined to extract data from the operating system and to interact with users in a natural language. The system also needs to be constantly updated to improve accuracy.

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Section: Related Workmentioning

confidence: 99%

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

Raju

Ramaswamy

Eid

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, there is a significant lack of studies showing the cost-benefit analysis of their proposed ML techniques, which would be vital for ML-based approaches to be feasible for adaptation in the industry. 190,191,192,193,194,195,196,197,198,199,200,201,202,203…”

Section: Applications Of ML Aiming At Software Maintenancementioning

confidence: 99%

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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Earth Observation Satellite Imagery Information Based Decision Support Using Machine Learning

Ferreira

Silva²,

Iten

2022

Remote Sensing

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This paper presented a review on the capabilities of machine learning algorithms toward Earth observation data modelling and information extraction. The main purpose was to identify new trends in the application of or research on machine learning and Earth observation—as well as to help researchers positioning new development in these domains, considering the latest peer-reviewed articles. A review of Earth observation concepts was presented, as well as current approaches and available data, followed by different machine learning applications and algorithms. Special attention was given to the contribution, potential and capabilities of Earth observation-machine learning approaches. The findings suggested that the combination of Earth observation and machine learning was successfully applied in several different fields across the world. Additionally, it was observed that all machine learning categories could be used to analyse Earth observation data or to improve acquisition processes and that RF, SVM, K-Means, NN (CNN and GAN) and A2C were among the most-used techniques. In conclusion, the combination of these technologies could prove to be crucial in a wide range of fields (e.g., agriculture, climate and biology) and should be further explored for each specific domain.

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Fault Prediction for Software System in Industrial Internet: A Deep Learning Algorithm via Effective Dimension Reduction

Cited by 3 publications

References 9 publications

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Earth Observation Satellite Imagery Information Based Decision Support Using Machine Learning

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