Network mining techniques to analyze the risk of the occupational accident via bayesian network

Nayak, Nihar Ranjan; Kumar, Sumit; Gupta, Deepak; Suri, Ashish; Naved, Mohd; Soni, Mukesh

doi:10.1007/s13198-021-01574-1

Cited by 50 publications

(11 citation statements)

References 27 publications

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“…Hoogesteger and Rivara think that farmers run, economic crimes, death/ escape/kidnapping of the head of the mutual aid society, natural disasters that cause the cooperative to fail to operate normally, abnormal computer network paralysis, mass petitions and demonstrations, and other cases that may cause great harm are the risks and unexpected risk events of mutual funds [17]. Nayak et al think that whether to develop big cities, medium cities, small cities, or small towns should be mainly regulated by the market, and the government can only guide them [18]. Delcroix believes that the development direction of urbanization in China is the development of medium-sized cities in urban circle, and at the same time, the further development of medium-sized cities will form a new urban circle [19].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Farmer Relocation Selection Behavior Based on Bayesian Network

Guo

2022

Journal of Function Spaces

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The study of farmers’ migration choice behavior can reflect people’s needs for the future living environment from the side and has practical and important guiding significance for adjusting and improving the focus of future development. In order to make a detailed analysis of the prediction of migrant workers’ behavior in cities, this paper studies the internal mechanism of the change of commuters’ commuting patterns under the background of urban suburbanization. Using Bayesian network, this paper establishes a model of commuting mode transfer of urban immigrants, taking personal and family attributes as control variables. The influence of factors such as migration attributes and changes in building environment perception before and after migration is analyzed. Based on the survey data, a Bayesian network of farmers’ land use behavior is established by using Bayesian network method to investigate the choice of farmers’ migration direction. In the research process of migration direction, the research structure shows that 60.1% of farmers want to stay in the countryside, rather than migrate through Bayesian network algorithm. The results of regression analysis by establishing a multivariate logistic model show that there is a strong willingness to choose small towns and large cities. However, the willingness to choose not to relocate to local villages is relatively weak.

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Section: Related Workmentioning

confidence: 99%

Analysis of Farmer Relocation Selection Behavior Based on Bayesian Network

Guo

2022

Journal of Function Spaces

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“…It then uses a support vector machine to build a classification model to detect equipment failures. Nayak et al [ 12 ] proposed a transfer factor analysis algorithm (TCA) based on the Gaussian latent factor model for feature extraction, and this basis trained a linear classifier for fault detection. Lang et al [ 13 ] proposed a method of mixing machine learning models, that is, constructing two machine learning models simultaneously, and the correct output of one model is used to train the other machine learning models to train a higher-precision classifier to achieve the purpose of fault detection.…”

Section: Related Workmentioning

confidence: 99%

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

Soni

Khan

Baseer

et al. 2022

Computational Intelligence and Neuroscience

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Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model’s online detection and update. The method’s efficacy is demonstrated by experiments based on real data from power plant sensors.

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“…It is the platform where the expectations of employees and organizations both get their way (Walby, Lippert &Luscombe 2020). Two valuable assets like time and efforts both have been secured with the operations of accountability (Nayak et al, 2022). Sometimes volatile performances make the barriers for time and effort suitable for the corporate sector.…”

Section: Identification Of the Need Of Accountability In Corporate Or...mentioning

confidence: 99%

“…Table2: various aspects in accountability mindset (Source: Nayak et al, 2022) This procedure appears to have gone quickest in the U.S., where banks have expected to adjust to expansive changes that in a few cases go past the multilateral motivation, as required by Dodd-Frank enactment, and have balanced their business models in like manner. In the meantime, U.S. banks have too possessed the capacity to raise capital and re-establish productivity, and bank loaning conditions have been great, especially concerning the Euro Area, since 2012.…”

Section: Identification Of the Need Of Accountability In Corporate Or...mentioning

confidence: 99%