The Use of an Internet of Things Data Management System Using Data Mining Association Algorithm in an E-Commerce Platform

Wang, Guan; Zhang, Xuan; Gao, Yifan; Yee, Austin Lin; Wang, Xue

doi:10.4018/joeuc.322553

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…These studies demonstrate the effectiveness of machine-learning techniques for predictive modeling tasks. Similarly, machine-learning methods, such as the support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbors (KNN) have been commonly employed by researchers for fatigue detection using EEG signals [10][11][12]. However, more recently, deeplearning methods have gained prominence over traditional machine-learning methods in fatigue detection from EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Xu,

Li,

Feng

2023

Sensors

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Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes. Examining the detection of miner fatigue is important because it can potentially prevent work accidents and improve working efficiency in underground coal mines. Many previous studies have introduced feature-based machine-learning methods to estimate miner fatigue. This work proposes a method that uses electroencephalogram (EEG) signals to generate topographic maps containing frequency and spatial information. It utilizes a convolutional neural network (CNN) to classify the normal state, critical state, and fatigue state of miners. The topographic maps are generated from the EEG signals and contrasted using power spectral density (PSD) and relative power spectral density (RPSD). These two feature extraction methods were applied to feature recognition and four representative deep-learning methods. The results showthat RPSD achieves better performance than PSD in classification accuracy with all deep-learning methods. The CNN achieved superior results to the other deep-learning methods, with an accuracy of 94.5%, precision of 97.0%, sensitivity of 94.8%, and F1 score of 96.3%. Our results also show that the RPSD–CNN method outperforms the current state of the art. Thus, this method might be a useful and effective miner fatigue detection tool for coal companies in the near future.

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Section: Introductionmentioning

confidence: 99%

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Xu,

Li,

Feng

2023

Sensors

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“…Then, the feature selection technology is used to screen out the features that have important influence on the ability evaluation, and the corresponding ability evaluation model is constructed. Finally, the cross-validation method is used to verify and evaluate the model (Wang et al, 2023).…”

mentioning

confidence: 99%

Construction of Human Resource Manager Competency Model Based on Data Mining Technology in Cross-Cultural Background

Yang,

Shi,

Song

et al. 2024

International Journal of Information Technologies and Systems Approach

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In the era of knowledge economy, enterprises are facing unprecedented competition and challenges. The process of globalization has accelerated the communication and integration between different cultures, making cross-cultural management an indispensable ability for enterprises. Especially in the field of human resource management, it is of great significance to build a competency model based on data mining technology to improve the cross-cultural communication ability, teamwork ability, and innovation ability of enterprise managers. The application of competency theory in the field of human resource management has the potential to completely revolutionize traditional management practices, enabling better adaptation to the continuous changes in the management environment. It plays a significant role in promoting the reform of the human resource management system. The aim of this paper is to build a competency model of human resource managers based on data mining technology and verify its effectiveness in cross-cultural background. It is assumed that the model can accurately evaluate the ability level of human resource managers and provide reference for enterprises to select and train outstanding talents. The experimental results demonstrate that the model achieves an accuracy of approximately 89%, a 93.3% improvement in efficiency, and a salary range exceeding 90%. The model is deemed reasonable, feasible, and highly precise in terms of competency assessment.

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Big data in relation with business intelligence capabilities and e-commerce during COVID-19 pandemic in accountant’s perspective

Huy,

Phuc

2023

Futur Bus J

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This research aims at producing a thorough and accurate analysis on how big data as a service and business intelligence capabilities provide a series of opportunities for small and medium enterprises to succeed in E-commerce. The statistical data were drawn from samples of accountants within small and medium enterprises, spanning two periods, the period of COVID-19 pandemic and the period of new normal. The structural equation modeling was applied in this study. The value of fine-grained insights in this work could guide policy-makers and practitioners to sense and seize how to implement E-commerce in an efficient and effective manner.

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The Use of an Internet of Things Data Management System Using Data Mining Association Algorithm in an E-Commerce Platform

Cited by 4 publications

References 36 publications

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Construction of Human Resource Manager Competency Model Based on Data Mining Technology in Cross-Cultural Background

Big data in relation with business intelligence capabilities and e-commerce during COVID-19 pandemic in accountant’s perspective

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