Learning and predicting operation strategies by sequence mining and deep learning

Dörgő, Gyula; Abonyi, János

doi:10.1016/j.compchemeng.2019.06.006

Cited by 23 publications

(8 citation statements)

References 21 publications

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“…Some existing virtual reality systems have already used these human motion postures, such as application of scientific visualization with gestures that represent activities, referential navigation using sign postures, and sign language interpretation [32]. The researchers analyzed the "pointing gestures" in virtual reality [33]. They defined the pointing gestures for processing one or more object selection operations based on the raw data of human motion captured in motion tracking.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Chu

2021

Journal of Sensors

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This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real-time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first-level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete’s gripping style, and the second-level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Chu

2021

Journal of Sensors

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“…The neural network system performs classification operations, and then combines the output results and uses formula (15) to fuse the results, so as to present different academic warnings for students.…”

Section: Research On Early Warning Algorithm Based Onmentioning

confidence: 99%

“…LSTM combines the convolutional neural network, retains the advantages of the LSTM method itself and time-frequency information, which can be used for the prediction of remaining lifespan and help solve health management problems [14]. In the early warning of industrial systems, Dorgo et al used LSTM to predict rare operations in chemical technology when processing the information obtained by data mining, and at the same time analyzed time series information to predict the possible consequences of rare operations [15]. Based on its excellent analytical ability, LSTM is also used in the field of education.…”

Section: Introductionmentioning

confidence: 99%

Research on the Academic Early Warning Model of Distance Education based on Student Behavior Data in the Context of COVID-19

Qu¹,

Sun²,

Liu³

2022

IJACSA

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The COVID-19 epidemic has caused great impact on the entire society, and the spread of novel coronavirus has brought a lot of inconvenience to the education industry. To ensure the sustainability of education, distance education plays a significant role. During the process of distance education, it is necessary to examine the learning situation of students. This study proposes an academic early warning model based on long-and short-term memory (LSTM), which firstly extracts and classifies students' behavior data, and then uses the optimized LSTM to establish an academic early warning model. The precision rate of the optimized LSTM algorithm is 0.929, the recall rate is 0.917 and the F value is 0.923, showing a higher degree of convergence than the basic LSTM algorithm. In the actual case analysis, the accuracy rate of the academic early warning system is 92.5%. The LSTM neural network shows high performance after parameter optimization, and the academic early warning model based on LSTM also has high accuracy in the actual case analysis, which proves the feasibility of the established academic early warning model.

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“…Gao et al (2017) proposed a new machine learning method and applied it to the analysis and management of multimedia data [ 19 ]. Dorgo and Abonyi (2019) studied the learning event sequence of associated events in the database by introducing machine learning algorithms, which provided a direction for the management of industrial system alarms and event logs [ 20 ]. Poh et al (2018) combined machine learning methods and data mining methods to propose a machine learning method for classifying sites based on the safety risks of construction projects; the prediction and severity assessment of accidents proved the effectiveness of the proposed method [ 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

The influence of machine learning-based knowledge management model on enterprise organizational capability innovation and industrial development

Zhou

Liu²,

Hao

et al. 2020

PLoS ONE

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The aims are to explore the construction of the knowledge management model for engineering cost consulting enterprises, and to expand the application of data mining techniques and machine learning methods in constructing knowledge management model. Through a questionnaire survey, the construction of the knowledge management model of construction-related enterprises and engineering cost consulting enterprises is discussed. First, through the analysis and discussion of ontology-based data mining (OBDM) algorithm and association analysis (Apriori) algorithm, a data mining algorithm (ML-AR algorithm) on account of ontology-based multilayer association and machine learning is proposed. The performance of the various algorithms is compared and analyzed. Second, based on the knowledge management level, analysis and statistics are conducted on the levels of knowledge acquisition, sharing, storage, and innovation. Finally, according to the foregoing, the knowledge management model based on engineering cost consulting enterprises is built and analyzed. The results show that the reliability coefficient of this questionnaire is above 0.8, and the average extracted value is above 0.7, verifying excellent reliability and validity. The efficiency of the ML-AR algorithm at both the number of transactions and the support level is better than the other two algorithms, which is expected to be applied to the enterprise knowledge management model. There is a positive correlation between each level of knowledge management; among them, the positive correlation between knowledge acquisition and knowledge sharing is the strongest. The enterprise knowledge management model has a positive impact on promoting organizational innovation capability and industrial development. The research work provides a direction for the development of enterprise knowledge management and the improvement of innovation ability.

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Learning and predicting operation strategies by sequence mining and deep learning

Cited by 23 publications

References 21 publications

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Research on the Academic Early Warning Model of Distance Education based on Student Behavior Data in the Context of COVID-19

The influence of machine learning-based knowledge management model on enterprise organizational capability innovation and industrial development

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