Large group activity security risk assessment and risk early warning based on random forest algorithm

Chen, Yanyu; Zheng, Wenzhe; Li, Wenbo; Huang, Yimiao

doi:10.1016/j.patrec.2021.01.008

Cited by 222 publications

(114 citation statements)

References 9 publications

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“…Process the data transmitted by the network layer and send the results obtained after algorithm analysis to different systems [21], which facilitates the operation and use of end users, including production, management, and maintenance. e application layer is the outermost layer of the three-tier structure of the Internet of ings, and its most important function is processing, which is to use cloud computing platforms for data processing.…”

Section: Application Layermentioning

confidence: 99%

Design of an Intelligent Virtual Classroom Platform for Ideological and Political Education Based on the Mobile Terminal APP Mode of the Internet of Things

Zhang

Gao

et al. 2021

Mathematical Problems in Engineering

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This article uses a software architecture model that combines two architectures, based on the traditional Android environment. Through comparative research on network technology today, Java technology has been selected as a tool for developing network education systems for future research and maintenance upgrades. The underlying network education technology relies on APP to enable education between teachers and students. This technology can not only give play to the advantages of computers in network data transmission, but also embody a new teaching model with teachers and students as the main body. It can solve various practical problems in teacher and student education and educate teachers and students by simulating the actual educational environment of the classroom and educational process using a variety of practical education management strategies. The experimental results prove that with the help of this virtual classroom platform for ideological and political education, it can provide students with more ideological and political learning resources, increase students' interest and time in learning, and promote the diversification of ideological and political learning methods. The quality of teaching is more conducive to the learning of knowledge points for students. At the same time, the system is operating in good condition. Switching between different interfaces is maintained at about 0.1 seconds. The system performance is also perfect. The overall analysis function is basically designed. It is a better learning platform.

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Section: Application Layermentioning

confidence: 99%

Design of an Intelligent Virtual Classroom Platform for Ideological and Political Education Based on the Mobile Terminal APP Mode of the Internet of Things

Zhang

Gao

et al. 2021

Mathematical Problems in Engineering

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show abstract

“…is thesis is based on the analysis and summary of academic research on arid and semiarid mining ecosystems, ecological environment remote sensing monitoring, quantitative ecological evaluation model, ecological restoration of mining areas, and so on [18][19][20][21][22]. It introduces optical remote sensing and radar remote sensing as monitoring means to study the mining ecosystem and evaluate the ecological environment monitoring results of mining areas [23][24][25]. It also uses GIS technology to zone the degree of damage to the ecological environment in the mining area and adopts targeted restoration and treatment strategies for different areas.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Mine Geological Environment Monitoring and Risk Assessment in Arid and Semiarid Areas

et al. 2021

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Along with the accelerated shift of coal mining to the ecologically fragile west, the contradiction between coal resource development and ecological protection in the western arid and semiarid coal mining areas is rapidly intensifying. Based on the above background, this thesis takes the coal mining area in the arid and semiarid regions as an example; applies the theories of ecology, coal mining subsidence, geodesy, and ecological restoration; uses remote sensing in synthetic aperture radar (SAR), geographic information system (GIS), and mathematical modelling to reveal the ecological evolution law of the mining area; measures the ecological damage of the mining area; and then proposes a reasonable ecological restoration strategy. The surface deformation monitoring study in the study area shows that on the whole, some areas in the study area have different degrees of surface subsidence disasters, and the maximum surface subsidence value exceeds 800 mm. From the distribution of surface subsidence in the study area, surface subsidence disasters mainly occur in the eastern and central mountainous areas rich in coal resources, as well as in the mining areas west of the Yellow River, and the subsidence basins are distributed in a series of irregular concentric ovals. In terms of the scale of surface subsidence in the study area, a total of 230.03 km2 of land in the study area showed surface subsidence hazards during the monitoring period, accounting for 13.78% of the total area of the study area, of which the area of severe subsidence was 44.98 km2 (2.69%). The area of more serious subsidence area is 101.33 km2 (6.07%), and the area affected by subsidence is 83.72 km2 (5.01%).

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“…In classification process, existing classification techniques are applied to evaluate the proposed prediction model on the collected QoE instances. We applied machine learning algorithms such as multilayer perceptron (MLP) (Yu et al 2021 ), C4.5 (J48) (Zhang et al 2021 ), KStar classifier (Gholipour et al 2020 ), sequential minimal optimization (SMO) (Wang et al 2021 ), K -nearest neighbors (IBk)(Xu et al 2018 ), Random Forest (Chen et al 2021 ), Naïve Bayes (NB) (Li et al 2021 ) and hybrid J48 + BinarySplites algorithm (Zuo et al 2015 ). In the collected QoE data set, 70% of the instances are divided as train and 30% of them are applied as testing data set.…”

Section: Proposed Qoe-based Prediction Modelmentioning

confidence: 99%

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Tan

Lin

2021

Soft Comput

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Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.

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Large group activity security risk assessment and risk early warning based on random forest algorithm

Cited by 222 publications

References 9 publications

Design of an Intelligent Virtual Classroom Platform for Ideological and Political Education Based on the Mobile Terminal APP Mode of the Internet of Things

Design of an Intelligent Virtual Classroom Platform for Ideological and Political Education Based on the Mobile Terminal APP Mode of the Internet of Things

[Retracted] Mine Geological Environment Monitoring and Risk Assessment in Arid and Semiarid Areas

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

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