Random Forest Feature Selection and Back Propagation Neural Network to Detect Fire Using Video

Liang, Jin-xing; Zhao, Jun; Sun, Ning; Shi, Baohui

doi:10.1155/2022/5160050

Cited by 6 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The machine learning method [20], the PDE model [21], system dynamics [22], the exponential smoothing forecasting model [23], the grey model [24], random forest [25][26][27][28], and neural network [29][30][31][32] are the main tools used in population forecasting research. The majority of scholars utilize gray models, random forests, and neural networks [33].…”

Section: Forecast Methodsmentioning

confidence: 99%

Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models

Zhao

Feng

2022

Sustainability

View full text Add to dashboard Cite

The economic development of various regions is influenced by high-quality population mobility. The research object of this article is the employment mobility data of “Double First-Class” university graduates from 2014 to 2019; the subsequent analysis is based on these data. First, this paper summarizes the current state of university graduates’ employment mobility. Second, this paper employs the fixed-effect model and PCA method to conclude that economic factors are the primary factors influencing university graduates’ employment mobility. Finally, based on the nonlinear, small sample, and high-dimensional characteristics of university graduates’ employment mobility data, this paper employs the random forest and BP neural network methods to build a prediction model for university graduates’ employment retention rate. The results show that the BP neural network model outperforms the random forest model in terms of prediction accuracy. The BP neural network model can accurately predict the employment retention rate of “Double First-Class” university graduates, which can guide the reasonable mobility of university graduates and provide a reference for government universities and individuals to make decisions.

show abstract

Section: Forecast Methodsmentioning

confidence: 99%

Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models

Zhao

Feng

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…e most famous supervised algorithm is the error back propagation (BP) algorithm of a multilayer feedforward artificial neural network [17][18][19][20]. Structurally speaking, the network is composed of layers or layers above, that is the input layer, the hidden layer, and the output layer.…”

Section: Evaluation System Of the Green Space System Planningmentioning

confidence: 99%

Urban Green Space Planning and Design for Sponge City

Hong

Han

2022

Scientific Programming

View full text Add to dashboard Cite

Urban green space designed based on the concept of sponge city is an important sponge carrier. Guided by the theory of landscape ecology, this article introduces the artificial neural network (ANN) model and uses the research method of landscape pattern index to establish a model for the proportion of urban green space types and the characteristics and dynamic changes of landscape spatial pattern. Based on the optimal landscape pattern index, the theoretical value of the optimal proportion of ecological green space, community park, comprehensive park, roadside green space, protective green space, and strip green space is obtained through the model. Based on this proportion value, combined with the ANN model, the reasonable evaluation results of patch density and average perimeter area ratio, spread degree, and diversity index of planned green space are obtained. Then, according to the influence of various green spaces on each landscape index, the development trend of urban green space construction in the future is predicted.

show abstract

“…The phenomenon of images degradation from fire scenarios is usually caused by the large number of suspended particles generated during combustion. When executing the robot rescue in such scenes, the quality of the images collected from the fire scenarios will be seriously affected [ 1 ]. For example, most of the current research in the computer vision community is based on the assumption that the input datasets are clear images or videos.…”

Section: Introductionmentioning

confidence: 99%

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

Wang

Luo

et al. 2022

Sensors

View full text Add to dashboard Cite

The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE’20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin.

show abstract

Random Forest Feature Selection and Back Propagation Neural Network to Detect Fire Using Video

Cited by 6 publications

References 24 publications

Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models

Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models

Urban Green Space Planning and Design for Sponge City

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

Contact Info

Product

Resources

About