Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure

Lü, Yongtao; Huo, Yi; Yang, Zhuoyue; Niu, Yibiao; Zhao, Ming; Босяков, Сергей Михайлович; Li, Lei

doi:10.3389/fbioe.2022.985688

Cited by 7 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, there is currently no consensus on the optimal settings for each parameter within the CNN model. [35] An architecture algorithm, along with a Convolutional Neural Network (CNN), can accurately identify bamboo stem anatomy. The results of various architectures, including ResNet50, ResNet101, and DenseNet201, were compared to assess accuracy.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

Rustandi,

Sony Hartono Wijaya,

Mushthofa

et al. 2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

It is important to note that some species of bamboo are protected and considered endangered. However, distinguishing between traded and protected bamboo species or differentiating between bamboo species for various purposes remains a challenge. This requires specialized skills to identify the type of bamboo, and currently, the process can only be carried out in the forest for bamboo that is still in clump form by experienced researchers or officers. However, a study has been conducted to develop an easier and faster method of identifying bamboo species. The study aims to create an automatic identification system for bamboo stems based on their anatomical structure (ASINABU). The bamboo identification algorithm was developed using macroscopic images of cross-sectioned bamboo stems and the research method used was the convolutional neural network (CNN). CNN was designed to identify bamboo species with images taken using a cellphone camera equipped with a lens. The final product is an Android automatic identification application that can detect bamboo species with an accuracy of 99.9%.

show abstract

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

Rustandi,

Sony Hartono Wijaya,

Mushthofa

et al. 2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

show abstract

“…Therefore, for each data set, it is important to investigate the sensitivity of the parameters in the CNN model to make predictions with high accuracy. To develop any deep learning model, the optimal values of a set of hyperparameters must be decided, such as activation functions, batch size, and learning rate, among others, in order to fine-tune each of these layers [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Effects of Training Parameters of AlexNet Architecture on Wound Image Classification

Eldem¹,

Ülker²,

Işıklı³

2023

View full text Add to dashboard Cite

Deep learning is more extensively used in image analysis-based classification of wounds with an aim to facilitate the monitoring of wound prognosis in preventive treatments. In this paper, the classification success of AlexNet architecture in pressure and diabetic foot wound images is discussed. Optimizing training parameters in order to increase the success of Convolutional Neural Network (CNN) architectures is a frequently discussed problem. This paper comparatively examines the effects of optimization of the training parameters of CNN architecture on classification success. The paper examines how the optimizer algorithm, mini-batch size (MBS), maximum epoch number (ME), learning rate (LR), and LearnRateSchedule (LRS) parameters, which are among the training parameters used in combination in architectural training, perform at different values. The best results were obtained with an accuracy of 95.48% at the 10 e-4 value of the LR parameter. When the changes in the evaluation metrics during the parameter optimization experiments were examined, it was seen that the LR parameter produced optimum values at 10 e-4 . As a result, when the Accuracy metric and standard deviations were examined, it was determined only with the LR parameter. No general conclusion could be reached regarding the other parameters.

show abstract

“…The parameters of each layer of the neural network affect the training speed and prediction ability of the whole CNN, so one of the critical parts of CNN training is the design of optimal parameters. Using optimal parameters saves calculation time and increases model performance [70].…”

mentioning

confidence: 99%

A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes

et al. 2023

View full text Add to dashboard Cite

The visual qualities of the urban environment influence people’s perception and reaction to their surroundings; hence the visual quality of the urban environment affects people’s mental states and can have detrimental societal effects. Therefore, people’s perception and understanding of the urban environment are necessary. This study used a deep learning-based approach to address the relationship between effective spatial criteria and people’s visual perception, as well as spatial modeling and preparing a potential map of people’s visual perception in urban environments. Dependent data on people’s visual perception of Tehran, Iran, was gathered through a questionnaire that contained information about 663 people, 517 pleasant places, and 146 unpleasant places. The independent data consisted of distances to industrial areas, public transport stations, recreational attractions, primary streets, secondary streets, local passages, billboards, restaurants, shopping malls, dilapidated areas, cemeteries, religious places, traffic volume, population density, night light, air quality index (AQI), and normalized difference vegetation index (NDVI). The convolutional neural network (CNN) algorithm created the potential map. The potential visual perception map was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC), with the estimates of AUC of 0.877 and 0.823 for pleasant and unpleasant visuals, respectively. The maps obtained using the CNN algorithm showed that northern, northwest, central, eastern, and some southern areas of the city are potent in pleasant sight, and southeast, some central, and southern regions had unpleasant sight potential. The OneR method results demonstrated that distance to local passages, population density, and traffic volume is most important for pleasant and unpleasant sights.

show abstract

Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure

Cited by 7 publications

References 28 publications

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

Effects of Training Parameters of AlexNet Architecture on Wound Image Classification

A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes

Contact Info

Product

Resources

About