Efficiency of convolutional neural networks (CNN) based image classification for monitoring construction related activities: A case study on aggregate mining for concrete production

Yeşilmen, Seda; Tatar, Bahadır

doi:10.1016/j.cscm.2022.e01372

Cited by 13 publications

(7 citation statements)

References 33 publications

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“…The results showed very good accuracy. It is clear that the prediction of concrete properties can be efficiently performed using machine learning technology [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

et al. 2022

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In the 21st century, numerous numerical calculation techniques have been discovered and used in several fields of science and technology. The purpose of this study was to use an artificial neural network (ANN) to forecast the compressive strength of waste-based concretes. The specimens studied include different kinds of mineral additions: metakaolin, silica fume, fly ash, limestone filler, marble waste, recycled aggregates, and ground granulated blast furnace slag. This method is based on the experimental results available for 1303 different mixtures gathered from 22 bibliographic sources for the ANN learning process. Based on a multilayer feedforward neural network model, the data were arranged and prepared to train and test the model. The model consists of 18 inputs following the type of cement, water content, water to binder ratio, replacement ratio, the quantity of superplasticizer, etc. The ANN model was built and applied with MATLAB software using the neural network module. According to the results forecast by the proposed neural network model, the ANN shows a strong capacity for predicting the compressive strength of concrete and is particularly precise with satisfactory accuracy (R² = 0.9888, MAPE = 2.87%).

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“…The results showed very good accuracy. It is clear that the prediction of concrete properties can be efficiently performed using machine learning technology [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

et al. 2022

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“…Finally, all results will be compiled to assess and evaluate which architecture provides a better performance. This structured approach enabled a comprehensive understanding of the CNN model's behavior and performance, facilitating iterative improvements toward enhanced accuracy and interpretability in image classification tasks [46].…”

Section: Methodsmentioning

confidence: 99%

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

Prianes,

Fortuno,

Onesa

et al. 2024

IJACSA

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In the era of rapid technological advancement, the integration of cutting-edge technologies plays a pivotal role in enhancing the efficiency and responsiveness of critical systems. iRESPOND, a real-time Geospatial Information and Alert System, stands at the forefront of such innovations, facilitating timely and informed decision-making in dynamic environments. As the demand for accurate and swift responses, the role of CNN models in iRESPOND becomes significant. The study focuses on seven prominent CNN architectures, namely EfficientNet (B0, B7, V2B0, and V2L), InceptionV3, ResNet50, and VGG19 and with the integration of different optimizers and learning rates. The methodology employed a strategic implementation of looping during the training phase. This iterative approach is designed to systematically re-train the CNN models, emphasizing identifying the most suitable architecture among the seven considered variants. The primary objective is to discern the optimal architecture and fine-tune critical parameters, explicitly targeting the optimizer and learning rate values. The differential impact of each model on the system's ability is to discern patterns and anomalies in the image datasets. ResNet50 exhibited robust performance showcasing suitability for real-time processing in dynamic environments with a better accuracy result of 95.02%. However, the EfficientNetV2B0 model, characterized by its advancements in network scaling, presented promising results with a lower loss of 0.187. Generally, the findings not only contribute valuable insights into the optimal selection of architectures for iRESPOND but also highlight the importance of fine-tuning hyperparameters through an iterative training approach, which paves the way for the continued enhancement of iRESPOND as an adaptive system.

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“…It is a neural network architecture that aims to improve the work of CNN. EfficientNet itself makes the model more efficient in terms of the number of parameters and computational costs [19]. EfficientNet uses a combination of depthbased scaling and convolution, which allows it to achieve good performance with fewer parameters as depicted in figure 4.…”

Section: Efficientnetb0mentioning

confidence: 99%

Skin Cancer Detection Approach Using Convolutional Neural Network Artificial Intelligence

Hayat

2024

IJIIS Int. J. Informatics Inf. Syst.

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Skin cancer is a type of cancer that can cause death, where skin cancer is included in the 15 common cancers that occur in Indonesia. The number of skin cancer sufferers was around 6,170 cases of non-melanoma skin cancer and 1,392 cases of melanoma skin cancer in 2018 in Indonesia. Therefore, research related to skin cancer classification is increasing. This is done as an initial step in detecting whether a lesion can be said to be cancerous or not. The deep learning approach has certainly shown promising results in carrying out classification, so this research proposes a deep learning-based method used for skin cancer classification. The proposed approach involves Convolutional Neural Networks with the ISIC 2017 dataset. The models used for skin cancer classification are InceptionV3, EfficientNetB0, ResNet50, MobileNetV2, and NASNetMobile. The highest accuracy of the single model produced reached 69.3% using the MobileNetV2 model. An ensemble model combining the five models was also tested and produced the highest accuracy compared to other single models with an accuracy result of 80.6%.

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Efficiency of convolutional neural networks (CNN) based image classification for monitoring construction related activities: A case study on aggregate mining for concrete production

Cited by 13 publications

References 33 publications

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

Skin Cancer Detection Approach Using Convolutional Neural Network Artificial Intelligence

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