Neural Network Estimation for Attenuation Coefficients for Gamma-Ray Angular Distribution

Akkoyun, Serkan; Yıldız, Nihat; Kaya, Hüseyin

doi:10.1134/s1547477119040034

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…It consist of artificial neurons that imitate the biological neuron abilities that exist in the human brain. Artificial neurons are the smallest unit that can store and process information [18]. ANN consist of an input layer, one or more hidden layers, and an output layer.…”

Section: Neural Networkmentioning

confidence: 99%

Powdery Mildew Detection in Hazelnut with Deep Learning

BOYAR

Yildiz

2022

Hittite Journal of Science and Engineering

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Hazelnut cultivation is widely practiced in our country. One of the major problems in hazelnut cultivation is powdery mildew disease on hazelnut tree leaves. In this study, the early detection of powdery mildew disease with the YOLO model based on machine learning was tested on a unique data set. Object detection on the image, which is widely applied in the detection of plant diseases, has been applied for the detection of powdery mildew diseases. According to the results obtained, it has been seen that powdery mildew disease can be detected on the image. In the network trained with the Yolov5 model, diseased areas were detected with 95% accuracy in leaf images containing many diseases. Detection of healthy leaves, on the other hand, was tried on images with complex backgrounds and could detect more than one leaf on an image with 85% accuracy. The Yolov5 model, which has been used in many studies for disease detection on plant leaves, also gave effective results for the detection of powdery mildew disease on hazelnut leaves. Early detection of powdery mildew with a method based on machine learning; will stop the possible spread of disease; It will increase the efficiency of hazelnut production by preventing the damage of hazelnut producers.

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Section: Neural Networkmentioning

confidence: 99%

Powdery Mildew Detection in Hazelnut with Deep Learning

BOYAR

Yildiz

2022

Hittite Journal of Science and Engineering

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“…To develop a simple ANN model for any particular system, one needs to define a topology of the network, the training algorithm as well as activation functions, respectively. In each ANN architecture, the simplest processing element is called an artificial neuron, which imitates the behavior and the functions of biological neurons in the human brain where large amounts of information can be stored and processed simultaneously by neurons (Akkoyun, Yildiz, & Kaya, 2019). ANN topology basically consists of a number of artificial neurons, similar to biological neurons.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Jelatin Çözeltilerinin Dinamik Viskozitesine Yapay Sinir Ağı (YSA) Yaklaşımı: Esnek Hesaplama Çalışması

Demirbay¹,

Karakullukçu²

2020

European Journal of Science and Technology

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In this research, we present a multi-layered feed-forward neural network (ANN) model developed for prediction of dynamic viscosity of aqueous gelatin solutions using experimental data collected from a number of measurements. In ANN architecture, shear stress, shear strain, torque of spindle, the angular velocity of spindle together with mass concentrations of gelatin solutions were introduced as input neurons, whereas dynamic viscosity of aqueous gelatin solutions was assigned as a single output neuron to be predicted. Developed ANN model was trained using backpropagation algorithm optimized with Bayesian regulation. Optimal geometry of the hidden layer was first studied to search out the ANN architecture which yields the most accurate performance results. Mean squared error (MSE), mean absolute error (MAE), root-mean-squared error (RMSE), determination of coefficient ( 2 ), the variance accounted for (VAF) and regression analyses were used as performance assessment parameters for suggested network models. Sensitivity analysis was carried out to investigate the most effective input neuron strongly influencing the performance of the developed ANN model. As a result, the use of 8 neurons in the hidden layer has shown excellent performance results yielding the least MSE and the highest 2 values compared to other suggested ANN models. Upon sensitivity analysis, the shear rate was found to be the most effective input neuron significantly affecting network performance. ANN-based predicted dynamic viscosity values were found to be in excellent agreement with measured viscosity values, demonstrating the robustness as well as the accuracy of the developed ANN model. Developed ANN model can, therefore, be effectively used to predict the dynamic viscosity of aqueous polymer solutions using the same input and output parameters in specific data range reported in this paper with statistical details.

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