Deep learning audio magnetotellurics inversion using residual-based deep convolution neural network

2021 ◽  
pp. 104309
Author(s):  
Zhengguang Liu ◽  
Huang Chen ◽  
Zhengyong Ren ◽  
Jingtian Tang ◽  
Zhimin Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network

scholarly journals A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

2021 ◽  
Author(s):  
Neeraj Kumar Rathore ◽  
Varshali Jaiswal ◽  
Varsha Sharma ◽  
Sunita Varma
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Deep Learning ◽  
Computation Time ◽  
Detection Algorithm ◽  
Convolution Neural Network ◽  
Color Model ◽  
Blue Color ◽  
Deep Convolution Neural Network ◽  
Hybrid Methodology

Abstract Deep-Convolution Neural Network (CNN) is the branch of computer science. Deep Learning CNN is a methodology that teaches computer systems to do what comes naturally to humans. It is a method that learns by example and experience. This is a heuristic-based method to solve computationally exhaustive problems that are not resolved in a polynomial computation time like NP-Hard problems. The purpose of this research is to develop a hybrid methodology for the detection and segmentation of flower images that utilize the extension of the deep CNN. The plant, leaf, and flower image detection are the most challenging issues due to a wide variety of classes, based on their amount of texture, color distinctiveness, shape distinctiveness, and different size. The proposed methodology is implemented in Matlab with deep learning Tool Box and the dataset of flower image is taken from Kaggle with five different classes like daisy, dandelion, rose, tulip, and sunflower. This methodology takes an input of different flower images from data sets, then converts it from RGB (Red, Green, Blue) color model to the L*a*b color model. L*a*b has reduced the effort of image segmentation. The flower image segmentation has been performed by the canny edge detection algorithm which provided better results. The implemented extended deep learning convolution neural network can accurately recognize varieties of flower images. The learning accuracy of the proposed hybrid method is up to 98% that is maximizing up to + 1.89% from state of the art.

scholarly journals Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon

Horticulturae ◽  
2021 ◽  
Vol 7 (11) ◽  
pp. 489
Author(s):  
Liying Chang ◽  
Daren Li ◽  
Muhammad Khalid Hameed ◽  
Yilu Yin ◽  
Danfeng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Nitrogen Fertilizer ◽  
Precision Agriculture ◽  
Short Term Memory ◽  
Nitrogen Nutrition ◽  
Nitrogen Concentration ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network ◽  
Nutrition Diagnosis

In precision agriculture, the nitrogen level is significantly important for establishing phenotype, quality and yield of crops. It cannot be achieved in the future without appropriate nitrogen fertilizer application. Moreover, a convenient and real-time advance technology for nitrogen nutrition diagnosis of crops is a prerequisite for an efficient and reasonable nitrogen-fertilizer management system. With the development of research on plant phenotype and artificial intelligence technology in agriculture, deep learning has demonstrated a great potential in agriculture for recognizing nondestructive nitrogen nutrition diagnosis in plants by automation and high throughput at a low cost. To build a nitrogen nutrient-diagnosis model, muskmelons were cultivated under different nitrogen levels in a greenhouse. The digital images of canopy leaves and the environmental factors (light and temperature) during the growth period of muskmelons were tracked and analyzed. The nitrogen concentrations of the plants were measured, we successfully constructed and trained machine-learning- and deep-learning models based on the traditional backpropagation neural network (BPNN), the emerging convolution neural network (CNN), the deep convolution neural network (DCNN) and the long short-term memory (LSTM) for the nitrogen nutrition diagnosis of muskmelon. The adjusted determination coefficient (R2) and mean square error (MSE) between the predicted values and measured values of nitrogen concentration were adopted to evaluate the models’ accuracy. The values were R2 = 0.567 and MSE = 0.429 for BPNN model; R2 = 0.376 and MSE = 0.628 for CNN model; R2 = 0.686 and MSE = 0.355 for deep convolution neural network (DCNN) model; and R2 = 0.904 and MSE = 0.123 for the hybrid model DCNN–LSTM. Therefore, DCNN–LSTM shows the highest accuracy in predicting the nitrogen content of muskmelon. Our findings highlight a base for achieving a convenient, precise and intelligent diagnosis of nitrogen nutrition in muskmelon.

Exploiting deep learning for predictable carbon dot design

2021 ◽  
Author(s):  
Xiao-Yuan Wang ◽  
Bin-Bin Chen ◽  
Jie Zhang ◽  
Ze-Rui Zhou ◽  
Jian Lv ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Properties ◽  
Deep Learning ◽  
Ultraviolet Irradiation ◽  
Carbon Dots ◽  
Spectral Properties ◽  
Convolution Neural Network ◽  
Carbon Dot ◽  
Fluorescence Color ◽  
Deep Convolution Neural Network

This work developed a multi-layer deep convolution neural network (DCNN) model for predicting the optical properties of carbon dots (CDs), including spectral properties and fluorescence color under ultraviolet irradiation.

scholarly journals Park Assist: A Deep Convolution Neural Network based Parking Assistant system

2020 ◽  
Vol 8 (6) ◽  
pp. 14-18
Author(s):  
Sukhdeep Sharma ◽  
Aayushya ‎ ◽  
Dr. Madhumita Kathuria ◽  
Pronika Chawla
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Convolution Neural Network ◽  
The Other ◽  
Human Labour ◽  
Deep Convolution Neural Network ◽  
Classification Tasks ◽  
Parking Facilities

With the proliferation in number of vehicles an unnoticeable problem regarding parking of these vehicles has emerged in places like shopping complexes where current car parking facilities are incapable of managing the parking of vehicles without human labour . Even in current automated PGI’s human labour is required in some or the other way . Motivated by the affordable and remarkable performance of Convolutional Nueral Network in various image classification tasks, this paper presents a review on the automated parking systems based on the CNN technique . The classifier are trained and tested by deep learning of nueral network thus using of PHP and HTML to create the UI and knowledge of MySQL to create a database to store information about vehicles .Similarly by converting the process into three small procedures we will be able to evaluate the bill in accordance to the timestamp of the parked vehicle without the use of human efforts.

An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

2020 ◽  
pp. 1-9
Author(s):  
Yiming Guo ◽  
Hui Zhang ◽  
Zhijie Xia ◽  
Chang Dong ◽  
Zhisheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Convolution Neural Network ◽  
Dual Channel ◽  
Time Domain Data ◽  
Channel Input ◽  
Deep Convolution Neural Network ◽  
Useful Life

The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.

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