scholarly journals Plant leaf Disease Identification using Deep learning techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 462-464
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Plant Disease ◽  
Environmental Changes ◽  
Vital Role ◽  
Network Algorithm ◽  
The Neural Network ◽  
Learning Techniques ◽  
Neural Network Algorithm ◽  
Leaf Image

Agriculture is an important source of our country’s growth. The major loss in an agricultural economy is because of the plant disease. Though technology plays a vital role in all the fields still today the agriculture field is using the old methodologies. Successful cultivation depends on identifying plant disease. Previously the identification was done manually by the experienced people but now it became difficult due to environmental changes. By using the deep learning techniques the plant disease can be identified effectively. Vgg16 and ResNet are the proposed techniques to increase accuracy than the existing system. The disease can be identified with images of the leaves by applying those deep learning techniques. Detection can be involved in steps like image acquisition, image pre-processing, image segmentation, feature extraction, and classification. By controlling the disease, productivity can be increased. The features of the leaf image are taken and trained using the neural network algorithm and then the prediction is done by testing the images. The features of the leaf image are taken and trained using the neural network algorithm and then the prediction is done by testing the images

scholarly journals Extraction of Ship Images using Deep Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 635-638
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Environmental Changes ◽  
The Other ◽  
Traditional Methods ◽  
Network Algorithm ◽  
Marine Industry ◽  
The Neural Network ◽  
Learning Techniques ◽  
Neural Network Algorithm

Ship Extraction is very important in the marine industry. Extraction of ships is helpful to the fishers to find the other ships nearly around the particular area. Still today the fishers are to find the ships using some traditional methods. But now it became difficult due to environmental changes. So, by using the deep learning techniques like the CNN algorithm the ship extraction can be identified effectively. Generally, the ships are identified as narrow bow and parallel hull edge, etc. Here, the Existing system they have used the Tensor flow, to see the performance of the datasets, using Recall and precision. In the proposed system, we are using CNN deep learning techniques to identify the ships. By finding the ships with the techniques, the time will be saved and the productivity can be increased. The features of the ship image are taken and trained using the neural network algorithm and then the prediction is done by testing the images.

scholarly journals FPGA Accelerating Core Design Based on XNOR Neural Network Algorithm

2018 ◽  
Vol 173 ◽  
pp. 01024
Author(s):  
Su Yi ◽  
Hu Xiao ◽  
Sun Yongjie
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Computational Efficiency ◽  
Large Scale ◽  
Structural Characteristics ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Computing Platforms ◽  
Fpga Acceleration

The current deep learning application scenario is more and more extensive. In terms of computing platforms, the widely used GPU platforms have lower computational efficiency. The flexibility of APU-dedicated processors is difficult to deal with evolving algorithms, and the FPGA platform takes into account both computational flexibility and computational efficiency. At present, one of the bottlenecks for limiting large-scale deep learning algorithms on FPGA platforms is the large-scale floating-point computing. Therefore, this article studies single-bit parameterized quantized neural network algorithm (XNOR), and optimizes the neural network algorithm based on the structural characteristics of the FPGA platform., Design and implementation of the FPGA acceleration core, the experimental results show that the acceleration effect is obvious.

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

scholarly journals WRF-Chem Simulation of Winter Visibility in Jiangsu, China, and the Application of a Neural Network Algorithm

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 520
Author(s):  
Peishu Zong ◽  
Yali Zhu ◽  
Huijun Wang ◽  
Duanyang Liu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Extinction Coefficient ◽  
Wrf Model ◽  
Jiangsu Province ◽  
Network Algorithm ◽  
The Neural Network ◽  
Noticeable Improvement ◽  
Neural Network Algorithm ◽  
Meteorological Elements

In this paper, the winter visibility in Jiangsu Province is simulated by WRF-Chem (Weather Research and Forecasting (WRF) model coupled with Chemistry) with high spatiotemporal resolutions. Simulation results show that WRF-Chem has good capability to simulate the visibility and related local meteorological elements and air pollutants in Jiangsu in the winters of 2013–2017. For visibility inversion, this study adopts the neural network algorithm. Meteorological elements, including wind speed, humidity and temperature, are introduced to improve the performance of WRF-Chem relative to the visibility inversion scheme, which is based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) extinction coefficient algorithm. The neural network offers a noticeable improvement relative to the inversion scheme of the IMPROVE visibility extinction coefficient, substantially improving the underestimation of winter visibility in Jiangsu Province. For instance, the correlation coefficient increased from 0.17 to 0.42, and root mean square error decreased from 2.62 to 1.76. The visibility inversion results under different humidity and wind speed levels show that the underestimation of the visibility using the IMPROVE scheme is especially remarkable. However, the underestimation issue is essentially solved using the neural network algorithm. This study serves as a basis for further predicting winter haze events in Jiangsu Province using WRF-Chem and deep-learning methods.

Neural Network Algorithm for Solving Large Scale Travelling Salesman Problems

2012 ◽  
Vol 542-543 ◽  
pp. 1398-1402
Author(s):  
Guo Zhong Cheng ◽  
Wei Feng ◽  
Fang Song Cui ◽  
Shi Lu Zhang
Keyword(s):  
Neural Network ◽  
Time Complexity ◽  
Large Scale ◽  
Travelling Salesman Problem ◽  
Effective Algorithm ◽  
Travelling Salesman ◽  
Satisfactory Solution ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

This study improves the neural network algorithm that was presented by J.J.Hopfield for solving TSP(travelling salesman problem) and gets an effective algorithm whose time complexity is O(n*n), so we can solve quickly TSP more than 500 cities in microcomputer. The paper considers the algorithm based on the replacement function of the V Value. The improved algorithm can greatly reduces the time and space complexities of Hopfield method. The TSP examples show that the proposed algorithm could efficiently find a satisfactory solution and has a fast convergence speed.

Health Monitoring: New Techniques Based on Vibrations Measurements and Identification Algorithms on an Aeronautical Composite Panel

2005 ◽  
Vol 293-294 ◽  
pp. 575-582 ◽  
Author(s):  
Igor Bovio ◽  
M. Della Ragione ◽  
Leonardo Lecce
Keyword(s):  
Neural Network ◽  
Health Monitoring ◽  
Structural Damage ◽  
Environmental Parameters ◽  
Composite Panel ◽  
Maintenance Cost ◽  
New Techniques ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

Purpose of the paper is to present a new application of a Non Destructive Test based on vibrations measurements, developed by the authors and already tested for analysing damage of many structural elements. The proposed new method is based on the acquisition and comparison of Frequency Response Functions (FRFs) of the monitored structure before and after an occurred damage. Structural damage modify the dynamical behaviour of the structure such as mass, stiffened and damping, and consequently its FRFs, making possible to identify and quantify a structural damage. The activities, presented in the paper, mostly focused on a new FRFs processing technique based on a dedicated neural network algorithm aimed at obtaining a “recognition-based learning”; this kind of learning methodology permits to train the neural network in order to let it recognise only “positive” examples discarding, as a consequence, the “negative” ones. Within the structural NDT a “positive” example means “healthy” state of the analysed structural component and, obviously, a “negative” one means a “damaged” or perturbed state. The developed NDT has been tested for identifying and analysing damage on an aeronautical composite panel to validate the method and calibrate the neural network algorithm. These tests have permitted to understand the influence of environmental parameters on the neural network training capability. Thanks to these new techniques it is possible to carry out a smart Health Monitoring system which is going to lead to the reduction of time and maintenance cost and to the increase of the aeronautical structure safety and reliability.

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