A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia Lake drought in Iran

In this current era of modern online shopping, people want to spend as little time as possible when it comes to buying products, therefore they prefer online shopping. People go shopping when the weather gets changed. For travelers, there is no such E-commerce platform that can recommend clothes according to any city weather. Even when people want to gift clothes to someone living in another country there is no such platform that gives recommendation of clothes according to that city's weather. They usually face problems when they want to buy weather-based products from various E-commerce platforms where they see mixed clothes of all types of weather which is very time-consuming, they become so confused most of the time that they think about whether they should buy or not. In this paper, we proposed a novel idea by using Convolutional Neural Network Algorithm of deep learning for developing an e-commerce platform that is unique in a way that it recommends clothes according to the city weather which provides hassle-free environment eventually saves customer's time thereby increasing customer satisfaction.

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A Convolutional Neural Network Algorithm Based on Rough Set Rule Extraction

2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) ◽

10.1109/ipec51340.2021.9421230 ◽

2021 ◽

Author(s):

Xuesi Ding

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Rough Set ◽

Rule Extraction ◽

Network Algorithm ◽

Neural Network Algorithm

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A Deep Learning Artificial Neural Network Algorithm for Instance-based Arabic Language Authorship Attribution

Advances in Data Science and Adaptive Analysis ◽

10.1142/s2424922x21430026 ◽

2021 ◽

Author(s):

Mohammad Al-Sarem ◽

Abdullah Alsaeedi ◽

Faisal Saeed

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Deep Learning ◽

Arabic Language ◽

Authorship Attribution ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Artificial Neural ◽

Artificial Neural Network Algorithm

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

10.1101/2021.08.04.21261235 ◽

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.

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