A typology of adopters and nonadopters of improved sorghum seeds in Tanzania: A deep learning neural network approach

2020 ◽  
Vol 127 ◽  
pp. 104839 ◽  
Author(s):  
Aloyce R. Kaliba ◽  
Richard J. Mushi ◽  
Anne G. Gongwe ◽  
Kizito Mazvimavi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Approach ◽  
Neural Network Approach ◽  
Deep Learning Neural Network

A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area

2020 ◽  
Vol 701 ◽  
pp. 134413 ◽  
Author(s):  
Dieu Tien Bui ◽  
Nhat-Duc Hoang ◽  
Francisco Martínez-Álvarez ◽  
Phuong-Thao Thi Ngo ◽  
Pham Viet Hoa ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Frequency ◽  
Flash Flood ◽  
Tropical Storm ◽  
Network Approach ◽  
Neural Network Approach ◽  
Flood Susceptibility ◽  
Deep Learning Neural Network

scholarly journals An Efficient Method for Detection of DDoS Attacks on the Web Using Deep Learning Algorithms

2021 ◽  
Vol 10 (4) ◽  
pp. 2821-2829
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Ddos Attacks ◽  
Problem Statement ◽  
Neural Network Approach ◽  
Learning Techniques ◽  
Attack Data ◽  
Deep Learning Neural Network

Recently, DDoS attacks is the most significant threat in network security. Both industry and academia are currently debating how to detect and protect against DDoS attacks. Many studies are provided to detect these types of attacks. Deep learning techniques are the most suitable and efficient algorithm for categorizing normal and attack data. Hence, a deep neural network approach is proposed in this study to mitigate DDoS attacks effectively. We used a deep learning neural network to identify and classify traffic as benign or one of four different DDoS attacks. We will concentrate on four different DDoS types: Slowloris, Slowhttptest, DDoS Hulk, and GoldenEye. The rest of the paper is organized as follow: Firstly, we introduce the work, Section 2 defines the related works, Section 3 presents the problem statement, Section 4 describes the proposed methodology, Section 5 illustrate the results of the proposed methodology and shows how the proposed methodology outperforms state-of-the-art work and finally Section VI concludes the paper.

scholarly journals Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Class

2019 ◽  
Vol 36 (12) ◽  
pp. 2349-2363 ◽  
Author(s):  
Veljko Petković ◽  
Marko Orescanin ◽  
Pierre Kirstetter ◽  
Christian Kummerow ◽  
Ralph Ferraro
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Model Development ◽  
Precipitation Rate ◽  
Neural Network Approach ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Microwave Imager ◽  
Deep Learning Neural Network

AbstractA decades-long effort in observing precipitation from space has led to continuous improvements of satellite-derived passive microwave (PMW) large-scale precipitation products. However, due to a limited ability to relate observed radiometric signatures to precipitation type (convective and stratiform) and associated precipitation rate variability, PMW retrievals are prone to large systematic errors at instantaneous scales. The present study explores the use of deep learning approach in extracting the information content from PMW observation vectors to help identify precipitation types. A deep learning neural network model (DNN) is developed to retrieve the convective type in precipitating systems from PMW observations. A 12-month period of Global Precipitation Measurement mission Microwave Imager (GMI) observations is used as a dataset for model development and verification. The proposed DNN model is shown to accurately predict precipitation types for 85% of total precipitation volume. The model reduces precipitation rate bias associated with convective and stratiform precipitation in the GPM operational algorithm by a factor of 2 while preserving the correlation with reference precipitation rates, and is insensitive to surface type variability. Based on comparisons against currently used convective schemes, it is concluded that the neural network approach has the potential to address regime-specific PMW satellite precipitation biases affecting GPM operations.

scholarly journals Identification of Skin Disease Using Deep Learning

2020 ◽  
pp. 1011-1116
Author(s):  
Shravani Kharat ◽  
Pooja Shinde ◽  
Preeti Malwadkar ◽  
Dipti Chaudhari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Skin Disease ◽  
Skin Diseases ◽  
Disease Prediction ◽  
Learning Technology ◽  
Network Approach ◽  
Neural Network Approach ◽  
Prevention And Early Detection ◽  
Common Skin

Globally, skin diseases are among the most common health problems in all humans irrespective of age. Prevention and early detection of these diseases can improve the chance of surviving. This model illustrates the identification of skin diseases providing more objective and reliable solutions using deep learning technology and convolutional neural network approach. In this model, the system design, implementation and identification of common skin diseases such as acne, blister, eczema, warts etc. are explained. The system applies deep learning technology to train itself with various images of skin diseases from the Kaggle platform. The accuracy obtained by using deep learning is 83.23%. The main objective of this system is to achieve maximum accuracy of skin disease prediction. Moreover, if the disease is identified the system provides detailed information about the diseases along with home remedies.

Basic Food Basket Monthly Price of Southern Bahia Cities: A Time Series Forecasting with Deep Learning Using a Recurrent Neural Network Approach

2020 ◽  
Vol 12 (4) ◽  
pp. 146-159
Author(s):  
Murillo A. S. Torres ◽  
Mateus S. Marinho ◽  
Dany S. Dominguez ◽  
Dárcio R. Silva ◽  
Hélder Conceição Almeida
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Time Series Forecasting ◽  
Network Approach ◽  
Neural Network Approach ◽  
Southern Bahia ◽  
Food Basket
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