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

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

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 Low-order Spherical Harmonic HRTF Restoration using a Neural Network Approach

2020 ◽  
Author(s):  
Benjamin Tsui ◽  
William A. P. Smith ◽  
Gavin Kearney
Keyword(s):  
Neural Network ◽  
Spherical Harmonic ◽  
High Frequency ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Network Approach ◽  
Spectral Difference ◽  
Neural Network Approach ◽  
Frequency Representation ◽  
Head Related Transfer Function

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

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