scholarly journals Tissue specific prediction of N6-methyladenine sites based on an ensemble of multi-input hybrid neural network

BIOCELL ◽  
2022 ◽  
Vol 46 (4) ◽  
pp. 1105-1121
Author(s):  
CANGZHI JIA ◽  
DONG JIN ◽  
XIN WANG ◽  
QI ZHAO
Keyword(s):  
Neural Network ◽  
Hybrid Neural Network ◽  
Tissue Specific ◽  
Specific Prediction

A hybrid neural network for prediction of flow shop scheduling performance

2015 ◽  
Author(s):  
J. R. Yang ◽  
W. Xu ◽  
M. Rajora ◽  
A. H. Shih ◽  
H. Z. Tan ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Neural Network ◽  
Shop Scheduling

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

scholarly journals A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules

Soft Matter ◽  
2021 ◽  
Author(s):  
Tao Lin ◽  
Zhen Wang ◽  
Wen Wang ◽  
Yi Sui
Keyword(s):  
Neural Network ◽  
High Throughput ◽  
Viscoelastic Properties ◽  
Dynamic Deformation ◽  
Membrane Elasticity ◽  
Hybrid Neural Network ◽  
High Throughput Method

We have developed a high-throughput method, by combining a hybrid neural network with a mechanistic capsule model, to predict membrane elasticity and viscosity of microcapsules from their dynamic deformation in a branched microchannel.

Station-level short-term demand forecast of carsharing system via station-embedding-based hybrid neural network

2021 ◽  
pp. 1-19
Author(s):  
Feifei Zhao ◽  
Weiping Wang ◽  
Huijun Sun ◽  
Hongming Yang ◽  
Jianjun Wu
Keyword(s):  
Neural Network ◽  
Demand Forecast ◽  
Short Term ◽  
Hybrid Neural Network

scholarly journals Speech Enhancement With Phase Sensitive Mask Estimation Using a Novel Hybrid Neural Network

2021 ◽  
Vol 2 ◽  
pp. 136-150
Author(s):  
Mojtaba Hasannezhad ◽  
Zhiheng Ouyang ◽  
Wei-Ping Zhu ◽  
Benoit Champagne
Keyword(s):  
Neural Network ◽  
Speech Enhancement ◽  
Hybrid Neural Network ◽  
Phase Sensitive ◽  
Mask Estimation

A syntactic path-based hybrid neural network for negation scope detection

2018 ◽  
Vol 14 (1) ◽  
pp. 84-94 ◽  
Author(s):  
Lydia Lazib ◽  
Bing Qin ◽  
Yanyan Zhao ◽  
Weinan Zhang ◽  
Ting Liu
Keyword(s):  
Neural Network ◽  
Hybrid Neural Network
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