Deep recurrent neural network‐based residual control chart for autocorrelated processes

2019 ◽  
Vol 35 (8) ◽  
pp. 2687-2708
Author(s):  
Shumei Chen ◽  
Jianbo Yu
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Recurrent Neural Network ◽  
Deep Recurrent Neural Network ◽  
Autocorrelated Processes ◽  
Residual Control

Jaya Ant Lion Optimization-Driven Deep Recurrent Neural Network for Cancer Classification Using Gene Expression Data (Preprint)

2020 ◽  
Author(s):  
Ramachandro Majji
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Early Stage ◽  
Data Transformation ◽  
Cancer Classification ◽  
Data Normalization ◽  
Ant Lion Optimization ◽  
Deep Recurrent Neural Network ◽  
Novel Strategy ◽  
Feature Dimension

BACKGROUND Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. OBJECTIVE Propose an automatic prediction system for classifying cancer to malignant or benign. METHODS This paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is the data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer-based on the reduced dimension features to produce a satisfactory result. RESULTS The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, the maximal sensitivity of 95.95%, and the maximal specificity of 96.96%. CONCLUSIONS The resulted output of the proposed JayaALO-based DeepRNN is used for cancer classification.

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model

2021 ◽  
Vol 193 (12) ◽  
Author(s):  
Salar Valizadeh Moghadam ◽  
Ahmad Sharafati ◽  
Hajar Feizi ◽  
Seyed Mohammad Saeid Marjaie ◽  
Seyed Babak Haji Seyed Asadollah ◽  
...  
Keyword(s):  
Neural Network ◽  
Dissolved Oxygen ◽  
Oxygen Concentration ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Dissolved Oxygen Concentration ◽  
Efficient Strategy ◽  
Deep Recurrent Neural Network
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