Cervical Cancer Classification Using Elman Recurrent Neural Network and Genetic Algorithm

2021 ◽  
Author(s):  
Rocky Yefrenes Dillak ◽  
Petrisia Widyasari Sudarmadji
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Cervical Cancer ◽  
Recurrent Neural Network ◽  
Cancer Classification ◽  
Elman Recurrent Neural Network

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.

scholarly journals ПІДХОДИ ДО СТВОРЕННЯ ПРОГРАМНОГО АГЕНТА НА ОСНОВІ МЕТАЕВРИСТИК І ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ

2019 ◽  
pp. 58-65
Author(s):  
Євген Євгенович Федоров ◽  
Марина Володимирівна Чичужко ◽  
Владислав Олегович Чичужко
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Internal State ◽  
Numerical Study ◽  
Agent Based ◽  
Development Framework ◽  
Number Of Visits ◽  
Artificial Neural ◽  
The Creation ◽  
Elman Recurrent Neural Network

In this article, has been developed a software agent based on meta-heuristics and artificial neural networks. The analysis of existing classes of agents and the selected reactive agent with internal state, which is well suited for partially observable, dynamic and non-episodic media, was carried out, and this agent has an internal state that preserves the state of the environment, obtained on the basis of the history of acts of perception, in the form of structured data. Were proposed approaches to create an agent based on meta-heuristics and an agent based on an artificial neural network. The development of reactive agents with internal state, based on the PSO (particle swarm optimization) metaheuristics, which are related to individual particles and to a whole swarm and interact by messages was proposed. Also, has been proposed an approach to the creation of a reactive agent with an internal state based on the Elman recurrent neural network. The agent-based approach allows combining different areas of artificial intelligence, digital signal processing, mathematical modeling, and game theory. The proposed agents were implemented using the JADE (Java Agent Development Framework) toolkit, which is one of the most popular tools for the creation of agent systems. A numerical study was made to determine the parameters of the swarm PSO metaheuristics and the Elman recurrent neural network. As a purpose function, the Rastrigin test function has been used. The number of visits to the website of DonNTU was used as an input sample for the Elman network. The minimum average square error forecast was the criterion for choosing the structure of a network model. 10 hiding neurons were used to predict the number of visits to the website page, since, with increasing of hidden neurons number, the change in the error value is small. To determine the number of particles in the swarm, a series of experiments was conducted, the results of which are presented by graphs. The proposed approaches can be used in intelligent computer systems.

scholarly journals ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

SINERGI ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 29
Author(s):  
Widi Aribowo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Power System ◽  
Recurrent Neural Networks ◽  
Load Shedding ◽  
Feed Forward Neural Network ◽  
Linear Problems ◽  
Elman Recurrent Neural Network ◽  
And Control

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility.  In recent years, Neural networks have been very victorious in several signal processing and control applications.  Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods. 

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