Integrated Genetic Algorithm and Artificial Neural Network

Nezamoddini, Nasim; Gholami, Amirhosein

doi:10.1109/cse/euc.2019.00057

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Using the artificial intelligent technique, the complexity of steam turbine can be studied and analysed which considers the high, medium and low-pressure stage of steam turbine. Using the model prepared by artificial neural network can be used for designing, synthesis, generating simulation and to monitor the power plant control system [12][13][14][15][16][17]. 160-Megawatt steam power plant has different motives like steam extraction, heater for feeding water and separator for moisture.…”

Section: Data Collection and Proposed Methodsmentioning

confidence: 99%

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Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Abdullah¹,

Hamoodi²,

Mohammed³

2021

JESA

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Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.

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Section: Data Collection and Proposed Methodsmentioning

confidence: 99%

“…In testing phase, a split technique is used to split the whole data into a bag of data and other dataset is used as a training data and the accuracy of the model is determined using remaining dataset. Depending on the accuracy of the model it is used for the predication or decision-making purpose [7][8][9][10].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Abdullah¹,

Hamoodi²,

Mohammed³

2021

JESA

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“…GA algorithm is the most commonly used in searchbased software engineering (SBSE) field [23]. The searching process using GA need to evolve in multiple iteration to get a better solution [24].…”

Section: Table 1 -Related Work On Search Engine Optimization Researchersmentioning

confidence: 99%

“…By applying ANN-GA algorithm, the optimization process can be faster than normal process [29]. ANN-GA algorithm mainly to find the best weights, find optimal solutions and give high accuracy results for ANN [24]. Furthermore, it also calculates fitness function for each chromosome in ANN-GA combined algorithm [24].…”

Section: Table 1 -Related Work On Search Engine Optimization Researchersmentioning

confidence: 99%

AComparative Study of Artificial Neural Network and Genetic Algorithm in Search Engine Optimization

Mohamad Madon,

Mohd. Yasin

2023

JSCDM

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Search engine optimization applies search principles in search engines to assign a higher ranking to the most suitable webpage. Nowadays, information searching is done ubiquitously on the World Wide Web with the help of search engines. However, the process needs to be efficient and produce accurate results simultaneously. In this research, the objectives are to implement and evaluate the Artificial Neural Network and Genetic Algorithms. The accuracy result for both algorithms is compared by implementing keyword ranking, Search Engine Result Page visibility, and time retrieval for document-based and e-commerce websites. To achieve them, firstly, the problem and data are defined. Next, two datasets are importedfrom Kaggle and transformed into a more helpful format. Then, the Artificial Neural Network and Genetic Algorithms are implemented on these datasets in Python using Jupyter Notebook tools. Subsequently, the accuracy of these datasetskeyword ranking, Search Engine Result Page visibility, and time retrieval areobserved based on the output and graph. Lastly, an analysis of the results is performed. Conclusively, the Genetic Algorithm demonstrates a higher percentage of accuracy results than the Artificial Neural Network algorithm in keyword ranking and SERP visibility. However, the accuracy results of time retrieval are vice versa. The results in Genetic Algorithm show 9.0%, 9.0%, and 3.0% in the e-commerce dataset for keyword ranking and 4.0%, 51.0%, and 1.0% in the document-based dataset for SERP visibility. Next, the Artificial Neural Network algorithm shows results of 8.0%, 7.0%, and 7.0% in the e-commerce dataset and 3.0%, 50.0%, and 4.0% in the document-based dataset for time retrieval. Therefore, the results validated the ability of the Genetic Algorithm as one of the most applied algorithms in the search engine optimization field.

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Cerebral Infarction Classification Using Genetic Algorithm Neural Network and Stochastic Neural Network

Wirasati

Rustam

Aurellia

2022

Advanced Intelligent Systems for Sustainable Development (AI2SD’2020)

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Integrated Genetic Algorithm and Artificial Neural Network

Cited by 5 publications

References 8 publications

Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

AComparative Study of Artificial Neural Network and Genetic Algorithm in Search Engine Optimization

Cerebral Infarction Classification Using Genetic Algorithm Neural Network and Stochastic Neural Network

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