Market zinciri ürün dağıtımı probleminin farklı genetik algoritma versiyonları ile çözümü ve karşılaştırması

Gülmez, Burak

doi:10.47495/okufbed.1117220

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…Bad solutions are eliminated with low probability. This process continues until the stopping criterion is met (Gülmez, 2023 ). GA flowchart is seen in Fig.…”

Section: Algorithmsmentioning

confidence: 99%

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

Gülmez

2022

Ann Oper Res

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The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively.

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“…Bad solutions are eliminated with low probability. This process continues until the stopping criterion is met (Gülmez, 2023 ). GA flowchart is seen in Fig.…”

Section: Algorithmsmentioning

confidence: 99%

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

Gülmez

2022

Ann Oper Res

Self Cite

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“…The quality of the solution is then gauged by a fitness function, which is used to assess the new individuals. Until a workable solution is identified, this procedure is repeated over several generations [33], [34]. GA has three essential components [35]:…”

Section: B Genetic Algorithmmentioning

confidence: 99%

Stock Market Volatility Estimation: A Case Study of the Hang Seng Index

Wu,

Lin,

liu

2024

ijacsa

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Among the influential elements in the national economy is the stock market. The stock market is a multifaceted system that combines economics, investor psychology, and other market mechanics. The objective of the financial market investment is to maximize profits; but, due to the market's complexity and the multitude of factors that might impact it, it is challenging to predict its future behavior. The challenging process of stock price prediction requires the analysis of a wide range of social, political, and economic factors. These variables include market trends, financial statements, earnings reports, and other data. The goal of this project is to develop an accurate hybrid stock price forecasting model using Random Forest which is combined with the optimization. Random Forest is one type of machine learning that is often used in time series analysis. This study provides stock price forecasting using the Hang Seng index market, which consists of the largest and most liquid corporations that are publicly traded on the Hong Kong Stock Exchange, data from 2015 to 2023. The Dow Jones and KOSPI were evaluated as two additional indices. This study demonstrates some optimization approaches including genetic algorithm, grey wolf optimization, and biogeography-based optimization, which drew inspiration from the phenomenon of species migrating between islands in search of a suitable habitat. Biogeography-based optimization has shown the best result among these optimizations. The proposed hybrid model obtained the values 0.992, 0.997, and 0.9937 for the coefficient of determination for HSI, Dow Jones, and KOSPI markets, respectively. These results indicate the ability of the model in order to predict the stock market with a high degree of accuracy.

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“…Today, artificial intelligence (AI) has a wide variety of different application areas [ [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] ]. The integration of AI into agriculture has opened up new avenues for improving crop management, and one notable application is in the detection of rice diseases.…”

Section: Introductionmentioning

confidence: 99%

Advancements in rice disease detection through convolutional neural networks: A comprehensive review

Gülmez

2024

Heliyon

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Market zinciri ürün dağıtımı probleminin farklı genetik algoritma versiyonları ile çözümü ve karşılaştırması

Cited by 6 publications

References 37 publications

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

Stock Market Volatility Estimation: A Case Study of the Hang Seng Index

Advancements in rice disease detection through convolutional neural networks: A comprehensive review

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