Classification of MRI Brain Images Using DNA Genetic Algorithms Optimized Tsallis Entropy and Support Vector Machine

Zang, Wenke; Wang, Zehua; Jiang, Dong; Liu, Xiyu; Jiang, Zhenni

doi:10.3390/e20120964

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…On the other hand, the time entering this stage was short, and research in this field also required further generalization. With the rapid development of object-oriented smart technologies, the current research hotspots and future development trends are the formation of new integrated technologies, namely, the Hybrid Intellectual System [85][86][87]. Advanced algorithms represented by Logical Regression, Internet of Things, cloud computing, and big data have emerged one after another, making artificial intelligence a breaking point in recent years [88].…”

Section: Evolution Analysis Of Ga-luomentioning

confidence: 99%

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Ding

Zheng

2021

Land

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Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has been widely used in this field. However, there are no comprehensive reviews concerning the development process for the application of the Genetic Algorithm in land use optimization (GA-LUO). This article used a bibliometric analysis method to explore current state and development trends for GA-LUO from 1154 relevant documents published over the past 25 years from Web of Science. We also displayed a visualization network from the aspects of core authors, research institutions, and highly cited literature. The results show the following: (1) The countries that published the most articles are the United States and China, and the Chinese Academy of Sciences is the research institution that publishes the most articles. (2) The top 10 cited articles focused on describing how to build GA models for multi-objective LUO. (3) According to the number of keywords that appear for the first time in each time period, we divided the process of GA-LUO into four stages: the presentation and improvement of methods stage (1995–2004), the optimization stage (2005–2008), the hybrid application of multiple models stage (2009–2016), and the introduction of the latest method stage (after 2017). Furthermore, future research trends are mainly manifested in integrating together algorithms with GA and deepening existing research results. This review could help researchers know this research domain well and provide effective solutions for land use problems to ensure the sustainable use of land resources.

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Section: Evolution Analysis Of Ga-luomentioning

confidence: 99%

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Ding

Zheng

2021

Land

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“…The best model in this experiment had a fitness value of 0.9054 with gene ( (3,4,16), (101, 98, 101)). The maximum mean generation fitness obtained was 0.8687.…”

mentioning

confidence: 93%

“…Deep learning techniques play an essential role in the classification process. Deep learning is a subcategory of machine learning that deals with the architecture of neural networks [4]. Various deep learning algorithms have been designed to solve many complex real-world problems.…”

Section: Introductionmentioning

confidence: 99%

Emerging Applications of Artificial Intelligence, Machine learning and Data Science

Dangi¹,

Bhagat²,

Dixit³

2022

Computers, Materials &Amp; Continua

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There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites. Today, customers throughout the world share their points of view on all kinds of topics through these sources. The massive volume of data created by these customers makes it impossible to analyze such data manually. Therefore, an efficient and intelligent method for evaluating social media data and their divergence needs to be developed. Today, various types of equipment and techniques are available for automatically estimating the classification of sentiments. Sentiment analysis involves determining people's emotions using facial expressions. Sentiment analysis can be performed for any individual based on specific incidents. The present study describes the analysis of an image dataset using CNNs with PCA intended to detect people's sentiments (specifically, whether a person is happy or sad). This process is optimized using a genetic algorithm to get better results. Further, a comparative analysis has been conducted between the different models generated by changing the mutation factor, performing batch normalization, and applying feature reduction using PCA. These steps are carried out across five experiments using the Kaggle dataset. The maximum accuracy obtained is 96.984%, which is associated with the Happy and Sad sentiments.

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“…Support vector machine (SVM), deep learning, and other clustering approaches are widely used for feature recognition [35][36][37]. Especially, SVM has the advantage of simple structure, fast learning speed, and wide applicability.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering

et al. 2022

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Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive a stable entropy due to intensity information loss during the coarse-graining process. For this problem, an improved multiscale permutation entropy (IMPE) is proposed in this work. IMPE is obtained through refining and reconstructing MPE. Compared with MPE, the IMPE overcomes the deficiency of amplitude information loss due to the coarse-graining process when computing signal complexity. Through the simulation of calculating MPE and IMPE from white Gaussian noise, it is found that the entropy derived by IMPE is more stable than that derived by MPE. The processing method based on IMPE feature extraction is applied to the experimental ultrasonic scattered echo signals in HIFU treatment. Support vector machine and Gustafson–Kessel fuzzy clustering based on MPE and IMPE feature extraction are also used for biological tissue denaturation classification and recognition. The results calculated from the different combination algorithms show that the recognition of biological tissue denaturation based on IMPE-GK clustering is more reliable with the accuracy of 95.5%.

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Classification of MRI Brain Images Using DNA Genetic Algorithms Optimized Tsallis Entropy and Support Vector Machine

Cited by 6 publications

References 28 publications

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Emerging Applications of Artificial Intelligence, Machine learning and Data Science

Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering

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