Visually evoked brain signals guided image regeneration using GAN variants

Kumari, Nandini; Anwar, Shamama; Bhattacherjee, Vandana; Sahana, Sudip Kumar

doi:10.1007/s11042-023-14769-4

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…The abstract view of our proposed methodology is presented in Figure 1. Machine learning techniques have played an important role in healthcare applications, and several researchers have pursued this area [1][2][3][4][5], proposing the detection of Covid 19 disease through Chest x ray images, or for image based disease prediction and diabetes retinopathy detection. In [6] Jha et al propose another machine learning approach as a step towards better health for society.…”

mentioning

confidence: 99%

Genetic Algorithm based optimization of Clustering Algorithms for the Healthy Aging Dataset

Kouser,

Priyam,

Gupta

et al. 2024

Preprint

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Clustering is a crucial at the same time challenging task in several application domains. It is important to incorporate the optimum feature finding into our clustering algorithms for getting better prediction accuracy but this is difficult when there is no or little information about the importance or relevance of features. To tackle this task in an efficient manner we employ the natural evolution process inherent in genetic algorithms (GA) to find the optimum features for clustering for the healthy aging dataset. In order to empirically verify the findings, genetic algorithms were combined with a number of clustering algorithms including parti-tional, density based as well as agglomerative. A variant of the popular KMeans algorithm, named KMeans++ gave the best performance on all performance metrics when combined with GA.

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mentioning

confidence: 99%

Genetic Algorithm based optimization of Clustering Algorithms for the Healthy Aging Dataset

Kouser,

Priyam,

Gupta

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The Discussion and Conclusion are given in Sections 5 and 6, respectively. Machine learning techniques have played an important role in healthcare applications, and several researchers have pursued this area [1][2][3][4][5], proposing the detection of COVID-19 through chest X-ray images or for image-based disease prediction and diabetes retinopathy detection. In [6], Jha et al propose another machine learning approach as a step towards better health for our society.…”

mentioning

confidence: 99%

Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset

Kouser,

Priyam,

Gupta

et al. 2024

Applied Sciences

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Clustering is a crucial and, at the same time, challenging task in several application domains. It is important to incorporate the optimum feature finding into our clustering algorithms for better exploration of features and to draw meaningful conclusions, but this is difficult when there is no or little information about the importance or relevance of features. To tackle this task in an efficient manner, we employ the natural evolution process inherent in genetic algorithms (GAs) to find the optimum features for clustering the healthy aging dataset. To empirically verify the findings, genetic algorithms were combined with a number of clustering algorithms, including partitional, density-based, and agglomerative clustering algorithms. A variant of the popular KMeans algorithm, named KMeans++, gave the best performance on all performance metrics when combined with GAs.

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Generating personalized facial emotions using emotional EEG signals and conditional generative adversarial networks

Esmaeili,

Kiani

2023

Multimed Tools Appl

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Visually evoked brain signals guided image regeneration using GAN variants

Cited by 4 publications

References 36 publications

Genetic Algorithm based optimization of Clustering Algorithms for the Healthy Aging Dataset

Genetic Algorithm based optimization of Clustering Algorithms for the Healthy Aging Dataset

Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset

Generating personalized facial emotions using emotional EEG signals and conditional generative adversarial networks

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