Variational Autoencoder-Based Imbalanced Alzheimer Detection Using Brain MRI Images

Maity, Soumyajit; Mandal, Rohit Prasan; Bhattacharjee, Saranya; Chatterjee, Sankhadeep

doi:10.1007/978-981-19-1657-1_14

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…We proposed K-means clustering with the elbow method [11,13,14] to group the data samples into 11 clusters. In each cluster, we augmented the data samples using a Variational Auto-Encoder (VAE) [12,15,16] to resolve the class imbalance in the data samples. The augmented data samples were used to predict gasoline orders with regression models [17][18][19][20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

“…The Variational Auto-Encoder (VAE) is a generative model that models the distribution of given datasets and generates new datasets [12,15,16]. VAE is used when the amount of data is sparse and is useful for augmenting datasets while preserving the distribution of their features.…”

Section: Variational Auto-encodermentioning

confidence: 99%

“…The datasets generated through the decoder layer are similar to the input datasets but have non-linear lower dimensions. Maity et al [15] augmented outliers using VAE to detect Alzheimer's disease in brain MRI data samples. The Alzheimer's patient data samples were augmented to match the class ratio of healthy brain datasets.…”

Section: Variational Auto-encodermentioning

confidence: 99%

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Prediction of Gasoline Orders at Gas Stations in South Korea Using VAE-Based Machine Learning Model to Address Data Asymmetry

Yoon,

Park

2023

Applied Sciences

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South Korea has developed road-based transportation and uses a lot of gasoline. South Korea imports gasoline since it is not produced domestically. So, fluctuations in gasoline prices have a significant impact on the national economy. Currently, gasoline orders, which are based on gasoline consumption, are analyzed in relation to fluctuations in gasoline prices. However, gasoline orders can also change due to various non-price factors. Therefore, to understand the trend of gasoline orders, it is important to identify additional factors that gas stations consider when determining orders. We collected 180 monthly samples of data on 167 variables. Sudden international issues lead to rapid fluctuations in gasoline orders, which can lead to outliers. A class imbalance occurs because outliers are generally fewer in number than the normal data points. Therefore, to address the class imbalance, we proposed a method that grouped the data samples into 11 clusters using the K-means clustering algorithm and then augmented the data into 85 datasets in each cluster through the Variational Auto-Encoder. We evaluated the augmented datasets through the R-Squared, Root Mean Squared Errors, and accuracy of various regression models. Based on the experimental results, when predicting gasoline orders at gas stations in South Korea using augmented datasets, linear regression showed the best performance.

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Section: Related Workmentioning

confidence: 99%

Section: Variational Auto-encodermentioning

confidence: 99%

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Prediction of Gasoline Orders at Gas Stations in South Korea Using VAE-Based Machine Learning Model to Address Data Asymmetry

Yoon,

Park

2023

Applied Sciences

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Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling

et al. 2023

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Pattern Analysis on Wireless Network Coverage in DISPERKIM, South Sumatra

Al Akbar,

et al. 2024

intij

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This paper introduces a novel Heterogeneous Ensemble Machine Learning (HEML) approach designed to detect bipolar disorder, a significant healthcare challenge that demands precise and prompt diagnosis for effective treatment. The HEML method integrates multiple machines learning models, incorporating various physiological, behavioral, and contextual data from patients. By using a comprehensive feature selection technique, relevant features are extracted from each data source and utilized to train individual classifiers for detecting mental disorders. The classifiers include Adaboost, Decision Tree, K-nearest neighbors, Multilayer Perceptron, Random Forest, Relevance Vector Machine, and XGB, with Logistic Regression serving as the meta-model. This ensemble of classifiers enhances overall performance by capturing a wider range of characteristics related to mental disorders. The research evaluates the HEML method across three bipolar disorder datasets: Dataset1 (a multimodal dataset), Dataset2 (a sensor-based dataset), and Dataset3 (a real-time dataset). The HEML approach surpasses traditional methods, achieving superior accuracy rates of 95.21% with Dataset 1, 99.28% with Dataset 2, and 99% with Dataset 3. It outperforms individual models in detecting bipolar disorder, delivering the best Precision, Recall, F1 score, and Kappa Score. This comparative analysis advances the field of mental health diagnosis by leveraging the strengths of ensemble machine learning to improve accuracy and reliability in detection methods.

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Variational Autoencoder-Based Imbalanced Alzheimer Detection Using Brain MRI Images

Cited by 3 publications

References 12 publications

Prediction of Gasoline Orders at Gas Stations in South Korea Using VAE-Based Machine Learning Model to Address Data Asymmetry

Prediction of Gasoline Orders at Gas Stations in South Korea Using VAE-Based Machine Learning Model to Address Data Asymmetry

Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling

Pattern Analysis on Wireless Network Coverage in DISPERKIM, South Sumatra

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