[Retracted] Feature Learning‐Based Generative Adversarial Network Data Augmentation for Class‐Based Few‐Shot Learning

B Subedi, VE Sathishkumar… - Mathematical …, 2022 - Wiley Online Library
As training deep neural networks enough requires a large amount of data, there have been
a lot of studies to deal with this problem. Data augmentation techniques are basic solutions …

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm

M Talebi Moghaddam, Y Jahani, Z Arefzadeh… - BMC Medical Research …, 2024 - Springer
Background Imbalanced datasets pose significant challenges in predictive modeling,
leading to biased outcomes and reduced model reliability. This study addresses data …

Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images

K Shanmugavadivel, VE Sathishkumar… - … Methods in Medicine, 2022 - Wiley Online Library
The level of patient's illness is determined by diagnosing the problem through different
methods like physically examining patients, lab test data, and history of patient and by …

[HTML][HTML] EDFA: Ensemble deep CNN for assessing student's cognitive state in adaptive online learning environments

S Gupta, P Kumar, RK Tekchandani - International Journal of Cognitive …, 2023 - Elsevier
Ensuring student engagement is crucial for effective learning outcomes in any classroom
setting, including e-learning environments. However, the absence of immediate supervision …

[PDF][PDF] A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

WY Wong, K Hasikin, M Khairuddin… - Comput. Mater …, 2023 - cdn.techscience.cn
A common difficulty in building prediction models with real-world environmental datasets is
the skewed distribution of classes. There are significantly more samples for day-to-day …

Multiconvolutional transfer learning for 3D brain tumor magnetic resonance images

SKB Sangeetha, V Muthukumaran… - Computational …, 2022 - Wiley Online Library
The difficulty or cost of obtaining data or labels in applications like medical imaging has
progressed less quickly. If deep learning techniques can be implemented reliably …

Unbalanced Learning for Diabetes Diagnosis Based on Enhanced Resampling and Stacking Classifier

N Zemmal, NE Benzebouchi, N Azizi… - International Journal of …, 2022 - igi-global.com
Diabetes is characterized by an abnormally enhanced concentration of glucose in the blood
serum. It has a damaging impact on several noble body systems. Today, the concept of …

Comparative Analysis of Resampling Techniques and Machine Learning Classifiers in Multiclass Classification of Diabetes Mellitus

A Hashmi, MT Nafis, S Naaz… - … Conference on Self …, 2023 - ieeexplore.ieee.org
This research study explores the effects of various resampling techniques with different
machine learning classifiers on the accuracy of multi-class classification of Diabetes using …

[PDF][PDF] Research Article Feature Learning-Based Generative Adversarial Network Data Augmentation for Class-Based Few-Shot Learning

B Subedi, VE Sathishkumar, V Maheshwari, MS Kumar… - 2022 - academia.edu
As training deep neural networks enough requires a large amount of data, there have been
a lot of studies to deal with this problem. Data augmentation techniques are basic solutions …