[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 …
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
Background Imbalanced datasets pose significant challenges in predictive modeling,
leading to biased outcomes and reduced model reliability. This study addresses data …
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 …
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
Ensuring student engagement is crucial for effective learning outcomes in any classroom
setting, including e-learning environments. However, the absence of immediate supervision …
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 …
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 …
progressed less quickly. If deep learning techniques can be implemented reliably …
Unbalanced Learning for Diabetes Diagnosis Based on Enhanced Resampling and Stacking Classifier
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 …
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
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 …
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 …
a lot of studies to deal with this problem. Data augmentation techniques are basic solutions …