Related Experiment Video
Updated: Jan 28, 2026
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
Published on: December 15, 2023
Efficient feature selection with attention based deep cat convolutional stacked sparse autoencoder for diabetes
G Thilagavathi1, N K Karthikeyan1
1Department of Information Technology, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India.
Abstract:
Diabetes, one of the most serious diseases in the world, however, early detection can prevent diabetes. This work proposes a novel approach to identifying early signs of diabetes based on deep learning methods. First, the input data is pre-processed and the features are selected using an improved Cheetah Optimization (ICO). Finally, diabetes is classified using a dual attention-based deep cat convolutional stacked sparse autoencoder model (DA_DCC_SSAE). The proposed study improves the results and proves that the proposed method produces better results in terms of accuracy (98.4% - dataset-1, 98% - dataset-2, 97.4% - dataset-3, and 96.8% - dataset-4.
Related Concept Videos
01:17Convolution Properties II
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
01:20Convolution Properties I
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
02:27Predicting Molecular Geometry
00:57Antibiotic Selection
01:20Pathophysiology of Diabetes
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.