It is obvious that the results of an ensemble of CNNs are better than just one single CNNs. Also, the proposed method introduces a new simple type of multi-focus images dataset. It simply changes the arranging of the patches of the multi-focus datasets, which is very useful for obtaining...
close to the infrared/visible source and pushed far away from the visible/infrared source in the representation space. We further exploit image characteristics to provide data-sensitive weights, allowing our loss function to build a more reliable relationship with source images. A multi-level attentio...
The major contributions of this research are: 1) Investigate two different feature fusion approaches (Fusion-1 & Fusion-2) for effective feature extraction, 2) Investigate the effect of ensemble learning using three different classifiers in the last block of CNN, 3) Embedding separable convolution ...
Recent research has focused on improving the resolution and feature fusion capabilities of deep learning models in remote sensing applications. Wang et al. proposed MSWAGAN, a multiscale window attention transformer for multispectral image super-resolution, which significantly enhances the quality and deta...
for Intrusion Detection (HAEnID), an innovative and powerful method to enhance intrusion detection, different from the conventional techniques. HAEnID is composed of a string of multi-layered ensemble, which consists of a Stacking Ensemble (SEM), a Bayesian Model Averaging (BMA), and a ...
fine-tuned a pre-trained multitask RNA binding protein model to develop DeepLocRNA for predicting subcellular localization of various RNAs [39]. In summary, an increasing number of studies have explored the application of machine learning methods to predict the subcellular localization of lncRNA, ...
The fusion-based bag-of-neural network(FuBoNN) model for forecasting Factor of Safety (FOS) employs a suite of IoT devices in edge networks. The newly integrated dataset is fed into the population-based neural network (NN), with the top-performing NN being selected in each iteration to impa...
Laryngeal cancer exhibits a notable global health burden, with later-stage detection contributing to a low mortality rate. Laryngeal cancer diagnosis on throat region images is a pivotal application of computer vision (CV) and medical image diagnoses in
Taherkhani, Cosma, and McGinnity (2020) presented AdaBoost-CNN for multi-class imbalanced datasets using transfer learning. Kumar, Biswas, and Devi (2019) presented Tomek link undersampling-based boosting (TLUSBoost), which combines Tomek link and redundancy-based undersampling (TLRUS) (Devi, ...
What is the comparative performance of different CNN architectures in the classification of ECG images? 3. Can ensemble-learning methods enhance the accuracy of ECG image classification, and what is the optimal ensemble configuration for this task? The answers to these questions hold profound implica...