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Found 15 papers, 3 papers with code
COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images
Moreover, the various boosted channels are obtained by introducing the new CB and Transfer Learning (TL) concept in STM blocks to capture small illumination and texture variations of COVID-19-specific images.
A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection
In this regard, we have developed a new malware detection framework, Deep Squeezed-Boosted and Ensemble Learning (DSBEL), comprised of novel Squeezed-Boosted Boundary-Region Split-Transform-Merge (SB-BR-STM) CNN and ensemble learning.
Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework
The proposed DBEL framework implicates the stacking of prominent and diverse boosted channels and provides the generated discriminative features of the developed Boosted-BR-STM to the ensemble of ML classifiers.
Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN
Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients.
COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs
In the first phase, a novel SB-STM-BRNet CNN is developed, incorporating a new channel Squeezed and Boosted (SB) and dilated convolutional-based Split-Transform-Merge (STM) block to detect COVID-19 infected lung CT images.
Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D Environments with Dynamic Obstacles
An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation.
A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron
The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide, creating a health crisis that infected millions of lives, as well as devastating the global economy.
IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN
The proposed architecture exploits the concepts of edge and smoothing, multi-path dilated convolutional operations, channel squeezing, and boosting in CNN.
A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI
While in the second phase, a new hybrid features fusion-based brain tumor classification approach is proposed, comprised of dynamic-static feature and ML classifier to categorize different tumor types.
Detection of Tumour Infiltrating Lymphocytes in CD3 and CD8 Stained Histopathological Images using a Two-Phase Deep CNN
Results: The empirical evaluation on samples from LYSTO dataset shows that the proposed LSTAM-Net can learn variations in the images and precisely remove the hard negative stain artifacts with an F-score of 0. 74.
Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder
The performances of the proposed MRI segmentation based DAE architectures have been tested using a 3D MRI shoulder muscle dataset using the hold-out cross-validation technique.
Malware Classification Using Deep Boosted Learning
In the hybrid learning, Deep features are extracted from customized CNN architectures and fed into the conventional machine learning classifier to improve the classification performance.
COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN
In this work, a new classification technique CB-STM-RENet based on deep Convolutional Neural Network (CNN) and Channel Boosting is proposed for the screening of COVID-19 in chest X-Rays.
Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks
In the second stage, the CT images classified as infectious images are provided to the segmentation models for the identification and analysis of COVID-19 infectious regions.
Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry
However, the development of a churn prediction system for a telecom industry is a challenging task, mainly due to the large size of the data, high dimensional features, and imbalanced distribution of the data.
