Specifically, the Resnet152v2 model serves as foundational architecture for the model, enabling accurate classification of distinct objects. In addition to Resnet152V2 base, the proposed work incorporates two supplementary dense neural network layers. These additional layers enhance the model capacity to...
In this environment, existing intrusion detection algorithms fail to satisfy the requirements of prompt responses, heavy network load management, inadequate extraction of features, and imprecise model classification. In this work, the imbalanced data problem in the input dataset is mitigated using the ...
The primary objective is to design and deploy this modified ResNet152v2 model for pneumonia prediction from chest X-rays, achieving high accuracy while minimizing computational complexity and reducing computation time. This model outperformed well when compared with the existing methods and produced ...
The model underwent rigorous training, resulting in a minimum training loss of 0.0397 and a validation loss of 0.0044. The achieved average precision (98.66%), recall (97.73%), and F1-score (98.2%) values further validate the model's effectiveness. This study presents a novel combination of ...
The experimental outcomes demonstrate an impressive 100% overall accuracy in type comparison using ResNet152V2, thereby substantiating its viability as a model for mammogram type detection and classification. This study thus provides a compelling argument for the application of ResNet152V2 in...
Furthermore, simulation experiments are carried out on the ToN-IoT and BoT-IoT datasets, and the outcomes demonstrate that our suggested model performs better than the existing models, with accuracy levels of 99.20% and 99.31%, respectively. These findings show that this approach is successful in...