In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments.

In the current study, a new pretrained Convolutional Neural Network (CNN) model is proposed for the extraction of deep features. In the CNN architecture, an average-pooling layer and a max-pooling ...

A hydraulic piston pump is an essential component of a hydraulic transmission system and is extensively used in contemporary industrial settings. Therefore, fault diagnosis of piston pumps is a ...

This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and edge servers’ available ...

In recent past, Convolutional Neural Networks (CNN) have been utilized in kind of areas, including style order. Web-based media, web-based business, and legitimate code are widely appropriate during ...

Accurate image segmentation of skin lesions is crucial for the detection and treatment of skin cancer. Based on the modern state space model Mamba, a novel hybrid CNN-Mamba network (BEFNet) is ...

This paper presents a block-based embedded convolutional neural network (CNN) for gesture classification on field-programmable gate array (FPGA) in real time. Gesture recognition is an important tool ...

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new ...

In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic ...

The project developed a highly accurate CNN-based model for plant disease detection, achieving perfect performance metrics (1.0 accuracy, precision, recall, F1-score), offering significant benefits ...