This paper proposes a hybrid CNN-transformer network called HCTSpeckle, an encoder-decoder network with a fusion block designed to enhance ultrasound images. The fusion block combines swin ...

Motivated by the three-blade symmetrical structure of WTs, we propose a new symmetry-aware pitch feature encoder-decoder network named PitchNet. A group feature encoding strategy is first designed to ...

Feature selection is one of the most important techniques for dimension reduction in a wide range of tasks. This paper presents a simple yet efficient method for unsupervised feature selection. To ...

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 ...

The proposed encoder–decoder-based ERSCDNet model uses an attention-based encoder and decoder block and a modified new spatial pyramid pooling block at each stage of the decoder part, which ...

The objective is to present EddyNet, a cutting-edge deep-learning framework specifically developed for the automatic identification and categorization of ocean eddies. EddyNet incorporates a ...

Recent advances in generalized image understanding have seen a surge in the use of deep convolutional neural networks (CNN) across a broad range of image-based detection, classification and prediction ...

This paper proposes an Encoder-Decoder neural network architecture with Attention Mechanism for solving the DRC-FJSSP using Deep Q-Learning. In the DRC-FJSSP the number of operations to schedule is ...

Wireless networks are vital for implementing flexible Networked Controlled Systems (NCS) in distributed applications, yet they introduce sampling errors, delays, and packet losses that can compromise ...

Classifying elephant and mouse flows is pivotal for meeting Quality of Service requirements in Software-Defined Data Center Networks. Diverse approaches have addressed this classification problem by ...

The fully convolutional network (FCN) with an encoder-decoder architecture has been the standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an encoder to capture ...

To overcome these limitations, we proposed an attention guided encoder-decoder network with multi-scale context aggregation to achieve more accurate segmentation of land cover. Based on the structure ...