DFPF-Net: Dynamically Focused Progressive Fusion Network for Remote Sensing Change Detection by Chengming Wang, Peng Duan, Jinjiang Li

“…To address these challenges, we propose the dynamically focused progressive fusion network (DFPF-Net) to simultaneously tackle global and local noise influences. On one hand, we utilize a pyramid vision transformer (PVT) as a weight-shared siamese network to implement change detection, efficiently fusing multilevel features extracted from the pyramid structure through a residual based progressive enhanced fusion module (PEFM). …”
Get full text

Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines by Anxin Zhao, Qiuhong Zheng, Liang Li

“…For the discriminator network, a global-local discriminator structure is designed to optimize overall illumination while adaptively enhancing shadow and highlight regions. …”
Get full text

Research on Diffusion Kurtosis Imaging of the Brain Based on Deep Learning by Rui Chen, Jingwen Yue, Rong Li, Zijian Jia

“…The DKI-Transformer model can extract global voxel correlation characteristics, the estimation results have a high structural similarity index compared to the reference labeling and exhibit distinct boundaries of microscopic features. …”
Get full text

A hybrid learning approach for MRI-based detection of alzheimer’s disease stages using dual CNNs and ensemble classifier by Sepideh Zolfaghari, Atra Joudaki, Yashar Sarbaz

“…Initially, these images were resized and augmented before being input into Network 1 and Network 2, which have different structures and layers to extract important features. …”
Get full text

Bearing fault diagnosis based on improved DenseNet for chemical equipment by Wu Huiyong, Jiang Kuan, Wang Yanyu

“…The alternating stacking strategy of channel and spatial attention further improves the feature extraction ability at different scales. This optimized structure increases the diversity and discriminative power of feature representations, enhancing the model’s performance in fault diagnosis tasks. …”
Get full text

Distinct brain age gradients across the adult lifespan reflect diverse neurobiological hierarchies by Nicholas Riccardi, Alex Teghipco, Sarah Newman-Norlund, Roger Newman-Norlund, Ida Rangus, Chris Rorden, Julius Fridriksson, Leonardo Bonilha

“…We address this gap by leveraging a data-driven, region-specific brain age approach in 335 neurologically intact adults, using a convolutional neural network (volBrain) to estimate regional brain ages directly from structural MRI without a predefined set of morphometric properties. …”
Get full text

GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

“…To overcome these limitations, this paper introduces GhostConv+CA-YOLOv8n, a lightweight object detection framework was proposed, which incorporates several innovative features: GhostConv replaces standard convolutional operations with computationally efficient ghost modules in the YOLOv8n’s backbone structure, reducing parameters by 40,458 while maintaining feature richness; a Context Aggregation (CA) module is applied after the large and medium-sized feature maps were output by the YOLOv8n’s neck structure. …”
Get full text

MRFP-Mamba: Multi-Receptive Field Parallel Mamba for Hyperspectral Image Classification by Xiaofei Yang, Lin Li, Suihua Xue, Sihuan Li, Wanjun Yang, Haojin Tang, Xiaohui Huang

“…The proposed MRFP-Mamba introduces two key innovation modules: (1) A multi-receptive-field convolutional module employing parallel <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mo>×</mo><mn>1</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3</mn><mo>×</mo><mn>3</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5</mn><mo>×</mo><mn>5</mn></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7</mn><mo>×</mo><mn>7</mn></mrow></semantics></math></inline-formula> kernels to capture fine-to-coarse spatial features, thereby improving discriminability for multi-scale objects; and (2) a parameter-optimized Vision Mamba branch that models global spatial–spectral relationships through structured state space mechanisms. …”
Get full text

An Effective Feature Extraction Method for Tomato Leafminer - Tuta Absoluta (Meyrick) (Lepidoptera: Gelechiidae) Classification by Tahsin Uygun, Serhat Kiliçarslan, Cemil Közkurt, Mehmet Metin Ozguven

“…Abstract Global warming caused by climate change causes some problems in agricultural production. …”
Get full text

Leveraging artificial intelligence for diagnosis of children autism through facial expressions by Mahmood A. Mahmood, Leila Jamel, Nazik Alturki, Medhat A. Tawfeek

“…Using autism spectrum disorder (ASD) diagnosed child RGB images data, researchers evaluated six prevalent deep learning structures: DenseNet201, ResNet152, VGG16, VGG19, MobileNetV2, and EfficientNet-B0. …”
Get full text

Transformer-based ensemble model for dialectal Arabic sentiment classification by Omar Mansour, Eman Aboelela, Remon Talaat, Mahmoud Bustami

“…The complexity arises from the language’s intricate semantic and morphological structures, along with the existence of multiple dialects. …”
Get full text

Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…Abstract Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. …”
Get full text

InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. …”
Get full text

GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Current methods struggle to simultaneously preserve global structure, model cellular dynamics, and handle technical noise effectively. …”
Get full text

BDSER-InceptionNet: A Novel Method for Near-Infrared Spectroscopy Model Transfer Based on Deep Learning and Balanced Distribution Adaptation by Jianghai Chen, Jie Ling, Nana Lei, Lingqiao Li

“…The key contributions include: (1) RX-Inception multi-scale structure: Combines Xception’s depthwise separable convolution with ResNet’s residual connections to strengthen global–local feature coupling. (2) Squeeze-and-Excitation (SE) attention: Dynamically recalibrates spectral band weights to enhance discriminative feature representation. (3) Systematic evaluation of six transfer strategies: Comparative analysis of their impacts on model adaptation performance. …”
Get full text

Cross-Domain Person Re-Identification Based on Multi-Branch Pose-Guided Occlusion Generation by Pengnan Liu, Yanchen Wang, Yunlong Li, Deqiang Cheng, Feixiang Xu

“…Secondly, a multi-branch feature fusion structure is constructed. By fusing different feature information from the global and occlusion branches, the diversity of features is enriched. …”
Get full text

Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy by Renyuan Liu, Yunyu Shi, Xian Tang, Xiang Liu

“…In this paper, we propose a convolutional neural network (CNN) architecture that combines the attention mechanism with multi-scale feature fusion; specifically, the features are weighted by designing a bottleneck layer that combines the Squeeze-and-Excitation (SE) attention mechanism to highlight the important information and by applying a multi-scale feature fusion method to enable the network to capture both the global structure and the local details of Chinese characters. …”
Get full text

Bidirectional Mamba with Dual-Branch Feature Extraction for Hyperspectral Image Classification by Ming Sun, Jie Zhang, Xiaoou He, Yihe Zhong

“…The HSI classification methods based on convolutional neural networks (CNNs) have greatly improved the classification performance. …”
Get full text

A New and Tested Ionospheric TEC Prediction Method Based on SegED-ConvLSTM by Yuanhang Liu, Yingkui Gong, Hao Zhang, Ziyue Hu, Guang Yang, Hong Yuan

“…We compared our model with traditional image-based models such as convolutional neural networks (CNNs), convolutional long short-term memory networks (ConvLSTMs), a self-attention mechanism-integrated ConvLSTM (SAM-ConvLSTM) model, and one-day predicted ionospheric products (C1PG) provided by the Center for Orbit Determination in Europe (CODE). …”
Get full text

SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting by Zhuang Li, Zhenyu Lu, Yizhe Li, Xuan Liu

“…Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. Specifically, the multi-scale convolutional block attention module (MSCBAM) captures local multi-scale features, while the gated attention feature fusion unit (GAFFU) adaptively regulates the fusion intensity, thereby enhancing spatial structure and temporal continuity in a synergistic manner. …”
Get full text