AFN-Net: Adaptive Fusion Nucleus Segmentation Network Based on Multi-Level U-Net by Ming Zhao, Yimin Yang, Bingxue Zhou, Quan Wang, Fu Li

“…This DSCOM effectively preserves high-resolution information and improves the segmentation accuracy of small targets and boundary regions through multi-level convolution operations and channel optimization. Finally, we proposed an Adaptive Fusion Loss Module (AFLM) that effectively balances different lossy targets by dynamically adjusting weights, thereby further improving the model’s performance in segmentation region consistency and boundary accuracy while maintaining classification accuracy. …”
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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

“…Traditional modeling methods exhibit certain limitations in handling these factors, making it difficult to achieve effective adaptation across different scenarios. Specifically, data distribution shifts and mismatches in multi-scale features hinder the transferability of models across different crop varieties or instruments from different manufacturers. …”
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FruitQuery: A lightweight query-based instance segmentation model for in-field fruit ripeness determination by Ziang Zhao, Yulia Hicks, Xianfang Sun, Chaoxi Luo

“…FruitQuery runs in an end-to-end way and incorporates the convolution and Transformer to capture fine-grained features related to different fruits at different ripeness stages.Extensive experiments on the combined fruit dataset demonstrate that our FruitQuery achieves the highest average precision of 67.02 with only 14.08M parameters, outperforming 13 state-of-the-art models with 33 variants. …”
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Multi-level User Interest and Multi-intent Fusion for Next Basket Recommendation by WEI Chuyuan, YUAN Baojie, WANG Changdong

“…A cross-level contrastive learning paradigm is also designed to combine item representations from different levels in order to enhance the semantic information between items at different levels. …”
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Multi-Dimensional Feature Perception Network for Open-Switch Fault Diagnosis in Grid-Connected PV Inverters by Yuxuan Xie, Yaoxi He, Yong Zhan, Qianlin Chang, Keting Hu, Haoyu Wang

“…This network captures multi-scale fault features under complex operating conditions through a multi-dimensional dilated convolution feature enhancement module and extracts non-causal relationships under different conditions using convolutional feature fusion with a Transformer. …”
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A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine-Harmony Loss by Xuefeng Chen, Liangyu Huang

“…The Spatial Bias module enables the model to focus on local facial features while capturing the dependencies between different facial regions. Additionally, a new loss function called Cosine-Harmony Loss is designed. …”
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Enhanced YOLOv8-based pavement crack detection: A high-precision approach. by ZuXuan Zhang, HongLi Zhang, TongJia Zhang

“…At the same time, the recognition rate of different types of cracks in the model reached more than 86%, which increased by 20.5% compared with the YOLO11 model. …”
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Hyperspectral target detection based on graph sampling and aggregation network. by Tie Li, Hongfeng Jin, Zhiqiu Li

“…Concurrently, it exhibits a remarkable adaptability to the diverse characteristics of different datasets, thus validating its high level of accuracy and robustness.…”
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Logatome and Sentence Recognition Related to Acoustic Parameters of Enclosures by Jedrzej KOCIŃSKI, Edward OZIMEK

“…Six enclosures were chosen: a church, an assembly hall of a music school, two courtrooms of different volumes, a typical auditorium and a university concert hall. …”
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A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave Radar by Zisheng Li, Ken Chen, Yaoqin Xie

“…To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. …”
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A YOLOv8 algorithm for safety helmet wearing detection in complex environment by Chunning Song, Yinzhong Li

“…Finally, propose a new structure of information aggregation, It better fuses information about target characteristics and context at different scales, allowing the information to flow between channels, thus improving the algorithm’s performance. …”
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An Efficient Recommendation Algorithm Based on Heterogeneous Information Network by Ying Yin, Wanning Zheng

“…Heterogeneous information networks can naturally simulate complex objects, and they can enrich recommendation systems according to the connections between different types of objects. At present, a large number of recommendation algorithms based on heterogeneous information networks have been proposed. …”
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Proposal of a Viscous Model for Nonviscously Damped Beams Based on Fractional Derivatives by Mario Lázaro, Jose M. Molines-Cano, Ignacio Ferrer, Vicente Albero

“…The theoretical results are contrasted with two different numerical examples.…”
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A deep learning short-term traffic flow prediction method considering spatial-temporal association by Yang ZHANG, Yue HU, Dongrong XIN

“…The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.…”
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AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN by Zhige He, Yuanqing He

“…The most important part of this is the capability of discriminate the different type of objects correctly. However, the existing detection models have the problems of degradation, lacking of detection capability for deformed and small objects and single feature extraction, causing low detection accuracy. …”
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Image semantic segmentation with hierarchical feature fusion based on deep neural network by Dawei Yang, Yan Du, Hongli Yao, Liyan Bao

“…Deep neural network can not effectively use the feature information between different levels. The accuracy of image semantic segmentation is damaged. …”
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A Multi-Source Domain Adaptation Method for Bearing Fault Diagnosis with Dynamically Similarity Guidance on Incomplete Data by Juan Tian, Shun Zhang, Gang Xie, Hui Shi

“…It enhances diagnostic performance in the target domain by transferring knowledge across diverse domains. DS-MDAN uses convolution kernels of different scales to extract multi-scale feature information and achieves feature fusion through upsampling and operations like addition and concatenation. …”
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Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants by Xiandan Du, Zhongfa Zhou, Denghong Huang

“…The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. …”
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A Survey on Immersive Cyber Situational Awareness Systems by Hussain Ahmad, Faheem Ullah, Rehan Jafri

“…In particular, our survey has identified visualization and interaction techniques, evaluation mechanisms, and different levels of cyber situational awareness (i.e., perception, comprehension, and projection) for ICSA systems. …”
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Multimodal anomaly detection in complex environments using video and audio fusion by Yuanyuan Wang, Yijie Zhao, Yanhua Huo, Yiping Lu

“…Multi-stream network architecture and cross-attention fusion mechanism are also adopted to comprehensively consider different factors such as color, texture, and motion, and further improve the robustness and generalization ability of the model. …”
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