Image dehazing algorithm based on light-value weighted allocation and multi-layer restricted perception by Dongyang Shi, Sheng Huang, Wei Zhao

“…We propose an image dehazing algorithm based on light-value weighted allocation and multi-layer restricted perception (DWARP) to address these issues. The proposed algorithm first constructs an atmospheric light estimation module based on weighted allocation. …”
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Research on Method for Intelligent Recognition of Deep-Sea Biological Images Based on PSVG-YOLOv8n by Dali Chen, Xianpeng Shi, Jichao Yang, Xiang Gao, Yugang Ren

“…Finally, an improved detection head synergistically fuses all the modules, yielding substantial enhancements in the overall accuracy. …”
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EMSAM: enhanced multi-scale segment anything model for leaf disease segmentation by Junlong Li, Quan Feng, Jianhua Zhang, Jianhua Zhang, Sen Yang

“…EMSAM employs the Local Feature Extraction Module (LFEM) and the Global Feature Extraction Module (GFEM) to extract local and global features from images respectively. …”
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A multi-scale cross-dimension interaction approach with adaptive dilated TCN for RUL prediction by Zhe Lu, Bing Li, Changyu Fu, Liang Xu, Bai Jiang, Zelong Li, Junbao Wu, Siye Jia

“…To address the aforementioned issues, this paper proposes an Adaptive Dilated Temporal Convolutional Network (AD-TCN) approach, incorporating a Multi-Scale Cross-Dimension Interaction Module (MSCDIM) to enhance feature extraction and interaction. …”
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PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection by Haomeng Guo, Zheming Chai, Huize Dai, Lei Yan, Pengle Cheng, Jianhua Yang

“…In order to improve the algorithm’s ability to detect multi-scale defects, this study introduced the ShareSepHead (Share Separated Head) and C2f_SAC (C2f module with Switchable Atrous Convolution) modules. …”
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Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8 by Peidong Jiang, Lai Jiang, Fengyan Wu, Tengteng Che, Ming Wang, Chuandong Zheng

“…This study developed an improved YOLOv8 model, named MCD-YOLOv8, for identifying traces of soil-dwelling termite activity, based on the Monte Carlo random sampling algorithm and a lightweight module. The Monte Carlo attention (MCA) module was introduced in the backbone part to generate attention maps through random sampling pooling operations, addressing cross-scale issues and improving the recognition accuracy of small targets. …”
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DVCW-YOLO for Printed Circuit Board Surface Defect Detection by Pei Shi, Yuyang Zhang, Yunqin Cao, Jiadong Sun, Deji Chen, Liang Kuang

“…Next, within the neck structure, the C2f module is substituted with the more lightweight VOVGSCSP module, thereby reducing model redundancy, simplifying model complexity, and enhancing detection speed. …”
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WormNet: A Multi-View Network for Silkworm Re-Identification by Hongkang Shi, Minghui Zhu, Linbo Li, Yong Ma, Jianmei Wu, Jianfei Zhang, Junfeng Gao

“…Specifically, we introduce a multi-order feature extraction module that captures a wide range of fine-grained features by utilizing convolutional kernels of varying sizes and parallel cardinality, effectively mitigating issues of high individual similarity and diverse poses. …”
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PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion by Heqi Yang, Junming Dong, Cancan Wang, Zhida Lian, Hui Chang

“…First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. The ConvNeXtBlock with inverted bottleneck is introduced in the P4 layer, greatly improving small-target feature capture and semantic understanding. …”
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Using Blockchain in the Registration and Authentication of a Carpooling Application: From Review to Proposal by Lina Sofía Cardona Martínez, Cesar Andrés Sandoval Muñoz, Ricardo Salazar-Cabrera, Álvaro Pachón de la Cruz, Juan Manuel Madrid Molina

“…The registration and authentication module developed in this work allows increased security, scalability, and user adoption for any type of application, e.g., carpooling.…”
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Reliability, validity, and acceptability of the simplified Mandarin Chinese EORTC QLQ-OPT30 for uveal melanoma patients by Yonghui Huang, Panpan Cui, Guangyan Dong, Tongfang Fan

“…The module measures various aspects such as ocular irritation, visual impairment, headaches, concerns about disease recurrence, appearance issues, functional problems related to vision impairment, reading difficulties, functional issues in the treated eye, and driving problems. …”
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Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes by Fukai Zhang, Shuo Zhao, Haiyan Zhang, Yongqiang Ma, Qiang Zhang, Shaopu Wang, Wenjing Chang

“…DrillNet comprises two main components: the YOLO with Multi-Scale Global Context Aggregation Network (YOLO-GC) and the Drill-Count module.The core architecture of YOLO-GC integrates the WaveletPool module, C2f-EMSCP feature extraction unit, GCFPN global context fusion pyramid network, and the oriented bounding box detection head (OBBHead), thereby effectively tackling the issues of insufficient detection accuracy and robustness in challenging coal mine scenarios. …”
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Multi-anchor adaptive fusion and bi-focus attention for enhanced gait-based emotion recognition by Jincheng Li, Xuejing Dai, Ruiao Yan, Chengqing Tang, Yunpeng Li

“…MDT-GCN extracts pose and action features from bone nodes using GCN and TCN networks, respectively. The MAAF module captures multi-scale temporal features to understand emotional expressions across different time ranges, while the BFA module focuses on both local and global features, enhancing the model’s ability to capture complex emotional information. …”
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A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering by Bahman Arasteh, Peri Gunes, Asgarali Bouyer, Farhad Soleimanian Gharehchopogh, Hamed Alipour Banaei, Reza Ghanbarzadeh

“…This paper applied the horse herd optimization algorithm, a distinctive population-based and discrete metaheuristic technique, in clustering software modules. The proposed method’s effectiveness in addressing the module clustering problem was examined by ten real-world standard software test benchmarks. …”
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Semantic enhancement and cross-modal interaction fusion for sentiment analysis in social media. by Guangyu Mu, Ying Chen, Xiurong Li, Li Dai, Jiaxiu Dai

“…Thus, this paper presents a Semantic Enhancement and Cross-Modal Interaction Fusion (SECIF) model for sentiment analysis to address these issues. Firstly, BERT and ResNet extract feature representations from text and images. …”
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Deep Contextual Structure and Semantic Feature Enhancement Stereo Network by Guowei An, Yaonan Wang, Kai Zeng, Qing Zhu, Xiaofang Yuan, Yang Mo

“…At present, thin structure regions, depth discontinuity regions, and large textureless regions are still the difficult issues for stereo matching. To address the blur in thin structure regions and the dilation in depth discontinuity regions, the contextual structure enhancing module is proposed to enhance the extraction ability for local contextual features of the feature extraction network. …”
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Study of conveyor belt deviation detection based on improved YOLOv8 algorithm by Yunfeng Ni, Haixin Cheng, Ying Hou, Ping Guo

“…Firstly, an Enhanced Squeeze-and-Excitation (ESE) module is incorporated into C2f to boost feature extraction for rollers and belts. …”
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Background-Aware Cross-Attention Multiscale Fusion for Multispectral Object Detection by Runze Guo, Xiaojun Guo, Xiaoyong Sun, Peida Zhou, Bei Sun, Shaojing Su

“…First, a background-aware module is designed to calculate the light and contrast to guide the fusion. …”
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Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism by Ding Hu, Zihao Zheng, Yafei Liu, Chengkang Liu, Xiaoguo Zhang

“…Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. …”
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RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning by Yang Jing, Li Weiya

“…The RL-QPSO Net combines quantum-inspired particle swarm optimization (QPSO) and deep reinforcement learning (DRL) modules through a dual control mechanism to achieve path optimization and environmental adaptation. …”
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