The Application of the Gestalt Theory in Music Psychotherapy for Piano by Fukun Cai

“…The wavelet energy entropy (WEE) characteristics of EEG signals were extracted as the input of the designed and optimized deep belief network model, and the average emotional classification accuracy of EEG signals in the left and right brain regions could be obtained as 84.20% and 83.07%, respectively, under the condition of distinguishing brain regions and different music environments. …”
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Rice disease and pest identification integrating albert pre-trained language model and improved BILSTM by Zhigui Dong, Tianyi Yang, Yanchao Wang

“…At the same time, a bidirectional long short-term memory network is introduced for further recognition, and the model is optimized using conditional random fields to design a fusion algorithm for rice disease and pest recognition. …”
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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

“…To address these challenges, we propose Med-DGTN, a dynamically integrated framework designed to advance multi-label classification performance in clinical imaging analytics.MethodsThe proposed Med-DGTN (Dynamic Graph Transformer Network with Adaptive Wavelet Fusion) introduces three key innovations: (1) A cross-modal alignment mechanism integrating convolutional visual patterns with graph-based semantic dependencies through conditionally reweighted adjacency matrices; (2) Wavelet-transform-enhanced dense blocks (WTDense) employing multi-frequency decomposition to amplify low-frequency pathological biomarkers; (3) An adaptive fusion architecture optimizing multi-scale feature hierarchies across spatial and spectral domains.ResultsValidated on two public medical imaging benchmarks, Med-DGTN demonstrates superior performance across modalities: (1) Achieving a mean average precision (mAP) of 70.65% on the retinal imaging dataset (MuReD2022), surpassing previous state-of-the-art methods by 2.68 percentage points. (2) On the chest X-ray dataset (ChestXray14), Med-DGTN achieves an average Area Under the Curve (AUC) of 0.841. …”
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Dual-robot formation transport in random environment and narrow restricted area via improved DDPG navigation by Liang Tang, Ronggeng Ma, Bowen Chen, Yisen Niu

“…Specifically, the paper first optimizes the reward function module in the DDPG network to incorporate a decision-swapping reward mechanism for training the formation's navigation capability. …”
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Comparison of Electric Vehicle’s Energy Consumption Factors for Different Road Types by Enjian Yao, Zhiqiang Yang, Yuanyuan Song, Ting Zuo

“…Obtaining the cost for each link (i.e., link energy consumption) in road networks plays a key role in energy-optimal route planning process. …”
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Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk by Changxiao Zhao, Wei Zhang, Fangzheng Dong, Jun Dai, Lei Dong

“…The simulation results show that the average propagation probability of the optimized resource allocation scheme decreases by 17.4%, and the overall fault propagation risk of the resource network decreases by 50.3%, indicating that the proposed resource allocation method can effectively improve the safety of the IMA system.…”
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A Hierarchical Control Framework for Coordinating CAV-Dedicated Lane Allocation and Signal Timing at Isolated Intersections in Mixed Traffic Environments by Feng Chen, Cunbao Zhang, Yu Cao

“…The framework employs two collaborative agents based on the dueling double deep Q-network (D3QN) algorithm. The upper-level agent recommends optimal CAV-DLs configurations based on long-term traffic flow patterns, while the lower-level agent focuses on real-time signal control by adjusting signal parameters and green time allocations in response to current traffic demand. …”
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Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM by Xiao Lai, Guanglong Fu

“…At the target detection level, we integrate a Convolutional Block Attention Module (CBAM) into the YOLOv5s backbone network. This significantly reduces missed detections and low-confidence predictions in dense stacking scenarios, improving detection speed by 28.04% and increasing mean average precision (mAP) by 5.31%. …”
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RE-YOLOv5: Enhancing Occluded Road Object Detection via Visual Receptive Field Improvements by Tianyu Li, Xuanrui Xiong, Yuan Zhang, Xiaolin Fan, Yushu Zhang, Haihong Huang, Dan Hu, Mengting He, Zhanjun Liu

“…The proposed method achieves a mean average precision (mAP) of 82.04% on KITTI, representing an improvement of 2.34% over the baseline model. …”
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Genetic Profiles of Purine, Uric Acid, Superoxide Dismutase, and Growth in Thai Slow-Growing Chickens by Wuttigrai Boonkum, Vibuntita Chankitisakul, Srinuan Kananit, Veeraya Tuntiyasawasdikul, Vatsana Sirisan, Wootichai Kenchaiwong

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Forest Three-Dimensional Reconstruction Method Based on High-Resolution Remote Sensing Image Using Tree Crown Segmentation and Individual Tree Parameter Extraction Model by Guangsen Ma, Gang Yang, Hao Lu, Xue Zhang

“…Then, a Tree Parameter and Reconstruction Network (TPRN) was constructed to directly estimate key structural parameters (height, DBH etc.) from crown images and generate tree 3D models. …”
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Photovoltaic Farm Power Generation Forecast Using Photovoltaic Battery Model with Machine Learning Capabilities by Agboola Benjamin Alao, Olatunji Matthew Adeyanju, Manohar Chamana, Stephen Bayne, Argenis Bilbao

“…The lowest average computational time of 17.99s was achieved with the ANFIS model across all the datasets compared to other models.…”
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Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System by Yongkang Li, Qing He, Yongqiang Liu, Amina Maituerdi, Yang Yan, Jiao Tan

“…Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. …”
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Deep Joint Demosaicking and Super-Resolution for Spectral Filter Array Images by Abdelhamid N. Fsian, Jean-Baptiste Thomas, Jon Y. Hardeberg, Pierre Gouton

“…To this end, we use a two-branch framework, namely a pseudo-panchromatic image network and a pre-demosaicking sub-branch coupled with a novel deep residual demosaicking and super resolution module. …”
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Deep learning based quantitative cervical vertebral maturation analysis by Fulin Jiang, Abbas Ahmed Abdulqader, Yan Yan, Fangyuan Cheng, Tao Xiang, Jinghong Yu, Juan Li, Yong Qiu, Xin Chen

“…Results Upon precise calibration with optimal ROI size, the landmark localization registered an average error of 0.66 ± 0.46 mm and a success detection rate of 98.10% within 2 mm. …”
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A cluster of paediatric hydrocephalus in Mohale’s Hoek district of Lesotho, 2013-2016 by Rudzani Climentine Mathebula, Motheba Lerotholi, Olufemi Olamide Ajumobi, Thabelo Makhupane, Limpho Maile, Lazarus Rugare Kuonza

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Improved Asphalt Pavement Crack Detection Model Based on Shuffle Attention and Feature Fusion by Tursun Mamat, Abdukeram Dolkun, Runchang He, Yonghui Zhang, Zulipapar Nigat, Hanchen Du

“…The experimental results demonstrate a stepwise improvement in the model’s mean average precision (mAP) with each enhancement to the original network. …”
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YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection by Yuxi Huang, Hong Zhao, Jie Wang

“…Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. …”
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Dimensionless Analysis of Rough Roadway Airflow Distribution Based on Numerical Simulations by Zongcheng Jia, Qiang Zhao, Yan Zhao, Baoyu Cui, Tao Song

“…As resources are extracted from the deeper sections of a mine, the ventilation network becomes increasingly complex. Consequently, determining the optimal installation location for speed-measuring equipment that accurately reflects the average wind speed along the roadway remains a challenging task. …”
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Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images by Hiroki Naito, Tomohiko Ota, Kota Shimomoto, Fumiki Hosoi, Tokihiro Fukatsu

“…The system integrates a deep learning model, the Mask ResNet-50 convolutional neural network, to count harvestable fruits from images and a predictive algorithm to estimate working time based on the fruit count. …”
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