Crop field extraction from high resolution remote sensing images based on semantic edges and spatial structure map by Liegang Xia, Ruiyan Liu, Yishao Su, Shulin Mi, Dezhi Yang, Jun Chen, Zhanfeng Shen

“…In recent years, deep convolutional neural networks (CNNs) have gained significant attention for edge detection tasks. …”
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Deep Learning Architectures for Single-Label and Multi-Label Surgical Tool Classification in Minimally Invasive Surgeries by Hisham ElMoaqet, Hamzeh Qaddoura, Mutaz Ryalat, Natheer Almtireen, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Thomas Neumuth, Knut Moeller

“…We explored different modeling scenarios with respect to the location and number of SE blocks for multi-label surgical tool classification in the Cholec80 dataset. …”
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Rural Renewable Energy Resources Assessment and Electricity Development Scenario Simulation Based on the LEAP Model by Hai Jiang, Haoshuai Jia, Yong Qiao, Wenzhi Liu, Yijun Miao, Wuhao Wen, Ruonan Li, Chang Wen

“…The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. …”
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Short‐term load forecasting facilitated by edge data centres: A coordinated edge‐cloud approach by Junlong Li, Lurui Fang, Xiangyu Wei, Mengqiu Fang, Yue Xiang, Peipei You, Chao Zhang, Chenghong Gu

“…Then, it adopts the rough forecasting results and accessible data for all LV networks within an MV network to train the convolutional neural networks and gated recurrent unit (CNN‐GRU) network. …”
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PZS‐Net: Incorporating of Frame Sequence and Multi‐Scale Priors for Prostate Zonal Segmentation in Transrectal Ultrasound by Jianguo Ju, Qian Zhang, Pengfei Xu, Tiange Liu, Cheng Li, Ziyu Guan

“…Then, a multi‐scale fusion (MSF) module that utilizes three parallel branches with different atrous convolutions is designed. The MSF module is placed at the bottleneck layer to dynamically fuse multi‐scale context information from high‐level features. …”
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TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction by Wenlong Wang, Peng Yu, Mengmeng Li, Xiaojing Zhong, Yuanrong He, Hua Su, Yunxuan Zhou

“…Therefore, methods integrating convolutional neural networks (CNNs) and visual transformers (ViTs) are popular nowadays. …”
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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

“…First, a dynamic adaptive dilation factor is incorporated into the TCN, thereby enabling the model to adjust its receptive field dynamically, which facilitates the capture of long- and short-term dependencies across different scales, allowing a more comprehensive representation of equipment degradation patterns. …”
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An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians by Abdurrahim Yilmaz, Kadir Gem, Mucahit Kalebasi, Rahmetullah Varol, Zuhtu Oner Gencoglan, Yegor Samoylenko, Hakan Koray Tosyali, Guvenir Okcu, Huseyin Uvet

“…The YOLOv5 architecture was employed for the object detection model, while three different pre-trained deep neural network (DNN) architectures were used for classification, applying transfer learning. …”
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Lightweight CNN model for automatic detection and depth estimation of subsurface voids using GPR B-scan data by Abdelaziz Mojahid, Driss EL Ouai, Khalid EL Amraoui, Khalil EL-Hami, Hamou Aitbenamer, Jochem Verrelst, Pier Matteo Barone

“…The model was trained on 1408 augmented B-scans collected with 200 and 400 ​MHz antennas across various subsurface materials, ensuring exposure to a wide range of material types with different electromagnetic properties. Testing experiments were performed using eight profiles where cavity detection was confirmed by borehole data. …”
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SVD-Based Feature Reconstruction Metric Network With Active Contrast Loss for Few-Shot SAR Target Recognition by Jia Zheng, Ming Li, Xiang Li, Peng Zhang, Yan Wu

“…Synthetic aperture radar (SAR) automatic target recognition (ATR) methods based on convolutional neural networks require a large number of samples to achieve good generalize. …”
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PC3D-YOLO: An Enhanced Multi-Scale Network for Crack Detection in Precast Concrete Components by Zichun Kang, Kedi Gu, Andrew Yin Hu, Haonan Du, Qingyang Gu, Yang Jiang, Wenxia Gan

“…Furthermore, evaluations on the CRACKS_MANISHA and DECA datasets also confirm the proposed model’s strong generalization capability across different data domains.…”
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Hyperspectral Detection of Pesticide Residues in Black Vegetable Based on Multi-Classifier Entropy Weight Method by Rongchang Jiang, Guoqiang Zhuang, Shijie Xie, Yang Wang, Guoqi Zhang, Dandan Qu, Wanzhi Wen

“…Bailey) by proposing a multi-classifier entropy weighted method algorithm that combines hyperspectral technology and the entropy weight method. 10 black vegetable samples were sprayed with each of the four different pesticides (trichlorfon, propargite, cypermethrin, and imidacloprid) at concentrations of 0.10, 2.00, 0.20, and 2.00 mg/kg, respectively. …”
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Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

“…Cut selection scoring rules are usually weighted sums of different measurements, where the weights are parameters. …”
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Hybrid modeling approaches for predicting COVID-19 mortality: A comparative study across USA, France, and India by B. Uppalaiah, D. Mallikarjuna Reddy, K. Rajalakshmi, P. Vignesh, V. Govindan, Siriluk Donganont

“…The findings indicate notable disparities in prediction accuracy, with each model exhibiting both advantages and drawbacks across different epidemiological contexts. The GP-LSTM and GP-CNN hybrid models surpassed the GP-RBM model in accuracy and efficiency, especially with the datasets from France and India. …”
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Hybrid Wavelet-Attention Model for Detecting Changes in High-Resolution Remote Sensing Images by Lhuqita Fazry, MGS M. Luthfi Ramadhan, Alif Wicaksana Ramadhan, Muhammad Febrian Rachmadi, Aprinaldi Jasa Mantau, Lukito Edi Nugroho, Chi-Hung Chi, Wisnu Jatmiko

“…Change detection is a remote sensing task for detecting a change from two satellite images in the same area, while being taken at different times. Change detection is one of the most difficult remote sensing tasks because the change to be detected (real-change) is mixed with apparent changes (pseudo-change) due to differences in the two images, such as brightness, humidity, seasonal differences, etc. …”
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Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN by Haijun Liu, Yan Ma, Huijun Le, Liangchao Li, Rui Zhou, Jian Xiao, Weifeng Shan, Zhongxiu Wu, Yalan Li

“…The proposed Multichannel ED-PredRNN was compared with COPG, ConvLSTM, and convolutional gated recurrent unit (ConvGRU) from multiple perspectives on a data set of 6 years, including comparisons at different solar activities, time periods, latitude regions, single stations, and geomagnetic storm periods. …”
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An Automated Image-Based Dietary Assessment System for Mediterranean Foods by Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis

“…The food volume estimation subsystem achieves an overall mean absolute percentage error 10.5&#x0025; for 148 different food dishes. <italic>Conclusions:</italic> The proposed automated image-based dietary assessment system provides the capability of continuous recording of health data in real time.…”
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Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios by Shi Lei, He Yi, Jeffrey S. Sarmiento

“…The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. …”
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Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones by Andrew P. Creagh, Frank Dondelinger, Florian Lipsmeier, Michael Lindemann, Maarten De Vos

“…The ability to understand the impact of disease on daily-life between clinical visits, through objective digital outcomes, paves the way forward to better measure and identify signs of disease progression that may be occurring out-of-clinic, to monitor how different patients respond to various treatments, and to ultimately enable the development of better, and more personalised care.…”
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YOLOv8-SDC: An Improved YOLOv8n-Seg-Based Method for Grafting Feature Detection and Segmentation in Melon Rootstock Seedlings by Lixia Li, Kejian Gong, Zhihao Wang, Tingna Pan, Kai Jiang

“…Additionally, the DWR module enables the network to more flexibly adapt to the perception accuracy of different cotyledons, growth points, stem edges, and contours. …”
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