Seed multispectral imaging combined with machine learning algorithms for distinguishing different varieties of lettuce (Lactuca sativa L.) by Jinpeng Wei, Zhangyan Dai, Qi Zhang, Le Yang, Zhaoqi Zeng, Yuliang Zhou, Jun Liu, Bingxian Chen

“…This study explores feasibility of rapid, non-destructive identification of different lettuce varieties using multispectral imaging combined with machine learning. We firstly collected seed morphological and spectral data from 15 lettuce varieties using multispectral imaging. …”
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INTERBLOCK ZONES IN THE CRUST OF THE SOUTHERN REGIONS OF EAST SIBERIA: TECTONOPHYSICAL INTERPRETATION OF GEOLOGICAL AND GEOPHYSICAL DATA by K. Zh. Seminsky, N. O. Kozhevnikov, A. V. Cheremnykh, E. V. Pospeeva, A. A. Bobrov, V. V. Olenchenko, M. A. Tugarina, V. V. Potapov, R. M. Zaripov, A.S. Cheremnykh

“…We used structural geological methods for studying faults and fractures, morphostructural analysis (including interpretation of satellite images), self-potential (SP) and resistivity profiling, magnetotelluric (MT) sounding, radon emanation survey, and hydrogeological studies of water occurrences. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. …”
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International trade market forecasting and decision-making system: multimodal data fusion under meta-learning by Yiming Bai, Muhammad Asif

“…Traditional market analysis tools primarily rely on unidimensional data, such as historical trading records and price trends. …”
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High-quality control of receiver functions using a capsule neural networkKey points by Mona H. Hegazi, Ahmad M. Faried, Omar M. Saad

“…The proposed capsule neural network achieved an average precision of 80% on the test set. …”
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Cultural Heritage Color Regeneration: Interactive Genetic Algorithm Optimization Based on Color Network and Harmony Models by Zhonghua Jiang, Qianlong Xia, Zhizhou Wang, Kaiwei Zhu, Qianyu Su, Jiajun Wang, Yirui Huang, Bo Wu, Yan Hong

“…A prototype system for color regeneration is built in Python, and a user interface is designed. The example analysis is conducted using the Yungang Grottoes as the source of color imagery, and image colorization is tested. …”
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Root segmentation of horticultural plants in X-Ray CT images by integrating 2D instance segmentation with 3D point cloud clustering by Mary E. Cassity, Paul C. Bartley, Yin Bao

“…Accurate root segmentation from CT images is integral to studying RSA. Research studies on segmenting roots from CT images have been mainly limited to image processing-based approaches which may require parameter tuning and often lack common segmentation metrics, e.g., Dice and IoU. …”
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Automatic cattle identification system based on color point cloud using hybrid PointNet++ Siamese network by Pyae Phyo Kyaw, Pyke Tin, Masaru Aikawa, Ikuo Kobayashi, Thi Thi Zin

“…The proposed approach employs a hybrid detection method that first applies a 2D depth image detection model before converting the detected region into a color point cloud, allowing for robust feature extraction. …”
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VTA projections to M1 are essential for reorganization of layer 2-3 network dynamics underlying motor learning by Amir Ghanayim, Hadas Benisty, Avigail Cohen Rimon, Sivan Schwartz, Sally Dabdoob, Shira Lifshitz, Ronen Talmon, Jackie Schiller

“…Previous studies demonstrated that skill acquisition requires dopaminergic VTA (ventral-tegmental area) signaling in M1, however little is known regarding the effect of these inputs at the neuronal and network levels. Using dexterity task, calcium imaging, chemogenetic inhibiting, and geometric data analysis, we demonstrate VTA-dependent reorganization of M1 layer 2-3 during motor learning. …”
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High-accuracy prediction of mutations in nine genes in lung adenocarcinoma via two-stage multi-instance learning on large-scale whole-slide images by Lingyu Zhao, Na Zhao, Ruiqi Zhong, Yiru Niu, Ziyi Chang, Peng Su, Zhihui Wang, Lifang Cui, Bei Wang, Huang Chen, Xiaowen Wang, Xiangbing Kong, Baolin Du, Fei Ren, Dingrong Zhong

“…Methods We collected 2,221 slides from 1999 patients diagnosed with lung adenocarcinoma. The data include whole-slide images data as well as information on gene mutations in EGFR, KRAS, ALK, HER2, and other rare genes (ROS1, RET, BRAF, PIK3CA, NRAS), and related clinical information. …”
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Facilitating real-time LED-based photoacoustic imaging with DenP2P: An optimized conditional generative adversarial deep learning solution by Avijit Paul, Srivalleesha Mallidi

“…The recent advancement of moderate pulse width LED (e.g., Acoustic-X) illuminating devices makes PAI more affordable, mobile, and fast with a trade-off for low illumination energy, leading to low signal-to-noise-ratio (SNR) images, which are averaged over time to get high SNR images. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. Our datasets included field and high-resolution topographic metrics including elevation, slope, aspect, and solar radiation and canopy height derived from lidar (1 m) and 44 vegetation indices derived from high-resolution (1 m) visible to near infrared (VNIR) hyperspectral data collected by NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) sensor. …”
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Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant Models by Jae Young Chang, Kwan-Young Oh, Kwang-Jae Lee

“…In this study, we propose a method to mitigate these inconsistencies by utilizing redundant models and verify the improvement using a public dataset based on Google Earth images. Redundant models share the same network architecture and hyperparameters but are trained with different combinations of training and validation data on the same dataset. …”
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Robust scale fusion and edge-aware feature attention network for remote sensing UAV road detection under harsh weather by Jialang Liu, Jialei Zhan, Jiehua Zhang, Jiangming Chen, Yan Song, Lixing Tang, Le Zhou, Chengsi Du, Yingmei Wei, Yanming Guo

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Leveraging airport CCTV footage through video understanding techniques for visibility prediction by Zeonlung Pun, Xinyu Tian, Shan Gao

“…However, existing visibility prediction methods predominantly rely on single-image analysis, using either traditional image processing techniques or deep learning models, which often fail to fully capture the dynamic and temporal characteristics inherent in video data. …”
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Quantification of CO₂ hotspot emissions from OCO-3 SAM CO₂ satellite images using deep learning methods by J. Dumont Le Brazidec, J. Dumont Le Brazidec, P. Vanderbecken, A. Farchi, G. Broquet, G. Kuhlmann, M. Bocquet

“…We present an end-to-end convolutional neural network (CNN) approach, processing the satellite XCO<span class="inline-formula">₂</span> images to derive estimates of the power plant emissions, that is resilient to missing data in the images due to clouds or to the partial view of the plume owing to the limited extent of the satellite swath.…”
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White-Matter Connectivity and General Movements in Infants with Perinatal Brain Injury by Ellen N. Sutter, Jose Guerrero-Gonzalez, Cameron P. Casey, Douglas C. Dean, Andrea de Abreu e Gouvea, Colleen Peyton, Ryan M. McAdams, Bernadette T. Gillick

“…Tractography was used to identify the corticospinal tract, a key motor pathway often affected by perinatal brain injury, and tract-based spatial statistics (TBSS) were used to examine broader white matter networks. Diffusion parameters from the diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models were compared between infants with and without typical general movements. …”
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Discrimination of the Skin Cells from Cellular-Resolution Optical Coherence Tomography by Deep Learning by Jui-Yun Yi, Sheng-Lung Huang, Shiun Li, Yu-You Yen, Chun-Yeh Chen

“…We used four models to classify the images of five types of 2D-OCT skin cells. Based on the ResNet-15 model, the mean accuracy (average accuracy of 10-fold cross-validation) reaches 98.47%, and the standard deviation is only 0.28% with the data augmentation method. …”
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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

“…Methods This study analyzed 2100 cephalometric images. The data distribution to an 8:1:1 for training, validation, and testing. …”
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Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography by Yujie Ma, Maged Ali Al-Aroomi, Yutian Zheng, Wenjie Ren, Peixuan Liu, Qing Wu, Ye Liang, Canhua Jiang

“…For the verification of tooth recognition, the data from the validation set were randomly selected for analysis. …”
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