Pesticide Residue Detection in Broccoli Based on Hyperspectral Technology and Convolutional Neural Network by Dan WANG, Yuqing LUAN, Zuojun TAN, Wei WEI

“…A one-dimensional convolutional neural network (1D-CNN) model was established using raw spectral data, which achieved recognition accuracy of 94.29%, 95.71%, 94.29%, and 97.14% for high-efficiency cypermethrin, chlorpyrifos, imidacloprid, and water, all of which were higher than the SVM model. …”
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Hybrid Neural Network Models to Estimate Vital Signs from Facial Videos by Yufeng Zheng

“…The hybrid model integrates convolutional neural network (CNN), convolutional long short-term memory (convLSTM), and video vision transformer (ViViT) architectures to ensure comprehensive analysis. …”
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Overall Layout Method of Frame Structure Plane Based on Generative Adversarial Network by ZHONG Yan, LEI Xin, LONG Danbing, FANG Changjian, KANG Yongjun

“…The discriminator determines whether the generated image is real or synthetic. Through adversarial training, the generator and discriminator iteratively improve until reaching a Nash equilibrium. …”
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Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy. by Andrei C Aioanei, Regine R Hunziker-Rodewald, Konstantin M Klein, Dominik L Michels

“…This comprehensive corpus provides a robust volume of data for training a residual neural network (ResNet) to classify highly degraded Aramaic letters. …”
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Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE by Nan Wei, René Michael Mathy, De-Hua Chang, Philipp Mayer, Jakob Liermann, Christoph Springfeld, Michael T Dill, Thomas Longerich, Georg Lurje, Hans-Ulrich Kauczor, Mark O. Wielpütz, Osman Öcal

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Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks by Georgios Lekkas, Eleni Vrochidou, George A. Papakostas

“…<b>Background:</b> Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. …”
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Surface Deformation Monitoring and Prediction of Longtantian Open-Pit Mine Based on SBAS-InSAR and CNN-BiLSTM Techniques by Xiaoxiao Zhang, Qi Chen, Mengshi Yang, Zhifang Zhao, Yu Zheng, Qixue Dai, Yang He, Dayu Cai, Ting Xu

“…This study used the small baseline subset interferometric synthetic aperture radar technique to extract surface deformation time-series data from Sentinel-1A images between 4 January 2020 and 19 January 2024. …”
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Alterations in static and dynamic functional network connectivity in subcortical vascular cognitive impairment by Haixia Mao, Yachen Shi, Qianqian Gao, Min Xu, Xiaoyun Hu, Feng Wang, Xiangming Fang

“…We extracted network independent components for sFNC and dFNC using independent component analysis with resting-state functional MRI data. …”
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Pine Wilt Disease Monitoring Using Multimodal Remote Sensing Data and Feature Classification by Xin Ye, Hanwen Yu, Yan Yan, Tieming Liu, Yan Zhang, Taoli Yang

“…To facilitate practical implementation, we developed a deep learning-based model, PWD-Net, which efficiently integrates these multimodal data for the monitoring of diseased pine trees. Statistical analysis of SAR-derived moisture content reveals significant differences in moisture variation patterns between diseased and healthy trees, enhancing the interpretability of the input features for the neural network. …”
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A method for time difference correction for real-time seismic-while-mining data by Dan JIN

“…Given that the time synchronization accuracy directly affects the quality of data processing and imaging, the time difference correction of real-time SWM data is essential for accurate SWM detection. …”
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Automated Cardiac Disease Prediction Using Composite GAN and DeepLab Model by Sohail Jabbar, Umar Raza, Muhammad Asif Habib, Muhammad Farhan, Saqib Saeed

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Insulin resistance, cognition, and functional brain network topology in older adults with obesity by Clayton C. McIntyre, Robert G. Lyday, Yixin Su, Barbara Nicklas, Sean L. Simpson, Gagan Deep, Shannon L. Macauley, Christina E. Hugenschmidt

“…An executive function composite score, summed scores to the Auditory Verbal Learning Test (AVLT) trials 1–5, and functional brain networks generated from resting-state functional magnetic resonance imaging data were outcomes in analyses. …”
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Differential dynamic functional network connectivity in different motor subtypes of Parkinson's disease by Yu Pan, Hang Qu, Yi Zhao, Zhaoxia Qin, Jiangbing Liu, Wei Wang

“…Resting-state functional magnetic resonance imaging (fMRI) data were obtained from all participants. …”
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Multi-omics insights into Palmitoylation-mediated brain network remodeling and glioblastoma risk by Shasha Tan, Jinliang You, Huijun Wang, Long Zhao, Shun Li, Xiaoping Tang, Hongjun Liu

“…Leveraging two-sample Mendelian randomization (MR) and multi-omics data from FinnGen (406 GBM cases/378,749 controls) and UK Biobank (3,935 brain imaging phenotypes), we identified protective effects of palmitoylation-related genes (ZDHHC3: OR = 0.17, 95%CI = 0.0602–0.4824; ZDHHC6: OR = 0.41, 0.2330–0.7267; ZDHHC13: OR = 0.64, 0.4745–0.8618; PPT2: OR = 0.59, 0.3568–0.9843) against GBM risk. …”
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Measurement of spatial heterogeneity in street restorative perceptions and street refinement design by Yalun Lei, Qingqing Li, Jingwen Tian, Jia Hu, Jixiang Jiang

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RON‐based cross‐chain routing optimization strategy in metaverse by Junjie Huang, Liang Tan, Jianmei Xiao

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A HYBRID APPROACH FOR MALARIA CLASSIFICATION USING CNN-BASED FEATURE EXTRACTION AND TRADITIONAL MACHINE LEARNING CLASSIFIERS by omar Mohammed Alzakholi, Walat A. Ahmed, Bafreen N. Mohammed, Asaad Kh. Ibrahim

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Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data by Zhu Zhimeng

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Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction by Varun Shinde, Vineet Dhanawat, Ahmad Almogren, Anjanava Biswas, Muhammad Bilal, Rizwan Ali Naqvi, Ateeq Ur Rehman

“…By pulling data from this image, GCN is able to obtain content rich features that are very powerful at detecting CMF regions. …”
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Advancing Underwater Vision: A Survey of Deep Learning Models for Underwater Object Recognition and Tracking by Mahmoud Elmezain, Lyes Saad Saoud, Atif Sultan, Mohamed Heshmat, Lakmal Seneviratne, Irfan Hussain

“…Beyond optical imaging, this review explores alternative modalities such as sonar, hyperspectral imaging, and event-based vision, which provide complementary data to enhance underwater vision systems. …”
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