Deep learning-based system for prediction of work at height in construction site by Ibrahim Karatas

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Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction by Wei Png Chua, Chien Chern Cheah

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Deep Learning-based Identification of Personal Protective Equipment in Construction Area by Mutia Fadhilla, Sapitri Sapitri, Rizky Wandri

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Deep learning-based computer vision in project management: Automating indoor construction progress monitoring by Biyanka Ekanayake, Johnny Kwok Wai Wong, Alireza Ahmadian Fard Fini, Peter Smith, Vishal Thengane

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Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot by Guang-Xin Zhou, Yu-Kun Tao, Jin-Zheng Hou, Hui-Juan Zhu, Li Xiao, Na Zhao, Xiao-Wen Wang, Bao-Lin Du, Da Zhang

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Construction of machine learning-based models for screening the high-risk patients with gastric precancerous lesions by Shuxian Yu, Haiyang Jiang, Jing Xia, Jie Gu, Mengting Chen, Yan Wang, Xiaohong Zhao, Zehua Liao, Puhua Zeng, Tian Xie, Xinbing Sui

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Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions by LIU Qixing, WANG Huogen, CIDAN Wangjiu, TUDAN Awang, YANG Meijie, PUQIONG Qiongda, YANG Xiao, PAN Hui, WANG Fengdan

“…ObjectiveTo construct and validate a deep learning-based bone age prediction model for children living in both plain and highland regions.MethodsA model named "ethnicity vision gender-bone age net (EVG-BANet)" was trained using three datasets, including the Radiology Society of North America (RSNA) dataset [training set(n=12 611), validation set (n=1425), test set (n=200)], the Radiological Hand Pose Estimation (RHPE) dataset[training set (n=5491), validation set (n=713), test set (n=79)], and a self-established dataset[training set (n=825), test set (n=351)], and it was validated using an external test set. …”
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Deep Learning-Based Real-Time 6D Pose Estimation and Multi-Mode Tracking Algorithms for Citrus-Harvesting Robots by Hyun-Jung Hwang, Jae-Hoon Cho, Yong-Tae Kim

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Survey on Deep Learning-Based Marine Object Detection by Ruolan Zhang, Shaoxi Li, Guanfeng Ji, Xiuping Zhao, Jing Li, Mingyang Pan

“…Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. …”
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Deep Learning-Based Algorithm for Road Defect Detection by Shaoxiang Li, Dexiang Zhang

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Construction of a transfer learning-based depression detection model for female breast cancer patients: text sentiment analysis by Jiaqi Fu, Shisi Deng, Wanting Zheng, Chunrao Zheng, Jianhong Liu, Wenji Li, Yinghua Zeng, Hongpo Xie, Yuchang Mai, Chaixiu Li, Jie Lai, Yujie Zhang, Zihan Guo, Jianyao Tang, Chuhan Zhong, Huihui Zhao, Yanni Wu

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Construction of a deep learning-based predictive model to evaluate the influence of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts by Ruozu Xiao, Haowei Zhou, Zhen Shi, Rong Huang, Yuheng Zhang, Jing Li

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Energy Minimization for Federated Learning Based Radio Map Construction by Fahui Wu, Yunfei Gao, Lin Xiao, Dingcheng Yang, Jiangbin Lyu

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DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States by Parnian Ghaneei, Hamid Moradkhani

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Deep Learning-Based Multimode Fiber Distributed Temperature Sensing by Luxuan Yang, Xiaoyan Wang, Tong Wu, Huichuan Lin, Songjie Luo, Ziyang Chen, Yongxin Liu, Jixiong Pu

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Deep learning based stacking ensembles for tropical sorghum classification by Muhammad Aqil, Muhammad Azrai, Roy Efendi, Nining Nurini Andayani, Suwardi, Bunyamin Zainuddin, Suarni, Herawati, Andi Irma Damayanti, Muhammad Jihad, Syafruddin, Ramlah Arief, Paesal, Yustisia, Rahman

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Construction of a deep - learning - based rehabilitation prediction model for lower-limb motor dysfunction after stroke using synchronous EEG-EMG and fMRI by Jiaqi Shi, Hongyu Wang, Haiyan Gou, Yan Chen, Jia He, Youyang Qu, Xinya Wei, Mingyue Fan, Yanlong Wang, Yanmei Zhu, Yulan Zhu

“…The area under the ROC curve was 0.8665, accuracy was 0.8031, F1-score was 0.7829, recall was 0.774, and precision was 0.833.ConclusionA deep learning-based rehabilitation prediction model utilizing multimodal signals was successfully developed. …”
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deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria by Yao Xiao, Yan Zhang

“…In this study, we have developed a deep learning-based algorithm, deep-Sep, for quickly and precisely identifying selenoprotein genes in bacterial genomic sequences. …”
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A deep learning-based algorithm for the detection of personal protective equipment. by Bo Tong, Guan Li, Xiangli Bu, Yang Wang, Xingchen Yu

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Deep learning-based strategies for evaluating and enhancing university teaching quality by Ying Gao

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