Method of tail beam posture prediction of top coal caving hydraulic support based on LSTM by Yupeng YAO, Jinglin ZHANG, Wu XIONG

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Gas concentration prediction in photoacoustic spectroscopy using PSO-EAP-CNN to address correlation degradation by Zhanshang Su, Pengpeng Wang, Zhengzhuo Li, Yawen Li, Tianxiang Zhao, Yujie Duan, Fupeng Wang, Cunguang Zhu

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Ultrasound combined with serological markers for predicting neonatal necrotizing enterocolitis: a machine learning approach by Yi Yang, Shoulan Zhou, Xiaomin Liu, Yanhong Zhang, Liping Lin, Chenhan Zheng, Xiaohong Zhong

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ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure by Bowen Du, Haohan Liang, Yuhang Wang, Junchen Ye, Xuyan Tan, Weizhong Chen

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Integrating artificial intelligence with network evolution theory for community behavior prediction in dynamic complex systems by Yongyan Zhao, Jian Li

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Short-term Wind Speed Prediction Based on Wavelet Packet Decomposition and BP Neural Network by WANG Ning, LUO Rubin, LIAO Jun, LI Jun, JIANG Yi, YANG Zechuan, YUAN Junjie

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A Discrete Element Model for Characterizing Soil-Cotton Seeding Equipment Interactions Using the JKR and Bonding Contact Models by Xuyang Ran, Long Wang, Jianfei Xing, Lu Shi, Dewei Wang, Wensong Guo, Xufeng Wang

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HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Framework for Software Defect Prediction by Madhusmita Das, Biju R. Mohan, Ram Mohana Reddy Guddeti

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An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi, Manar Ali

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Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

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An artificial intelligence platform for predicting postoperative complications in metastatic spinal surgery: development and validation study by Weihao Jiang, Juan Zhang, Weiqing Shi, Xuyong Cao, Xiongwei Zhao, Bin Zhang, Haikuan Yu, Shengjie Wang, Yong Qin, Mingxing Lei, Yuncen Cao, Boyu Zhu, Yaosheng Liu

“…These variables were thus incorporated as key features in all the predictive models. Among the six machine learning models, the eXGBM model demonstrated the highest prediction accuracy, with an area under the curve (AUC) of 0.924 (95% CI: 0.884–0.965), outperforming the RF model (AUC = 0.892, 95% CI: 0.846–0.938) and the KNN model (AUC = 0.869, 95% CI: 0.818–0.920). …”
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Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma. by Han Wang

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Machine-learning approach to atrial fibrillation prediction among individuals without prior cardiovascular diseases by Niklas Hammar, Javier Louro, Karin Modig, Shunsuke Murata, Mozhu Ding

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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

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Research on Pressure Exertion Prediction in Coal Mine Working Faces Based on Data-Driven Approaches by Yiqi Chen, Changyou Liu, Ningbo Zhang, Huaidong Liu, Xin Yu, Shibao Liu, Jianning Hu

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Value of multi-modal MRI in predicting the effect of high-intensity focused ultrasound for uterine fibroids by Si-Qi Tao, Li-Ye Shi, Xiao-Shan Chai, Xiao-Rui Yuan, Shi-Xiong Tang, Jun Zhang, Du-Jun Bian, Chun Fu

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A nomogram for predicting postpartum post-traumatic stress disorder: a prospective cohort study by Xiaoting Wang, Jiaxin Ren, Linping Kang, Jing Lu, Hongxia Wang

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Comparative Study on Total Organic Carbon Content Logging Prediction Method Based on Machine Learning by TANG Shengshou, YANG Bin, JIN Jiulong, LIU Hongrui, DAI Xingyu, PU Jincheng

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Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud by Hang Mi, Bo Ai, Ruisi He, Anuraag Bodi, Raied Caromi, Jian Wang, Jelena Senic, Camillo Gentile, Yang Miao

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AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease. by Jiansong Fang, Ling Wang, Yecheng Li, Wenwen Lian, Xiaocong Pang, Hong Wang, Dongsheng Yuan, Qi Wang, Ai-Lin Liu, Guan-Hua Du

“…In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. …”
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