Evaluation method of hydrophobicity of composite insulators based on improved Mask R-CNN by SHENG Fei, CAO Liu, LIU Yulong, HUANG Jie, HUANG Yaqian, ZHU Yanqing

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A composite Feature Selection Method to improve Classifying Imbalanced Big Data by Shaymaa Razoqi, Ghayda Al-Talib

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Investigate the composition effect of Cu-Nb-B-Fe soft magnetic metallic glasses using deep learning method by ZhaoHui Huang, TianTian Zhang

“…This method was based on the concept of amorphousness degree derived from bond orientational analysis. …”
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Machine learning methods for predicting human-adaptive influenza A virus reassortment based on intersegment constraint by Dan-Dan Zeng, Dan-Dan Zeng, Yu-Rong Cai, Sen Zhang, Fang Yan, Tao Jiang, Jing Li

“…IntroductionIt is not clear about mechanisms underlining the inter-segment reassortment of Influenza A viruses (IAVs).We analyzed the viral nucleotide composition (NC) in coding sequences,examined the intersegment NC correlation, and predicted the IAV reassortment using machine learning (ML) approaches based on viral NC features.MethodsUnsupervised ML methods were used to examine the NC difference between human-adapted and zoonotic IAVs. …”
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Hierarchical Service Composition via Blockchain-enabled Federated Learning by Li Huang, Lu Zhao, Yansong Liu, Yao Zhao

“…To address these limitations, we propose the Hierarchical Service Composition (HSC) approach, leveraging blockchain and federated learning to minimize computational complexity. …”
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A Novel Rubber Composite Sleeper-Deformation-Prediction Model Based on Response Surface Method (RSM) and Machine Learning (ML) Techniques by Abdulmumin Ahmed Shuaibu, Zhiping Zeng, Ibrahim Hayatu Hassan, Wang Weidong, Hassan Suleiman Otuoze, Suleiman Abdulhakeem, Bushrah Baba Abdulrahman

“…This study attempts to develop a novel deformation model of rubber composite sleepers using response surface methodology (RSM) and machine learning (ML) techniques. …”
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Application of machine learning methods to restore size-sex composition in catches of snow crab by I. S. Chernienko, A. G. Slizkin, V. V. Kulik

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Deep learning-based GNSS composite jamming detection and recognition technology by Chenxuan Liu, Chenxuan Liu, Binbin Ren, Binbin Ren, Yuchen Xie, Yuchen Xie, Feiqiang Chen, Feiqiang Chen

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FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT) by PENG Fan, ZOU SiNong, REN YiRu

“…A prediction model was constructed based on limited training samples to predict the peak failure load of bolted composite joints. …”
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ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites by K. Ramesha, N. Santhosh, B. A. Praveena, Banakara Nagaraj, N. Channa Keshava Naik, Quadri Noorulhasan Naveed, Ayodele Lasisi, Anteneh Wogasso Wodajo

“…Abstract This study investigates the effectiveness of Abrasive Water Suspension Jet (AWSJ) Machining, a non-conventional erosion-based method, for machining carbon fiber-reinforced polymer (CFRP) composites. …”
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A cross-sectional study on dentists’ learning preferences for learning about light-curing units and resin-based composites by Afnan O. Al-Zain, Khlood Baghlaf, Omar Abdulwassi, Reem Almukairin, Maram Alanazi, Elaf Alshomrani, Sultan Alftaikhah, Richard B. Price

“…Aim: To identify the parameters dentists use when choosing an LCU or resin-based composite (RBC) and to determine the most effective educational method for dentists to learn about LCUs. …”
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Machine Learning Approach for Predicting Hypertension Based on Body Composition in South Korean Adults by Jeong-Woo Seo, Sanghun Lee, Mi Hong Yim

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The analysis of Chinese National ballad composition education based on artificial intelligence and deep learning by Chao Zhang

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A Data Protection Method for the Electricity Business Environment Based on Differential Privacy and Federal Incentive Mechanisms by Xu Zhou, Hongshan Luo, Simin Chen, Yuling He

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Prediction on Slip Modulus of Screwed Connection for Timber–Concrete Composite Structures Based on Machine Learning by Wen-Wu Lu, Yu-Wei Chen, Ji-Gang Xu, Hui-Feng Yang, Hao-Tian Tao, Wei Zheng, Ben-Kai Shi

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A novel machine learning based framework for developing composite digital biomarkers of disease progression by Song Zhai, Andy Liaw, Judong Shen, Yuting Xu, Vladimir Svetnik, James J. FitzGerald, James J. FitzGerald, Chrystalina A. Antoniades, Dan Holder, Marissa F. Dockendorf, Jie Ren, Richard Baumgartner

“…However, the complexity of DHT datasets and the potential to derive numerous digital features that were not previously possible to measure pose challenges, including in selection of the most important digital features and construction of composite digital biomarkers.MethodsWe present a comprehensive machine learning based framework to construct composite digital biomarkers for progression tracking. …”
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AAGP integrates physicochemical and compositional features for machine learning-based prediction of anti-aging peptides by Saptashwa Datta, Jen-Chieh Yu, Yi-Hsiang Lin, Yun-Chen Cheng, Ching-Tai Chen

“…We propose AAGP, an anti-aging peptide predictor based on diverse physicochemical and compositional features. …”
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Enhancing satellite image compositing with temporal proximity weighting for deep learning–based cropland segmentation by Reza Maleki, Falin Wu, Guoxin Qu, Amel Oubara, Gongliu Yang

“…This study proposes a compositing method that improves temporal coherence for tracking phenological stages in deep learning–based cropland segmentation. …”
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Composite Learning-Based Inverse Optimal Fault-Tolerant Control for Hierarchy-Structured Unmanned Helicopters by Qingyi Liu, Ke Zhang, Bin Jiang, Yushun Tan

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Machine learning-assisted determination of material chemical compositions: a study case on Ni-base superalloy by Sae Dieb, Yoshiaki Toda, Keitaro Sodeyama, Masahiko Demura

“…In this work, we present an efficient machine learning-assisted method to optimize the chemical compositions of materials for desired mechanical properties. …”
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