Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model by Nhat-Duc Hoang, Anh-Duc Pham, Quoc-Lam Nguyen, Quang-Nhat Pham

“…Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network. Furthermore, GPR model is strongly recommended for estimating HPC strength because this method demonstrates good learning performance and can inherently express prediction outputs coupled with prediction intervals.…”
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Refinement Method of Evaluation and Ranking of Innovation and Entrepreneurship Ability of Colleges and Universities Based on Optimal Weight Model by Wei Guo, Lihua Liu, Yao Yao, Tong Shen

“…This paper proposes a sorting refinement method based on the optimal weight model and uses the BP neural network to determine the optimal weight. Weight is a scoring mechanism for comprehensive ranking, that is, a scoring system. …”
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Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning by Won Joong Kim, Gunho Jung, Sun-Yong Choi

“…In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. …”
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Visualization of Multivariate Time-Series Characteristics of Ground Loss Caused by Shield Tunneling by Zhu Wen, Xiaoli Rong, Fei Gao, Zhen Wang, Dong An

“…A method of visualizing MTS features based on a residual network and multichannel fully convolutional neural network is also presented. The validity of the proposed ground-loss model is verified via calculation and comparison with 13 EPB shield construction projects carried out in typical urban areas featuring soft soil. …”
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An algorithm of blood typing using serological plate images by S.A. Korchagin, E.E. Zaychenkova, D.A. Sharapov, E.I. Ershov, Y.V. Butorin, Y.Y. Vengerov

“…The proposed recognition algorithm allows the alveolus boundaries to be accurately determined and the agglutination degree to be evaluated using a lightweight convolutional neural network. A unique dataset was collected with the independent assessment of agglutination degree conducted by medical experts. …”
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The process of budgeting the strategy of industrial complex transformation in the context of digitalisation by A. G. Boev

“…The author determines the recommended amount and horizon of the strategy budget planning and also presents a 6-stage algorithm of methodical reception on the use of neural network modeling tools to optimize the budget of the strategy of changes in the industrial complex. …”
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Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments by Leyang Zhao, Weixi Wang, Qizhi He, Li Yan, Xiaoming Li

“…Initially, image enhancement techniques are integrated with neural network-based visual feature extraction. Subsequently, employs a high-dimensional error-state optimizer coupled with a low-dimensional height filter to achieve high-precision localization of the UAV in under-canopy environments. …”
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Semantic-Based Classification of Long Texts on Higher Education in China by Chun Li, Yanying Fei

“…To solve these problems, this paper improves the convolutional neural network (CNN) into the HE-CNN classification model for HE texts. …”
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Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control by Yufei Ruan, Huang Gao, Dequn Li

“…Once the optimal compensation time is learned, a deep Q-learning algorithm which combined Q-learning with an artificial neural network (ANN) is proposed to learn the optimal compensation quantity. …”
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Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique by Saud Altaf, Muhammad Waseem Soomro, Mirza Sajid Mehmood

“…This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. …”
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Mitigating Sinkhole Attacks in MANET Routing Protocols using Federated Learning HDBNCNN Algorithm by Sherril Sophie Maria Vincent

“…Further, the Hierarchical Deep Belief Network Convolutional Neural Network (HDBNCNN) algorithm has analysed the accumulated data in detecting the anomalies revealing the sinkhole activity centred on learning routing patterns. …”
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Deep Learning for Plastic Waste Classification System by Janusz Bobulski, Mariusz Kubanek

“…One of the opportunities is the use of deep learning and convolutional neural network. In household waste, the most problematic are plastic components, and the main types are polyethylene, polypropylene, and polystyrene. …”
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LazyAct: Lazy actor with dynamic state skip based on constrained MDP. by Hongjie Zhang, Zhenyu Chen, Hourui Deng, Chaosheng Feng

“…However, the high computational cost of policies based on deep neural networks restricts their practical application. …”
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MICE city ecosystems: building collaboration through information systems – a case study from Thailand by Pattaraporn Warintarawej, Nattaporn Thongsri, Jariya Seksan, Witchuta Marchoo

“…Objectives include developing an information system based on design thinking, investigating factors affecting the intention to use the system by examining MICE background knowledge, benefits, and Technology Acceptance Model (TAM) factors using correlation analysis and neural network techniques, and studying stakeholder satisfaction with the system. …”
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Application of CNN-LSTM Model for Vehicle Acceleration Prediction Using Car-following Behavior Data by Shuning Tang, Yajie Zou, Hao Zhang, Yue Zhang, Xiaoqiang Kong

“…Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. …”
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A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model by Meng He, Hui Wang, Myo Thwin

“…GRU is a recurrent neural network that has the ability to comprehend intricate temporal relationships within the input data. …”
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A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network by Yulian Li, Yang Su

“…Routing deployment and resource scheduling in communication networks require accurate traffic prediction. Neural network-based models that extract the time-correlated or space-correlated features of traffic flow have been developed for traffic prediction. …”
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Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach by Yang Liu, Qingguo Zeng, Joaquín Ordieres Meré, Huanrui Yang

“…A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.…”
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A disproportionality analysis of FDA adverse event reporting system events for misoprostol by Li Yang, Wenting Xu

“…This study used proportional disequilibrium methods such as reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM) to detect AEs. …”
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A Modified Time Reversal Method for Guided Wave Detection of Bolt Loosening in Simulated Thermal Protection System Panels by Guan-nan Wu, Chao Xu, Fei Du, Wei-dong Zhu

“…To analyze a large number of tightness indices, a principle component analysis method is introduced, and a neural network-based loosening detection method is proposed. …”
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