Modeling Autonomous Vehicles’ Altruistic Behavior to Human-Driven Vehicles in the Car following Events and Impact Analysis by Wenyun Tang, Le Xu, Jianxiao Ma

“…An extension car following framework with two possible soft optimization targets was proposed in this article to improve the experience of HDV followers with different following strategies by deep deterministic policy gradient (DDPG) algorithm. …”
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Exploring the Current Status of Risk Stratification in Hypertrophic Cardiomyopathy: From Risk Models to Promising Techniques by Alexandros Kasiakogias, Christos Kaskoutis, Christos-Konstantinos Antoniou, Stavros Georgopoulos, Dimitrios Tsiachris, Petros Arsenos, Alexandrina Kouroutzoglou, Dimitrios Klettas, Charalambos Vlachopoulos, Konstantinos Tsioufis, Konstantinos Gatzoulis

“…Some research suggests that integrating electrophysiological studies into traditional risk assessment models may further optimize risk prediction and significantly improve accuracy in detecting high risk patients. …”
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Research on Maneuvering Motion Prediction for Intelligent Ships Based on LSTM-Multi-Head Attention Model by Dongyu Liu, Xiaopeng Gao, Cong Huo, Wentao Su

“…The results demonstrate that our proposed model significantly improves the accuracy of ship maneuvering predictions compared to standalone LSTM and MHAM algorithms and exhibits superior generalization performance.…”
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Integrating direct observation and environmental DNA data to enhance species distribution models in riverine environments by Luca Carraro

“…When eDNA and direct sampling sites are spatially arranged in an optimized fashion, the highest prediction skill for a fixed total number of sampling sites deployed is reached when both data types are included in the model fitting. …”
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Exploring the Effects of Pre-Processing Techniques on Topic Modeling of an Arabic News Article Data Set by Haya Alangari, Nahlah Algethami

“…This research investigates the impacts of pre-processing techniques on the effectiveness of topic modeling algorithms for Arabic texts, focusing on a comparison between BERTopic, Latent Dirichlet Allocation (LDA), and Non-Negative Matrix Factorization (NMF). …”
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The evaluation model of engineering practice teaching with complex network analytic hierarchy process based on deep learning by Xianlong Han, Xiaohui Chen

“…This study aims to help reveal the relationship between students’ performance and teaching evaluation factors, deepen the understanding of the evaluation model of engineering practice teaching in colleges and universities, and provide valuable guidance for optimizing teaching.…”
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State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model by Jun Chen, Yan Liu, Jun Yong, Cheng Yang, Liqin Yan, Yanping Zheng

“…This optimization enhances the ELM’s performance, addressing instability issues in the standard algorithm. …”
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Research on Feed Forward Adaptive-generalized Model Predictive Control for High-speed Train Automatic Operation by LUO Yuan, YI Jie, BAI Jinlei, ZHANG Zhengfang

“…In view of the characteristics of nonlinearity and time-variability of high-speed train model parameters, this paper proposes a feed forward adaptive-generalized model predictive control (FA-GPC) method for dynamic optimization control of the ATO system along with a constrained multi-object predictive controller. …”
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MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images by Shengbang Zhou, Song Zhang, Chuanqi Li, Shutian Liu, Dong Chen

“…This article introduces a drone detection model, MEP-YOLOv5s, which optimizes the Backbone, Neck layer, and C3 module based on YOLOv5s, and combines effective attention mechanisms to improve the training efficiency of the model by replacing the traditional CIoU loss (Complete Intersection over Union) with MPDIoU (Minimum Point Distance-based Intersection over Union) loss. …”
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Development of an AI-Based Image Analysis Model for Verifying Partial Defects in Nuclear Fuel Assemblies by Seulah Kim, Dayun Park, Hyung-Joo Choi, Chulhee Min, Jaejoon Ahn

“…This study aims to increase the accuracy of SNF defect detection by optimizing artificial intelligence (AI)-based classification algorithms. …”
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A study on the risk prediction model for venous thromboembolism in orthopedic inpatients based on machine learning by Bo Zhang, Yumei Qin, Liandi Jiu, Chunming Qin, Jiangbo Wang, Haiqing Zhao

“…Feature analysis and data mining were conducted, and eight different machine learning algorithms were used to build the prediction model. …”
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Cooperative longevity of interaction model for addressing paused handoff problem in smart city intelligent transportation systems. by Abdullah Faiz Al Asmari, Tariq Alqubaysi, Fayez Alanazi, Ahmed Almutairi, Ammar Armghan

“…The model employs a hybrid trained herd optimization algorithm to improve the longevity for interaction between vehicles and roadside units, minimizing handoff interruptions. …”
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A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. …”
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ARMC-RL: Adaptive Caching With Reinforcement Learning for Efficient 360° Video Streaming in Edge Networks by Minji Choi, Somin Park, Jin-Hyun Ahn, Dong Ho Kim, Cheolwoo You

“…In particular, ARMC improved the hit rate by up to 29% compared to Least Frequency Used algorithm under constrained cache conditions of 6% or less of the total data. …”
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Machine Learning-Driven Prediction of CO₂ Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach by Seyed Hossein Hashemi, Farshid Torabi, Paitoon Tontiwachwuthikul

“…In this study, Gaussian Process Regression (GPR) with eight different kernels was optimized using the Grey Wolf Optimizer (GWO) algorithm to model this important phase behavior. …”
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Research on High Arch Dam Deformation Monitoring Model with Deep Capturing Related Features in Factor-time Dimensions by XUE Jianghan, ZHANG Pengtao, TIAN Jichen, LU Xiang, CHEN Jiankang, Guo Yinju

“…However, at the present stage, the dam prediction model based on machine learning mostly adopts the means of data preprocessing, using optimization algorithm, and using the model's characteristics to stack multiple models, lacking in in-depth consideration of the physical mechanism of dam deformation. …”
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Two-Step Estimation Procedure for Parametric Copula-Based Regression Models for Semi-Competing Risks Data by Qingmin Zhang, Bowen Duan, Małgorzata Wojtyś, Yinghui Wei

“…Due to the complexity of the copula structure, we propose a new method that integrates a novel two-step algorithm with the Bound Optimization by Quadratic Approximation (BOBYQA) method. …”
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Application of data twinning based on deep time series model in smart city traffic flow prediction by Li Gao

“…The information fusion mechanism combines real-time data and forecast data to optimize the model inputs, and the PSO algorithm is used to optimize the model parameters. …”
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An intelligent fault diagnosis model for bearings with adaptive hyperparameter tuning in multi-condition and limited sample scenarios by Jianqiao Li, Zhihao Huang, Liang Jiang, Yonghong Zhang

“…The proposed model not only improves diagnostic performance but also enhances optimization efficiency, achieving faster results within the same time frame. …”
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A synergistic approach using digital twins and statistical machine learning for intelligent residential energy modelling by Ahmad Almadhor, Shtwai Alsubai, Natalia Kryvinska, Nejib Ghazouani, Belgacem Bouallegue, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…Traditional energy management techniques often fail to address dynamic energy demands and user preferences, leading to inefficiencies and increased costs. This paper proposes a framework that integrates Digital Twin (DT) systems with Artificial Intelligence (AI) algorithms for intelligent building energy consumption assessment by developing real-time virtual twin representations. …”
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