Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling by Xuanzhu Sheng, Chao Yu, Xiaolong Cui, Yang Zhou

“…By analysising and comparing and with other existing asynchronous federated learning algorithms, the experimental results show that our proposed method outperforms other algorithms in terms of performance, such as model accuracy and running time. …”
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Stackelberg Game Based on Trajectory Prediction for Lane Change in Mixed Traffic by Baichuan Shi, Li Zhai, Chang Liu

“…The method develops a utility function for human-driven vehicles incorporating driving styles and safety-comfort-efficiency factors, with a corresponding cost function for autonomous vehicles. An improved Stackelberg game model integrates trajectory prediction of human-driven vehicles, while a bi-level optimization algorithm combining model predictive control and genetic algorithms jointly optimizes acceleration sequences and lane-change timing. …”
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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms by Marwan Dhuheir, Aiman Erbad, Bechir Hamdaoui, Samir Brahim Belhaouari, Mohsen Guizani, Thang X. Vu

“…Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. Extensive simulations show that our model outperforms competitive methods by around 29% in terms of latency and around 23% in terms of transmission power improvements while delivering results comparable to the traditional LDTP optimization solution by around 9% in terms of latency.…”
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New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing by Sahar Habbadi, Ismail El Mouayni, Brahim Herrou, Souhail Sekkat

“…To identify the optimal buffer locations, a Genetic Algorithm (GA) is employed. …”
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Adaptive data driven multi period power supply recovery method for distribution networks by Xi Ye, Meng Yang, Zhihong Yang, Qing Xiang, Yazhuo Li

“…Finally, simulations on the improved IEEE-33 bus system and actual example systems verify that the adaptive data driven power supply recovery model for distribution networks can reduce conservatism and improve the robustness of the optimization results. …”
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Theoretical analysis of MOFs for pharmaceutical applications by using machine learning models to predict loading capacity and cell viability by Bader Huwaimel, Saad Alqarni

“…Principal Component Analysis (PCA) was applied to reduce dimensionality, and the Water Cycle Algorithm was used to optimize hyperparameters. Evaluation metrics, including R2, Root Mean Squared Error (RMSE), and maximum error, indicated that the QR-MLP model outperformed the other models, achieving test R2 scores of 0.99917 for Drug Loading Capacity and 0.99111 for Cell Viability. …”
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Research on Lightweight Dynamic Security Protocol for Intelligent In-Vehicle CAN Bus by Yuanhao Wang, Yinan Xu, Zhiquan Liu, Suya Liu, Yujing Wu

“…To address these issues, we propose an improved dynamic compression algorithm that achieves higher compression rates and efficiency by optimizing header information processing during data reorganization. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…Oversampling techniques, model optimization, and reduced communication rounds were used to mitigate the issues. …”
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Real-time urban regional route planning model for connected vehicles based on V2X communication by Pangwei Wang, Hui Deng, Juan Zhang, Mingfang Zhang

“…Advancement in the novel technology of connected vehicles has presented opportunities and challenges for smart urban transport and land use. To improve the capacity of urban transport and optimize land-use planning, a novel real-time regional route planning model based on vehicle to X communication (V2X) is presented in this paper. …”
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BERT-OntoSent: combining BERT language model with sentiment ontology for enhanced sentiment analysis on social media by Abdelweheb Gueddes, Mohamed Ali Mahjoub

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An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome by Wanyi Li, Hangyu Zhou, Yingxue Zou

“…This study used eight machine learning algorithms to construct predictive models. Recursive feature elimination with cross-validation is used to screen features, and cross-validation-based Bayesian optimization is used to filter the features used to find the optimal combination of hyperparameters for the model. …”
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Environmental Risk Mitigation via Deep Learning Modeling of Compressive Strength in Green Concrete Incorporating Incinerator Ash by Amin Amraee, Seyed Azim Hosseini, Farshid Farokhizadeh, Mohammad Hassan Haeri

“…A database for deep learning modeling was created using Convolutional Neural Networks (CNNs) and the Multi-Verse Optimizer (MVO) algorithm. …”
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Machine Learning-Based Prediction of Resilience in Green Agricultural Supply Chains: Influencing Factors Analysis and Model Construction by Daqing Wu, Tianhao Li, Hangqi Cai, Shousong Cai

“…Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. …”
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Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC by Muhammad Ricky Perdana Putra, Ema Utami

“…The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. …”
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A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs by Yue Li, Yu Jiang, Jianhua Guo, Dong Xie

“…Consequently, we propose an improved Fuzzy C-Means (FCM) clustering algorithm that use historical traffic data and dynamic traffic information accurately identify traffic conditions. …”
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UAM Vertiport Network Design Considering Connectivity by Wentao Zhang, Taesung Hwang

“…To efficiently solve the problem and improve solution quality, a hybrid genetic algorithm is developed by incorporating a Minimum Spanning Tree (MST)-based connectivity enforcement mechanism, a fundamental concept in graph theory that connects all nodes in a given network with minimal total link cost, enhanced by a greedy initialization strategy. …”
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Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus by Xiyao Wan, Yuan Wang, Ziyi Liu, Ziyan Liu, Shuting Zhong, Xiaohua Huang

“…Patients were classified into PPDM-A (n = 109) and non-PPDM-A groups (n = 162), and split into training (n = 189) and testing (n = 82) cohorts at a 7:3 ratio. 1223 radiomic features were extracted from CT images in the plain, arterial and venous phases, respectively. The radiomics model was developed based on the optimal features retained after dimensionality reduction, utilizing the extreme gradient boosting (XGBoost) algorithm. …”
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A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention by Huifeng Wang, Jianchuan Yin, Jianchuan Yin, Nini Wang, Lijun Wang, Lijun Wang

“…Finally,the improved dung beetle optimization (IDBO) algorithm is used to optimize the hyper-parameters of the model. …”
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A Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches by Sam Ansari, Soliman Mahmoud, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny

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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.ObjectiveThis study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.MethodsA retrospective analysis was conducted on 847 pediatric patients with AH. …”
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