Underwater Target 3D Reconstruction via Integrated Laser Triangulation and Multispectral Photometric Stereo by Yang Yang, Yimei Liu, Eric Rigall, Yifan Yin, Shu Zhang, Junyu Dong

“…At the same time, we propose to optimize the laser place calibration and laser line separation processes, further improving the reconstruction performance of underwater laser triangulation and multispectral photometric stereo. …”
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Predicting hospital outpatient volume using XGBoost: a machine learning approach by Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang

“…Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allocation of medical resources. This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. …”
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A Metaheuristic Method for the Task Assignment Problem in Continuous-Casting Production by Hongtao Hu, Yiwei Wu, Tingsong Wang

“…An improved solution algorithm based on particle swam optimization is developed to efficiently solve the proposed model. …”
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High-Dimensional Projected Clustering for Learner Competency Analysis in Medical Training Programs by Sandhya Harikumar, C. S. Jayamohan Pillai, V. Vani Chithra, Raghu Raman, Mr Kaimal, Kai-Yu Tang, Prema Nedungadi

“…Additionally, weak learners deficient in crucial healthcare areas are identified, and the model recommends the most qualified professionals for specific critical care cases.…”
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Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction by Qian Zeng, Xiaobo Li, Yixuan Chen, Minghao Yang, Xingbang Liu, Yuetian Liu, Shiwei Xiu

“…By leveraging attention parameters, ELPM acquires spatio-temporal adaptive weights, enabling accurate location predictions. We also design an improved service allocation strategy, MESDA, based on the Gray Wolf Optimization (GWO) algorithm. …”
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Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning by Jian Ma, Hua Su, Wan-lin Zhao, Bin Liu

“…However, the hyperparameters of the deep learning, which significantly impact the feature extraction and prediction performance, are determined based on expert experience in most cases. The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. …”
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A Semi-Supervised Abbreviation Disambiguation Method Based on ACNN and Bi-LSTM by ZHANG Chun-xiang, PANG Shu-yang, GAO Xue-yao

“…Training corpus is extended by using Xgboost algorithm and LightGBM algorithm， and then expanded training corpus is input into this model. …”
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A Method Adjusting Consistency and Consensus for Group Decision-Making Problems with Hesitant Fuzzy Linguistic Preference Relations Based on Discrete Fuzzy Numbers by Meng Zhao, Ting Liu, Jia Su, Meng-Ying Liu

“…Besides, an illustrative example and comparative analysis are conducted through the proposed calculation process and the optimization algorithm. Finally, the analysis on the threshold values is made to help the decision-maker determine critical consensus level. …”
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A Generalized Shape Function for Vibration Suppression Analysis of Acoustic Black Hole Beams Based on Fractional Calculus Theory by Jun Xu, Ning Chen

“…To obtain the best parameters of the shape function under various parameters, the Particle Swarm Optimization (PSO) algorithm is employed. The results demonstrate that by selecting appropriate ML parameters and viscoelastic materials, the dissipation characteristics of the structure can be significantly improved. …”
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Neural Network-Based Imitation Learning for Approximating Stochastic Battery Management Systems by Andrea Pozzi, Alessandro Incremona, Daniele Toti

“…Lithium-ion batteries play a pivotal role in enabling eco-friendly mobility, particularly in electric vehicles, but optimizing their charging process to improve battery lifespan, safety, and overall efficiency remains a significant challenge. …”
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A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data by Zhenzhen Wang, Junde Xie, Jia Zhang

“…In addition, a data-driven based particle swarm optimization algorithm automatically is used to select the value of parameters for base classifiers. …”
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Performance Analysis of CO2 Systems Integrated with Ejector and Dedicated Mechanical Subcooling by Dai Baomin, Zhao Ruirui, Liu Shengchun, Qian Jiabao, Xu Tianyahui, Liu Chen, Yang Peifang

“…A thermodynamic model of the system was devised. Then, the discharge pressure and subcooling degree were optimized using a genetic algorithm by considering the coefficient of performance (COP) as the objective function. …”
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Prediction of Drifter Trajectory Using Evolutionary Computation by Yong-Wook Nam, Yong-Hyuk Kim

“…In contrast to existing numerical models that use the Lagrangian method, we used an optimization algorithm to predict the trajectory. …”
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Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision by Zheng Zhang, Cong Huang, Fei Zhong, Bote Qi, Binghong Gao

“…The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. …”
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Deep learning-based prediction of individualized Real-time FSH doses in GnRH agonist long protocols by Na Kong, Yu Xia, Zhilong Wang, Hui Zhang, Liyan Duan, Yingchun Zhu, Chenyang Huang, Guijun Yan, Jie Mei, Wujun Li, Haixiang Sun

“…Most existing models are limited to static predictions of initial doses and do not support real-time adjustments throughout stimulation. …”
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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

“…Three sequential deep learning models—Recurrent Neural Network and Gated Recurrent Unit—were developed and optimized using the Adam algorithm. …”
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Cloud edge end network resource allocation for thermostatically controlled load aggregation regulation by Yi LIU, Xin WU

“…Thermostatically controlled load is a flexible load that controls temperature regulation, such as air conditioning and electric water heaters.As a crucial demand side resource, flexible aggregation and regulation of load clusters can fully mobilize clean energy consumption capacity and ensure the balance between supply and demand of the power grid.Due to the common occurrence of thermostatically controlled loads in commercial office buildings and residential areas, a relatively stable control and transmission method can be adopted.Therefore, an efficient hierarchical transmission network is introduced to achieve data transmission and information interaction between loads and the power grid, and to flexibly, real-time, and accurately utilize the adjustable potential of load clusters.Firstly, an information interaction architecture of load IoT which structured “central cloud-edge cloud-regional load controller-thermostatically controlled load”was proposed.Then, for the “end edge”part, considering the requirements of different aggregation control tasks, an improved clustering algorithm was used to classify the tasks and reduce transmission overhead.For the “end-side” part, an improved clustering algorithm was used to optimize the transmission distance.For the edge-cloud collaboration part, a subchannel resource allocation algorithm was designed based on stable matching and water injection algorithms.The binary particle swarm optimization algorithm was used to solve the task upload decision problem.Finally, the effectiveness of the proposed model and algorithm is verified through simulation, and comparative experiments are also conducted.…”
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Research on data completion and generation of downhole drilling tool attitude based on Long Short-Term Memory neural networks by Qiwei Liu, Fanmin Kong, Xiaolong Chen, Kang Li

“…To overcome these limitations, a Long Short-Term Memory (LSTM) model, a type of deep learning algorithm known for capturing complex time-series dependencies, is proposed for data completion. …”
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Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgrids by Changchun Cai, Fenglu Gan, Yong Cui, Bo Li, Shixi Hou

“…First, with critical load restoration as the objective function, coordinated optimization model for MESS dispatch and network reconfiguration is constructed, which comprehensively considers security constraints of both distribution networks and microgrids. …”
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Influence of artificial intelligence on higher education reform and talent cultivation in the digital intelligence era by Limin Qian, Weiran Cao, Lifeng Chen

“…Abstract In order to solve the problems of inefficient allocation of teaching resources and inaccurate recommendation of learning paths in higher education, this paper proposes a smart education optimization model (SEOM) by combining the improved random forest algorithm (RFA) based on adaptive enhancement mechanism and the Graph Neural Network (GNN) algorithm. …”
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