Evaluation Method for Remaining Life of XLPE Insulated Power Cable by MA Hanchao, GAO Baoqi, LI Xiangyang, WU Suzhou, ZHANG Xiaojun

“…The results show that this method can obtain the optimal solution fitness value quickly, improve the efficiency of the surplus life evaluation of the analysis object, determine the weight value of the assessment factor under the measurement of the iterative period, and improve the residual life of the cable. …”
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Advanced long-term actual evapotranspiration estimation in humid climates for 1958–2021 based on machine learning models enhanced by the RReliefF algorithm by Ahmed Elbeltagi, Salim Heddam, Okan Mert Katipoğlu, Abdullah A. Alsumaiei, Mustafa Al-Mukhtar

“…To address this issue and guarantee more accurate ET predictions, this study attempts the following: i) to assess the performance of five machine learning (ML) models optimized by the RReliefF algorithm in estimating actual ET values for each month in four Chinese provinces under various agroclimatic conditions; and ii) to select the optimal model based on statistical metrics while minimizing discrepancies between the estimated and actual ET values. …”
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Construction of Clinical Predictive Models for Heart Failure Detection Using Six Different Machine Learning Algorithms: Identification of Key Clinical Prognostic Features by Qu FZ, Ding J, An XF, Peng R, He N, Liu S, Jiang X

“…Finally, a correlation analysis was conducted to examine the relationships between these features and other significant clinical features.Results: The logistic regression (LR) model was determined to be the optimal machine learning algorithm in this study, achieving an accuracy of 0.64, a precision of 0.45, a recall of 0.72, an F1 score of 0.51, and an AUC of 0.81 in the training set and 0.91 in the testing set. …”
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Dynamic and static integrated classification model of gas well based on XGBoost algorithm—an example from block S of Sulige tight sandstone gas field by Shuangshuang Zhang, Shuangshuang Zhang, Xiangdong Yu, Xiuli Gao, Donglin Li, Shijun Huang

“…Aiming at this problem, this paper establishes a set of dynamic and static integrated classification model of tight sandstone gas wells in Sulige based on XGBoost algorithm. …”
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A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation by Zhechuan Jin, Jiale Dong, Chengxiang Li, Yi Jiang, Jian Yang, Lei Xu, Ping Li, Zhun Xie, Yulin Li, Dongjin Wang, Zhili Ji

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Autonomous Robotic Path Planning Based on the Gaussian Mixture Model in Complex Manufacturing Environment by Rui Sun, Yuanmin Wang, Wenzheng Zhao, Yinhua Liu

“…To solve this problem, this paper proposes a path segment directed evolution algorithm (PSDEA) based on the Gaussian mixture model and a heuristic optimization algorithm. …”
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Research on Gating Fusion Algorithm for Power Grid Survey Data Based on Enhanced Mamba Spatial Neighborhood Relationship by Aiyuan Zhang, Jinguo Lv, Yu Geng, Xiaolei Wang, Xianhu Li

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Machine learning-based prediction method for open-pit mining truck speed distribution in manned operation by Changyou XU, Gang CHEN, Qiuxia ZHANG, Bo WANG, Hongwang ZHANG, Hongrui LI, Weiwei QIN, Muyang LI

“…Using machine learning to achieve accurate prediction of vehicle speed, in order to improve production efficiency, reduce costs, and enhance work safety. …”
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Predictive models for overall health of hydroelectric equipment based on multi-measurement point output by Liu Dong, Kong Lijun, Song Jinghui, Zhou Yiming

“…By comparing and analyzing the predictive performance, error results, and real-time prediction performance before and after model optimization, it was concluded that the prediction model constructed by SBM, hypersphere algorithm, and LSTM network had an overall average improvement of 23.7% in the prediction precision of 12 parameters, including temperature, vibration frequency, pressure, and lubrication degree, for the optimized upper guide bearing, thrust guide bearing, and water guide bearing systems. …”
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An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

“…To enhance the training and convergence of the GAN component, we integrate an improved chimp optimization algorithm (IChOA) for hyperparameter tuning and feature refinement. …”
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Multi-Objective Optimal Scheduling of Water Transmission and Distribution Channel Gate Groups Based on Machine Learning by Yiying Du, Chaoyue Zhang, Rong Wei, Li Cao, Tiantian Zhao, Wene Wang, Xiaotao Hu

“…A one-dimensional hydrodynamic model based on St. Venant’s system of equations is built to generate the feature dataset, which is then combined with the random forest algorithm to create a nonlinear prediction model. …”
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A simulation-driven computational framework for adaptive energy-efficient optimization in machine learning-based intrusion detection systems by Ripal Ranpara, Osamah Alsalman, Om Prakash Kumar, Shobhit K. Patel

“…Extensive simulations conducted on the KDD 1999 dataset demonstrate that GreenMU achieves a detection accuracy close to 99%, significantly surpassing standard baseline models while reducing energy consumption by 31%. Furthermore, the framework improves computational efficiency, reducing processing time by 15% and making it highly effective for resource-constrained environments such as IoT and edge computing. …”
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Cost-Effective Multitask Active Learning in Wearable Sensor Systems by Asiful Arefeen, Hassan Ghasemzadeh

“…Multitask learning models provide benefits by reducing model complexity and improving accuracy by concurrently learning multiple tasks with shared representations. …”
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Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study by Shahryar K. Ahmad, Sujay V. Kumar, Clara Draper, Rolf H. Reichle

“…Scaling and bias correction factors, often used in ML approaches for enhancing generalizability, were found to limit the transferability of the optimized physical parameters to the land model. The global objective function further compromises the algorithm's ability to simultaneously capture contrasting moisture regimes. …”
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Optimizing Social Security Contributions for Spanish Self-Employed Workers: Combining Data Preprocessing and Ensemble Models for Accurate Revenue Estimation by Luis Palomero, Vicente García, José Salvador Sánchez

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BERT Mutation: Deep Transformer Model for Masked Uniform Mutation in Genetic Programming by Eliad Shem-Tov, Moshe Sipper, Achiya Elyasaf

“…We introduce BERT mutation, a novel, domain-independent mutation operator for Genetic Programming (GP) that leverages advanced Natural Language Processing (NLP) techniques to improve convergence, particularly using the Masked Language Modeling approach. …”
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Energy Efficiency Optimization of Air Conditioning Systems Towards Low-Carbon Cleanrooms: Review and Future Perspectives by Xinran Zeng, Chunhui Li, Xiaoying Li, Chennan Mao, Zhengwei Li, Zhenhai Li

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Crucial heat damage analysis and optimization of a mid-sized pickup truck based on a deep Gaussian process model by Zebin Zhang, Sisi Liu, Xianzong Meng, Tingting Wang, Shizhao Jing, Chuanrui Wang, Dongchen Qin

“…Based on simulation results, a multi-objective two-layer deep Gaussian process model predicted heat source temperatures. The positions of cooling components were optimized using a genetic algorithm with heat-sensitive locations as the objectives. …”
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Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-II by Qunfeng Liu, Zhen Liu, Jun Zhao, Yuhang Lei, Shimin Zhu, Xing Wu

“…The second strategy employs a data-driven surrogate model, specifically an Artificial Neural Network (ANN), integrated with the NSGA-II optimization algorithm. …”
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Intelligent rockburst level prediction model based on swarm intelligence optimization and multi-strategy learner soft voting hybrid ensemble by Qinghong Wang, Tianxing Ma, Shengqi Yang, Fei Yan, Jiang Zhao

“…The data preprocessing method proposed in this study, based on an improved version of the Student t-SNE algorithm, effectively reduced the negative impact of data noise on model performance, enhancing the reliability of predictions. …”
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