New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading by Dingyi Zhou, Zhifang Zhao, Wenfei Xi, Xin Zhao, Jiangqin Chao

“…The landslide susceptibility model is developed utilizing the Particle Swarm Optimization-Back Propagation (PSO-BP) algorithm. It includes evaluation techniques for regions without deformation rates. …”
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Energy storage efficiency modeling of high-entropy dielectric capacitors using extreme learning machine and swarm-based hybrid support vector regression computational methods by Yas Al-Hadeethi, Taoreed O. Owolabi, Mouftahou B. Latif, Bahaaudin M. Raffah, Ahmad H. Milyani, Saheed A. Tijani

“…The developed sigmoid (SG) activation function-based ELM (SG-ELM) shows performance improvement over sine (SI) function-based ELM (SI-ELM) model and PS-SVR model with an improvement of 79.25 % and 89.4 % using root mean square error (RMSE) performance measuring parameter. …”
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A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals by Lei Liu, Ziyi Wang, Xiaohan Zhang, Yan Zhuang, Yongbo Liang

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Corrosion rate prediction for long-distance submarine pipelines based on MWIWOA-SVM by Zhengshan LUO, Haipeng LYU, Jihao LUO

“…MethodsTo address these issues, Multi-Way Improved Whale Optimization Algorithm (MWIWOA) was proposed to optimize the SVM-based prediction model for the internal corrosion rate of long-distance submarine pipelines. …”
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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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Constrained total least squares localization using angle of arrival and time difference of arrival measurements in the presence of synchronization clock bias and sensor position er... by Ruirui Liu, Ding Wang, Jiexin Yin, Ying Wu

“…Based on measurements of angle of arrival and time difference of arrival, a method is proposed to improve the accuracy of localization with imperfect sensors. …”
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Broad learning system based on attention mechanism and tracking differentiator by LIAO Lüchao, ZOU Weidong, YANG Jialong, LU Huihuang, XIA Yuanqing, GAO Jianlei

“…In terms of model structure, A-TD-BLS introduced self-attention mechanism to the original BLS, and further fused and transformed the extracted features through attention weighting to improve the feature learning ability.In terms of model training methods, a weight optimization algorithm based on tracking differentiator was designed.This method effectively alleviates the overfitting phenomenon of the original BLS by limiting the size of the weight values, significantly reduces the influence of the number of hidden layer nodes on model performance and makes the generalization performance more stable.Moreover, the training algorithm was extended to the BLS incremental learning framework, so that the model can improve performance by dynamically adding hidden layer nodes.Multiple experiments conducted on some benchmark datasets show that compared to the original BLS, the classification accuracy of A-TD-BLS is increased by 1.27% on average on classification datasets and the root mean square error of A-TD-BLS is reduced by 0.53 on average on regression datasets.Besides, A-TD-BLS is less affected by the number of hidden layer nodes and has more stable generalization performance. …”
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Accurate extraction of electrical parameters in three-diode photovoltaic systems through the enhanced mother tree methodology: A novel approach for parameter estimation. by Mouncef El Marghichi, Abdelilah Hilali, Abdelkhalek Chellakhi, Mohamed Makhad, Azeddine Loulijat, Najib El Ouanjli, Abdelhak Essounaini, Vikash Kumar Saini, Ameena Saad Al-Sumaiti

“…This balance between exploration and exploitation allows the algorithm to dynamically and effectively identify optimal parameters. …”
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Domain Knowledge-Enhanced Process Mining for Anomaly Detection in Commercial Bank Business Processes by Yanying Li, Zaiwen Ni, Binqing Xiao

“…Additionally, we employed large language models (LLMs) for root cause analysis and process optimization recommendations. …”
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Protein docking by the underestimation of free energy funnels in the space of encounter complexes. by Yang Shen, Ioannis Ch Paschalidis, Pirooz Vakili, Sandor Vajda

“…This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. …”
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Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds by Peng Zhang, Jiangping Liu

“…Subsequently, a dual-optimized neural network model, termed Bayes-ASFSSA-BP, was developed by incorporating Bayesian optimization and the Adaptive Spiral Flight Sparrow Search Algorithm (ASFSSA). …”
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Research on subway settlement prediction based on the WTD-PSR combination and GSM-SVR model by Miren Rong, Chao Feng, Yinping Pang, Hailong Wang, Ying Yuan, Wensong Zhang, Lanxin Luo

“…Furthermore, Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Marine Predators Algorithm (MPA), and Whale Optimization Algorithm (WOA) are introduced to optimize the SVR model, and the prediction performance is compared with that of the Long Short-Term Memory (LSTM) model. …”
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A Short-Term Load Forecasting Method Considering Multiple Factors Based on VAR and CEEMDAN-CNN-BILSTM by Bao Wang, Li Wang, Yanru Ma, Dengshan Hou, Wenwu Sun, Shenghu Li

“…Finally, the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) is used to optimize the parameters of the combinative model to improve the forecasting accuracy. …”
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Enhancing 4G/LTE Network Path Loss Prediction with PSO-GWO Hybrid Approach by Messaoud Garah, Nabil Boukhennoufa

“…Furthermore, a hybrid optimization model, PSO-GWO, is proposed to improve prediction accuracy. …”
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Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for advanced pharmaceutical manufacturing by Tareq Nafea Alharby, Bader Huwaimel

“…Each model’s performance was assessed via measuring Root Mean Square Error (RMSE), R2, Mean Absolute Error (MAE), and Monte Carlo Cross-Validation (CV) scores using a Tabu Search method for optimization. …”
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Classification-based point cloud denoising and 3D reconstruction of roadways by Denghong CHEN, Ning PANG, Wen NIE, Juqiang FENG, Jiliang KAN, Jinjing ZHANG

“…By integrating a mean value method, an improved density-based spatial clustering of applications with noise (DBSCAN) algorithm, and an improved bilateral filtering algorithm, this study constructed a technical framework for classification processing. …”
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An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems by Jiahao Zhang, Kaiqiang Feng, Jie Li, Chunxing Zhang, Xiaokai Wei

“…On this basis, fully considering the high-order moments of estimation errors, the maximum versoria criterion is introduced as the optimization criterion to construct a novel cost function, further effectively suppressing deviations caused by non-Gaussian disturbances and improving system navigation accuracy. …”
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Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…Prior to model training, the dataset underwent rigorous preprocessing including outlier removal using the z-score method and normalization. To improve model performance, hyperparameters were optimized using the bio-inspired Barnacles Mating Optimizer (BMO) algorithm. …”
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Inversion model of stress state reconstruction for geological hazard pipelines based on digital twin by Xue Luning, Tian Mingliang, Zhao Juncheng

“…The mechanical state of the physical pipeline is mapped in real time by the digital twin, the numerical simulation and multi-source monitoring data are integrated, and the parameters of the twin model are dynamically optimized by combining the optimization algorithms of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM), so as to realize the real-time prediction of the pipeline stress state and the dynamic updating of the disaster scenario. …”
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An integrated IKOA-CNN-BiGRU-Attention framework with SHAP explainability for high-precision debris flow hazard prediction in the Nujiang river basin, China. by Hao Yang, Tianlong Wang, Nikita Igorevich Fomin, Shuoting Xiao, Liang Liu

“…This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. …”
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