Recent Advances in Creep Modelling Using the <i>θ</i> Projection Method by William Harrison

“…Using a power law approach along with optimisation algorithms, the residual error between predicted and experimentally observed creep curves is reduced. …”
Get full text

Event recognition technology and short-term rockburst early warning model based on microseismic monitoring and ensemble learning by Zibin Li, Dengpan Qiao, Tianyu Yang, Jun Wang, Hao Chen

“…The rockburst early warning model V-soft achieves peak accuracy and F1 scores of 0.9394 and 0.9173, respectively, demonstrating performance improvements of 6.59–15.68% in accuracy and 14.11–27.21% in F1 score compared to conventional machine learning algorithms and ensemble classifiers. This highlights its superior discriminative capability and robustness in predicting high-intensity rockburst events. …”
Get full text

Nondestructive Detection of Rice Milling Quality Using Hyperspectral Imaging with Machine and Deep Learning Regression by Zhongjie Tang, Shanlin Ma, Hengnian Qi, Xincheng Zhang, Chu Zhang

“…Among single-task models, BPNNs outperformed the others in predicting BRR and HRR, with correlation coefficients (<i>r</i>) up to 0.9. …”
Get full text

Macro-microscopic study on the damage threshold strain of particle-filled polymer composites by Wang Jiaxiang, Qiang Hongfu, Wang Xueren, Wang Zhejun, Li Shiqi

“…This study proposes a damage threshold strain prediction model for typical particle filled polymer composites (composite solid propellants) based on the theory of particle inclusion micromechanics. …”
Get full text

Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models by Jae-Woo Song, Jaho Seo, Wang-Hee Lee

“…This distribution currently covers approximately 9.53% of the global land area; however, the model predicted this distribution would decrease to 6.85%. …”
Get full text

Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. I. Data Set Preparation by Thomas Williams, Christopher B. Prior, David MacTaggart

“…This publicly available living data set will allow users to incorporate these data into their own flare prediction algorithms.…”
Get full text

Impact of morphological traits and irrigation levels on fresh herbage yield of sorghum x sudangrass hybrid: Modelling data mining techniques. by Halit Tutar, Senol Celik, Hasan Er, Erdal Gönülal

“…For this purpose, Artificial Neural Networks (ANN), Automatic Linear Model (ALM), Random Forest (RF) Algorithm and Multivariate Adaptive Regression Spline (MARS) Algorithm were used, and the prediction performances of these methods were compared. …”
Get full text

Servo Control of Shunt-Active Power Filters Using Kalman Filter With DC-Link Reference by Vijay Kumar C. V., Srinivasa Rao Gundala, Sri Kavya G., Pavan K., Murali R., Meerimatha G.

“…This reference approach may autonomously regulate adapting dc-link voltage anticipating current at most permissible source reference under varying load circumstances. Predictive modelling does this. MATLAB/Simulink integrates SAPF with the control algorithm. …”
Get full text

Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study by Jiexin Chen, Qiongbing Zheng, Youmian Lan, Meijing Li, Ling Lin

“…LASSO regression, XGBoost algorithm, and RF algorithm were used to identify significant indicators, and a OA prediction nomogram was developed. …”
Get full text

Impedance-Driven Decoupling Water–Nitrogen Stress in Wheat: A Parallel Machine Learning Framework Leveraging Leaf Electrophysiology by Shuang Zhang, Xintong Du, Bo Zhang, Yanyou Wu, Xinyi Yang, Xinkang Hu, Chundu Wu

“…A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. …”
Get full text

AgriChainSync: A Scalable and Secure Blockchain-Enabled Framework for IoT-Driven Precision Agriculture by Hassam Ahmed Tahir, Walaa Alayed, Waqar Ul Hassan, Fahad Nabi, Truong X. Tran

“…The rapid advancement of precision farming, automated irrigation systems, and predictive analytics has revolutionized agriculture, but these innovations also introduce new challenges, particularly in data integrity and security. …”
Get full text

SHapley Additive exPlanations (SHAP) for Landslide Susceptibility Models: Shedding Light on Explainable AI by H. Al-Najjar, B. Kalantar, B. Pradhan, G. Beydoun, N. Ueda

“…The first approach incorporates geoenvironmental factors, while the second integrates both geoenvironmental factors and an additional physically based model. Random Forest (RF) algorithm is used to develop and compare these landslide susceptibility models. …”
Get full text

An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks by Xianglong Luo, Fengrong Yu, Jing Qian, Biao An, Nengpeng Duan

“…The IGTO algorithm is used to optimize the parameters of VMD and LSTM, enhancing signal decomposition and feature extraction. …”
Get full text

Intelligent design of mixture proportions of manufactured sand concrete from environmental, economical and mechanical perspectives by Junfei Zhang, Changhai Xu, Lei Zhang, Ling Wang

“…This study proposes a multi-objective optimization (MOO) method based on machine learning (ML) and the non-dominated sorting genetic algorithm II (NSGA-II) to optimize MSC mixtures. The results indicate that the extremely randomized trees (ERT) model exhibits the best predictive performance for UCS, with an R value of 0.988 on the test set. …”
Get full text

Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…Comparative analysis demonstrated that the LDA–BA–ANN configuration delivered the highest prediction precision and computational speed over traditional models. …”
Get full text

Imbalanced feature generation based on bootstrap power spectral curve for estimating respiratory rate by Soojeong Lee, Gyanendra Prasad Joshi, Gangseong Lee

“…However, machine learning algorithm-related errors are unsuitable for medical decision-making processes because some data have a much larger sample size in the training set than in other sets. …”
Get full text

Machine learning-based forecasting of ground surface settlement induced by metro shield tunneling construction by Qiankun Wang, Chuxiong Shen, Chao Tang, Zeng Guo, Fangqi Wu, Wenyi Yang

“…The maximum relative error of the prediction based on the data preprocessing method and PSO-BP algorithm is 0.46%, demonstrating a good fitting effect. …”
Get full text

Investigating methods to enhance interpretability and performance in cardiac MRI for myocardial scarring diagnosis using convolutional neural network classification and One Match. by Michael H Udin, Sara Armstrong, Alice Kai, Scott T Doyle, Saraswati Pokharel, Ciprian N Ionita, Umesh C Sharma

“…To address this, we developed One Match (OM), an algorithm that builds on template matching to improve on both the explainability and performance of ML myocardial scaring classification. …”
Get full text

Experimental and Theoretical Study of the Axial Distribution of Solid Phase Particles in a Fluidized Bed by A. V. Mitrofanov, V. E. Mizonov, N. S. Shpeynova, S. V. Vasilevich, N. K. Kasatkina

“…The algorithm involved splitting the image by height into separate rectangular areas, identifying the particles and counting their number in each of these areas. …”
Get full text

EXPLORING A NOVEL HEXACO PERSONALITY TRAITS ON TWITTER: AN ENSEMBLE-BASED NLP METHODOLOGY by Tanvi Desai, Divyakant Meva

“…Feature selection employs the Hybrid Kepler Inspired Secretary Bird (HKISP) algorithm, a combination of the Kepler Optimization Algorithm (KOA) and Secretary Bird Optimization (SBO). …”
Get full text