Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task. by ZeSheng Lin

“…Secondly, the WOA's position update formula was modified by incorporating inertia weight ω and enhancing convergence factor α, thus improving its capability for local search. Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. …”
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Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments by Shiwei Lin, Jianguo Wang, Bomin Huang, Xiaoying Kong, Hongwu Yang

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Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large‐scale soybean dataset by Hawlader A. Al‐Mamun, Monica F. Danilevicz, Jacob I. Marsh, Cedric Gondro, David Edwards

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A New Robust Algorithm for Fault-Plane Parameters Identification: The 2009 L’Aquila (Central Italy) Seismic Sequence Case by Annarita Mangiacapra, Zaccaria Petrillo, Sergio Scippacercola, Luisa Valoroso, Rosa Nappi, Giuliana Alessio, Simona Tripaldi

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Applications of Multi-Robotic Arms to Assist Agricultural Production: A Review by Xiaojian Gai, Chang Xu, Yajia Liu, Qingchun Feng, Shubo Wang

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A novel method based on improved SFLA for IP information extraction from TEM signals by Ruiyou Li, Ruiheng Li, Guang Li, Yong Zhang, Xiaohui Ding, Long Zhang

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RAPO: An Automated Performance Optimization Tool for Redis Clusters in Distributed Storage Metadata Management by Yunkai Zhu, Tian Xia, Te Zhu, Zhoujie Zhao, Kexin Li, Xingbo Hu

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Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO by Hongbo Xu, Jundong Zhang, Jiale Liu, Yang Cao, Ao Ma

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Sliding Mode Thrust Control Strategy for Electromagnetic Energy-Feeding Shock Absorbers Based on an Improved Gray Wolf Optimizer by Wenqiang Zhang, Jiayu Lu, Wenqing Ge, Xiaoxuan Xie, Cao Tan, Huichao Zhang

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Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids by Nityananda Giri, Pravati Nayak, Ranjan Kumar Mallick, Sairam Mishra, Aymen Flah, Habib Kraiem, Lukas Prokop, Mohammad Kanan

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Reducing bias in coronary heart disease prediction using Smote-ENN and PCA. by Xinyi Wei, Boyu Shi

“…To address the data imbalance issue, SMOTE-ENN is utilized, and five machine learning algorithms-Decision Trees, KNN, SVM, XGBoost, and Random Forest-are applied for classification tasks. …”
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Novel neoadjuvant therapies for muscle‐invasive bladder cancer: Systematic review and meta‐analysis by Shugo Yajima, Naoki Imasato, Hitoshi Masuda

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16-channel photonic solver for optimization problems on a silicon chip by Jiayi Ouyang, Shengping Liu, Ziyue Yang, Wei Wang, Xue Feng, Yongzhuo Li, Yidong Huang

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Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks by Ahmad Gendia, Osamu Muta, Sherief Hashima, Kohei Hatano

“…In addition, exhaustive and random search benchmarks are provided as baselines for the achievable upper and lower sum-rate levels, respectively. …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…Despite advancements in satellite-based radiometry such as NASA’s Soil Moisture Active Passive (SMAP), significant challenges persist in coastal SSS retrieval due to radio frequency interference (RFI), land-sea contamination, and complex interactions of nearshore dynamic processes.MethodThis study proposes a deep neural network (DNN) framework that integrates SMAP L-band brightness temperature data with ancillary oceanographic and geographic parameters such as sea surface temperature, the shortest distance to the coastline (dis) to enhance SSS estimation accuracy in the Yellow and East China Seas. The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Supporting self-management with an internet intervention for low back pain in primary care: a RCT (SupportBack 2) by Adam W A Geraghty, Taeko Becque, Lisa C Roberts, Jonathan Hill, Nadine E Foster, Lucy Yardley, Beth Stuart, David A Turner, Gareth Griffiths, Frances Webley, Lorraine Durcan, Alannah Morgan, Stephanie Hughes, Sarah Bathers, Stephanie Butler-Walley, Simon Wathall, Gemma Mansell, Malcolm White, Firoza Davies, Paul Little

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