Online variational Gaussian process for time series data by Weidong Wang, Mian Muhammad Yasir Khalil, Leta Yobsan Bayisa

“…Abstract Gaussian processes (GPs) are a powerful and popular framework for addressing machine learning problems, particularly for time-dependent data such as that generated by the Internet of Things (IoT). …”
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Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation by Stephanie Batista, Miguel Couceiro, Ricardo Filipe, Paulo Rachinhas, Jorge Isidoro, Inês Domingues

“…Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imaging by enabling precise liver segmentation, a crucial task for diagnosing and treating liver diseases. …”
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Energy optimization control of extended-range hybrid combine harvesters based on quasi-cycle power demand estimation by Shuofeng Weng, Chaochun Yuan, Youguo He, Jie Shen, Lizhang Xu, Zhihao Zhu, Qiuye Yu, Xiaowei Yang

“…By segmenting harvesting processes into quasi-periodic cycles linked to machine dynamics, the method integrates component-specific power models (header, conveyor, drum) for accurate energy estimation. …”
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High-throughput discovery of genetic determinants of circadian misalignment. by Tao Zhang, Pancheng Xie, Yingying Dong, Zhiwei Liu, Fei Zhou, Dejing Pan, Zhengyun Huang, Qiaocheng Zhai, Yue Gu, Qingyu Wu, Nobuhiko Tanaka, Yuichi Obata, Allan Bradley, Christopher J Lelliott, Sanger Institute Mouse Genetics Project, Lauryl M J Nutter, Colin McKerlie, Ann M Flenniken, Marie-France Champy, Tania Sorg, Yann Herault, Martin Hrabe De Angelis, Valerie Gailus Durner, Ann-Marie Mallon, Steve D M Brown, Terry Meehan, Helen E Parkinson, Damian Smedley, K C Kent Lloyd, Jun Yan, Xiang Gao, Je Kyung Seong, Chi-Kuang Leo Wang, Radislav Sedlacek, Yi Liu, Jan Rozman, Ling Yang, Ying Xu

“…By collecting and analyzing indirect calorimetry (IC) data from more than 2000 wild-type mice available from the International Mouse Phenotyping Consortium (IMPC), we show that the onset time and peak phase of activity and food intake rhythms are reliable parameters for screening defects of circadian misalignment. We developed a machine learning algorithm to quantify these two parameters in our misalignment screen (SyncScreener) with existing datasets and used it to screen 750 mutant mouse lines from five IMPC phenotyping centres. …”
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Stochastic Variance Reduced Primal–Dual Hybrid Gradient Methods for Saddle-Point Problems by Weixin An, Yuanyuan Liu, Fanhua Shang, Hongying Liu

“…Our algorithms have a simpler structure and lower per-iteration complexity than SOTA ADMMs. …”
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Research review on intelligent object detection technology for coal mines based on deep learning by Fan ZHANG, Jiarong ZHANG, Haixing CHENG

“…Firstly, a brief overview of object detection technology was provided, and the evolution process and algorithm classification of object detection technology based on deep learning were introduced. …”
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Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate by Shiyang Lv, Xiangying Ran, Mengsheng Xia, Yehong Zhang, Ting Pang, Xuezhi Zhou, Zongya Zhao, Yi Yu, Zhixian Gao

“…Significant features were used to construct classification models using Linear Discriminant Analysis(LDA), Support Vector Machines(SVM), and K-Nearest Neighbors(KNN) algorithms. …”
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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…The high water content of iron ore will reduce its machinability, which is not conducive to the smooth progress of mineral processing, sintering, smelting and tailings treatment. …”
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Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings by Vinoth Kanna I, Raja Subramani, Maher Ali Rusho, Shubham Sharma, Ramachandran T, Abinash Mahapatro, Deepak Gupta, Jasmina Lozanovic

“…Future research will utilize machine learning combined with swarm intelligence for adaptive and predictive HVAC control.…”
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Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…To further enhance the LSTM performance, the GWO is employed to adaptively optimize the key parameters of the LSTM. …”
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CAFiKS: Communication-Aware Federated IDS With Knowledge Sharing for Secure IoT Connectivity by Ogobuchi Daniel Okey, Demostenes Zegarra Rodriguez, Frederico Gadelha Guimaraes, Joao Henrique Kleinschmidt

“…In many cases, deep neural networks (DNNs) serve as the backbone algorithm in federated processes. However, their computational demands make them impractical for deployment in Internet of Things (IoT) environments, which are characterised by resource-constrained devices; hence, the need for lightweight and adaptable solutions. …”
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Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River by Mohammad Uzair Anwar Qureshi, Afshin Amiri, Isa Ebtehaj, Silvio José Guimere, Juraj Cunderlik, Hossein Bonakdari

“…Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. …”
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Equity in Digital Mental Health Interventions in the United States: Where to Next? by Athena Robinson, Megan Flom, Valerie L Forman-Hoffman, Trina Histon, Monique Levy, Alison Darcy, Toluwalase Ajayi, David C Mohr, Paul Wicks, Carolyn Greene, Robert M Montgomery

“…For products with artificial intelligence/machine learning, maintaining a “human in the loop” as well as prespecified and adaptive analytic frameworks to monitor and remediate potential algorithmic bias can reduce the risk of increasing inequity. …”
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A Synergistic CNN-DF Method for Landslide Susceptibility Assessment by Jiangang Lu, Yi He, Lifeng Zhang, Qing Zhang, Jiapeng Tang, Tianbao Huo, Yunhao Zhang

“…In addition, the SHAP algorithm was used to quantify the contribution of features to the prediction results both globally and locally, further explaining the model. …”
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“Decision Tree” as a Tool for Analyzing the Psychodiagnostics Data Latent Connections by Elena V. Slavutskaya, Leonid A. Slavutsky

“…Background. Machine learning methods turn to be an effective tool for data processing and provide ample opportunities for analyzing the results of psychodiagnostics. …”
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Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach by Weibo Yin, Qingfeng Hu, Jinping Liu, Peipei He, Dantong Zhu, Abdolhossein Boali

“…Six remote sensing indices were selected to model desertification using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM). …”
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Application of microscopic smartphone attachment for remote preoperative lab testing by Kefan Song, Kefan Song, Alexander T. Adams, Alexander T. Adams

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Physics-Informed Neural Networks: A Review of Methodological Evolution, Theoretical Foundations, and Interdisciplinary Frontiers Toward Next-Generation Scientific Computing by Zhiyuan Ren, Shijie Zhou, Dong Liu, Qihe Liu

“…The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>72</mn><mo>%</mo></mrow></semantics></math></inline-formula> cost reduction compared to FEM in high-dimensional spaces (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.01</mn><mo>,</mo><mi>n</mi><mo>=</mo><mn>15</mn></mrow></semantics></math></inline-formula> benchmarks). …”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…With the help of machine learning algorithms, the VNF resource capacity demand prediction method VNFPre proposed for polymorphic network scenarios,it can judge the future VNF resource capacity demand of network slices, and provide a priori information for the placement and mapping of VNFs carried by network slices.…”
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Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study by Yi Xie, Yi Xie, Xinmeng Wang, Huiwen Yang, Huiwen Yang, Jiayao Zhang, Honglin Wang, Zineng Yan, Jiaming Yang, Zhiyuan Yan, Zhiwei Hao, Pengran Liu, Yijie Kuang, Zhewei Ye, Zhewei Ye

“…Finally, the SL system was appraised through a prospective cohort to aid 6 clinicians in the preoperative assessment of 112 patients with knee joint injuries.ResultsThe YOLOv8n-cls algorithm demonstrated superior performance in centralized experiments and was adapted for SL implementation. …”
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