A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite by S. Zhao, S. Zhao, Y. Zhang, Y. Zhang, S. Zhao, S. Zhao, X. Wang, X. Wang, D. J. Varon

“…We evaluate the ability of the algorithm to discover new methane sources with a suite of transfer tasks, in which training and evaluation data come from different regions. …”
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Adaptive respiratory muscle trainer based on hybrid nanogenerator sensor and artificial intelligence by Ziao Xue, Puchuan Tan, Jiangtao Xue, Yuan Xi, Minghao Liu, Yang Zou, Qiang Zheng, Zhou Li, Yuxiang Wu

“…Our findings inspire researchers in the field of rehabilitation and sports training to evaluate training status and improve training efficiency.…”
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Domain-Adaptive and Per-Fraction Guided Deep Learning Framework for Magnetic Resonance Imaging-Based Segmentation of Organs at Risk in Gynecologic Cancers by Reza Kalantar, PhD, Manasi Ingle, FRCR, Romelie Rieu, BA, BmBCh, Sebastian Curcean, MD, Jessica Mary Winfield, PhD, Gigin Lin, MD, PhD, Christina Messiou, MD, MRCP, FRCR, Susan Lalondrelle, FRCR, Dow-Mu Koh, MD, FRCP, FRCR, Matthew David Blackledge, PhD

“…Subsequently, a domain-adaptive segmentation network was trained to segment the 6 organs at risk on acquired MRL images. …”
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Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study by Xinzhang Zhang, Yapeng Si, Xin Shi, Yiwen Zhang, Liuyang Yang, Junfeng Yang, Ye Zhang, Jinjun Leng, Pingping Hu, Hao Liu, Jiaqi Chen, Wenliang Li, Wei Song, Jianping Zhu, Maolin Yang, Wei Li, Junfeng Wang

“…Results After a comprehensive evaluation of the predictive indicators, the logistic regression classifier model based on the combined clinico-radiological and radiomic features had an AUC of (0.945, 0.927, 0.856) for aldosterone-producing adenoma (APA), (0.963, 0.889, 0.887) for cortisol-producing adenoma (CPA), and (0.940, 0.765, 0.816) for non-functioning adrenal adenoma (NAA) in the training set, validation set, and external test set, respectively. …”
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Measurement Error Estimation Method of Field Service Electricity Energy Meters under the Condition of Big Data by DAI Yan-jie, DONG Xian-guang, LIU Ya-qi, LIANG Ya-jie, LIU Xiao, QI Jia, SUN Yong-quan

“…Firstly, the K-Means clustering algorithm is improved by optimizing the clustering evaluation index, and the field environmental data is analyzed by clustering. …”
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Improving the Performance of Individually Calibrated SSVEP Classification by Rhythmic Entrainment Source Separation by Wei Xu, Yufeng Ke, Dong Ming

“…The supervised decoding algorithms of Steady-State Visual Evoked Potentials (SSVEP) have achieved remarkable performance with sufficient training data. …”
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Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks by Yuan Wang, Yuan Wang, Yutong Zhang, Yongxin Li, Tianyu She, Meiqing He, Hailing He, Dong Zhang, Dong Zhang, Jue Jiang

“…The dataset was divided into a training set (n = 296) and a test set (n = 75) in an 8:2 ratio for further analysis and model evaluation. …”
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Evolution of landslide susceptibility in the Three Gorges Reservoir area over the three decades from 1991 to 2020 by Jiahui Dong, Jinrong Duan, Runqing Ye, Ming Li, Runze Wu, Ruiqing Niu

“…We applied two machine learning models—Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost)—along with the isolation Forest (iForest) algorithm. The iForest-LightGBM model achieved the highest accuracy and demonstrated efficient training performance. …”
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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

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Lightweight Dual-Stream SAR–ATR Framework Based on an Attention Mechanism-Guided Heterogeneous Graph Network by Xuying Xiong, Xinyu Zhang, Weidong Jiang, Tianpeng Liu, Yongxiang Liu, Li Liu

“…The experiments use two more rigorous evaluation protocols on MSTAR and OpenSARShip, namely, once-for-all and less-for-more, which can rigorously assess the efficacy and generalization capability of the algorithms. …”
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Correlation learning based multi-task model and its application by XU Wei, LUO Jianping, LI Xia, CAO Wenming

“…Meanwhile, the proposed multi-task learning network as a proxy model is applied to the Bayesian optimization algorithm, which not only reduces the evaluation times of model to target problem, but also enlarges the number of training data exponentially and further improves the model accuracy.…”
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Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods by Yihao Yu, Yuqi Yang, Qian Li, Jing Yuan, Yan Zha

“…With 50 medical characteristics easily obtained, six ML algorithms were used to develop prediction models. Several evaluation parameters were used to compare the predictive performance, including the area under the receiver-operating-characteristic curve (AUC) and precision-recall (P-R) curve. …”
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Two-sided Energy Storage Cooperative Scheduling Method for Transmission and Distribution Network Based on Multi-agent Attention-deep Reinforcement Learning by CHEN Shi, ZHU Yujie, LIU Yihong, XU Liuchao, TANG Guodeng

“…The attention mechanism is introduced into the evaluation network to capture interdependencies among agents, enabling potential intent recognition and cooperative behavior perception, thereby improving algorithm convergence. …”
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Land Use Analysis Using Machine-Learning Based on Cloud Computing Platform by Syukur Toha Prasetyo, Fahmi Arief Rahman, Sinar Suryawati, Slamet Supriyadi, Eko Setiawan

“…Satellite image analysis using the Random Forest algorithm on the GEE platform with the JavaScript API, including masking, cloud masking, class and sampling, training, and testing sample data. …”
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Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…The experimental data show that the relevant evaluation metrics MAE, RMSE, MSE, and R² of the model are within the reliable range. …”
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Polarization of road target detection under complex weather conditions by Feng Huang, Junlong Zheng, Xiancai Liu, Ying Shen, Jinsheng Chen

“…Additionally, a dataset of Polarized Images of Road Targets in Complex Weather conditions (PIRT-CW) is proposed for training and evaluation. Experimental results on the PIRT-CW show that the YOLO-PRTD algorithm achieves a mAP0.5 of 89.83%, reducing the error rate by 15.54% compared to the baseline network YOLOX.…”
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Identification of Energy Metabolism-Related Subtypes and Diagnostic Biomarkers for Osteoarthritis by Integrating Bioinformatics and Machine Learning by Xu S, Ye J, Cai X

“…We construct a comprehensive nomogram, and the diagnostic performance of both the nomogram and feature genes was evaluated using operating characteristic curve (ROC) and calibration curves. …”
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Vibration, current, torque, RPM dataset for multiple fault conditions in industrial-scale electric motors under randomized speed and load variationsMendeley DataMendeley DataMendel... by Wonho Jung, Junho Kim, Kangmin Jang, Sung-Hyun Yun, Daeguen Lim, Minje Jin, Yong-Hwa Park

“…The inclusion of multi-fault, multi-severity, and variable-condition data makes it especially suitable for training generalizable diagnostic algorithms in both academic and industrial contexts. …”
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Development and validation of a radiomics-based nomogram for predicting pathological grade of upper urinary tract urothelial carcinoma by Yanghuang Zheng, Hongjin Shi, Shi Fu, Haifeng Wang, Xin Li, Zhi Li, Bing Hai, Jinsong Zhang

“…The training set comprised 116 patients with a mean age of 63.5 ± 9.38 years and 38 males. …”
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Among Artificial Intelligence/Machine Learning Methods, Automated Gradient-Boosting Models Accurately Score Intraoral Plaque in Non-Standardized Images by Eric Coy, William Santo, Bonnie Jue, Helen Betts, Francisco Ramos-Gomez, Stuart A. Gansky

“…Average and dominant hue, saturation, and brightness values were features for training plaque-scoring algorithms.Results Best performing models were: Support Vector Machine-Gaussian for image selection, 5-CV AUC-ROC of 0.99 and 0.76s of training time; Gradient-Boosting classification and regression models for individual teeth (5-CV AUC-ROC of 0.99 with 105s training); and mean plaque-scoring algorithms (5-CV R2 of 0.72 with 1415s training).Conclusions Accurate automated plaque-scoring is attainable without the high computational and financial costs of deep learning (DL) models. …”
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