Blended Ensemble Learning for Robust Normal Behavior Modeling of Wind Turbines by Jianghao Zhu, Tingting Pei, Le Su, Bin Lan, Wei Chen

“…Our findings establish a new paradigm for wind turbine monitoring, combining computational efficiency with unprecedented accuracy in normal behavior prediction.…”
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Real-Time Algal Monitoring Using Novel Machine Learning Approaches by Seyit Uguz, Yavuz Selim Sahin, Pradeep Kumar, Xufei Yang, Gary Anderson

“…Among the models tested, DTS achieved the highest accuracy for cell count prediction (R² = 0.77), while RF demonstrated superior performance for dry biomass estimation (R² = 0.66). …”
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Wide-Angle Image Distortion Correction and Embedded Stitching System Design Based on Swin Transformer by Shiwen Lai, Zuling Cheng, Wencui Zhang, Maowei Chen

“…The model integrates multi-scale feature extraction, Thin Plate Spline (TPS) control point prediction, and optical flow-guided constraints, balancing correction accuracy and computational efficiency. …”
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Characterization of the immune cell infiltration patterns in lung adenocarcinoma to facilitate immunotherapy by Haili Li, Zanmei Qin, Xuemei Huang, Zuyou Wei, JingMin Deng

“…This study demonstrated that ICI scores serve as an original and effective indicator for independent prognostic prediction as well as customized immune therapy of LUAD. …”
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Research on Multi-Objective Optimization Design of High-Speed Train Wheel Profile Based on RPSTC-GJO by Mao Li, Hao Ding, Meiqi Wang, Xingda Yang, Bin Kong

“…Firstly, the chaotic map is introduced into the population initialization process of the golden jackal algorithm. In the later stage of the algorithm iteration, random disturbance is introduced with optimization algebra as the switching condition to obtain an improved optimization algorithm, and the performance index of the optimization algorithm is verified to be superior to other algorithms. …”
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Comprehensive VR dataset for machine learning: Head- and eye-centred video and positional dataThe Adaptive Mind Datahub by Alexander Kreß, Markus Lappe, Frank Bremmer

“…Researchers can leverage this extensive dataset to develop and refine algorithms requiring comprehensive and annotated video and positional data. …”
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Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study by Shiying Shen, Wenhao Qi, Sixie Li, Jianwen Zeng, Xin Liu, Xiaohong Zhu, Chaoqun Dong, Bin Wang, Qian Xu, Shihua Cao

“…Research hotspots are concentrated in diabetes and cardiovascular disease, focusing mainly on risk prediction, disease diagnosis, and personalized treatment. …”
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MW-UNet: Multi-Scale Weighted Connection UNet for Identification and Classification of Non-Meteorological Clutter over Big Radar Data by Mengmeng Cui, Chen Zeng, Xiaolong Xu, Muhammad Bilal, Xiaoyu Xia

“…The field of weather forecasting makes extensive use of radar big data to extract information about precipitation, storms, lightning, and other weather phenomena to aid in the prediction and monitoring of weather changes. To improve the quality of radar data, machine learning and fuzzy logic algorithms are often used to identify and classify non-meteorological clutter in weather data. …”
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Maritime Traffic Knowledge Discovery via Knowledge Graph Theory by Shibo Li, Jiajun Xu, Xinqiang Chen, Yajie Zhang, Yiwen Zheng, Octavian Postolache

“…To enhance the perception, prediction, and decision-making capabilities of these ships, the present study proposes a novel approach for constructing a time-series knowledge graph, utilizing real-time Automatic Identification System (AIS) data analyzed via a sliding window technique. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…By combining both types of data, this framework enhances the generalisability and prediction accuracy of trained models. The results of the data experiments demonstrate that the multi-fidelity framework outperforms models trained solely on low- or high-fidelity data, achieving a coefficient of determination of 0.980 and a root mean square error of 0.078 m. …”
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A Hybrid Analytical Framework for Cracking and Some Fruit Quality Features in Sweet Cherries by Erol Aydın, Mehmet Ali Cengiz, Leyla Demirsoy, Hüsnü Demirsoy

“…Key fruit characteristics were measured, and novel machine learning algorithms were applied to identify associations between variables. …”
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Few-shot learning for novel object detection in autonomous driving by Yifan Zhuang, Pei Liu, Hao Yang, Kai Zhang, Yinhai Wang, Ziyuan Pu

“…Perception, a critical component, extracts real-time traffic information essential for various system functionalities, such as agent behavior prediction. However, the quality of information derived from perception greatly influences overall system performance. …”
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MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers by Huaxu Yu, Jun Ding, Tong Shen, Min Liu, Yuanyue Li, Oliver Fiehn

“…Final data tables undergo quality controls and can be used for metabolome-informed phenotype prediction. Peak detection in MassCube achieves 100% signal coverage with comprehensive reporting of chromatographic metadata for quality assurance. …”
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Vehicle Flow Detection and Tracking Based on an Improved YOLOv8n and ByteTrack Framework by Jinjiang Liu, Yonghua Xie, Yu Zhang, Haoming Li

“…These improvements represent increases of 2.7% and 3.16%, respectively, compared to baseline algorithms. This method provides effective technical support for intelligent traffic management, traffic flow monitoring, and congestion prediction.…”
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Low-latency neuromorphic air hockey player by Juan P Romero B, Dimitrios Korakovounis, Jens E Pedersen, Jorg Conradt

“…We believe our air-hockey platform provides a versatile testbed for evaluating neuromorphic systems and invites further exploration of advanced algorithms, such as those incorporating trajectory prediction or adaptive learning, which could significantly enhance real-time decision-making and control.…”
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Digital health technologies in Kurdistan region of Iraq: a narrative review of enhancing healthcare accessibility and quality by Zhinya Kawa Othman, Shuaibu Saidu Musa, Mohamed Mustaf Ahmed, Olalekan John Okesanya, Omar Abdulkarim Saeed Alhammadi, Don Lucero-Prisno Eliseo

“…This review describes several digital health technologies in Kurdistan, including software implementation for data management, such as District Health Information Software 2, the establishment of telemedicine services, and the utilization of machine learning algorithms for mortality prediction, particularly during the COVID-19 pandemic. …”
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State-Dependent Transcranial Magnetic Stimulation Synchronized with Electroencephalography: Mechanisms, Applications, and Future Directions by He Chen, Tao Liu, Yinglu Song, Zhaohuan Ding, Xiaoli Li

“…Future directions emphasize adaptive algorithms for neural state prediction, phase-specific stimulation protocols, and standardized methodologies to enhance reproducibility. …”
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Computational fluid dynamics and machine learning integration for evaluating solar thermal collector efficiency -Based parameter analysis by Xiaoyu Hu, Lanting Guo, Jiyuan Wang, Yang Liu

“…The methodology addresses the fundamental challenge of balancing computational efficiency with prediction accuracy in thermal system design. A validated CFD model generated 935 numerical cases across diverse operational and design parameters, which were used to train and evaluate three machine learning algorithms: linear regression (LR), support vector regression (SVR), and artificial neural networks (ANN). …”
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Advanced Technologies for Smart Fertilizer Management in Agriculture: A Review by Jian-Jun Liu, Hao Wu, Imran Riaz

“…Additionally, the study explores key applications of precision fertilization, including data-driven fertilizer recommendations, aerial crop monitoring, real-time nutrient deficiency prediction, automated and optimized fertilization systems, and organic and eco-friendly nutrient management practices. …”
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Pistachio Classification Based on Acoustic Systems and Machine Learning by Yavuz Türkay, Zekiye Seyma Tamay

“…These features are given as input to a support vector machine algorithm called FITCSVM for classification. FITCSVM is an algorithm that can perform one-class and two-class (binary) classification on low or medium-sized prediction datasets. …”
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