Estimation of Three-Dimensional Ground Reaction Force and Center of Pressure During Walking Using a Machine-Learning-Based Markerless Motion Capture System by Ru Feng, Ukadike Christopher Ugbolue, Chen Yang, Hui Liu

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Unsupervised machine learning-based multi-attributes analysis for enhancing gas channel detection and facies classification in the serpent field, offshore Nile Delta, Egypt by Shaimaa A. El-Dabaa, Farouk I. Metwalli, Ali Maher, Amir Ismail

“…Abstract The prediction of highly heterogeneous reservoir parameters from seismic amplitude data is a major challenge. …”
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Study on the Forecasting of Internal Solitary Wave Propagation in the Andaman Sea Using Joint Ascending-Descending Orbit Sentinel-1A Data and Machine Learning by Zexiang Cao, Junmin Meng, Jing Wang, Lina Sun, Hao Zhang

“…Running the model over two semidiurnal tidal cycles produced similar results. Compared with other machine learning models, the prediction performance shows improvements across various metrics, demonstrating the model's robustness in predicting ISW.…”
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Exploring Machine Learning's Potential for Estimating High Resolution Daily Snow Depth in Western Himalaya Using Passive Microwave Remote Sensing Data Sets by Srinivasarao Tanniru, Dhiraj Kumar Singh, Kamal Kant Singh, RAAJ Ramsankaran

“…Spaceborne passive microwave (PMW) remote sensing data sets provides valuable information about SD; however, only a limited PMW SD studies that cover subregions of WH are available. Different machine learning (ML) methods viz. support vector machine, random forest, and Extremely Randomized Trees (ERT) were tested for estimating SD. …”
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Integrating data from unmanned aerial vehicles and Sentinel-2 with PROSAIL-5D-driven machine learning for fuel moisture content estimation in agroecosystems by Jinlong Liu, Jia Jin, Jing Huang, Mengjuan Wu, Shaozheng Hao, Haoyi Jia, Tengda Qin, Yuqing Huang, Dan Chen, Nathsuda Pumijumnong

“…This study presents an advanced framework integrating multi-source remote sensing data fusion, physically based modeling, and machine learning to enable high-resolution and high-precision FMC estimation. …”
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Predicting grip strength-related frailty in middle-aged and older Chinese adults using interpretable machine learning models: a prospective cohort study by Lisheng Yu, Lisheng Yu, Shunshun Cao, Botian Song, Yangyang Hu

“…The feature performance of six ML models was compared based on the area under the receiver operating characteristic curve (AUROC) and the light gradient boosting machine (LightGBM) model was selected as the best predictive frailty model. …”
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Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

“…Independent risk factors were identified through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Machine learning algorithms were applied to construct predictive models for hematoma expansion. …”
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Durum Wheat (<i>Triticum durum</i> Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions by Giuseppe Badagliacca, Gaetano Messina, Emilio Lo Presti, Giovanni Preiti, Salvatore Di Fazio, Michele Monti, Giuseppe Modica, Salvatore Praticò

“…In contrast, for Ciclope, several vegetation indices (VIs) (i.e., CVI, GNDRE, and SR_RE) performed well (r-value > 0.7) in estimating both productive parameters. The implementation of ML approaches, particularly random forest (RF) regression, neural network (NN), and support vector machine (SVM), overcame the limitations of correlation in estimating the grain yield (R² > 0.6, RMSE = 0.56 t ha⁻¹, MAE = 0.43 t ha⁻¹) and protein (R² > 0.7, RMSE = 1.2%, MAE 0.47%) in Timilia, whereas for Ciclope, the RF approach outperformed the other predictive methods (R² = 0.79, RMSE = 0.56 t ha⁻¹, MAE = 0.44 t ha⁻¹).…”
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Development and validation of a machine learning-based diagnostic model for identifying nonneutropenic invasive pulmonary aspergillosis in suspected patients: a multicenter cohort... by Xinyu Wang, Yajie Lu, Chao Sun, Huanhuan Zhong, Yuchen Cai, Min Cao, Xuefan Cui, Wenkui Sun, Li Wang, Xin Lu, Cheng Chen, Yanbin Chen, Chunlai Feng, Yujian Tao, Jun Zhou, Jiaxin Shi, Guoer Ma, Yuanqin Li, Xin Su

“…The study developed a novel diagnostic framework by integrating clinical parameters, imaging features, and laboratory biomarkers using machine learning techniques. …”
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Circulating CCN6/WISP3 in type 2 diabetes mellitus patients and its correlation with insulin resistance and inflammation: statistical and machine learning analyses by Reza Afrisham, Yasaman Jadidi, Nariman Moradi, Seyed Mohammad Ayyoubzadeh, Reza Fadaei, Omid Kiani Ghalesardi, Vida Farrokhi, Shaban Alizadeh

“…Additionally, various machine learning models were employed to develop classifiers for predicting T2DM. …”
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ENHANCING STATE POLICY EFFECTIVENESS IN CINEMA THROUGH MACHINE LEARNING / Повышение эффективности государственной политики в сфере кинематографа с помощью машинного обучения... by DOZHDIKOV ANTON V. / ДОЖДИКОВ А.В.

“…A study was conducted on the distribution data of a range of Russian national films from 2004 to September 2023 using machine learning methods, with successful and unsuccessful films and patriotic projects considered separately. …”
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ED‐Autoformer: A New Model for Precise Global TEC Forecast by Jiawei Zhou, Hongtao Cai, Xu Yan, Hong‐wen Xu, Kun Hu, Chao Xiong

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Economy Optimization by Multi-Strategy Improved Whale Optimization Algorithm Based on User Driving Cycle Construction for Hybrid Electric Vehicles by Jie Ma, Mingzhang Pan, Wei Guan, Zhiqing Zhang, Jingcheng Zhou, Nianye Ye, Haifeng Qin, Lulu Li, Xingjia Man

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Integrated Intelligent Control of Redundant Degrees-of-Freedom Manipulators via the Fusion of Deep Reinforcement Learning and Forward Kinematics Models by Yushuo Chen, Shijie Su, Kai Ni, Cunjun Li

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Reliability Growth Method for Electromechanical Products Based on Organizational Reliability Capability Evaluation and Decision-Making by Zongyi Mu, Jian Li, Xiaogang Zhang, Genbao Zhang, Jinyuan Li, Hao Wei

“…Finally, the evaluation indicator framework and method are explained through practical application in CNC machine tool manufacturing enterprises, and the effectiveness of the framework and method is demonstrated through the MTBF growth of CNC machine tools.…”
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Improving network security using keyboard dynamics: A comparative study by Ugwunna, C.O., Chukwuogo, O.E., Alabi, O.A., Kareem, M.K., Belonwu, T.S., Oloyede, S.O.

“…To provide an accurate verification of whether a user is authentic or fraudulent, a model that integrates machine learning and dynamic keystroke models—Decision Tree, Random Forest, Support Vector Machine, and K-nearest Neighbors—is compared and utilized. …”
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Investigation Study of Structure Real Load Spectra Acquisition and Fatigue Life Prediction Based on the Optimized Efficient Hinging Hyperplane Neural Network Model by Lin Zhu, Benao Xing, Xingbao Li, Min Chen, Minping Jia

“…The prediction results of case structure indicate that the optimized EHH-NN model can achieve the high-accuracy load spectra, in comparison with support vector machine (SVM), random forest (RF) model and back propagation (BP) neural network. …”
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Estimation of Ground-Level NO₂ Concentrations Over Megacities Using Sentinel-5P and Machine Learning Models: A Case Study of Istanbul by N. Yagmur Aydin, I. Aydin

“…Integration of ground and satellite data using machine learning (ML) algorithms enables more accurate regional analysis. …”
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Microstructure simulation of maraging steel 1.2709 processed by powder bed fusion of metals using a laser beam: A cellular automata approach with varying process parameters by Kai-Uwe Beuerlein, Mohammad Shojaati, Mazyar Ansari, Hannes Panzer, Shahriar Imani Shahabad, Mohsen K. Keshavarz, Michael F. Zaeh, Saeed Maleksaeedi

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Experimental Investigation and Optimization of Material Removal Rate and Tool Wear in the Machining of Aluminum-Boron Carbide (Al-B4C) Nanocomposite Using EDM Process by A. Arunnath, S. Madhu, Mebratu Tufa

“…This work investigates the influence of EDM process parameters such as current (I), pulse on-time (ton), and tool diameter (d) during machining of Al-B4C composite on metal removal rate (MRR) and tool wear rate (TWR). …”
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