Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning by Giulia Corniani, Stefano Sapienza, Gloria Vergara-Diaz, Andrea Valerio, Ashkan Vaziri, Paolo Bonato, Peter M. Wayne

“…The movement identification model achieved a micro F1 score of 90.05%. The proficiency assessment models achieved a mean micro F1 score of 78.64%. …”
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Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study by Zhan Zhang, Yue Zhao, Yi-Jun Ma, Chuan-Qi Chen, Zhen-Yi Li, Yv-Kai Wang, Si-Jie Zhang, Hai-Ming Li, Yongmeng Li, Yu Tian, Hui Tian

“…Preoperative identification of STAS is crucial for optimizing surgical strategies. This study aimed to develop and validate machine learning models to predict the presence of STAS using preoperative clinical, radiological, and pathological data in lung cancer patients. …”
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Phenotyping for heat stress tolerance in wheat population using physiological traits, multispectral imagery, and machine learning approaches by Neelesh Sharma, Manu Kumar, Hans D Daetwyler, Richard M Trethowan, Matthew Hayden, Surya Kant

“…We classified the stress severity and yield using VIs with a machine learning approach. A model was created with a random forest classifier (RFC) trained to categorize genotypes based on the stress susceptibility index using Python libraries. …”
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‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion by Manash Sarma, Subarna Chatterjee

“…DL classifier is used for developing models of both categories while GB (Gradient Boost), SVM (Support Vector Machine) classifier based models are built to identify AD stages from NCBI participants. …”
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Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example by Jia Rong, Zongyuan Zheng, Xiaorong Luo, Chao Li, Yuping Li, Xiangfeng Wei, Quanchao Wei, Guangchun Yu, Likuan Zhang, Yuhong Lei

“…The total organic carbon content (TOC) is a core indicator for shale gas reservoir evaluations. Machine learning-based models can quickly and accurately predict TOC, which is of great significance for the production of shale gas. …”
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Decoding Flow-Ecology Relationships: A Machine learning framework for flow regime Characterization and riparian vegetation prediction by Yifan Huang, Xiang Zhang, Jing Xu, Liangkun Deng, Yilun Li

“…To efficiently obtain the flow sequences under the future climate scenarios, the study constructs two optimization algorithm-based LSTM-Transformer coupled models, achieving superior simulation results with NSE exceeding 0.95 during the historical period (1981–2023). …”
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Machine Learning-Based Prediction of Summer Extended-Range Precipitation and Possible Contribution of Soil Moisture over China by Yuchen YE, Haishan CHEN, Siguang ZHU, Yinshuo DONG

“…Low accuracy of extended forecast remains an important scientific problem in the current stage， and qualified extended forecast is of great significance for disaster prevention and mitigation.In this study， the machine learning method was used to forecast the summer precipitation during the extension period （5~30 days） in China， and explore the possible contribution of soil moisture to extended forecast of precipitation.Based on the results， machine learning methods remarkably outweigh traditional linear models in terms of forecast accuracy， with Catboost， Lightgbm and Adaboost being the optimal machine learning methods.According to further analysis， the abnormal evaporation and sensible heat anomaly caused by the surface soil moisture anomaly in the Yangtze River Basin can lead to the atmospheric circulation and vertical movement anomaly， which eventually affects summer precipitation.The set of three optimal machine learning methods was applied to calculate the contribution rate of each forecasting factor in the model.It was found that the local soil moisture dominated the extended precipitation in the Yangtze River Basin from the 5th day to the 10th day， while the local soil moisture played a dominant role on previous precipitation from the 10th day to the 15th day， and the extended precipitation in the Yangtze River Basin during the period of Day 20~30 was basically controlled by large-scale circulation.Besides， the influence of non-local soil moisture on extended precipitation was evaluated， the results of which showed that the surface soil moisture in Indo-China Peninsula mainly contributed to the extended precipitation in the Yangtze River Basin from the 15th day to the 30th day.By adding the surface soil moisture of Indo-China Peninsula to the extended precipitation model in Northeast China， it was found that surface the soil moisture failed to improve the extended forecast accuracy of precipitation in this area， which verified the availability of the machine learning model.This study provides a certain reference for forecasting precipitation in the extended period and exploring the contribution rate of forecasting factors.…”
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Improving the Accuracy of a Wearable Uroflowmeter for Incontinence Monitoring Under Dynamic Conditions: Leveraging Machine Learning Methods by Faezeh Shanehsazzadeh, John O. L. DeLancey, James A. Ashton-Miller

“…Both computational fluid dynamics simulations and experimental tests demonstrated that this flow conditioner significantly reduced turbulence intensity by 82% and stabilized the axial velocity profile by 67%, increasing the R² of flow rate measurements from 0.44 to 0.92. Furthermore, our machine learning framework—utilizing a support vector machine (SVM) and an extreme gradient boosting (XGBoost) model with principal component analysis (PCA)—accurately predicted the true flow rate with high correlations, robust performance, and minimal overfitting. …”
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Estimating Snow Coverage Percentage on Solar Panels Using Drone Imagery and Machine Learning for Enhanced Energy Efficiency by Ashraf Saleem, Ali Awad, Amna Mazen, Zoe Mazurkiewicz, Ana Dyreson

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Predicting the diffusion coefficients of rejuvenators into bitumens using molecular dynamics, machine learning, and force field atom types by Eli I. Assaf, Xueyan Liu, Peng Lin, Shisong Ren, Sandra Erkens

“…This study explores the use of chemical descriptors derived from force field atom types to predict Fickian diffusion coefficients of rejuvenators in bitumen, utilizing machine learning models trained on data from 240 non-equilibrium molecular dynamics simulations. …”
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Predicting the Recurrence of Differentiated Thyroid Cancer Using Whale Optimization-Based XGBoost Algorithm by Keshika Shrestha, H. M. Jabed Omur Rifat, Uzzal Biswas, Jun-Jiat Tiang, Abdullah-Al Nahid

“…Both of our algorithms optimize the hyperparameters of the Extreme Gradient Boosting (XGBoost) model to increase the overall performance. …”
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Image-based machine learning and cluster analysis for urban road network: employing Orange for codeless visual programming by Zuo Zhang, Mengwei Zhang, Xiangxiang Song, Zhi Li

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Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using Machine Learning by Qiong Li, Hongde Liu

“…<b>Results</b>: A set of 19 key telomere- and senescence-related genes was identified as the optimal prognostic signature. The model demonstrated strong predictive accuracy and was successfully validated in multiple independent cohorts. …”
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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

“…Results showed improved accuracy (92.62 % Random forest, 90.16 % Support vector machine, 83.61 % K-nearest neighbours, and 81.97 % Naïve Bayes) and a reduction in the number of model parameters and memory usage (7.22 × 107 Random forest, 6.23 × 103 Support vector machine, 3.64 × 104 K-nearest neighbours and 1.88 × 102 Naïve Bayes) compared to using all features. …”
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An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems by Xinshuo Cui, Qingbo Meng, Jiacun Wang, Xiwang Guo, Peisheng Liu, Liang Qi, Shujin Qin, Yingjun Ji, Bin Hu

“…To solve the model, an evolutionary learning-based whale optimization algorithm is developed. …”
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Advanced machine learning techniques reveal multidimensional EEG abnormalities in children with ADHD: a framework for automatic diagnosis by Ying Mao, Ying Mao, Xuchen Qi, Xuchen Qi, Xuchen Qi, Lingyan He, Shan Wang, Zhaowei Wang, Fang Wang, Fang Wang

“…Then, four widely-employed machine learning algorithms (including random forest (RF), XGBoost, CatBoost, and LightGBM) were used for classification calculations, and the SHAP algorithm was then used to assess the importance of the contributing features to interpret the model’s decision process.ResultsThe results showed that the highest classification accuracy of 99.58% for pediatric ADHD detection was obtained with the CatBoost model based on the optimal feature subset of 206 features (PSD/FuzEn/MI = 53/5/148). …”
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Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. …”
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Pilot Study of Using Machine Learning to Detect Atherosclerotic Renal Artery Stenosis From Spectral Doppler Waveforms by Haseeb Mukhtar, Seyed Moein Rassoulinejad-Mousavi, Shahriar Faghani, Bradley J. Erickson, Sanjay Misra

“…Introduction: We investigated whether machine learning (ML) could be used to determine atherosclerotic renal artery stenosis (ARAS) using spectral Doppler waveforms in renal duplex ultrasound (DUS). …”
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Net-zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learning by Mohammadmahdi Abbaspour, Hamed Fazlollahtabar

“…This study presents an innovative approach to improving biomass energy sustainability by addressing the Productivity Opportunity Gap (POG) through machine learning techniques. A Multi-Layer Perceptron (MLP)-based model is employed to evaluate and rank sustainability strategies across environmental, economic, and social dimensions. …”
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Skin hyperspectral imaging and machine learning to accurately predict the muscular poly-unsaturated fatty acids contents in fish by Yi-Ming Cao, Yan Zhang, Qi Wang, Ran Zhao, Mingxi Hou, Shuang-Ting Yu, Kai-Kuo Wang, Ying-Jie Chen, Xiao-Qing Sun, Shijing Liu, Jiong-Tang Li

“…Finally, the visualization distribution maps under the optimal model would facilitate the direct determination of the fillet PUFAs and EPA + DHA contents. …”
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