Building radiomics models based on ACR TI-RADS combining clinical features for discriminating benign and malignant thyroid nodules by Xingxing Chen, Xingxing Chen, Lili Zhang, Bin Chen, Jiajia Lu

“…The calibration curve demonstrated that the mean absolute error in the training group was just 0.020 and in the test cohort was 0.033. …”
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A Framework for Predicting Winter Wheat Yield in Northern China with Triple Cross-Attention and Multi-Source Data Fusion by Shuyan Pan, Liqun Liu

“…The multi-source data processing module collects satellite, climate, and soil data based on the winter wheat planting range, and constructs a multi-source feature sequence set by combining statistical data. …”
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Investigating different methods of estimating annual flood discharge in basins and presenting a new regression model based on physiographic features by Yaser Hoseini

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Space Target Recognition of Radar Cross-Section Sequence Based on Transformer by Meng Xie, Weiwei Wu, Chenchen Fu, Sujunjie Sun

“…We then integrate period and size features into a unified feature set and introduce a cross-attention-based multi-feature interaction module to fuse physical and statistical features, learning dependencies between them to enhance target recognition accuracy. …”
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An edge awareness-enhanced visual SLAM method for underground coal mines by Qi MU, Xin LIANG, Yuanjie GUO, Yuhao WANG, Zhanli LI

“…Such feature extraction was followed by precise feature matching achieved using grid-based motion statistics (GMS) and ratio test matching algorithms. …”
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Individual psychological characteristics and features of coping with the disease in patients with the first psychotic episode and post-psychotic depression as targets for psychosoc... by E. Yu. Antokhin, A. V. Vasilyeva, T. A. Boldyreva, R. I. Antokhina

“…From the numerical characteristics of the samples, the arithmetic mean was determined with the calculation of the standard error of the mean, standard deviation. Statistically significant results were accepted at the p<0.05 significance level.Conclusions: the study found a significant effect on the clinical manifestations of postpsychotic depression in patients who underwent PES of schizophrenia, all studied individual psychological characteristics with the highest coping activity, which indicates the undoubted involvement of reactive mechanisms in the development of this type of pathology. …”
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Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques by Isteaq Kabir Sifat, Tajin Ahmed Jisa, Jyoti Shree Roy, Nourin Sultana, Farhana Hasan, Md Parvez Mosharaf, Md. Kaderi Kibria

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Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models by Rami Al-Hajj, Gholamreza Oskrochi, Mohamad M. Fouad, Ali Assi

“…After that, we incorporated the non-parametric kernel density estimation (KDE) method to statistically synthesize the wind speed prediction errors and estimate the prediction intervals (PI) with several confidence levels. …”
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SENTIMENT ANALYSIS OF REVIEWS ON X APPS ON GOOGLE PLAY STORE USING SUPPORT VECTOR MACHINE AND N-GRAM FEATURE SELECTION by Fahri Aimar Kusumo, Dewi Retno Sari Saputro, Purnami Widyaningsih

“…N-gram feature selection is a statistics-based approach to classifying text. …”
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Clinical Features and Body Composition in Men with Hormone-Sensitive Metastatic Prostate Cancer: A Pilot Study Examining Differences by Race by Patricia M. Sheean, Paula O’Connor, Cara Joyce, Vasilios Vasilopoulos, Ami Badami, Melinda Stolley

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Modelling effective soil depth at field scale from soil sensors and geomorphometric indices by Mauricio Castro Franco, Marisa Domenech, José Luis Costa, Virginia Carolina Aparicio

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Recursive feature elimination for summer wheat leaf area index using ensemble algorithm-based modeling: The case of central Highland of Ethiopia by Dereje Biru, Berhan Gessesse, Gebeyehu Abebe

“…Model performance validation analysis was evaluated via R-squared (R2), root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) statistical models. …”
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Continuous Patient-Independent Estimation of Respiratory Rate and Blood Pressure Using Robust Spectro-Temporal Features Derived From Photoplethysmogram Only by Muhammad Ahmad Sultan, Wala Saadeh

“…In addition, we derived a set of novel spectral and statistical features strongly correlated to BP. We proposed robust correlation-based feature selection methods for accurate RR estimates. …”
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Air quality prediction-based big data analytics using hebbian concordance and attention-based long short-term memory by Sathishkumar Sekar, Zhang Wei

“…They are preprocessing using the Statistical Normalization-based Preprocessing model, feature extraction employing the Generalised Hebbian Spatio Temporal Feature extraction model, feature selection using Concordance Correlation function, and Attention-based Long Short-Term Memory for air quality prediction. …”
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A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery by John B. Kilbride, Robert E. Kennedy

“…Our numerical experiments indicate that statistical features derived from GLCMs and spatial buffers yield the greatest improvement in AGB model performance out of the spatial feature engineering strategies considered. …”
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River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model by Khabat Khosravi, Salim Heddam, Changhyun Jun, Sayed M. Bateni, Dongkyun Kim, Essam Heggy

“…A greedy stepwise feature selection technique (GSFST) is employed to identify the optimal model inputs. …”
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Enhancing multi-temporal drought forecasting accuracy for Iran: Integrating an innovative hidden pattern identifier, recursive feature elimination, and explainable ensemble learnin... by Mahnoosh Moghaddasi, Mansour Moradi, Mehdi Mohammadi Ghaleni, Mehdi Jamei

“…The research employs an innovative hybrid approach that integrates a novel decomposition technique known as Hidden Pattern Feature Extraction Statistical Mode Decomposition (HPFE-SMD), along with Recursive Feature Elimination (RFE) for feature selection, and the Extra Tree Regressor (ETR) model. …”
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High-throughput method for improving rice AGB estimation based on UAV multi-source remote sensing image feature fusion and ensemble learning by Jinpeng Li, Jinpeng Li, Jinxuan Li, Jinxuan Li, Dongxue Zhao, Dongxue Zhao, Qiang Cao, Qiang Cao, Fenghua Yu, Fenghua Yu, Fenghua Yu, Yingli Cao, Yingli Cao, Yingli Cao, Shuai Feng, Shuai Feng, Shuai Feng, Tongyu Xu, Tongyu Xu, Tongyu Xu

“…Both single-type and multi-type features were used to develop individual and ensemble machine learning (ML) models for rice AGB estimation.ResultsThe findings indicate that: (i) Single-type features result in significant errors and low accuracy within the same sensor, but multi-feature fusion improves performance. …”
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Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework by Liang Yu, Yuanshen Hong, Hua Lin, Xu Jiang, Ziming Song

“…The BP neural network algorithm adjusts weights to minimize prediction errors, while multi-scale feature learning captures the diversity and regularity of user behavior by extracting data from various time dimensions, such as daily, weekly, and monthly intervals. …”
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One step hydrothermal synthesis of magnetically separable rGO supported Fe₃O₄ and Ag nanoparticles for adsorption and reduction of organic pollutants by Eman F. Aboelfetoh

“…The nanocomposite exhibited high adsorption efficiency toward methyl violet 2B (MV), with performance evaluated across varying dye concentrations, pH, temperature, and adsorbent dosages. Statistical error analysis (reduced χ2, RMSE, SSE) validated the applicability of a pseudo-second-order kinetics. …”
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