An Informer-based multi-scale model that fuses memory factors and wavelet denoising for tidal prediction by Peng Lu, Yuchen He, Wenhui Li, Yuze Chen, Ru Kong, Teng Wang

“…Tidal time series are affected by a combination of astronomical, geological, meteorological, and anthropogenic factors, revealing non-stationary and multi-period features. The statistical features of non-stationary data vary over time, making it challenging for typical time series forecasting models to capture their dynamism. …”
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Research on Walnut (<i>Juglans regia</i> L.) Yield Prediction Based on a Walnut Orchard Point Cloud Model by Heng Chen, Jiale Cao, Jianshuo An, Yangjing Xu, Xiaopeng Bai, Daochun Xu, Wenbin Li

“…By combining these morphological features with statistical models and machine learning methods, a prediction model between tree morphology and yield is established, achieving prediction accuracy with a mean absolute error (MAE) of 2.04 kg, a mean absolute percentage error (MAPE) of 17.24%, a root mean square error (RMSE) of 2.81 kg, and a coefficient of determination (R²) of 0.83. …”
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Finite-size effects in molecular simulations: a physico-mathematical view by Benedikt M. Reible, Carsten Hartmann, Luigi Delle Site

“…Such problems are the equivalent of the size effects discussed in the first part of the review. Here this feature is treated employing the same statistical mechanics framework developed for the first problem.…”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…The VAE primarily learns from normal state acoustic data and determines the occurrence of faults based on reconstruction error. To achieve this, statistical features of Mel frequency cepstral coefficients were extracted, generating features applicable regardless of signal length. …”
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Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes by Martin Hitziger, Mareike Ließ

“…The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. …”
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A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm by Rana H. Al-Abboodi, Ayad A. Al-Ani

“…The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. …”
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Sensitivity Analysis of the WRF Model to Simulate Precipitation in the Metropolitan Area of the Valley of Mexico for the Period June-September 2019 by Indalecio Mendoza Uribe, Víctor Kevin Contreras Tereza, Pamela Iskra Mejía Estrada, Olivia Rodríguez López

“…The performance of the model was evaluated through the Efficiency Multiparametric Index, considering as complementary statistical metrics Bias Percentage, Mean Absolute Error, Mean Square Error, Nash-Sutcliffe Index, and Pearson's Correlation. …”
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FINANCIAL AND ECONOMIC SAFETY OF ECONOMIC AGENTS by V. I. Avdiysky, V. M. Bezdenezhnyh, V. E. Liechtenstein, G. V. Ross, K. I. Solodovnikova

“…In particular, we performed a review of the literature, which allows to draw the following main conclusions: fi rst, there is no shortage in the number and variety of indicators and tests, no diffi culties in the development of new, able to take into account the specifi c characteristics of each economic agent in specifi c circumstances; secondly, all the indicators and tests (at least the ones that we have found in the press) do not take into account the impact on the emergence of threats to fi nancial bubbles, money laundering, fi nancial terrorism, systematic or intentional distortion of statistical reporting, the use of virtual currencies and online games to move the capital; third, all indicators and tests ignore the infl uence of psychology on the behavior of economic agents, in particular, the real and subjective risks and methodical features of the calculation of economic indicators. …”
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Boosting EMG classification: a hybrid NCA-driven evolutionary optimization approach for high accuracy and efficiency by X. Little Flower, S. Poonguzhali

“…Abstract A novel hybrid approach combining neighborhood component analysis (NCA) and metaheuristic optimization algorithms is proposed to improve the classification accuracy of electromyography (EMG) signals while reducing the feature set size and computational time. EMG signals were collected from six neck and shoulder muscles, and a total of 23 features were extracted, including 17 time domain and 6 frequency domain features. …”
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An Adaptive BiGRU-ASSA-iTransformer Method for Remaining Useful Life Prediction of Bearing in Aerospace Manufacturing by Youlong Lyu, Qingpeng Qiu, Ying Chu, Jie Zhang

“…Second, 13 time-domain, frequency-domain, and statistical features, derived from traditional expertise, are processed using iTransformer to encode temporal correlations. …”
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Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms by Ö. Şengül, Ş. Çelik

“…<span class="inline-formula"><i>R</i>²=0.633</span>, 0.633, 0.721, 0.637, 0.768, and 0.609), coefficient of variation (CV % <span class="inline-formula">=</span> 6.35 and 5.14, <span class="inline-formula"><i>P</i><i>&lt;</i>0.01</span>), mean square error (MSE <span class="inline-formula">=</span> 3.296, 3.296, 2.904, 4.461, 2.277, and 4.121), root mean square error (RMSE <span class="inline-formula">=</span> 1.815, 1.815, 1.704, 2.112, 1.509, and 2.030), mean absolute error (MAE <span class="inline-formula">=</span> 1.409, 1.409, 1.279, 1.702, 1.193, and 1.628), and mean absolute percentage error (MAPE <span class="inline-formula">=</span> 3.925, 3.925, 3.578, 4.002, 3.335, and 3.967), between actual and predicted values of live body weight. …”
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GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

“…Data preprocessing included IQR-based outlier removal and class balancing. Feature selection is carefully addressed via a combination of 14 statistical methods, tree-based methods, bio-inspired methods, and regularization methods so that only the most relevant features for modelling are chosen and included. …”
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Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework. by David Lunn, Robert J B Goudie, Chen Wei, Oliver Kaltz, Olivier Restif

“…The non-linear dynamics, multi-factorial design, multiple measurements of responses over time and sampling error that are typical features of experimental host-pathogen systems can also be naturally incorporated. …”
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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). …”
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Damage Identification of Railway Bridge KW51 Conditions Using Deep-Learning-Based 1D CNN Model by Ali Al-Ghalib, Sawsan Mahmoud

“…The 1D CNN classification algorithm is compared with a statistical-based ML model and another CNN model. The alternative classification model uses human-derived damage-sensitive features extracted from Principal Components Analysis (PCA) in the supervised Linear Discriminant Analysis (LDA) method. …”
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Presentating a multi-objective optimization model for resource-constrained project scheduling regarding financial costs, time delays and the reliability function by Saeideh Naderi, Mohsen Vaez-Ghasemi, Farzad Movahedi Sobhani

“…The number of errors in calculating the total cost was 26%, 19%, and 5%, respectively. …”
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Clinical and epidemiological characteristics of chickenpox in children aged 0–17 in Barnaul by E. A. Peredelskaya, T. V. Safyanova, M. M. Druchanov

“…Data processing was performed using calculation of intensive and extensive indicators, calculation of the arithmetic mean (X) and standard error of the average (m). Calculations were made using the STATISTICA-10 program.Results. …”
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Evaluating AI Methods for Pulse Oximetry: Performance, Clinical Accuracy, and Comprehensive Bias Analysis by Ana María Cabanas, Nicolás Sáez, Patricio O. Collao-Caiconte, Pilar Martín-Escudero, Josué Pagán, Elena Jiménez-Herranz, José L. Ayala

“…Linear regression models and deep neural networks (DNNs) emerged as the leading AI methodologies, utilizing features such as statistical metrics, signal-to-noise ratios, and intricate waveform morphology to enhance accuracy. …”
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Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. by Kirsten Voorhies, Ruofan Bie, John E Hokanson, Scott T Weiss, Ann Chen Wu, Julian Hecker, Georg Hahn, Dawn L Demeo, Edwin Silverman, Michael H Cho, Christoph Lange, Sharon M Lutz

“…To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using standard regression approaches. …”
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Decision tree for severity assessment of neurodegenerative diseases using possibility approach and gait dynamics by Preeti Khera, Ashok Kumar, Rajat Kapila

“…Gait instrumentation, on the other hand, can be used as a reliable tool to record various contrasting primary gait features. However, these features are agonized by individual’s physical dimensions causing data dispersion. …”
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