Generative Electronic Poetic Texts: Specifics of Verbal Component and Peculiarities of Code Structure by S. A. Kuchina

“…The novelty of the research is seen in the attempt to understand the program algorithms to generate a literary text not only as a method of text creation, but also as a substantive artistic expression, where the code is regarded as a meaning that needs no verbal accompaniment. …”
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On the Choice of Training Data for Machine Learning of Geostrophic Mesoscale Turbulence by F. E. Yan, J. Mak, Y. Wang

“…Abstract Data plays a central role in data‐driven methods, but is not often the subject of focus in investigations of machine learning algorithms as applied to Earth System Modeling related problems. …”
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Option Pricing Based on Modular Neural Network by Moslem Peymany Foroushani, mohamad ali dehghan dehnavi, Milad Kouhkan

“…In the neural network models, option prices were predicted using Python and its machine learning algorithms. Finally, the predicted prices from the models were compared with the market prices of the same options. …”
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The rational canonical form of a matrix by J. S. Devitt, R. A. Mollin

“…The purpose of this paper is to provide an efficient algorithmic means of determining the rational canonical form of a matrix using computational symbolic algebraic manipulation packages, and is in fact the practical implementation of a classical mathematical method.…”
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RETRACTED: Computerized Detection of Calcium Oxalate Crystal Progression by Hanan A. Hosni Mahmoud

“…The simulation results of the new temporal algorithm show an enhancement of the speed by 70% compared to well-known temporal algorithms, and increased accuracy when computing PRSN against other algorithms.…”
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Comparison of principal component analysis algorithms for imputation in agrometeorological data in high dimension and reduced sample size. by Valter Cesar de Souza, Sergio Augusto Rodrigues, Luís Roberto Almeida Gabriel Filho

“…Statistical performance evaluation utilized the following indicators: correlation coefficient (r), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Normalized Root Mean Square Error (nRMSE), Willmott Index (d), and performance index (c). …”
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Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms by Nian Liu, Yuehan Zhao

“…Results The experimental results show that the Mean Absolute Percent Error (MAPE) of the proposed algorithm is 8.62%, the Mean Absolute Error (MAE) is 1.30% and the Root Mean Square Error (RMSE) is 2.26%. …”
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Discrepancies between physician review and algorithmic detection of the zoll rescuenet post-cardiac arrest case review by Ayal Z. Pierce, Cody Couperus, Jordan Parker, Allison LaRocco, Michael Mazzeffi, Nicholas A. Morris

“…Results: Bland-Altman plots indicated overestimation of individual pause times (mean difference 4.00 s), max pause time per arrest (mean difference 24.57 s) total pause time per arrest (mean difference 0.73 min), and average number of pauses per arrest, with corresponding underestimation of CCF (mean difference 8.33%). …”
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A Penalized h-Likelihood Variable Selection Algorithm for Generalized Linear Regression Models with Random Effects by Yanxi Xie, Yuewen Li, Zhijie Xia, Ruixia Yan, Dongqing Luan

“…This paper focuses linear models with random effects in the mean structure and proposes a penalized h-likelihood algorithm which incorporates variable selection procedures in the setting of mean modeling via h-likelihood. …”
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Elevator fault precursor prediction based on improved LSTM-AE algorithm and TSO-VMD denoising technique. by Hao Cao, Xiaoyan Du

“…The results demonstrate that the VMD-BILSTM-AEAM algorithm achieves a mean True Positive Rate (TPR) of 0.919 with a 95% confidence interval of 0.915 to 0.924, a mean False Positive Rate (FPR) of 0.090 with a 95% confidence interval of 0.087 to 0.092, and a mean Area Under the Curve (AUC) of 0.919 with a 95% confidence interval of 0.915 to 0.923. …”
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An ensemble deep learning framework for energy demand forecasting using genetic algorithm-based feature selection. by Mohd Sakib, Tamanna Siddiqui, Suhel Mustajab, Reemiah Muneer Alotaibi, Nouf Mohammad Alshareef, Mohammad Zunnun Khan

“…The proposed model demonstrated exceptional precision, achieving a Root Mean Square Error (RMSE) of 130.6, a Mean Absolute Percentage Error (MAPE) of 0.38%, and a Mean Absolute Error (MAE) of 99.41 for weekday data. …”
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Enhanced Multi-Threshold Otsu Algorithm for Corn Seedling Band Centerline Extraction in Straw Row Grouping by Yuanyuan Liu, Yuxin Du, Kaipeng Zhang, Hong Yan, Zhiguo Wu, Jiaxin Zhang, Xin Tong, Junhui Chen, Fuxuan Li, Mengqi Liu, Yueyong Wang, Jun Wang

“…To overcome these issues, this study proposes an adaptive multi-threshold Otsu algorithm optimized by a Simulated Annealing-Enhanced Differential Evolution–Whale Optimization Algorithm (SADE-WOA). …”
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Integrated deep learning for cardiovascular risk assessment and diagnosis: An evolutionary mating algorithm-enhanced CNN-LSTM by Ahmed Mohammed Ahmed Alsarori, Mohd Herwan Sulaiman

“…The EMA-based model achieved superior results, with a Mean Absolute Error (MAE) of 0.018, Mean Squared Error (MSE) of 0.0006, Root Mean Squared Error (RMSE) of 0.024, and a coefficient of determination (R²) of 0.98 for risk prediction. …”
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Feasibility of using a novel automatic cardiac segmentation algorithm in the clinical routine of lung cancer patients. by Robert Neil Finnegan, Lucia Orlandini, Xiongfei Liao, Jun Yin, Jinyi Lang, Jason Dowling, Davide Fontanarosa

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Python algorithm package for automated Estimation of major legume root traits using two dimensional images by Amit Ghimire, Yong Suk Chung, Sungmoon Jeong, Yoonha Kim

“…All the traits showed a high correlation with an R² ≥0.98 (p < 0.001) with the ground truth data. The root mean square error (RMSE) and mean bias error (MBE) were also minimal when comparing the algorithm-derived values to the ground truth values, with RMSE and MBE both < 10 for TRL, < 6 for SA, and < 0.5 for AD and RV. …”
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Investigation of ML algorithms for prediction of CFD data of fluid flow inside a packed-bed reactor by Yujiang Qiu, Pinxiao Liu

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Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment by Dimple Dimple, Jitendra Rajput, Nadhir Al-Ansari, Ahmed Elbeltagi

“…The accuracy of these models was determined serially using the mean squared error (MSE), correlation coefficients (r), mean absolute error (MAE), and root mean square error (RMSE). …”
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Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations by J. Xia, L. Guan

“…Both RF and MLP models performed well in cloud fraction retrieval, showing lower mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) compared to operational products. …”
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The Hounsfield Units Algorithm Demonstrates Changes in Bone Density of the Distal Tibia in Patients with Talus Osteochondral Defect by Erik Jesus Huanuco Casas MD, François Lintz MD, PhD, Kepler A.M. Carvalho MD, Grayson M. Talaski, Antoine Acker MD, Emily Joan Luo BS, Tommaso Forin Valvecchi MD, Mark E. Easley MD, Samuel B. Adams MD, Cesar de Cesar Netto MD, PhD

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Advanced comparative analysis of machine learning algorithms for early Parkinson's disease detection using vocal biomarkers by Ajay Kumar, Jay Parkash Singh, Priyanka Paygude, Rachan Daimary, Sandeep Prasad

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