Genetic analysis of a serologically weak D phenotype caused by the p. R191G variant of the RHAG gene by ZHANG Xu, LI Xiaofeng, LI Jianping

“…R191Q) mutation was predicted to be “probably damaging”, “deleterious” and “affected” by PolyPhen2, PROVEAN and Mutation Taster algorithms, respectively. …”
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Modeling and long-term forecasting of CO2 emissions in Asia: An optimized Artificial Neural Network approach with consideration of renewable energy scenarios by Erfan Abbasian Hamedani, S. Talebi

“…After preprocessing the data, a 5-6-1 MLP-ANN that is optimized with two metaheuristic algorithms (PSO and GWO) is utilized to train and validate the model for each country. …”
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From molecules to data: the emerging impact of chemoinformatics in chemistry by Anup Basnet Chetry, Keisuke Ohto

“…Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly improved the ability to analyze complex datasets, predict molecular properties, and design new compounds. …”
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Field Grading of Longan SSC via Vis-NIR and Improved BP Neural Network by Jun Li, Meiqi Zhang, Kaixuan Wu, Hengxu Chen, Zhe Ma, Juan Xia, Guangwen Huang

“…Initially, nine preprocessing methods were combined with six classification algorithms to develop the longan SSC grading prediction model. …”
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Features of Breast Cancer Progression after Comprehensive Treatment, Prognosis of Metastatic Spread by Yu. Chuprovska, V. Bodiaka, O. Ivashchuk, Ch. Tsagkaris

“…Still, there are no clear criteria and algorithms for predicting the occurrence of this complication. …”
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Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning by Shuang Yang, Yuanbo Yang, Yufeng Zhou

“…We utilized support vector machine (SVM), logistic regression (LogR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, linear regression (LR), and feedforward neural network (FNNs) for predicting the cerebral infarction volume ratio. …”
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A localization strategy combined with transfer learning for image annotation. by Zhiqiang Chen, Leelavathi Rajamanickam, Jianfang Cao, Aidi Zhao, Xiaohui Hu

“…The optimal K value is obtained experimentally and used to determine the number of predicted labels, thereby solving the empty label set problem that occurs when the predicted label values of images are below a fixed threshold. …”
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Spike Stall Precursor Detection in a Single-Stage Axial Compressor: A Data-Driven Dynamic Modeling Approach by Anish Thapa, Jichao Li, Marco P. Schoen

“…Reducing this margin through active control requires stall precursor detection and mitigation mechanisms. While several algorithms have shown promising results in predicting modal stalls, predicting spike stalls remains a challenge due to their rapid onset, leaving little time for corrective actions. …”
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A new deep learning-based fast transcoding for internet of things applications by Jia Yang, Yonghong Peng, Linbo Qing, Yajuan Xue, Hong Yang

“…The proposed transcoding algorithm operates efficiently at both CU and PU levels. …”
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Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos by Lisa Boucret, Floris Chabrun, Magalie Boguenet, Pascal Reynier, Pierre-Emmanuel Bouet, Pascale May-Panloup

“…Abstract Time-lapse imaging and deep-learning algorithms are promising tools to assess the most viable embryos and improve embryo selection in IVF laboratories. …”
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A novel machine learning-based approach to thermal integrity profiling of concrete pile foundations by Javier Sánchez Fernández, Agustín Ruiz López, David M.G. Taborda

“…This is followed by a regression algorithm that predicts the defect size and its location within the cross-section. …”
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Optimizing the learning rate for adaptive estimation of neural encoding models. by Han-Lin Hsieh, Maryam M Shanechi

“…Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. …”
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Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning by Karim Maghfour Sarkarabad, Mohsen Shayanmehr, Ahad Ghaemi

“…The effectiveness of sulfur removal was predicted by analyzing five important factors: temperature, concentration, surface area, fuel/adsorbent, and time. …”
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Recent Advances in Creep Modelling Using the <i>θ</i> Projection Method by William Harrison

“…Using a power law approach along with optimisation algorithms, the residual error between predicted and experimentally observed creep curves is reduced. …”
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Event recognition technology and short-term rockburst early warning model based on microseismic monitoring and ensemble learning by Zibin Li, Dengpan Qiao, Tianyu Yang, Jun Wang, Hao Chen

“…The rockburst early warning model V-soft achieves peak accuracy and F1 scores of 0.9394 and 0.9173, respectively, demonstrating performance improvements of 6.59–15.68% in accuracy and 14.11–27.21% in F1 score compared to conventional machine learning algorithms and ensemble classifiers. This highlights its superior discriminative capability and robustness in predicting high-intensity rockburst events. …”
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Nondestructive Detection of Rice Milling Quality Using Hyperspectral Imaging with Machine and Deep Learning Regression by Zhongjie Tang, Shanlin Ma, Hengnian Qi, Xincheng Zhang, Chu Zhang

“…Among single-task models, BPNNs outperformed the others in predicting BRR and HRR, with correlation coefficients (<i>r</i>) up to 0.9. …”
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Macro-microscopic study on the damage threshold strain of particle-filled polymer composites by Wang Jiaxiang, Qiang Hongfu, Wang Xueren, Wang Zhejun, Li Shiqi

“…This study proposes a damage threshold strain prediction model for typical particle filled polymer composites (composite solid propellants) based on the theory of particle inclusion micromechanics. …”
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Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models by Jae-Woo Song, Jaho Seo, Wang-Hee Lee

“…This distribution currently covers approximately 9.53% of the global land area; however, the model predicted this distribution would decrease to 6.85%. …”
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Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. I. Data Set Preparation by Thomas Williams, Christopher B. Prior, David MacTaggart

“…This publicly available living data set will allow users to incorporate these data into their own flare prediction algorithms.…”
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Impact of morphological traits and irrigation levels on fresh herbage yield of sorghum x sudangrass hybrid: Modelling data mining techniques. by Halit Tutar, Senol Celik, Hasan Er, Erdal Gönülal

“…For this purpose, Artificial Neural Networks (ANN), Automatic Linear Model (ALM), Random Forest (RF) Algorithm and Multivariate Adaptive Regression Spline (MARS) Algorithm were used, and the prediction performances of these methods were compared. …”
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