Analysis of drug crystallization by evaluation of pharmaceutical solubility in various solvents by optimization of artificial intelligence models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The results indicate that the BAG-DT model surpasses other models with the highest R2 scores in the training, validation, and test sets, as well as the lowest error rates. The results highlight the efficacy of ensemble methods in improving predictive precision and resilience in regression assignments, especially within intricate datasets characterized by high dimensionality and noise. …”
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An improved spatially downscaled solar-induced chlorophyll fluorescence dataset from the TROPOMI product by Siyuan Chen, Liangyun Liu, Lichun Sui, Xinjie Liu, Yan Ma

“…Compared with a machine learning (ML) SIF product and OSIF, TroDSIF can better reproduce the OSIF signals with higher R2, lower root mean square error (RMSE), and nearly zero residuals at different latitudes. …”
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Optimized CNN-LSTM with hybrid metaheuristic approaches for solar radiation forecasting by İrem Fatma Şener, İhsan Tuğal

“…A hybrid ensemble approach is then proposed, integrating the predictions of the three optimized CNN-LSTM models to reduce error and enhance forecasting stability. The results demonstrate that the hybrid model outperforms the individual models, achieving the lowest MAE, MSE, and RMSE while maximizing the R2 score. …”
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Enhanced Mobile Multiple-Input Multiple-Output Underwater Acoustic Communications by Kexin Zhao, Jun Ling, Jian Li

“…In particular, this paper reviews the linear minimum mean-squared error (LMMSE) based soft-input soft-output equalizer involved in the turbo equalization scheme and adopts a fast implementation of the equalizer that achieves negligible detection performance degradation compared to its direct implementation counterpart. …”
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Probabilistic Dressing of a Storm Surge Prediction in the Adriatic Sea by R. Mel, P. Lionello

“…Here, a sophisticated error distribution form is developed, which includes the superposition of the uncertainty caused by inaccuracies of the ensemble prediction system, which depends on surge level and lead time, and the uncertainty of the meteorological forcing, which is described using a combination of cross-basin pressure gradients. …”
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Small radius inclusive jet production at the LHC through NNLO+NNLL by Terry Generet, Kyle Lee, Ian Moult, Rene Poncelet, Xiaoyuan Zhang

“…We compare with CMS data for small-R jet production, and find that our results greatly improve the accuracy of the predictions at small-R, and stabilize the perturbative convergence and error estimates at larger R. Our approach is applicable to a wide class of jet substructure observables exhibiting similar factorization theorems, opening the door to an NNLO jet substructure program at the LHC.…”
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Design and Simulation of a Four-Legged Mobile Robot for Autonomous Navigation on a Spacecraft Hull by Motaz Hassan, Kayla Dremann, Ashton Orosa, Emily Metzger, Nathan Doty, Julia Patek, Siamak Farhad, Ajay Mahajan

“…The robot demonstrated the ability to follow the computed optimal path with a positional deviation of less than 0.5% by the third trial. Positional error was quantified per waypoint and summarized using average and standard deviation metrics, while total traversal distances ranged from 65.50 to 63.00 in., approaching the theoretical minimum of 62.70 in. …”
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Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based Approach by Najla Sassi, Wassim Jaziri

“…As data-centric applications become increasingly complex, understanding effective query optimization in large-scale relational databases is crucial for managing this complexity. …”
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Automated Classification Model for Elementary Mathematics Diagnostic Assessment Data Based on TF-IDF and XGBoost by Seonghyun Park, Seungmin Oh, Woncheol Park

“…The proposed system integrates Term Frequency-Inverse Document Frequency (TF-IDF) and eXtreme Gradient Boosting (XGBoost) to vectorize textual data and automatically classify learning error patterns. The study utilizes 15,000 diagnostic assessment records collected from 2020 to 2024. …”
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Optimizing balanced accuracy in medical data threshold models by Benjamin F. Dribus, Jamie Hill, Neil Goldsmith, Daniel Martingano

“…Our solution is expressed as a formula involving the Gaussian error function, which may be easily applied to real-world datasets computationally, as we demonstrate via the accompanying software. …”
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A nomogram for predicting early bacterial infection after liver transplantation: a retrospective study by Jie Yu, Jie Yu, Jichang Jiang, Jichang Jiang, Caili Fan, Caili Fan, Jinlong Huo, Jinlong Huo, Tingting Luo, Tingting Luo, Lijin Zhao, Lijin Zhao

“…These results indicate that the developed model exhibits good predictive performance and a moderate error in training and validation.ConclusionThe nomogram constructed in this study showed good differentiation, calibration, and clinical applicability. …”
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Interrelationships Among Government Participation, Population and Growth of per Capita Income: Inquiry on Top Twenty Income-Holding Countries in the World by Ramesh Chandra Das

“…The literature on growth in economics encompasses two main facets of thinking: the applicability of diminishing productivity of capital, as has been in the neoclassical growth model with exogenous technological progress, and the applicability of non-diminishing productivity of capital, as has been in the endogenous growth models. …”
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Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles by Diana-Andreea Sterpu, Daniel Măriuța, Grigore Cican, Ciprian-Marius Larco, Lucian-Teodor Grigorie

“…The novelty of this study lies in the demonstration that the same machine learning model, trained on identical data and architecture, can exhibit up to 250% variation in prediction error solely due to differences in random seed selection. …”
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Context-Aware Embedding Techniques for Addressing Meaning Conflation Deficiency in Morphologically Rich Languages Word Embedding: A Systematic Review and Meta Analysis by Mosima Anna Masethe, Hlaudi Daniel Masethe, Sunday O. Ojo

“…The results point to a sizable impact size, but the estimates are highly unclear, as evidenced by the huge standard error and non-significant <i>p</i>-value. The review concludes that although contextually aware embeddings have promise in treating Meaning Conflation Deficiency, there is a great deal of variability and uncertainty in the available data. …”
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Prediction of Rut Depth in Soil Caused by Wheels Using Artificial Neural Networks by N. Farhadi, A. Mardani, A. Hosainpour, B. Golanbari

“…Specifically, the root mean square error (RMSE) for the optimal MLP model, which utilized a learning rate of 0.001 and a momentum of 0.67, was 0.10. …”
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An Ensemble Learning-Based Predictive Parameterization Approach for Permanent Magnet Synchronous Machines by Sema Nur Ipek, Nur Bekiroglu, Murat Taskiran

“…It also outperformed individual models in error metrics, with a MAPE of 7.66% for (R) compared to 23.06% (TRF) and 29.42% (LRNN). …”
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A Voice-Based Personal Assistant for Mental Health in Kreol Morisien by B. Gobin-Rahimbux, N. Gooda Sahib, N. Peerthy, A. Taylor

“…Voice-based smart personal assistants (VSPAs) are applications that recognize speech-based input and perform a task. …”
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A study on monthly sales forecasting of new energy vehicles in urban areas using the WOA-BiGRU model. by Xiangtu Li

“…The WOA-BiGRU model outperforms both the standalone BiGRU and PSO models, achieving a Mean Absolute Error (MAE) of 3051.89, which is 526.18 lower than the BiGRU model and 104.72 lower than that of the PSO model. …”
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Empowering entity synonym set generation using flexible perceptual field and multi-layer contextual information. by Subin Huang, Daoyu Li, Chengzhen Yu, Junjie Chen, Qing Zhou, Sanmin Liu

“…Automatic generation of entity synonyms plays a pivotal role in various natural language processing applications, such as search engines, question-answering systems, and taxonomy construction. …”
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Classical Benchmarks for Variational Quantum Eigensolver Simulations of the Hubbard Model by Antonios M. Alvertis, Abid Khan, Thomas Iadecola, Peter P. Orth, Norm Tubman

“…We find that even when using the most accurate wavefunction ansätze for the Hubbard model, the error in its ground state energy and wavefunction plateaus for larger lattices, while stronger electronic correlations magnify this issue. …”
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