Raman‐Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations by Jeppe Hagedorn, Guilherme Ramos, Miguel Ressurreição, Ernst Broberg Hansen, Michael Sokolov, Carlos Casado Vázquez, Christos Panos

“…Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. …”
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High-Frequency Passive Acoustic Recognition in Underwater Environments: Echo-Based Coding for Layered Elastic Shells by Zixuan Dai, Zilong Peng, Suchen Xu

“…Jaccard similarity analysis of 3570 material combinations reveals a system-wide average recognition error rate of 0.41%, confirming robust encoding reliability. …”
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Tower Frequency Monitoring Solution and Implementation for Wind Turbine Generator Systems by LIU Jinrui, GUO Yanyifu

“…The research results showed the effectiveness of the proposed frequency monitoring solution in accurately identifying natural frequency changes, with an error of merely 0.012 2 Hz in the shutdown state, and in detecting potential structural damages and faults with high sensitivity and accuracy. …”
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Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index by Dania AL-Najjar, Hazem AL-Najjar, Nadia Al-Rousan, Hamzeh F. Assous

“…In order to test the performance of two prediction models, R2 and various error functions are used. The linear regression model results found that PM10, NO2, CO, month, day, and year are significant, whereas O3, SO2, and AQI indices are insignificant. …”
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Predicting diabetes using supervised machine learning algorithms on E-health records by Sulaiman Afolabi, Nurudeen Ajadi, Afeez Jimoh, Ibrahim Adenekan

“…These results indicate that the KNN model with a mean test error of 0.0391 is the most reliable for predicting diabetes within the studied dataset. …”
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Uncertainty monitoring in Eurasian jays (Garrulus glandarius) by M. Loconsole, A. K. Schnell, E. Garcia-Pelegrin, N. S. Clayton

“…Metacognition abilities encompass enhanced decision-making in uncertain situations, more efficient resource management, error detection and correction, and improved problem-solving skills. …”
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Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front by Weishuai Xu, Lei Zhang, Ming Li, Xiaodong Ma, Maolin Li

“…Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. …”
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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Additionally, SHAP analysis identified hs-CRP, BIL.D, ALT, and sex as the most influential predictors of MetS. These findings suggest that leveraging liver function biomarkers and hs-CRP within an automated ML pipeline can facilitate early, non-invasive detection of MetS, supporting clinical decision-making and risk stratification efforts in healthcare systems.…”
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Parameter Analysis of Pile Foundation Bearing Characteristics Based on Pore Water Pressure Using Rapid Load Test by Jing-Jie Su, Xue-Liang Zhao, Qing Guo, Wei-Ming Gong, Yu-Chen Wang, Tong-Xing Zeng

“…A numerical model incorporating the damping coefficient and permeability coefficient is developed, yielding the time history features of load, displacement, and acceleration. The findings indicate that (1) the long pulse vibration equation, predicated on dynamic effects, aligns more closely with the actual bearing capacity of pile foundations than traditional detection theory; (2) in the rapid load test method, the maximum load applied to sand pile foundations occurs prior to peak displacement, while the ultimate bearing capacity, after accounting for inertial forces, corresponds to the maximum displacement value; (3) the permeability coefficient significantly influences the ultra-static pore water pressure, and the testing error regarding the bearing capacity of low permeability sand pile foundations using the rapid loading method is elevated.…”
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THE CURRENT STATE OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY – A REVIEW OF THE BASIC CONCEPTS, APPLICATIONS, AND CHALLENGES by Mariana Yordanova

“…In radiology, AI aims to assist with image analysis and interpretation, potentially reducing human error and alleviating the radiological workload. …”
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Using fuzzy decision support to create a positive mental health environment for preschoolers by Xinyue Li

“…The FDS process identifies the relationship between a child’s behaviour and attention level with maximum accuracy (97.98%), specificity (96.79%), sensitivity (97.08%), and minimum error (0.28). Behavioural prediction helps improve preschoolers’ mental health and activities effectively. …”
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Enhancing Speaker Recognition with CRET Model: a fusion of CONV2D, RESNET and ECAPA-TDNN by Pinyan Li, Lap Man Hoi, Yapeng Wang, Xu Yang, Sio Kei Im

“…Our model performs the best on the VoxCeleb2 dataset with 1024 channels, achieving an accuracy of 0.97828, an equal error rate (EER) of 0.03612 on the VoxCeleb1-O dataset, and a minimum detection cost function (MinDCF) of 0.43967. …”
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Practice patterns in pediatric myopia management: Insights from a 2023 survey among Indian ophthalmologists by Lav Kochgaway, Anuradha Chandra, Ipsita Basu, Debarpita Chaudhury, Jitendra Jethani, Arup Bhaumik

“…The recommended mean age for a child's first eye checkup is 3 years, underscoring the importance of early detection. Refractive error thresholds for prescribing spectacles are set at 3.0 D for infants, 2.0 D for preschool children, and 1.0 D for school-going children older than 3 years. …”
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Implementation of MF block in CNN for advanced REB fault diagnosis by M. Pandiyan, Narendiranath Babu T.

“…Statistical significance tests and error bars validate the robustness of the model’s performance. …”
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Association of Conventional Cardiovascular Risk Factors with Carotid Atherosclerosis Prevalence in General Population by V. S. Kaveshnikov, V. N. Serebryakova, I. A. Trubacheva, S. A. Shalnova

“…Study of relationships was carried out with logistic regression analysis. The error probability of less than 5% was considered statistically significant.Results. …”
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Representation Learning for Place Recognition Using MIMO Radar by Prashant Kumar Rai, Nataliya Strokina, Reza Ghabcheloo

“…During runtime, for place recognition and loop closure detection, cosine similarity is applied to query scan feature representation and the database to find the closest matches. …”
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Estimating individual tree DBH and biomass of durable Eucalyptus using UAV LiDAR by Ning Ye, Euan Mason, Cong Xu, Justin Morgenroth

“…Model performance was evaluated using the root mean square error and coefficient of determination (R2). SHapley Additive exPlanations (SHAP) analysis was employed to explain model predictions and evaluate input variables. …”
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Robust Tracking Method for Small and Weak Multiple Targets Under Dynamic Interference Based on Q-IMM-MHT by Ziqian Yang, Hongbin Nie, Yuxuan Liu, Chunjiang Bian

“…Experimental results indicate that under high noise conditions, the Root Mean Square Error (RMSE) of position estimation decreases to 0.74 pixels, while the RMSE of velocity estimation falls to 0.04 pixels/frame. …”
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Mapping Subalpine Forest Aboveground Biomass in Qilian Mountain National Park Using UAV-LiDAR, GEDI, and Multisource Satellite Data by Yanyun Nian, Siwen Chen, Jie Chen, Minglu Che, Wenhui Zhang, Junejo Sikandar Ali, Hao Zhang, Xingbang Wang, Bingzhi Liao, Xufeng Wang

“…Third, by extrapolating biomass from discrete GEDI footprints and incorporating variables from Sentinel-1 and Landsat 8 OLI, a continuous, high-accuracy forest biomass map for the entire Qilian Mountain National Park was generated (<italic>R</italic>² &#x003D; 0.66, root-mean-square error &#x003D; 19.08 Mg&#x002F;ha, and relative root-mean-square error &#x003D; 11.04&#x0025;). …”
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Automated diagnosis and classification of metacarpal and phalangeal fractures using a convolutional neural network: a retrospective data analysis study by Michael Axenhus, Anna Wallin, Jonas Havela, Sara Severin, Ablikim Karahan, Max Gordon, Martin Magnéli

“… Background and purpose: Hand fractures are commonly presented in emergency departments, yet diagnostic errors persist, leading to potential complications. …”
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