Smartphone‐Embedded Artificial Intelligence‐Based Regression for Colorimetric Quantification of Multiple Analytes with a Microfluidic Paper‐Based Analytical Device in Synthetic Tea... by Meliha Baştürk, Elif Yüzer, Mustafa Şen, Volkan Kılıç

“…However, quantitative evaluation remains a challenge, as classification aims to categorize the color change into discrete class labels rather than a quantity. Therefore, in this study, an AI‐based regression model with enhanced accuracy is developed and integrated into a microfluidic paper‐based analytical device for simultaneous colorimetric measurements of glucose, cholesterol, and pH. …”
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High-fidelity surrogate modelling for geometric deviation prediction in laser powder bed fusion using in-process monitoring data by Zhengrui Tao, Mirko Sinico, Bey Vrancken, Wim Dewulf

“…This configuration achieves root mean squared errors of 6.1 µm (11.3% of the 54.1 µm average ground-truth deviation) for up-skin and 27.4 µm (9.4% of 291.7 µm) for down-skin surfaces, with R2 of 0.91 and 0.95, respectively. …”
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Dual U–Vision–Transformer for reconstructing the three-dimensional eddy-resolving oceanic physical parameters from satellite observations by Huarong Xie, Changming Dong, Qing Xu

“…Daily parameter profiles from a reanalysis product with a 0.083° grid serve as labels for establishing and evaluating the model. …”
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Predictive maintenance in naval vessel propulsion systems for enhanced marine operations using a BiGMM-HMM framework with divergence-based clustering by Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

“…Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. …”
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Heart Rate Variability-Based Stress Detection and Fall Risk Monitoring During Daily Activities: A Machine Learning Approach by Ines Belhaj Messaoud, Ornwipa Thamsuwan

“…K-means clustering identified three distinct physiological states based on HRV features, such as the high-frequency band power and the root mean square of successive differences between normal heartbeats, suggesting patterns that may reflect stress levels. …”
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Research on future trends of electricity consumption based on conditional generative adversarial network considering dual‐carbon target by Jinghua Li, Zibei Qin, Yichen Luo, Jianfeng Chen, Shanyang Wei

“…The results demonstrate that the authors’ method achieves lower root mean square error and mean absolute percentage error values, specifically 0.177% and 2.39%, respectively, outperforming established advanced methods such as SVM and LSTM.…”
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Cobb angle prediction for adolescent idiopathic scoliosis via an explainable machine learning model by Yu Ding, Bin Li, Xiaoyong Guo

“…The root mean square error (∘), the mean absolute percentage error (∘), and the Pearson correlation coefficient are 3.69 ± 1.23, 2.81 ± 1.69, and 0.92 ± 0.01, respectively. …”
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Using traffic data to identify land-use characteristics based on ensemble learning approaches by Jiahui Zhao, Zhibin Li, Pan Liu

“…The Random Forest model performs better in labels with high regularity, such as education, residence, and work activities. …”
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Spectroscopic Method for Detection of Soluble Solid Content in Cherry Tomato Using Deep Convolutional Generative Adversarial Network-Based Data Augmentation by WU Zhijing, LIU Fuqiang, LI Zhigang, CHEN Hui

“…The prediction accuracy of the model for the test set from the combined dataset was the highest, with correlation coefficient of prediction (rp) of 0.980 7, and root mean square error of prediction (RMSEp) of 0.192 9. …”
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From a Woolen Curl to an Emotional Wave by Nicolae STANCIU

“…Having been found in numerous groups of Indo-European languages, the root displaying a trichotomic meaning was constantly labelled as Slavic in Romanian without any etymological incursion into the origins and the historical evolution of the word. …”
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Enhancing Quality Control of Packaging Product: A Six Sigma and Data Mining Approach by Resty Ayu Ramadhani, Rina Fitriana, Anik Nur Habyba, Yun-Chia Liang

“…In response to the increasing defects of packaging product in a cosmetics industry in Indonesia, surpassing the specified 3% tolerance limit, this research conducts a thorough investigation into the root causes, corrective measures, and improvement proposals to elevate product quality. …”
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Machine Learning Model for Predicting Global Ionospheric TEC Maps Based on Constraint Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

“…The results show that the PGVAE-label model has superior TEC prediction capability, producing TEC prediction maps with the lowest average root-mean-square error values of 1.79, 1.80, and 1.83 TECU, and that the PGVAE-label model is also superior to the PGVAE and CODE models in the region of the peak ionospheric structure. …”
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Individual Cow Recognition Based on Ultra-Wideband and Computer Vision by Aruna Zhao, Huijuan Wu, Daoerji Fan, Kuo Li

“…This allows for accurate cow identification and number labelling when compared to the location coordinates. …”
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Large-Scale Completion of Ionospheric TEC Maps Using Machine Learning Models With Constraints Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

“…Results show that the CGVAE-label model excels in TEC completion, achieving a mean structural similarity of 93.80% and a root mean square error of 2.20 TECU. …”
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An unsupervised cross project model for crashing fault residence identification by Xiao Liu, Zhou Xu, Dan Yang, Meng Yan, Weihan Zhang, Haohan Zhao, Lei Xue, Ming Fan

“…Abstract It is a critical quality assurance activity to effectively detect the root cause of faults causing the software crashes (i.e. crashing faults). …”
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Predicting the Degree of Fresh Tea Leaves Withering Using Image Classification Confidence by Mengjie Wang, Yali Shi, Yaping Li, Hewei Meng, Zezhong Ding, Zhengrui Tian, Chunwang Dong, Zhiwei Chen

“…The moisture percentage of fresh tea leaves is calculated by developing a weighted method that combines confidence levels and moisture labels, and the degree of withering is ultimately determined by incorporating the standard for wilted moisture content. …”
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Unsupervised learning-based panoramic unfolded image stitching method for rock mass borehole wall by XIAO Yu, LI Zehao, WANG Chao

“…Meanwhile, supervised learning methods cannot obtain sufficiently precise labeled matching point pairs. To address these issues, an unsupervised learning-based panoramic unfolded image stitching method for rock mass borehole wall is proposed. …”
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A reliable NLOS error identification method based on LightGBM driven by multiple features of GNSS signals by Xiaohong Zhang, Xinyu Wang, Wanke Liu, Xianlu Tao, Yupeng Gu, Hailu Jia, Chuanming Zhang

“…The sample data are first labeled using a fisheye camera to classify the signals from visible satellites as Line-of-Sight (LOS) or NLOS signals. …”
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Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks by Joshua Larsen, Jeffrey Dunne, Robert Austin, Cassondra Newman, Michael Kudenov

“…Image processing algorithms then convert these labels into quality metrics that can efficiently score these images based on infected versus non-infected area. …”
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Estimating chlorophyll content in tea leaves using spectral reflectance and deep learning methods by Yuta Tsuchiya, Yuhei Hirono, Rei Sonobe

“…Among the three models, the SSL approach achieved the highest accuracy, with a root mean square error (RMSE) of 3.33 μg/cm2, outperforming both the 1D–CNN (5.05 μg/cm2) and ViT (4.28 μg/cm2). …”
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