A Low‐Latitude Three‐Dimensional Ionospheric Electron Density Model Based on Radio Occultation Data Using Artificial Neural Networks With Prior Knowledge by Ding Yang, Hanxian Fang

“…In addition, we corrected effectively the error of ANN‐IRI in the lower ionosphere source from COSMIC data based on IRI‐2016 and spline interpolation. …”
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View-label driven cross-space structure alignment for incomplete multi-view partial multi-label classification by Shenrun Ding, Jiarui Chen, Tongwei Gao, Yinghao Ye, Xiaohuan Lu

“…Abstract Despite significant advancements in multi-view multi-label learning driven by its broad applicability, real-world scenarios frequently suffer from dual incompleteness in both view and label spaces due to data acquisition uncertainties. …”
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Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy by Mahadee Al Mobin

“…In contrast, the random forest model outperformed others on the downscaled daily data, reaching an accuracy of $$95.8\%$$ 95.8 % , thereby supporting the efficacy of data downscaling for ML applications in epidemiology. Comparative analysis reveals that downscaling provided a $$28.5\%$$ 28.5 % improvement in accuracy and an $$89.3\%$$ 89.3 % reduction in mean absolute percentage error (MAPE) over non-downscaled data which has been proven to be statistically significant using the Wilcoxon signed rank test, illustrating the substantial advantages of employing downscaling for effective DENV forecasting. …”
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Acoustic Fabry–Perot Resonance Detector for Passive Acoustic Thermometry and Sound Source Localization by Yan Yue, Zhifei Dong, Zhi-mei Qi

“…The SSL function of the AFPRD array was demonstrated in the outdoor environment, and the SSL error of the acoustic target with a sound pressure of 35 mPa was less than 1.2°. …”
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Predicting wheat yield using deep learning and multi-source environmental data by Muhammad Ashfaq, Imran Khan, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…The results showed that all models achieved less than 10% yield error rates, highlighting their ability to effectively integrate spatial, temporal, and static data. …”
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Design and Development of a Self-tuning Fuzzy PID Controller for Evaporative Cooling system to Preserve Fruits and Vegetables by Dawit Ashagrie Tarekegn, Tefera Terefe Yetayew

“…The controllers further validated to the sudden change of temperature and humidity by varying gain to ± 50%, and the self-tunning fuzzy PID controller track the desired setpoint with approximately zero error. The desired set points of 22°C and 93% humidity also successfully achieved in closed-loop experimental tests on developed prototype by adjusting airflow and water flow rates based on the feedback from ambient conditions of 28°C and 58% humidity. …”
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Predictive modeling of ADME properties using M-polynomial based topological indices for biocompatible polysaccharides by W. Eltayeb Ahmed, Muhammad Naeem, Muhammad Kamran Siddiqui, Mohamed Abubakar Fiidow

“…An in-depth understanding of these structural properties is essential for applications in drug delivery, biomedical engineering, and polymer-based therapeutics. …”
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Research on Mechanical Properties of Non-Directly Welded Reinforced Casings Under High Stress Ratio by Yiwei Fang, Yuming Li, Kuntao Xing, Zhe Liu

“…Combining the theoretical calculations with numerical simulations, an empirical formula for estimating the ultimate bearing capacity of the reinforced pipe specimens was derived. The relative error of the formula is less than 10% with the experimental outcomes and the finite element analysis results thereby offering a reliable tool for engineering applications.…”
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Anesthesia depth prediction from drug infusion history using hybrid AI by Liang Wang, Yiqi Weng, Wenli Yu

“…Performance was assessed using Mean Squared Error (MSE) and compared against other models. Results The hybrid model demonstrated superior predictive performance compared to conventional regression approaches. …”
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Photonics-based modular multistate digital coherent system by I. V. Unchenko, A. A. Emelyanov

“…Calculations of the introduced phase error of a quartz singlemode fiber and graphs of the dependence of the change in the signal phase on external influencing factors are given. …”
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Magnetometry, Acoustical and Inertial Indoor-Positioning in Healthcare by I. V. Cherepanova, I. V. Pospelova, D. S. Bragin, V. N. Serebryakova

“…Inertial sensors possess high accuracy, but over time, the measurement error increases. There-fore, the sensors need to regular correction. …”
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Modeling and Performance Analysis of MDM−WDM FSO Link Using DP-QPSK Modulation Under Real Weather Conditions by Tanmeet Kaur, Sanmukh Kaur, Muhammad Ijaz

“…In the present work, empirical models have been derived in terms of visibility, considering fog, haze, and cloud conditions of diverse geographical regions of Delhi, Washington, London, and Cape Town. Mean square error (MSE) and goodness of fit (R squared) have been employed as measures for estimating model performance. …”
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Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis by Javad Shirani Shamsabadi, Saeid Ansari Mahyari, Mostafa Ghaderi-Zefrehei

“…In precision dairy farm production, these codes may serve as a foundation for developing mobile applications.…”
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Comparative Analysis of Lab-Data-Driven Models for International Friction Index Prediction in High Friction Surface Treatment (HFST) by Alireza Roshan, Magdy Abdelrahman

“…Model refinements are suggested to better represent HFST surface characteristics with the lowest testing Root Mean Squared Error (RMSE) (0.049) and the highest predictive accuracy R² (0.821); the logarithmic model was found to be the best. …”
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On the effectiveness of neural operators at zero-shot weather downscaling by Saumya Sinha, Brandon Benton, Patrick Emami

“…We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. …”
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Cross-thermal streamline patterns and heat transfer in EP-nanofluids: a neural network approach with uncertainty analysis by Umar Farooq, Ali Alshamrani, M. Mahtab Alam, Khadija Rafique

“…Cross-thermal streamline patterns further enhance insights into flow dynamics, underscoring the model's potential in biomedical applications.…”
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A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors by Nahshon Obiri, Kristof van Laerhoven

“…Compared to a baseline model that considers only Multiple Walls (LDPLSM-MW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R2) from 0.6917 to 0.8222. …”
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Speaker Identification and Verification Using Convolutional Neural Network CNN by Azhar S. Abdulaziz, Akram Dawood, Amar Daood

“…It is noteworthy that a small amount of data was enough to efficiently train the proposed model, with a verification error of around 3%, i.e., an accuracy of 97%. Meanwhile, 95% and 96% identification accuracy was achieved using two different datasets. …”
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Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review by José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, Eduardo Maio, Eduardo Maio, Eduardo Maio, Pedro Afonso, Pedro Afonso, Samuel Encarnação, Samuel Encarnação, Samuel Encarnação, Guilherme F. Machado, Guilherme F. Machado, Ryland Morgans, Tiago M. Barbosa, Tiago M. Barbosa, António M. Monteiro, António M. Monteiro, Pedro Forte, Pedro Forte, Pedro Forte, Ricardo Ferraz, Ricardo Ferraz, Luís Branquinho, Luís Branquinho, Luís Branquinho, Luís Branquinho

“…Concretely, the tactical behavior was expressed by spatiotemporal tracking data using convolutional neural networks, recurrent neural networks, variational recurrent neural networks, and variational autoencoders, Delaunay method, player rank, hierarchical clustering, logistic regression, XGBoost, random forest classifier, repeated incremental pruning produce error reduction, principal component analysis, and T-distributed stochastic neighbor embedding. …”
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Economic Efficiency of Renewable Energy Investments in Photovoltaic Projects: A Regression Analysis by Adem Akbulut, Marcin Niemiec, Kubilay Taşdelen, Leyla Akbulut, Monika Komorowska, Atılgan Atılgan, Ahmet Coşgun, Małgorzata Okręglicka, Kamil Wiktor, Oksana Povstyn, Maria Urbaniec

“…The developed multiple linear regression model achieved a predictive error margin of 14.7%, confirming its validity.</b> This study highlights the technical, economic, and environmental benefits of EPC applications in Türkiye’s public institutions and offers a practical decision-support framework for policymakers. …”
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