Improved forecasting of carbon dioxide emissions using a hybrid SSA ARIMA model based on annual time series data in Bahrain by Zahrah Fayez Althobaiti

“…The model performance was evaluated using standard error metrics—Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). …”
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Comparative Validation of the fBrake Method with the Conventional Brake Efficiency Test Under UNE 26110 Using Roller Brake Tester Data by Víctor Romero-Gómez, José Luis San Román

“…Criteria for the assessment of the equivalence of braking efficiency test methods in relation to the methods defined in ISO 21069), roller brake tester measurements were used to obtain force data under both conditions. The analysis showed that the extrapolated efficiencies agree within combined uncertainty limits, with normalized errors below 1 in all segments tested. …”
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High-Resolution Geochemical Data Mapping With Swin Transformer-Convolution-Based Multisource Geoscience Data Fusion by Ye Yuan, Shuguang Zhou, Jianhua Bian, Jinlin Wang, Wei Han, Jining Yan

“…However, the high economic cost of geochemical data analysis hinders large-scale studies, leading to low spatial resolution, especially in remote areas. …”
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A hybrid steganography framework using DCT and GAN for secure data communication in the big data era by Kaleem Razzaq Malik, Muhammad Sajid, Ahmad Almogren, Tauqeer Safdar Malik, Ali Haider Khan, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen

“…By leveraging deep learning techniques in both spatial and frequency domains, the proposed hybrid architecture offers a robust solution for applications requiring high levels of data integrity and security. While conventional steganography methods are typically classified into spatial and transform domains, extensive research and analysis demonstrate that the hybrid approach surpasses individual techniques in performance. …”
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A strategy for network multi-layer information fusion based on multimodel in user emotional polarity analysis by Ronghua Wang, Peng Zhuang

“…The study employed multiple emotional analysis models to analyze emotions in text data. Then the emotional analysis results of different models were integrated to improve accuracy through a hierarchical information fusion strategy. …”
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Enhancing construction safety management through multivariable grey model analysis and variable selection optimization by Jian Liu, Ye He, Rui Feng, Qinlin Chu

“…By conducting an exhaustive analysis of 511 potential combinations involving nine variables, it was observed that by integrating crucial external variables such as macroeconomic indicators and industry scale, the multivariable model achieved a prediction accuracy error rate of less than 0.5%, thereby significantly enhancing its information capture and forecasting precision. …”
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Experimental Comparison of Interference and Autocollimating Null Indicators by B. Nyamweru, E. V. Shishalova

“…As a result of conducting measurements and performing a cross-calibration procedure, four sets of data were obtained. An analysis of the processed data showed that the difference in the errors of the ring laser when using two NI did not exceed 0.06 arc seconds, being within the margin of random error. …”
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Data-Driven Energy Consumption Analysis and Prediction of Real-World Electric Vehicles at Low Temperatures: A Case Study Under Dynamic Driving Cycles by Yifei Zhao, Hang Liu, Jinsong Li, Hongli Liu, Bin Li

“…Here, we propose a data-driven energy consumption analysis and prediction approach for real-world electric vehicles in cold conditions. …”
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Proactive synergistic control of PM2.5 and ozone through urban planning: longitudinal data analysis of 274 Chinese cities from 2005 to 2020 by Sha Li, Bin Zou, Ning Liu, Chenhao Xue, Shenxin Li, Yulong Wang, Yong Xu

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Comprehensive Analysis of HY-2B/2C/2D Satellite-Borne GPS Data Quality and Reduced-Dynamic Precise Orbit Determination by Xin Jin, Guangzhe Wang, Jinyun Guo, Hailong Peng, Yongjun Jia, Xiaotao Chang

“…The experimental results demonstrate that the GPS data from the satellites exhibit consistent satellite visibility and minimal multipath errors, confirming the reliability and stability of the receivers. …”
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Differentiating placenta accreta spectrum from scar dehiscence with underlying, non‐adherent placenta: A systematic review of scoring systems and primary data analysis by Theophilus K. Adu‐Bredu, Rozi Aditya Aryananda, Joseph Arkorful, Sam Matthewlynn, Sally L. Collins

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Relationships between intensity, duration, cumulative dose, and timing of smoking with age at menopause: A pooled analysis of individual data from 17 observational studies. by Dongshan Zhu, Hsin-Fang Chung, Nirmala Pandeya, Annette J Dobson, Janet E Cade, Darren C Greenwood, Sybil L Crawford, Nancy E Avis, Ellen B Gold, Ellen S Mitchell, Nancy F Woods, Debra Anderson, Daniel E Brown, Lynnette L Sievert, Eric J Brunner, Diana Kuh, Rebecca Hardy, Kunihiko Hayashi, Jung Su Lee, Hideki Mizunuma, Graham G Giles, Fiona Bruinsma, Therese Tillin, Mette Kildevæld Simonsen, Mette Kildevæld Simonsen, Hans-Olov Adami, Elisabete Weiderpass, Marianne Canonico, Marie-Laure Ancelin, Panayotes Demakakos, Gita D Mishra

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Validating Data Interpolation Empirical Orthogonal Functions Interpolated Soil Moisture Data in the Contiguous United States by Haipeng Zhao, Haoteng Zhao, Chen Zhang

“…Previous studies have applied DINEOF+ to reconstruct the 1-km daily SM dataset but without further analysis of the reconstruction errors. In this study, we perform a comprehensive validation of DINEOF+ reconstructed SM by using both the original SMAP data and in situ measurements across the CONUS. …”
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Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model by Amirhossein Rostami, Chi-Hung Chang, Hyongki Lee, Hung-Hsien Wan, Tien Le Thuy Du, Kel N. Markert, Gustavious P. Williams, E. James Nelson, Sanmei Li, William Straka III, Sean Helfrich, Angelica L. Gutierrez

“…However, traditional forecasting methods face challenges in terms of implementation and scalability due to computational burdens and data availability issues. Current forecasting services in the U.S. largely rely on hydrodynamic modeling, limited to river reaches near in situ gauges and requiring extensive data for model setup and calibration. …”
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Integrating Satellite-Based Precipitation Analysis: A Case Study in Norfolk, Virginia by Imiya M. Chathuranika, Dalya Ismael

“…Bias adjustment methods include additive bias correction (ABC), which subtracts systematic errors; multiplicative bias correction (MBC), which scales satellite data to match observed data; and distribution transformation normalization (DTN), which aligns the statistical distribution of satellite data with observations. …”
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The topology of molecular representations and its influence on machine learning performance by Florian Rottach, Sebastian Schieferdecker, Carsten Eickhoff

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Channel Coding Toward 6G: Technical Overview and Outlook by Mohammad Rowshan, Min Qiu, Yixuan Xie, Xinyi Gu, Jinhong Yuan

“…Our evaluation includes a comparative analysis of error correction performance and the performance of hardware implementation for these coding schemes, providing insights into their potential and suitability for the upcoming 6G era. …”
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A novel behavioral health care dataset creation from multiple drug review datasets and drugs prescription using EDA by Gantela Prabhakar, George Merlin Linda, Bandi Sudheer Reddy, Samineni Nagamani, Chary Sonkoju Nagarjuna

“…Furthermore, the proposed work has a threefold unique approach that includes the integration of datasets, the creation of a new dataset, and the focus on exploratory data analysis. In the final step, a novel dataset is created from multiple datasets on behavioral healthcare drug reviews that are compared with individual datasets. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…In addition, the graphical-based and statistical-based evaluations illustrate that AdaBoost and ensemble learning models outperforms all other developed data-driven intelligent models as these two models are associated with lowest values of mean square error (adaptive boosting: 0.017 and ensemble learning: 0.021 based on unseen test data) and largest values of coefficient of determination (adaptive boosting: 0.986 and ensemble learning: 0.983 based on unseen test data).…”
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The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…These advancements have led to significant achievements in predictive modeling, data analysis, and decision-making processes. However, there remains a need for more sophisticated models that can accurately capture complex relationships in dynamic, real-world datasets. …”
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