An Efficient Estimation Method for Reducing the Axial Intensity Drop in Circular Cone-Beam CT by Lei Zhu, Jared Starman, Rebecca Fahrig

“…If the reconstruction algorithm assumes zeros for the missing data, such as the standard FDK algorithm, a major type of resulting CB artifacts is the intensity drop along the axial direction. …”
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Changes in Nutrition, Food Safety, and Physical Activity Behaviors: A Comparison Between the Peak Health and Performance and Teen Cuisine Curricula by Tyler B. Becker, Ronald J. Gibbs Jr.

“…Individual curriculum pre- and post-scores were compared using a paired t-test, and between-group changes were examined using a repeated-measures ANOVA. Missing data were excluded case-wise. The results showed that vegetable and fruit consumption significantly increased for both groups. …”
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Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography by Chun-Chi Chen, Song-Xian Lin, Hyundoo Jeong

“…However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame rates and missing data, leading to inaccuracies in HR measurements. …”
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Seasonality, Interannual Variability, and Linear Tendency of Wind Speeds in the Northeast Brazil from 1986 to 2011 by Alexandre Torres Silva dos Santos, Cláudio Moisés Santos e Silva

“…To this end, the following methods were used: filling of missing data, descriptive statistical calculations, boxplots, cluster analysis, and trend analysis using the Mann-Kendall statistical method. …”
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Comparative study of imputation strategies to improve the sarcopenia prediction task by Shakhzod Karimov, Dilmurod Turimov, Wooseong Kim, Jiyoun Kim

“…Objective Sarcopenia, a condition characterized by the progressive loss of skeletal muscle mass and strength, poses significant challenges in research due to missing data. Incomplete datasets undermine the accuracy and reliability of studies, necessitating effective imputation techniques. …”
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High-Speed Target HRRP Reconstruction Based on Fast Mean-Field Sparse Bayesian Unrolled Network by Hang Dong, Fengzhou Dai, Juan Zhang

“…To address these challenges, this paper proposes a model-driven deep network based on fast mean-field SBL (FMFSBL-Net) for the HRRP reconstruction of high-speed targets under missing data conditions. Specifically, we integrate a precise velocity compensation and HRRP reconstruction into the mean-field SBL framework, which introduces a unified SBL objective function and a mean-field variational family to avoid matrix inversion operations. …”
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The PIT: SToPP Trial—A Feasibility Randomised Controlled Trial of Home-Based Physiotherapy for People with Parkinson's Disease Using Video-Based Measures to Preserve Assessor Blind... by Emma Stack, Helen Roberts, Ann Ashburn

“…Remote outcome measurement was successful; questionnaire followup and further training in video production would reduce missing data. We advocate a fully powered trial, designed to minimise dropouts and preserve assessor blinding, to evaluate this intervention.…”
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Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction by Gamal M. Mahmoud, Wael Said, Magdy M. Fadel, Mostafa Elbaz

“…We propose a new GAN architecture incorporating an identity module and a sperm motility-inspired heuristic during filtration to optimize the selection of pixels used in reconstructing missing data. The intelligent sperm motility heuristic navigates the image’s pixel space, identifying the most influential neighboring pixels for accurate imputation. …”
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D-MGDCN-CLSTM: A Traffic Prediction Model Based on Multi-Graph Gated Convolution and Convolutional Long–Short-Term Memory by Linliang Zhang, Shuyun Xu, Shuo Li, Lihu Pan, Su Gong

“…The model uses the DTWN algorithm to fill in missing data. To better capture the dual characteristics of short-term fluctuations and long-term trends in traffic, the model employs the DWT for multi-scale decomposition to obtain approximation and detail coefficients. …”
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Initial data analysis for longitudinal studies to build a solid foundation for reproducible analysis. by Lara Lusa, Cécile Proust-Lima, Carsten O Schmidt, Katherine J Lee, Saskia le Cessie, Mark Baillie, Frank Lawrence, Marianne Huebner, TG3 of the STRATOS Initiative

“…IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. …”
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Advancements in Predictive Analytics: Machine Learning Approaches to Estimating Length of Stay and Mortality in Sepsis by Houssem Ben Khalfallah, Mariem Jelassi, Jacques Demongeot, Narjès Bellamine Ben Saoud

“…After rigorous preprocessing to address missing data and ensure consistency, multiple classifiers, including Random Forest, Extra Trees, and Gradient Boosting, were trained and validated. …”
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A Deep Learning Model for the Thermospheric Nitric Oxide Emission by Xuetao Chen, Jiuhou Lei, Dexin Ren, Wenbin Wang

“…Given that the 3‐D image of NO emission from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) onboard the Thermosphere Ionosphere Energetics and Dynamics satellite contains a large amount of missing data which is unobserved, a context loss function is applied to extract the features from the incomplete SABER NO emission images. …”
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Differences in Sexual Function Between Trimesters During Pregnancy: An Observational Study by Sunullah Soysal, Abdullah Sarioz, Umran Kilincdemir Turgut, Gokce Anik Ilhan, Yusuf Arman, Begum Yildizhan, Tanju Pekin

“…Seventy-two of the women did not complete the questionnaire (rejections or missing data) and the overall response rate was 80.6%. …”
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A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model by Xiaoliang Jia, Guoyan Zhang, Zhiqiang Wang, Huacong Li, Jing Hu, Songlin Zhu, Caiwei Liu

“…The data reconstruction method proposed in this study can accurately capture trends in missing data, without the need for manual hyperparameter tuning, and the reconstruction results are highly consistent with the real data.…”
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Signal Recovery in Power Systems by Correlated Gaussian Processes by Marcel Zimmer, Daniele Carta, Thiemo Pesch, Andrea Benigni

“…Based on only local power system topology, the presented algorithm combines cross-channel information of the considered signals with a universal, nonparametric probabilistic machine learning regression to recover missing data. Starting from the theoretical background, the proposed approach is presented and contextualized in the framework of signal recovery for power systems. …”
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Rehabilitation outcomes after comprehensive post-acute inpatient rehabilitation following moderate to severe acquired brain injury—study protocol for an overall prognosis study bas... by Uwe M. Pommerich, Peter W. Stubbs, Jørgen Feldbæk Nielsen

“…Descriptive statistics will be used to estimate average prognoses for the level of functioning at discharge from post-acute rehabilitation. The patterns of missing data will be investigated. Discussion This protocol is intended to provide transparency in our upcoming study based on routinely collected clinical data. …”
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A Data Quality Control Program for Computer-Assisted Personal Interviews by Janet E. Squires, Alison M. Hutchinson, Anne-Marie Bostrom, Kelly Deis, Peter G. Norton, Greta G. Cummings, Carole A. Estabrooks

“…Data quality was assessed using both survey and process data. Missing data and data errors were minimal. Mean and median values and standard deviations were within acceptable limits. …”
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Association between chronic pain and physical activity in a Swiss population-based cohort: a cross-sectional study by Pedro Marques-Vidal, Peter Vollenweider, Oriane Aebischer, Marc René Suter

“…Participants were excluded if they had missing data for the pain or the PA questionnaires, for accelerometry (defined as >20% of non-wear time or duration <7 days) or for covariates.Primary outcomes Primary outcomes were association between chronic pain and previous, subjectively assessed PA (questionnaire), and subsequent, objectively assessed PA (accelerometry). …”
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Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective dat... by Jiawen Deng, Hemang Yadav, Kiyan Heybati

“…Data preprocessing steps include missing data imputation, feature scaling and dimensionality reduction techniques. …”
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Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning by Ji Hao, Wenbin Jiao, Xinpo Xie, Dula Man, Shengwei Huang

“…It provides an integrated approach encompassing data collection, preprocessing, model training, prediction, and result comparison to effectively illustrate the framework. Initially, missing data were interpolated using the modified Akima interpolation method, with visualized results provided for discussion. …”
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