A Bayesian Network Approach to Predicting Severity Status in Nuclear Reactor Accidents with Resilience to Missing Data by Kaiyu Li, Ling Chen, Xinxin Cai, Cai Xu, Yuncheng Lu, Shengfeng Luo, Wenlin Wang, Lizhi Jiang, Guohua Wu

“…This study introduces a Bayesian Network (BN) framework used to enhance nuclear energy safety by predicting accident severity and identifying key factors that ensure energy production stability. With the integration of simulation data and physical knowledge, the BN enables dynamic inference and remains robust under missing-data conditions—common in real-time energy monitoring. …”
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Data-Driven Multiple ARIMA Through Neural Fusion for Enhanced Wind Power Prediction With Missing Data by Xiaoou Li, Wen Yu

“…This paper proposes a novel methodology that integrates a data-driven approach for creating multiple Autoregressive Integrated Moving Average (ARIMA) models with with neural network-based fusion strategy. …”
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An ensemble-based enhanced short and medium term load forecasting using optimized missing value imputation by Tania Gupta, Richa Bhatia, Sachin Sharma

“…Abstract Electricity load forecasting is integral to planning, energy management, and the energy market. …”
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An RMSprop-Incorporated Latent Factorization of Tensor Model for Random Missing Data Imputation in Structural Health Monitoring by Jingjing Yang

“…SHM data are prone to random missing values due to signal interference or connectivity issues, making precise data imputation essential. …”
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Mapping and quantifying near-miss events involving vehicles and vulnerable road users in Reno and Sparks, Nevada by Scott Kelley, Cole Peiffer, Fei Guan, Hao Xu, James Okorocha, Kelly Dunn, Carlos Cardillo

“…The observed high relative frequency of near-miss events underscores the importance of integrating near-miss events into countermeasure planning.…”
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Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos by Shadi Jaradat, Mohammed Elhenawy, Huthaifa I. Ashqar, Alexander Paz, Richi Nayak

“…This study underscores the potential of combining deep learning with MLLMs to enhance traffic safety analysis by integrating near-miss data as a key predictive layer. …”
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Adaptive biases-incorporated latent factorization of tensors for predicting missing data in water quality monitoring networks by Xuke Wu, Xuke Wu, Lan Wang, Lan Wang, Miao Ge, Miao Ge, Jing Jiang, Jing Jiang, Yu Cai, Bing Yang, Bing Yang

“…The findings highlight that integrating multiple bias schemes into tensor factorization models can effectively address the limitations of existing LFT models in capturing inherent data fluctuations, thereby enhancing the reliability of missing data imputation for water quality monitoring. …”
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A systematic literature review on incomplete multimodal learning: techniques and challenges by Yifan Zhan, Rui Yang, Junxian You, Mengjie Huang, Weibo Liu, Xiaohui Liu

“…However, data often suffers from missing or incomplete modalities in practical applications. …”
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XGBoost based enhanced predictive model for handling missing input parameters: A case study on gas turbine by Nagoor Basha Shaik, Kittiphong Jongkittinarukorn, Kishore Bingi

“…The proposed research establishes the framework for real-time integration of the developed XGBoost model with gas turbine control systems.…”
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Bayesian Random Forest with Multiple Imputation by Chain Equations for High-Dimensional Missing Data: A Simulation Study by Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani

“…The pervasive challenge of missing data in scientific research forces a critical trade-off: discarding incomplete observations, which risks significant information loss, while conventional imputation methods struggle to maintain accuracy in high-dimensional settings. …”
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Missing data approaches for longitudinal neuroimaging research: Examples from the Adolescent Brain and Cognitive Development (ABCD) Study® by Lin Li, Mohammadreza Bayat, Timothy B. Hayes, Wesley K. Thompson, Michael C. Neale, Arianna M. Gard, Anthony Steven Dick

“…This paper addresses the challenges of managing missing values within expansive longitudinal neuroimaging datasets, using the specific example of data derived from the Adolescent Brain and Cognitive Development (ABCD) Study®. …”
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Caveat emptor, computational social science: Large-scale missing data in a widely-published Reddit corpus. by Devin Gaffney, J Nathan Matias

“…In this paper, we document the dataset, substantial missing observations in the dataset, and the risks to research validity from those gaps. …”
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Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale by Monica Casella, Nicola Milano, Pasquale Dolce, Davide Marocco

“…IntroductionMissing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. …”
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Gaussian Process Regression (GPR)-based missing data imputation and its uses for bridge structural health monitoring by Matteo Dalmasso, Marco Civera, Valerio De Biagi, Bernardino Chiaia

“…Therefore, imputing missing values is necessary to maintain the integrity and utility of the SHM data. …”
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Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines by Xiaoming Yu, Jun Wang, Ke Zhang, Zhijun Chen, Ming Tong, Sibo Sun, Jiapeng Shen, Li Zhang, Chuyang Wang

“…With the increasing complexity of power systems, the monitoring data of UPFC submodules suffers from high missing rates due to sensor failures and environmental interference, significantly limiting equipment condition assessment and fault warning capabilities. …”
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Early intervention with transitional implants for congenitally missing lateral incisors in a pediatric patient: a case report by Ishani Rahate, Punit Fulzele, Bhushan Mundada, Dhruvi Solanki, Nilima Thosar, Madhavi Selukar, Aakriti Chandra

“…Orthodontic therapy was performed for the correction of malocclusion, and the missing maxillary lateral incisors were restored with MS transitional implants. …”
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Tracing Missing Surgical Specimens: A Quality Improvement Strategy for Adverse Events Based on Root Cause Analysis by Huang LL, Yang JH, Hong WW, Wang BL, Chen HF

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Technical note: Reconstructing missing surface aerosol elemental carbon data in long-term series with ensemble learning by Q. Meng, Y. Zhang, S. Zhong, J. Fang, L. Tang, Y. Rao, M. Zhou, J. Qiu, X. Xu, J.-E. Petit, O. Favez, X. Ge

“…We applied this approach to reconstruct hourly EC concentrations from 2013–2023 for four cities in eastern China, filling 45 %–79 % of missing data and improving prediction performance by 8 %–17 % compared to individual models. …”
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Missing value interpolation algorithm for long-term temperature observation data based on data augmentation multiple interpolation method by Xiaolin Liu, Bo Wang, Shuanglong Jin, Zongpeng Song

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Imputation Methods Based on Cluster-Wise Linear Regression: A Mathematical Programming Approach by Mahmoud M. Rashwan, Nouran M. Tawheed, Ahmed M. Gad

“…The cluster-wise linear regression is then integrated in the proposed imputation methods, to fill in the missing values in the response variable. …”
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