Performance evaluation of different estimation methods for missing rainfall data

“…In order to study the effectiveness of various methods to estimate missing data, by seven classic statistical methods and M5 model tree as one of efficient data mining methods, hypothetical missing values were estimated using precipitation data from neighbor station. …”
Get full text

Accelerated dynamic magnetic resonance imaging from Spatial-Subspace Reconstructions (SPARS). by Alexander J Mertens, Hai-Ling Margaret Cheng

“…Even state-of-the-art image reconstruction techniques that infer missing data in a sparse acquisition space cannot recover the loss of spatial detail, especially at high temporal acceleration rates. …”
Get full text

A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance by Nabil Belacel

“…Additionally, traditional methods often struggle with noisy or missing data. To address these issues, we propose novel classification methods based on feature partitioning and outranking measures. …”
Get full text

Flexible and modular latent transition analysis-A tutorial using R. by Lisbeth Lund, Christian Ritz

“…The proposed alternative approach offers more options in terms of choice of effect measures, model assumptions such as hierarchical structures and covariate adjustment, and differential handling of missing data. R code snippets are provided in the tutorial. …”
Get full text

Distribution Inference for Physical and Orbital Properties of Jupiter’s Moons by F. B. Gao, X. H. Zhu, X. Liu, R. F. Wang

“…Based on the inferred results, one can predict some physical or orbital features of moons with missing data or even new possible moons within a reasonable range. …”
Get full text

Analytical validation of the IBD segment-based tool KinSNP® for human identification applications by Bruce Budowle, Jianye Ge, Lee Baker, Kristen Mittelman, David Mittelman

“…The calculated values from KinSNP aligned closely with IBIS and Ped-sim benchmarks, and accuracy was maintained with up to 75% simulated missing data. However, even slight increases in simulated sequence error rates negatively impacted performance. …”
Get full text

A Long Short-Term Memory–Wasserstein Generative Adversarial Network-Based Data Imputation Method for Photovoltaic Power Output Prediction by Zhu Liu, Lingfeng Xuan, Dehuang Gong, Xinlin Xie, Dongguo Zhou

“…To address the challenges of the issue of inaccurate prediction results due to missing data in PV power records, a photovoltaic power data imputation method based on a Wasserstein Generative Adversarial Network (WGAN) and Long Short-Term Memory (LSTM) network is proposed. …”
Get full text

Later-Life Mortality and Longevity in Late-18th and 19th-Century Cohorts. Where Are We Now, and Where Are We Heading? by Rick Mourits

“…However, in order to find out the determinants of later-life mortality, external validity of results, blind spots due to missing data, and familial clustering need to be studied more thoroughly.…”
Get full text

Stroke Prediction Based on Machine Learning by Zhang Yuhan

“…Data preprocessing encompasses managing missing data, processing categorical variables, and tackling issues related to class imbalance. …”
Get full text

A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica by Xin Zou, Wen Long Yue

“…By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts’ knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. …”
Get full text

River Flow Estimation from Upstream Flow Records Using Support Vector Machines by Halil Karahan, Serdar Iplikci, Mutlu Yasar, Gurhan Gurarslan

“…The results show that the proposed architecture improves the model performance under noisy and missing data conditions and that support vector machines can be powerful alternative in flood routing modeling.…”
Get full text

Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing by Zepei Li, Peng Zheng, Yanjia Tian

“…Experiments conducted on the NASA Turbofan Engine Degradation and UCI Hydraulic Systems datasets demonstrate that BD-IoTQNet outperforms existing models in accuracy, precision, and data quality improvement, with reduced latency and enhanced robustness under noisy and missing data conditions. An ablation study validates the critical role of each component, showing significant performance drops when modules like DQM or blockchain are excluded. …”
Get full text

Proposition d’une démarche commune pour la sélection de variations asymptomatiques osseuses et dentaires répétables et reproductibles en anthropologie biologique by Ariane Ducher, Bérengère Saliba-Serre, Laurine Renaud, Bruno Foti, Émeline Verna

“…The quality of agreement was established according to the Fleiss et al. scale (2003). Although missing data were a hindrance in this study, several proposals were made to overcome the problem. …”
Get full text

Later-Life Mortality and Longevity in Late-18th and 19th-Century Cohorts. Where Are We Now, and Where Are We Heading? by Rick Mourits

“…However, in order to find out the determinants of later-life mortality, external validity of results, blind spots due to missing data, and familial clustering need to be studied more thoroughly.…”
Get full text

Spatial Multiple Description Coding for Scalable Video Streams by Roya Choupani, Stephan Wong, Mehmet Tolun

“…Classical multiple description methods interpolate the missing data which results in smoothing and artifact at object boundaries. …”
Get full text

Later-Life Mortality and Longevity in Late-18th and 19th-Century Cohorts. Where Are We Now, and Where Are We Heading? by Rich J. Mourits

“…However, in order to find out the determinants of later-life mortality, external validity of results, blind spots due to missing data, and familial clustering need to be studied more thoroughly. …”
Get full text

Students’ Online Learning Adoption during an Emergency Situation: Integrating the Self-Determination and Perceived Risk Theories by Sheng-Ju Chan, Thi Xuan Nong, Thi Thanh Truc Nguyen

“…A printed questionnaire was developed to collect data from 487 Vietnamese students using a quota sampling method. After missing data and outliers were removed, 450 questionnaires were found to be usable for data analysis. …”
Get full text

A systematic review of machine learning applications in predicting opioid associated adverse events by Carlos R. Ramírez Medina, Jose Benitez-Aurioles, David A. Jenkins, Meghna Jani

“…Transparent reporting of model development was often incomplete, with key aspects such as calibration, imbalance correction, and handling of missing data absent. Infrequent external validation limited the generalizability of current models. …”
Get full text

Protocol for generating protein profiles and distance-based network analysis of murine tissue slices by Luisa Schmidt, Philipp Antczak, Marcus Krüger

“…We detail procedures for scanning longitudinal protein profiles and replacing missing data using a sliding window approach. This protocol has potential for applications in spatial proteomics to other tissues with asymmetric patterns such as the brain and heart tissue.For complete details on the use and execution of this protocol, please refer to Schmidt et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.…”
Get full text

Later-Life Mortality and Longevity in Late-18th and 19th-Century Cohorts. Where Are We Now, and Where Are We Heading? by Rick Mourits

“…However, in order to find out the determinants of later-life mortality, external validity of results, blind spots due to missing data, and familial clustering need to be studied more thoroughly.…”
Get full text