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12 articles for “Imputation”
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Research Frame work of Missing Data Analysis using Mathematical Models
Abstract: AbstractThis article deals with providing an overall framework of missing data analysis using mathematical models. Mathematical models serves at large for missing data imputation. Several algorithms in machine learning techniques like Mean, Median, Standard Deviation, Regression and Naïve Bayesian classifier use Mathematical models for analysis. The performance of above mathematical models has been compared by using correlation statistics analysis gives the imputed values are positively related or negatively related or …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 7, Issue 3, 2020 · pp. 36–44 Read article
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Estimation of Population Mean Using Ratio cum Product Imputation Techniques in Sample Survey
Abstract: In this paper, three generalized classes of ratio cum product compromised imputation techniques in single phase sampling scheme and its properties have been studied. Their mean square error (MSE) expressions and percentage relative efficiency (PRE) are compared with the sample mean, ratio, compromised and Ahmed method of imputation in case of missing data, and necessary conditions are derived. Further, theoretical results have been verified through empirical studies with the help …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 38–49 Read article
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Estimation of Population Mean Under SRSWOR Using Imputation Technique
Abstract: This research work suggests four log-type imputation methods using auxiliary information for missing observations in a data set. These methods consist of corresponding point estimator for estimating population mean under simple random sampling without replacement. Bias and mean square error expressions are derived up-to first order of approximation. Percent relative efficiency of suggested estimators is compared with the existing estimators using four population real data sets. And it is empirically …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 2, 2018 · pp. 12–20 Read article
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Some Imputation Methods in Two-Phase Sampling Scheme for Estimation of Population Mean
Abstract: In this paper, authors have proposed a class of dual to ratio estimator for estimating the finite population mean in presence of missing values in two-phase sampling design. Two different sampling designs in two-phase sampling are compared under imputed data. The bias and MSE of suggested estimator is derived in the form of population parameters using the concept of large sample approximation. Numerical studies are performed over two populations using …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 1–16 Read article
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Imputing Missing Data Analysis in Web Service Quality Dataset for Improving QoS Prediction
Abstract: AbstractThe web services at present have countless options for similar tasks. This wide range in web services induce challenge to choose the best service among all available. QoS prediction is a key of the selection but it is very time-consuming affair. Any prediction strategy relies on accuracy and completeness of available data, especially in case of QOS Prediction. Feedback, throughput and response time are the major attribute that should not …
Published in Recent Trends in Programming languages · Vol. 6, Issue 2, 2019 · pp. 8–22 Read article
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Improving Dataset Integrity Through Automated Data Cleaning Techniques
Abstract: High-quality data is a fundamental requirement in data science for producing trustworthy analytical insights and effective machine learning models. Problems, including incomplete records, inconsistent entries, duplicate observations, and anomalous values, can severely reduce the accuracy and robustness of predictive systems. As modern datasets continue to expand in both volume and structural complexity, relying on manual data cleaning methods become time-consuming and error-prone, highlighting the growing importance of automated data preprocessing …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 40–45 Read article
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Predictive Modeling of Polymer Composites for Medical Implants Using Artificial Intelligence Techniques
Abstract: The use of polymers in biomaterials was now key to designing the next generation of medical implants, which need to be strong and also compatible with living tissue. Tests for biocompatibility, such as those done in the laboratory and by doing experiments on animals, require much time and many resources, so the need for computer-based approaches becomes clear. An artificial intelligence approach was provided in this study to determine how …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 665–692 Read article
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Disease Prediction Using Ensemble Learning Models: A Comprehensive Approach
Abstract: In recent years, ensemble learning techniques have become pivotal in advancing predictive analytics within healthcare, particularly for early disease detection. The inherent variability and complexity of medical data, often characterized by high dimensionality, class imbalance, and noise, make it challenging for standalone classifiers to maintain high predictive accuracy. Ensemble learning, by integrating multiple models through bagging, boosting, or stacking, offers a more robust and generalizable approach. This study explores the …
Published in Journal of Communication Engineering & Systems · Vol. 15, Issue 3, 2025 · pp. 26–33 Read article
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Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques
Abstract: The integration of hybrid ensemble learning and automated machine learning (AutoML) is revolutionizing disease prediction by addressing the complexity, imbalance, and high dimensionality inherent in medical datasets. This paper proposes an advanced pipeline that combines diverse ensemble learning models with AutoML-based optimization to predict chronic diseases such as kidney diseas-e, Parkinson’s disease, and lung cancer. Publicly available datasets from UCI and PhysioNet repositories were preprocessed using outlier removal, normalization, and …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 10–20 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Artificial Intelligence and Constitutive Modeling Equations for Predictive Design of High-Performance Polymer Composites
Abstract: Growing polymer composite applications demand accurate mechanical prediction, yet complex interactions and conventional constitutive models limit predictive capability and require extensive calibration. To report these challenges, this research recommends a combined Artificial Intelligence (AI) and constitutive modeling approach based on an Enhanced Tasmanian Devil Optimizer-tuned Residual Neural Network with Multilayer Perceptron (ETDO-ResNet-MLP) for the predictive design of high-performance polymer composites. The study uses a publicly available Polymer Composite Property Dataset …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Machine Learning-Based Structure–Property Quantification of Advanced Polymer Composites
Abstract: Advanced polymer composites are widely used in high-performance engineering due to their superior mechanical and multifunctional properties. Accurate structure–property quantification is essential for efficient material design and reducing experimental costs. Existing Machine Learning (ML) approaches often exhibit limited predictive generalization due to inadequate feature discrimination and suboptimal hyperparameter tuning. To address these limitations, the proposed method enhances the ability to capture the complex nonlinear interactions among composite structural descriptors. The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article