Thierry Mugenzi, Cahit Perkgoz^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.070381 - 10 November 2025

Abstract Missing data presents a crucial challenge in data analysis, especially in high-dimensional datasets, where missing data often leads to biased conclusions and degraded model performance. In this study, we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision. The proposed loss combines (i) a guided, masked mean squared error focusing on missing entries; (ii) a noise-aware regularization term to improve resilience against data corruption; and (iii) a variance penalty to encourage expressive yet stable reconstructions. We evaluate the proposed model across four missingness mechanisms, such as Missing… More >

Yunus Dogan¹, Goksu Tuysuzoglu¹, Elife Ozturk Kiyak², Bita Ghasemkhani³, Kokten Ulas Birant^1,4, Semih Utku¹, Derya Birant^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1677-1715, 2025, DOI:10.32604/cmes.2025.069255 - 31 August 2025

Abstract Accurate traffic flow prediction (TFP) is vital for efficient and sustainable transportation management and the development of intelligent traffic systems. However, missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision. This study introduces REPTF-TMDI, a novel method that combines a Reduced Error Pruning Tree Forest (REPTree Forest) with a newly proposed Time-based Missing Data Imputation (TMDI) approach. The REPTree Forest, an ensemble learning approach, is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urban mobility solutions. Meanwhile, the TMDI approach exploits temporal patterns… More >

Firdaus Firdaus, Siti Nurmaini^*, Anggun Islami, Annisa Darmawahyuni, Ade Iriani Sapitri, Muhammad Naufal Rachmatullah, Bambang Tutuko, Akhiar Wista Arum, Muhammad Irfan Karim, Yultrien Yultrien, Ramadhana Noor Salassa Wandya

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3419-3441, 2025, DOI:10.32604/cmc.2024.055906 - 17 February 2025

Abstract Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated… More >

Hyun Ahn¹, Kyunghee Sun², Kwanghoon Pio Kim^3,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 767-779, 2022, DOI:10.32604/cmc.2022.019369 - 07 September 2021

Abstract Time series forecasting has become an important aspect of data analysis and has many real-world applications. However, undesirable missing values are often encountered, which may adversely affect many forecasting tasks. In this study, we evaluate and compare the effects of imputation methods for estimating missing values in a time series. Our approach does not include a simulation to generate pseudo-missing data, but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom. In an experiment, therefore, several time series forecasting models are trained using different training datasets prepared More >

Fan Bingxu^*

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 549-555, 2020, DOI:10.32604/iasc.2020.013932

Abstract Poverty alleviation has always been the focus of China's work. According to the survey, the poverty population in rural areas has been reduced to a large extent, and the unemployed have had the lowest historical record in history. Big data technology is a new technology that has slowly emerged in recent years. The use of big data technology to create a visual platform for rural poverty alleviation is a relatively new idea at this stage. And we use the Map-reducebased big data missing value filling algorithm, which is designed to solve the data loss phenomenon More >