Converting tabular data into images for deep learning with convolutional neural networks
Sign Up to like & getrecommendations! Published in 2021 at "Scientific Reports"
DOI: 10.1038/s41598-021-90923-y
Abstract: Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume… read more here.
Keywords: image; neural networks; convolutional neural; tabular data ... See more keywords
A tabular data generation framework guided by downstream tasks optimization
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DOI: 10.1038/s41598-024-65777-9
Abstract: Recently, generative models have been gradually emerging into the extended dataset field, showcasing their advantages. However, when it comes to generating tabular data, these models often fail to satisfy the constraints of numerical columns, which… read more here.
Keywords: downstream; tabular data; generation framework; data generation ... See more keywords
Transforming tabular data into images via enhanced spatial relationships for CNN processing
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DOI: 10.1038/s41598-025-01568-0
Abstract: Convolutional neural networks (CNNs), renowned for their efficiency in image analysis, have revolutionized pattern and structure recognition in visual data. Despite their success in image-based applications, CNNs face challenges when applied to tabular data due… read more here.
Keywords: tabular data; image; data images; spatial relationships ... See more keywords
Advanced solar radiation prediction using combined satellite imagery and tabular data processing
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DOI: 10.1038/s41598-025-96109-0
Abstract: Accurate solar radiation prediction is crucial for optimizing solar energy systems. There are two types of data that can be used to predict solar radiation, such as satellite images and tabular satellite data. This research… read more here.
Keywords: tabular data; methodology; radiation; radiation prediction ... See more keywords
Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach
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DOI: 10.1109/access.2021.3059589
Abstract: Tabular data and images have been used from machine learning models as two diverse types of inputs, in order to perform path loss predictions in urban areas. Different types of models are applied on these… read more here.
Keywords: input; loss prediction; tabular data; loss ... See more keywords
Sparse Hierarchical Table Ensemble–A Deep Learning Alternative for Tabular Data
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DOI: 10.1109/access.2022.3190537
Abstract: Deep learning for tabular data is drawing increasing attention, with recent work attempting to boost the accuracy of neuron-based networks. However, such deep learning models are deserted when computational capacity is low, as in Internet… read more here.
Keywords: tabular data; learning; sparse hierarchical; deep learning ... See more keywords
Counterfactual and Prototypical Explanations for Tabular Data via Interpretable Latent Space
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DOI: 10.1109/access.2024.3496114
Abstract: Artificial Intelligence decision-making systems have dramatically increased their predictive power in recent years, beating humans in many different specific tasks. However, with increased performance has come an increase in the complexity of the black-box models… read more here.
Keywords: tabular data; prototypical explanations; space; counterfactual prototypical ... See more keywords
A Hierarchical Probabilistic Deep Learning Approach for Contextual Anomaly Detection in Mixed-Type Tabular Data
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DOI: 10.1109/access.2025.3617799
Abstract: A contextual anomaly is a subtype of anomaly that, when observed in isolation, may not have the characteristics of an anomaly but becomes one when observed within a given context. Contextual anomaly detection is applied… read more here.
Keywords: detection; contextual anomaly; tabular data; mixed type ... See more keywords
CNN-Based Approaches for Various Types of Tabular Data
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DOI: 10.1109/access.2025.3635724
Abstract: Deep learning (DL) includes various architectures, such as deep neural networks (DNNs) and convolutional neural networks (CNNs). DL is very powerful and flexible for non-tabular (non-structured) data (e.g. image, text). However, in tabular data, standard… read more here.
Keywords: tabular data; cnn; various types; cnn based ... See more keywords
Augmentation and Evaluation of Geological Tabular Data: Geo-TabGAN Model and Its Applications
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DOI: 10.1109/lgrs.2025.3541770
Abstract: Data augmentation plays a crucial role in data-driven geoscience research by minimizing sampling costs and improving the generalization and predictive accuracy of models utilized in mineral exploration and oil and gas development. Although geoscience data… read more here.
Keywords: tabular data; geoscience; tabgan model; geo tabgan ... See more keywords
Cross-Feature Interactive Tabular Data Modeling With Multiplex Graph Neural Networks
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2024.3440654
Abstract: The rising popularity of tabular data in data science applications has led to a surge of interest in utilizing deep neural networks (DNNs) to address tabular problems. Existing deep neural network methods are not effective… read more here.
Keywords: tabular data; graph neural; multiplex graph; cross feature ... See more keywords