Data Augmentation Approaches for Estimating Curtain Wall Construction Duration in High-Rise Buildings by Sang-Jun Park, Jin-Bin Im, Hye-Soon Yoon, Ju-Hyung Kim

“…The results showed that SMOTE and SMOTE–Tomek best represented the original dataset based on box plot analysis showcasing data distribution. Moreover, according to statistical performance criteria, it was found that the oversampling techniques improved the prediction performance, where Pearson correlation for linear, polynomial, and RBF increased by 0.611%, 4.232%, and 0.594%, respectively, for the best-performing sampling method. …”
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GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

“…Feature selection is carefully addressed via a combination of 14 statistical methods, tree-based methods, bio-inspired methods, and regularization methods so that only the most relevant features for modelling are chosen and included. …”
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Estimating and Testing Augmented Randomized Complete Block Designs: The Neutrosophic Approach by Abdulrahman AlAita, Hooshang Talebi, Yasser Al Zaim

“…Real data and a series of simulation studies numerically assess the performance of the present method. …”
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Study of the Application of Text Augmentation with Paraphrasing to Overcome Imbalanced Data in Indonesian Text Classification by Mutiara Indryan Sari, Lya Hulliyyatus Suadaa

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A data augmentation procedure to improve detection of spike ripples in brain voltage recordings by Emily D. Schlafly, Daniel Carbonero, Catherine J. Chu, Mark A. Kramer

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Doubly Structured Data Synthesis for Time-Series Energy-Use Data by Jiwoo Kim, Changhoon Lee, Jehoon Jeon, Jungwoong Choi, Joseph H. T. Kim

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Synthetic fibrosis distributions for data augmentation in predicting atrial fibrillation ablation outcomes: an in silico study by Alexander M. Zolotarev, Alexander M. Zolotarev, Kiane Johnson, Kiane Johnson, Yusuf Mohammad, Omnia Alwazzan, Omnia Alwazzan, Gregory Slabaugh, Gregory Slabaugh, Caroline H. Roney, Caroline H. Roney

“…While deep learning (DL) has shown promise in predicting ablation success, training such pipelines is limited by the availability of real patient data.MethodsIn this study, we generated synthetic fibrosis distributions using a denoising diffusion probabilistic model trained on a collection of 100 real LGE-MRI distributions. …”
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A Method of Simplified Synthetic Objects Creation for Detection of Underwater Objects from Remote Sensing Data Using YOLO Networks by Daniel Klukowski, Jacek Lubczonek, Pawel Adamski

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Enhancing voice spoofing detection in noisy environments using frequency feature masking augmentation by Soyul Han, Jaejin Seo, Sunmook Choi, Taein Kang, Sanghyeok Chung, Seungeun Lee, Seoyoung Park, Seungsang Oh, Il-Youp Kwak

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Enhancing precision in multiple sclerosis lesion segmentation: A U-net based machine learning approach with data augmentation by Oezdemir Cetin, Berkay Canel, Gamze Dogali, Unal Sakoglu

“…To address the issue of insufficient training data, data augmentation techniques have been implemented, enhancing the diversity and volume of the training set. …”
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Limited-Data Augmentation for Fault Diagnosis in Lithium-Ion Battery Energy Storage Systems via Transferable Conditional Diffusion by Zhipeng Yang, Yuhao Pan, Wenchao Liu, Jinhao Meng, Zhengxiang Song

“…This study addresses this critical issue by proposing a diffusion-based data augmentation methodology tailored explicitly for battery fault diagnosis scenarios. …”
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Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network for Aspect-Level Sentiment Analysis by Hongwei Tang, Haining Yan, Ran Song

“…To address these limitations, this paper presents a novel Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network (SSC-GRAN), a hybrid framework that synergistically integrates large language models (LLMs) with graph neural networks (GNNs). Our method introduces three key innovations: 1) a synthetic data augmentation paradigm leveraging LLMs to generate semantically coherent sentiment cues, thereby enriching aspect-opinion interactions; 2) a hierarchical graph architecture that models syntactic dependency structures and aspect-context relationships through relation-aware attention mechanisms; and 3) a contrastive learning objective that aligns representations from both authentic and synthetic data to enhance model robustness. …”
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Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions by Binrui Liu, Xinguang He, Wenkai Lyu, Lizhi Tao

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Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study by Sudeshna Das, Yao Ge, Yuting Guo, Swati Rajwal, JaMor Hairston, Jeanne Powell, Drew Walker, Snigdha Peddireddy, Sahithi Lakamana, Selen Bozkurt, Matthew Reyna, Reza Sameni, Yunyu Xiao, Sangmi Kim, Rasheeta Chandler, Natalie Hernandez, Danielle Mowery, Rachel Wightman, Jennifer Love, Anthony Spadaro, Jeanmarie Perrone, Abeed Sarker

“…MethodsWe proposed a two-layer RAG framework for query-focused answer generation and evaluated a proof of concept for the framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. …”
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Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan. by Chuan-Sheng Hung, Chun-Hung Richard Lin, Jain-Shing Liu, Shi-Huang Chen, Tsung-Chi Hung, Chih-Min Tsai

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Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments by Faridoon Khan, Amena Urooj, Saud Ahmed Khan, Abdelaziz Alsubie, Zahra Almaspoor, Sara Muhammadullah

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Mi-maml: classifying few-shot advanced malware using multi-improved model-agnostic meta-learning by Yulong Ji, Kunjin Zou, Bin Zou

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Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods by Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali

“…When developing the models, CatBoost artificial intelligence methods were used, regularization methods (Weight Decay, Decoupled Weight Decay Regularization, Augmentation) were used to improve the accuracy of the model, and the Z-Score method was used to normalize the data. …”
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Role of ratio-based metrics in cardiology by A. O. Konradi, A. L. Maslyansky, E. P. Kolesova, E. V. Shlyakhto, P. L.M. Kerkhof

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A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and... by Priya Mathur, Hammad Shaikh, Farhan Sheth, Dheeraj Kumar, Amit Kumar Gupta

“…Addressing the limitations of conventional empirical approaches, the study used a curated dataset of 436 samples from the peer-reviewed literature, which includes nine input parameters such as the nanoparticle, base fluid, temperature (&#x00B0;C), volume concentration (<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>), base fluid density (<inline-formula> <tex-math notation="LaTeX">$\rho _{\text {bf}}$ </tex-math></inline-formula>), density of primary and secondary nanoparticles (<inline-formula> <tex-math notation="LaTeX">$\rho _{\text {np1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\rho _{\text {np2}}$ </tex-math></inline-formula>), and volume mixture ratios of primary and secondary nanoparticles. Data preprocessing involved outlier removal via the Interquartile Range (IQR) method, followed by augmentation using either autoencoder-based or Gaussian noise injection, which preserved statistical integrity and enhanced dataset diversity. …”
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