Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data by Hiskias Dingeto, Juntae Kim

“…We use both augmentation techniques, customized for time-series and skeletal data, to improve the robustness and performance of HAR models by diversifying the data and overcoming the drawbacks of having limited training data. …”
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Dynamic optimizers for complex industrial systems via direct data-driven synthesis by Khalid Alhazmi, S. Mani Sarathy

“…By demonstrating significant efficiency improvement on a realistic industrial benchmark problem, this work paves the way for the adoption of data-driven optimization techniques in real-world CPI applications.…”
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Effective Prediction on Time Series Data Using Deep Learning: An Incisive Review by Rupa Rajakumari, Ujwal Ambadas Lanjewar

“…Time-series evaluation explores the way in which data differs over time and optimal prediction could determine the pathway wherein the data seems to be changing. …”
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Real-Time Multiresolution Management of Spatiotemporal Earth Observation Data Using DGGS by Amir Mirzai Golpayegani, Mahmudul Hasan, Faramarz F. Samavati

“…Our system enables the dynamic retrieval and visualization of time-varying data for a user-defined ROI. The obtained results demonstrate that our method ensures high data fidelity while making spatiotemporal data more accessible across various practical applications in Earth observation.…”
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High resolution building facade dataset for facade elements parsingMendeley Data by Shuchang Xu, Fan Wu, Junjie Cheng, Wenzhen Yang

“…This paper introduces a high-resolution building facade dataset—hznu_facade, designed to provide rich data support for urban modeling, digital twin city construction, and facade analysis tasks. …”
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The Role of Big Data Analytics in Revolutionizing Diabetes Management and Healthcare Decision-Making by Muhammad Nauman, Ahmad S. Almadhor, Mohammed Albekairi, Ali R. Ansari, Muhammad A. B. Fayyaz, Raheel Nawaz

“…The CDC’s Behavioral Risk Factor Surveillance System (BRFSS) was used to empirically demonstrate the advantages of integrating BDA with ML for medical decision-making in Big Data environments. The research finding highlighted the superior performance of Logistic Regression (LR) models compared to other models like Naive Bayes (NB), providing valuable insights for healthcare applications.…”
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Knowledge Distillation for Face Recognition Using Synthetic Data With Dynamic Latent Sampling by Hatef Otroshi Shahreza, Anjith George, Sebastien Marcel

“…State-of-the-art face recognition models are computationally expensive for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets, raising privacy and ethical concerns. …”
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Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning by Giuliana Monachino, Beatrice Zanchi, Michael Wand, Giulio Conte, Athina Tzovara, Francesca Dalia Faraci

“…One of the main challenges in building robust LTA detection algorithms is the limited availability of labeled LTA data. Methods We introduce an effective deep-learning algorithm for detecting LTAs from single-lead ECGs in out-of-hospital cardiac arrest applications. …”
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An end-to-end mass spectrometry data classification model with a unified architecture by Yinchu Wang, Wei Zhang, Lin Guo, Fengyi Zhang, Zilong Liu, Xingchuang Xiong, Xiang Fang

“…Despite progress in classification methods/tools for data collection in the past decade, problems such as complex data processing, weak model characterization, and large interbatch differences persist. …”
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Cleaning of Abnormal Wind Speed Power Data Based on Quartile RANSAC Regression by Fengjuan Zhang, Xiaohui Zhang, Zhilei Xu, Keliang Dong, Zhiwei Li, Yubo Liu

“…The research results indicate that the algorithm excels in cleaning effectiveness, efficiency, accuracy, and rationality of data deletion. The cleaning accuracy improvement is particularly significant when dealing with a high proportion of stacked anomaly data, thereby bringing significant value to wind power applications such as wind power prediction, condition assessment, and fault detection.…”
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Ideology and Policy Preferences in Synthetic Data: The Potential of LLMs for Public Opinion Analysis by Keyeun Lee, Jaehyuk Park, Suh-hee Choi, Changkeun Lee

“…In two key applications, we first explore whether LLMs can predict public sentiment on emerging or rapidly evolving issues using existing survey data. …”
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The Importance of Materials Data and Modelling Parameters in an FE Simulation of Linear Friction Welding by R. Turner, F. Schroeder, R. M. Ward, J. W. Brooks

“…The importance of reliable material data in performing these calculations is paramount. …”
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Federated learning for privacy-enhanced mental health prediction with multimodal data integration by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…Traditional centralised data collection methods expose sensitive user information, limiting large-scale adoption in real-world applications. …”
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A Transfer Learning Approach for Diverse Motion Augmentation Under Data Scarcity by Junwon Yoon, Jeon-Seong Kang, Ha-Yoon Song, Beom-Joon Park, Kwang-Woo Jeon, Hyun-Joon Chung, Jang-Sik Park

“…These findings suggest that the proposed approach can effectively augment small motion-capture datasets with greater variety, a capability expected to benefit applications that rely on diverse human-motion data across modern robotics, animation, and virtual reality.…”
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An NSCT-Based Multifrequency GPR Data-Fusion Method for Concealed Damage Detection by Junfang Wang, Xiangxiong Li, Huike Zeng, Jianfu Lin, Shiming Xue, Jing Wang, Yanfeng Zhou

“…Despite the advantages of NSCT in image processing, its applications to GPR data fusion for concealed damage identification of transport infrastructures are rarely reported. …”
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Regularization by neural style transfer for MRI field-transfer reconstruction with limited data by Guoyao Shen, Guoyao Shen, Yancheng Zhu, Mengyu Li, Mengyu Li, Ryan McNaughton, Ryan McNaughton, Hernan Jara, Sean B. Andersson, Sean B. Andersson, Chad W. Farris, Stephan Anderson, Stephan Anderson, Xin Zhang, Xin Zhang, Xin Zhang, Xin Zhang, Xin Zhang

“…RNST generates high-field-quality images from low-field inputs without requiring paired training data, leveraging style priors to address limited-data settings. …”
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View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes by Yingqiu Yang, Qianye Lin, Zeyue Li, Yakui Wang, Siyu Liang, Siyuan Zhang, Yiyan Wang, Chunli Lv

“…These results significantly outperform mainstream baseline models such as XGBoost, TabTransformer, and VIME, demonstrating the proposed method’s robustness and broad applicability across diverse real-world tasks. The findings suggest that the proposed method offers an efficient and reliable paradigm for modeling sparse structured data.…”
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Synthetic Data Generation Using Large Language Models: Advances in Text and Code by Mihai Nadas, Laura Diosan, Andreea Tomescu

“…This survey reviews how large language models (LLMs) are transforming synthetic training data generation in both natural language and code domains. …”
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Semi-Supervised Density Estimation with Background-Augmented Data for In Situ Seed Counting by Baek-Gyeom Sung, Chun-Gu Lee, Yeong-Ho Kang, Seung-Hwa Yu, Dae-Hyun Lee

“…The experimental results on field data from unknown domains show that our approach reduces seed counting errors by up to 58.5% compared to conventional methods, highlighting its potential as a scalable and effective solution for agricultural applications in real-world environments.…”
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A deep learning framework for instrument-to-instrument translation of solar observation data by R. Jarolim, A. M. Veronig, W. Pötzi, T. Podladchikova

“…We demonstrate that the available data sets can directly profit from instrumental improvements, by applying our method to four different applications of ground- and space-based solar observations. …”
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