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Thematische Bibliographien / Interpolation-Based data augmentation / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Interpolation-Based data augmentation“

Autor: Grafiati

Veröffentlicht am 28. September 2024

Zuletzt aktualisiert am 31. Juli 2025

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1

Oh, Cheolhwan, Seungmin Han, and Jongpil Jeong. "Time-Series Data Augmentation based on Interpolation." Procedia Computer Science 175 (2020): 64–71. http://dx.doi.org/10.1016/j.procs.2020.07.012.

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Li, Yuliang, Xiaolan Wang, Zhengjie Miao, and Wang-Chiew Tan. "Data augmentation for ML-driven data preparation and integration." Proceedings of the VLDB Endowment 14, no. 12 (2021): 3182–85. http://dx.doi.org/10.14778/3476311.3476403.

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In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques developed by the data management community for data preparation and data integration. In addition to surveying task-specific DA operators that leverage rules, transformations, and external knowledge for creating additional training data, we also explore the advanced DA techniques such as interpolation, conditional generation, and DA policy learning.

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Huang, Chenhui, and Akinobu Shibuya. "High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data." Remote Sensing 12, no. 12 (2020): 1991. http://dx.doi.org/10.3390/rs12121991.

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Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to me

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Tsourtis, Anastasios, Georgios Papoutsoglou, and Yannis Pantazis. "GAN-Based Training of Semi-Interpretable Generators for Biological Data Interpolation and Augmentation." Applied Sciences 12, no. 11 (2022): 5434. http://dx.doi.org/10.3390/app12115434.

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Single-cell measurements incorporate invaluable information regarding the state of each cell and its underlying regulatory mechanisms. The popularity and use of single-cell measurements are constantly growing. Despite the typically large number of collected data, the under-representation of important cell (sub-)populations negatively affects down-stream analysis and its robustness. Therefore, the enrichment of biological datasets with samples that belong to a rare state or manifold is overall advantageous. In this work, we train families of generative models via the minimization of Rényi diver

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Becerra-Suarez, Fray L., Halyn Alvarez-Vasquez, and Manuel G. Forero. "Improvement of Bank Fraud Detection Through Synthetic Data Generation with Gaussian Noise." Technologies 13, no. 4 (2025): 141. https://doi.org/10.3390/technologies13040141.

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Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contrasting it with SMOTE and ADASYN. By injecting controlled perturbations into the minority class, our approach mitigates overfitting risks inherent in interpolation-based techniques. Five classifiers, including XGBoost and a convolutional neural network (CNN), were evaluated on augmented datasets. XGBoost achieved superior performance with Gaussian noi

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Li, Jinyuan, Wenqing Wan, Yong Feng, and Jinglong Chen. "Meta-task interpolation-based data augmentation for imbalanced health status recognition of complex equipment." Computers in Industry 165 (February 2025): 104226. https://doi.org/10.1016/j.compind.2024.104226.

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Bi, Xiao-ying, Bo Li, Wen-long Lu, and Xin-zhi Zhou. "Daily runoff forecasting based on data-augmented neural network model." Journal of Hydroinformatics 22, no. 4 (2020): 900–915. http://dx.doi.org/10.2166/hydro.2020.017.

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Abstract Accurate daily runoff prediction plays an important role in the management and utilization of water resources. In order to improve the accuracy of prediction, this paper proposes a deep neural network (CAGANet) composed of a convolutional layer, an attention mechanism, a gated recurrent unit (GRU) neural network, and an autoregressive (AR) model. Given that the daily runoff sequence is abrupt and unstable, it is difficult for a single model and combined model to obtain high-precision daily runoff predictions directly. Therefore, this paper uses a linear interpolation method to enhance

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Halevy, Karina, Karly Hou, and Charumathi Badrinath. "Who’s the (Multi-)Fairest of Them All: Rethinking Interpolation-Based Data Augmentation Through the Lens of Multicalibration." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17014–22. https://doi.org/10.1609/aaai.v39i16.33870.

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Data augmentation methods, especially SoTA interpolation-based methods such as Fair Mixup, have been widely shown to increase model fairness. However, this fairness is evaluated on metrics that do not capture model uncertainty and on datasets with only one, relatively large, minority group. As a remedy, multicalibration has been introduced to measure fairness while accommodating uncertainty and accounting for multiple minority groups. However, existing methods of improving multicalibration involve reducing initial training data to create a holdout set for post-processing, which is not ideal wh

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de Rojas, Ana Lazcano. "Data augmentation in economic time series: Behavior and improvements in predictions." AIMS Mathematics 8, no. 10 (2023): 24528–44. http://dx.doi.org/10.3934/math.20231251.

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<abstract> <p>The performance of neural networks and statistical models in time series prediction is conditioned by the amount of data available. The lack of observations is one of the main factors influencing the representativeness of the underlying patterns and trends. Using data augmentation techniques based on classical statistical techniques and neural networks, it is possible to generate additional observations and improve the accuracy of the predictions. The particular characteristics of economic time series make it necessary that data augmentation techniques do not signific

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Becerra-Suarez, Fray L., Luciani J. Jiménez-Fernández, Estrella D. Ticona-Tapia, José Rolando Cárdenas-Gonzáles, and Pepe Humberto Bustamante-Quintana. "SynKGen: A kernel PCA-Based oversampling method for enhanced credit card fraud detection." Revista Científica de Sistemas e Informática 5, no. 2 (2025): e952. https://doi.org/10.51252/rcsi.v5i2.952.

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Credit card fraud detection is a growing challenge in the financial domain due to data imbalance, where fraudulent transactions are minimal compared to legitimate ones. This study presents SynKGen, a data augmentation method using Kernel PCA with Gaussian perturbations to generate synthetic samples of the minority class, contrasting it with ADASYN and SMOTE. By introducing variance analysis with controlled perturbations in the minority class, the proposed approach mitigates the risks of overfitting associated with traditional interpolation-based techniques. Four classifiers, XGBoost, RandomFor

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Xie, Xiangjin, Li Yangning, Wang Chen, Kai Ouyang, Zuotong Xie, and Hai-Tao Zheng. "Global Mixup: Eliminating Ambiguity with Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13798–806. http://dx.doi.org/10.1609/aaai.v37i11.26616.

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Data augmentation with Mixup has been proven an effective method to regularize the current deep neural networks. Mixup generates virtual samples and corresponding labels simultaneously by linear interpolation. However, the one-stage generation paradigm and the use of linear interpolation have two defects: (1) The label of the generated sample is simply combined from the labels of the original sample pairs without reasonable judgment, resulting in ambiguous labels. (2) Linear combination significantly restricts the sampling space for generating samples. To address these issues, we propose a nov

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Lim, Seong-Su, and Oh-Wook Kwon. "FrameAugment: A Simple Data Augmentation Method for Encoder–Decoder Speech Recognition." Applied Sciences 12, no. 15 (2022): 7619. http://dx.doi.org/10.3390/app12157619.

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As the architecture of deep learning-based speech recognizers has recently changed to the end-to-end style, increasing the effective amount of training data has become an important issue. To tackle this issue, various data augmentation techniques to create additional training data by transforming labeled data have been studied. We propose a method called FrameAugment to augment data by changing the speed of speech locally for selected sections, which is different from the conventional speed perturbation technique that changes the speed of speech uniformly for the entire utterance. To change th

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Guo, Hongyu. "Nonlinear Mixup: Out-Of-Manifold Data Augmentation for Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4044–51. http://dx.doi.org/10.1609/aaai.v34i04.5822.

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Data augmentation with Mixup (Zhang et al. 2018) has shown to be an effective model regularizer for current art deep classification networks. It generates out-of-manifold samples through linearly interpolating inputs and their corresponding labels of random sample pairs. Despite its great successes, Mixup requires convex combination of the inputs as well as the modeling targets of a sample pair, thus significantly limits the space of its synthetic samples and consequently its regularization effect. To cope with this limitation, we propose “nonlinear Mixup”. Unlike Mixup where the input and lab

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Khamlich, Moaad, Federico Pichi, Michele Girfoglio, Annalisa Quaini, and Gianluigi Rozza. "Optimal transport-based displacement interpolation with data augmentation for reduced order modeling of nonlinear dynamical systems." Journal of Computational Physics 531 (June 2025): 113938. https://doi.org/10.1016/j.jcp.2025.113938.

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Xie, Kai, Yuxuan Gao, Yadang Chen, and Xun Che. "Mask Mixup Model: Enhanced Contrastive Learning for Few-Shot Learning." Applied Sciences 14, no. 14 (2024): 6063. http://dx.doi.org/10.3390/app14146063.

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Few-shot image classification aims to improve the performance of traditional image classification when faced with limited data. Its main challenge lies in effectively utilizing sparse sample label data to accurately predict the true feature distribution. Recent approaches have employed data augmentation techniques like random Mask or mixture interpolation to enhance the diversity and generalization of labeled samples. However, these methods still encounter several issues: (1) random Mask can lead to complete blockage or exposure of foreground, causing loss of crucial sample information; and (2

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Liu, Ziwei, Jinbao Jiang, Mengquan Li, et al. "Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies." Foods 11, no. 8 (2022): 1156. http://dx.doi.org/10.3390/foods11081156.

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Aflatoxins in moldy peanuts are seriously toxic to humans. These kernels need to be screened in the production process. Hyperspectral imaging techniques can be used to identify moldy peanuts. However, the changes in spectral information and texture information caused by the difference in moisture content in peanuts will affect the identification accuracy. To reduce and eliminate the influence of this factor, a data augmentation method based on interpolation was proposed to improve the generalization ability and robustness of the model. Firstly, the near-infrared hyperspectral images of 5 varie

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Zhou, Xiaojing, Yunjia Feng, Xu Li, Zijian Zhu, and Yanzhong Hu. "Off-Road Environment Semantic Segmentation for Autonomous Vehicles Based on Multi-Scale Feature Fusion." World Electric Vehicle Journal 14, no. 10 (2023): 291. http://dx.doi.org/10.3390/wevj14100291.

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For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments often have the problems of single scene and low accuracy. Therefore, this paper proposes a semantic segmentation network based on LiDAR called Multi-scale Augmentation Point-Cylinder Network (MAPC-Net). The network uses a multi-layer receptive field fusion module to extract features from objects of different scales in off-road environments. Gated f

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Pizoń, Zofia, Shinji Kimijima, and Grzegorz Brus. "Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques." Energies 17, no. 10 (2024): 2413. http://dx.doi.org/10.3390/en17102413.

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Methane steam reforming is the foremost method for hydrogen production, and it has been studied through experiments and diverse computational models to enhance its energy efficiency. This study focuses on employing an artificial neural network as a model of the methane steam reforming process. The proposed data-driven model predicts the output mixture’s composition based on reactor operating conditions, such as the temperature, steam-to-methane ratio, nitrogen-to-methane ratio, methane flow, and nickel catalyst mass. The network, a feedforward type, underwent training with a comprehensive data

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Soni, Aparna. "Predictive Modeling of Soil Moisture Variability Using Machine Learning: Insights from Dry and Wet Soil Cultures." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 48–52. https://doi.org/10.22214/ijraset.2024.65700.

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Soil moisture prediction is crucial for optimizing irrigation practices and advancing precision agriculture. This study presents a predictive modeling approach leveraging machine learning techniques to analyze soil moisture variability in dry and wet soil cultures. Utilizing data collected through IoT-enabled sensors, this research applies regression-based algorithms to forecast moisture trends. A robust data preprocessing framework, including interpolation and augmentation, was implemented to address missing data challenges. Experimental results demonstrate high prediction accuracy, with minu

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Li, Muyang, Tuo Yao, Jian Liu, Ziyi Liu, Zhenguo Gao, and Junbo Gong. "Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis." Mathematics 12, no. 22 (2024): 3448. http://dx.doi.org/10.3390/math12223448.

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Deep learning-based in situ imaging and analysis for crystallization process are essential for optimizing product qualities, reducing experimental costs through real-time monitoring, and controlling the process. However, large and high-quality annotated datasets are required to train accurate models, which are time consuming. Therefore, we proposed a novel methodology that applied image synthesis neural networks to generate virtual information-rich images, enabling efficient and rapid dataset expansion while simultaneously reducing annotation costs. Experiments were conducted on the L-alanine

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Kim, Hyungju, and Nammee Moon. "TN-GAN-Based Pet Behavior Prediction through Multiple-Dimension Time-Series Augmentation." Sensors 23, no. 8 (2023): 4157. http://dx.doi.org/10.3390/s23084157.

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Behavioral prediction modeling applies statistical techniques for classifying, recognizing, and predicting behavior using various data. However, performance deterioration and data bias problems occur in behavioral prediction. This study proposed that researchers conduct behavioral prediction using text-to-numeric generative adversarial network (TN-GAN)-based multidimensional time-series augmentation to minimize the data bias problem. The prediction model dataset in this study used nine-axis sensor data (accelerometer, gyroscope, and geomagnetic sensors). The ODROID N2+, a wearable pet device,

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Guo, Xinshuai, Tianrui Hou, and Li Wu. "DAT-Net: Filling of missing temperature values of meteorological stations by data augmentation attention neural network." Journal of Physics: Conference Series 2816, no. 1 (2024): 012004. http://dx.doi.org/10.1088/1742-6596/2816/1/012004.

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Abstract For a long time, filling in the missing temperature data from meteorological stations has been crucial for researchers in analyzing climate variation cases. In previous studies, people have attempted to solve this problem by using interpolation and deep learning methods. Through extensive case studies, it is observed that the data utilization rate of convolutional neural networks based on PConv is low at a high missing rate, which will result in the poor filling performance of each model at a high missing rate. To solve these problems, a Data Augmentation Attention Neural Network (DAT

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Yildirim, Muhammed. "Diagnosis of Heart Diseases Using Heart Sound Signals with the Developed Interpolation, CNN, and Relief Based Model." Traitement du Signal 39, no. 3 (2022): 907–14. http://dx.doi.org/10.18280/ts.390316.

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The majority of deaths today are due to heart diseases. Early diagnosis of heart diseases will lead to early initiation of the treatment process. Therefore, computer-aided systems are of great importance. In this study, heart sounds were used for the early diagnosis and treatment of heart diseases. Diagnosing heart sounds provides important information about heart diseases. Therefore, a hybrid model was developed in the study. In the developed model, first of all, spectrograms were obtained from audio signals with the Mel-spectrogram method. Then, the interpolation method was used to train the

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Wang, Shuo, Jian Wang, Yafei Song, Sicong Li, and Wei Huang. "Malware Variants Detection Model Based on MFF–HDBA." Applied Sciences 12, no. 19 (2022): 9593. http://dx.doi.org/10.3390/app12199593.

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A massive proliferation of malware variants has posed serious and evolving threats to cybersecurity. Developing intelligent methods to cope with the situation is highly necessary due to the inefficiency of traditional methods. In this paper, a highly efficient, intelligent vision-based malware variants detection method was proposed. Firstly, a bilinear interpolation algorithm was utilized for malware image normalization, and data augmentation was used to resolve the issue of imbalanced malware data sets. Moreover, the paper improved the convolutional neural network (CNN) model by combining mul

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Wang, Kan, Ahmed El-Mowafy, Wei Wang, Long Yang, and Xuhai Yang. "Integrity Monitoring of PPP-RTK Positioning; Part II: LEO Augmentation." Remote Sensing 14, no. 7 (2022): 1599. http://dx.doi.org/10.3390/rs14071599.

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Low Earth orbit (LEO) satellites benefit future ground-based positioning with their high number, strong signal strength and high speed. The rapid geometry change with the LEO augmentation offers acceleration of the convergence of the precision point positioning (PPP) solution. This contribution discusses the influences of the LEO augmentation on the precise point positioning—real-time kinematic (PPP-RTK) positioning and its integrity monitoring. Using 1 Hz simulated data around Beijing for global positioning system (GPS)/Galileo/Beidou navigation satellite system (BDS)-3 and the tested LEO con

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Ratnam, D. Venkata. "ESTIMATION AND ANALYSIS OF USER IPP DELAYS USING BILINEAR MODEL FOR SATELLITE-BASED AUGMENTED NAVIGATION SYSTEMS." Aviation 17, no. 2 (2013): 65–69. http://dx.doi.org/10.3846/16487788.2013.805864.

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Several countries are involved in developing satellite-based augmentation systems (SBAS) for improving the positional accuracy of GPS. India is also developing one such system, popularly known as GPS-aided geo-augmented navigation (GAGAN), to cater to civil aviation applications. The ionospheric effect is the major source of error in GAGAN. An appropriate efficient and accurate ionospheric time model for GAGAN is necessary. To develop such a model, data from 17 GPS stations of the GAGAN network spread across India are used in modelling. The prominent model, known as bi-linear interpolation tec

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Ren, Yougui, Jialu Li, Yubin Bao, Zhibin Zhao, and Ge Yu. "An Optimized Object Detection Algorithm for Marine Remote Sensing Images." Mathematics 12, no. 17 (2024): 2722. http://dx.doi.org/10.3390/math12172722.

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In order to address the challenge of the small-scale, small-target, and complex scenes often encountered in offshore remote sensing image datasets, this paper employs an interpolation method to achieve super-resolution-assisted target detection. This approach aligns with the logic of popular GANs and generative diffusion networks in terms of super-resolution but is more lightweight. Additionally, the image count is expanded fivefold by supplementing the dataset with DOTA and data augmentation techniques. Framework-wise, based on the Faster R-CNN model, the combination of a residual backbone ne

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Tiwari, Nitin, Fabio Rondinella, Neelima Satyam, and Nicola Baldo. "Experimental and Machine Learning Approach to Investigate the Mechanical Performance of Asphalt Mixtures with Silica Fume Filler." Applied Sciences 13, no. 11 (2023): 6664. http://dx.doi.org/10.3390/app13116664.

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This study explores the potential in substituting ordinary Portland cement (OPC) with industrial waste silica fume (SF) as a mineral filler in asphalt mixtures (AM) for flexible road pavements. The Marshall and indirect tensile strength tests were used to evaluate the mechanical resistance and durability of the AMs for different SF and OPC ratios. To develop predictive models of the key mechanical and volumetric parameters, the experimental data were analyzed using artificial neural networks (ANN) with three different activation functions and leave-one-out cross-validation as a resampling meth

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Meshchaninov, Viacheslav Pavlovich, Ivan Andreevich Molodetskikh, Dmitriy Sergeevich Vatolin, and Alexey Gennadievich Voloboy. "Combining contrastive and supervised learning for video super-resolution detection." Keldysh Institute Preprints, no. 80 (2022): 1–13. http://dx.doi.org/10.20948/prepr-2022-80.

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Upscaled video detection is a helpful tool in multimedia forensics, but it’s a challenging task that involves various upscaling and compression algorithms. There are many resolution-enhancement methods, including interpolation and deep-learning based super-resolution, and they leave unique traces. This paper proposes a new upscaled-resolution-detection method based on learning of visual representations using contrastive and cross-entropy losses. To explain how the method detects videos, the major components of our framework are systematically reviewed — in particular, it is shown that most dat

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de Rezende, L. F. C., E. R. de Paula, I. J. Kantor, and P. M. Kintner. "Mapping and Survey of Plasma Bubbles over Brazilian Territory." Journal of Navigation 60, no. 1 (2006): 69–81. http://dx.doi.org/10.1017/s0373463307004006.

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Ionospheric plasma irregularities or bubbles, that are regions with depleted density, are generated at the magnetic equator after sunset due to plasma instabilities, and as they move upward they map along the magnetic field lines to low latitudes. To analyse the temporal and spatial evolution of the bubbles over Brazilian territory, the mapping of ionospheric plasma bubbles for the night of 17/18 March 2002 was generated using data collected from one GPS receiver array, and applying interpolation techniques. The impact on the performance of Global Navigation Satellites System (GNSS) and on the

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De-La-Cruz, Celso, Jorge Trevejo-Pinedo, Fabiola Bravo, et al. "Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose." Sensors 23, no. 13 (2023): 5864. http://dx.doi.org/10.3390/s23135864.

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Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin”. For signal processing, neural networks, multiclass support vector machines and random for

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González-Vidal, Aurora, José Mendoza-Bernal, Alfonso P. Ramallo, Miguel Ángel Zamora, Vicente Martínez, and Antonio F. Skarmeta. "Smart Operation of Climatic Systems in a Greenhouse." Agriculture 12, no. 10 (2022): 1729. http://dx.doi.org/10.3390/agriculture12101729.

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The purpose of our work is to leverage the use of artificial intelligence for the emergence of smart greenhouses. Greenhouse agriculture is a sustainable solution for food crises and therefore data-based decision-support mechanisms are needed to optimally use them. Our study anticipates how the combination of climatic systems will affect the temperature and humidity of the greenhouse. More specifically, our methodology anticipates if a set-point will be reached in a given time by a combination of climatic systems and estimates the humidity at that time. We performed exhaustive data analytics p

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Ku, Hyeeun, and Minhyeok Lee. "TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks." Applied Sciences 13, no. 8 (2023): 5098. http://dx.doi.org/10.3390/app13085098.

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Generative adversarial networks (GANs) have demonstrated remarkable potential in the realm of text-to-image synthesis. Nevertheless, conventional GANs employing conditional latent space interpolation and manifold interpolation (GAN-CLS-INT) encounter challenges in generating images that accurately reflect the given text descriptions. To overcome these limitations, we introduce TextControlGAN, a controllable GAN-based model specifically designed for text-to-image synthesis tasks. In contrast to traditional GANs, TextControlGAN incorporates a neural network structure, known as a regressor, to ef

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D'souza, Annie, Swetha M, and Sunita Sarawagi. "Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16127–34. https://doi.org/10.1609/aaai.v39i15.33771.

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Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training dataset with synthetically generated data. While classical augmentation techniques were limited to linear interpolation of existing minority class examples, recently higher capacity deep generative models are providing greater promise. However, handling of imbalance in class distribution when building a deep generative model is also a challenging problem, that has not been studied as extensively as imbalanced classif

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Jiang, Zuo, Haobo Chen, and Yi Tang. "Sample Inflation Interpolation for Consistency Regularization in Remote Sensing Change Detection." Mathematics 12, no. 22 (2024): 3577. http://dx.doi.org/10.3390/math12223577.

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Semi-supervised learning has gained significant attention in the field of remote sensing due to its ability to effectively leverage both a limited number of labeled samples and a large quantity of unlabeled data. An effective semi-supervised learning approach utilizes unlabeled samples to enforce prediction consistency under minor perturbations, thus reducing the model’s sensitivity to noise and suppressing false positives in change-detection tasks. This principle underlies consistency regularization-based methods. However, while these methods enhance noise robustness, they also risk overlooki

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Huang, Yongdi, Qionghai Chen, Zhiyu Zhang, et al. "A Machine Learning Framework to Predict the Tensile Stress of Natural Rubber: Based on Molecular Dynamics Simulation Data." Polymers 14, no. 9 (2022): 1897. http://dx.doi.org/10.3390/polym14091897.

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Natural rubber (NR), with its excellent mechanical properties, has been attracting considerable scientific and technological attention. Through molecular dynamics (MD) simulations, the effects of key structural factors on tensile stress at the molecular level can be examined. However, this high-precision method is computationally inefficient and time-consuming, which limits its application. The combination of machine learning and MD is one of the most promising directions to speed up simulations and ensure the accuracy of results. In this work, a surrogate machine learning method trained with

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Gil-Martín, Manuel, María Villa-Monedero, Andrzej Pomirski, Daniel Sáez-Trigueros, and Rubén San-Segundo. "Sign Language Motion Generation from Sign Characteristics." Sensors 23, no. 23 (2023): 9365. http://dx.doi.org/10.3390/s23239365.

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This paper proposes, analyzes, and evaluates a deep learning architecture based on transformers for generating sign language motion from sign phonemes (represented using HamNoSys: a notation system developed at the University of Hamburg). The sign phonemes provide information about sign characteristics like hand configuration, localization, or movements. The use of sign phonemes is crucial for generating sign motion with a high level of details (including finger extensions and flexions). The transformer-based approach also includes a stop detection module for predicting the end of the generati

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Wang, Sheng-Yu, David Bau, and Jun-Yan Zhu. "Rewriting geometric rules of a GAN." ACM Transactions on Graphics 41, no. 4 (2022): 1–16. http://dx.doi.org/10.1145/3528223.3530065.

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Deep generative models make visual content creation more accessible to novice users by automating the synthesis of diverse, realistic content based on a collected dataset. However, the current machine learning approaches miss a key element of the creative process - the ability to synthesize things that go far beyond the data distribution and everyday experience. To begin to address this issue, we enable a user to "warp" a given model by editing just a handful of original model outputs with desired geometric changes. Our method applies a low-rank update to a single model layer to reconstruct ed

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Rizvi, Syed Haider M., and Muntazir Abbas. "Lamb wave damage severity estimation using ensemble-based machine learning method with separate model network." Smart Materials and Structures 30, no. 11 (2021): 115016. http://dx.doi.org/10.1088/1361-665x/ac2e1a.

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Abstract Lamb wave-based damage estimation have great potential for structural health monitoring. However, designing a generalizable model that predicts accurate and reliable damage quantification result is still a practice challenge due to complex behavior of waves with different damage severities. In the recent years, machine learning (ML) algorithms have been proven to be an efficient tool to analyze damage-modulated Lamb wave signals. In this study, ensemble-based ML algorithms are employed to develop a generalizable crack quantification model for thin metallic plates. For this, the scatte

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Tang, Qunfeng, Zhencheng Chen, Carlo Menon, Rabab Ward, and Mohamed Elgendi. "PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals." Sensors 21, no. 12 (2021): 4007. http://dx.doi.org/10.3390/s21124007.

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An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched togethe

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Li, He, Shuaipeng Yang, Rui Zhang, et al. "Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment." Water 16, no. 1 (2023): 86. http://dx.doi.org/10.3390/w16010086.

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Aiming to solve the problems with easy false detection of small targets in river floating object detection and deploying an overly large model, a new method is proposed based on improved YOLOv5s. A new data augmentation method for small objects is designed to enrich the dataset and improve the model’s robustness. Distinct feature extraction network levels incorporate different coordinate attention mechanism pooling methods to enhance the effective feature information extraction of small targets and improve small target detection accuracy. Then, a shallow feature map with 4-fold down-sampling i

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Amirrajab, Sina, Yasmina Al Khalil, Cristian Lorenz, Jürgen Weese, Josien Pluim, and Marcel Breeuwer. "Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs." Machine Learning for Biomedical Imaging 2, June 2023 (2023): 288–311. http://dx.doi.org/10.59275/j.melba.2023-1g8b.

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We propose a method for synthesizing cardiac magnetic resonance (MR) images with plausible heart pathologies and realistic appearances for the purpose of generating labeled data for the application of supervised deep-learning (DL) training. The image synthesis consists of label deformation and label-to-image translation tasks. The former is achieved via latent space interpolation in a VAE model, while the latter is accomplished via a label-conditional GAN model. We devise three approaches for label manipulation in the latent space of the trained VAE model; i) intra-subject synthesis aiming to

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Hu, Wenyi, Wei Hong, Hongkun Wang, Mingzhe Liu, and Shan Liu. "A Study on Tomato Disease and Pest Detection Method." Applied Sciences 13, no. 18 (2023): 10063. http://dx.doi.org/10.3390/app131810063.

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In recent years, with the rapid development of artificial intelligence technology, computer vision-based pest detection technology has been widely used in agricultural production. Tomato diseases and pests are serious problems affecting tomato yield and quality, so it is important to detect them quickly and accurately. In this paper, we propose a tomato disease and pest detection model based on an improved YOLOv5n to overcome the problems of low accuracy and large model size in traditional pest detection methods. Firstly, we use the Efficient Vision Transformer as the feature extraction backbo

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Gautam, Vinay Kumar, Mahesh Kothari, Pradeep Kumar Singh, Sita Ram Bhakar, and Kamal Kishore Yadav. "Spatial mapping of groundwater quality using GIS for Jakham River basin of Southern Rajasthan." Environment Conservation Journal 23, no. 1&2 (2022): 234–43. http://dx.doi.org/10.36953/ecj.021936-2175.

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The physico-chemical analysis of groundwater quality plays a significant role to manage the water resources for drinking as well as irrigation in the sub-humid and semi-arid agro-climatic areas. In this study, the hydrogeochemical analyses and spatial mapping of groundwater quality in the Jakham River Basin located in the southern part of Rajasthan were investigated.The groundwater quality samples were collected from 76 wells marked on the grid map of 5×5 km2 area.A spatial distribution in sampling location in the basin was prepared using GIS (Geographical information system) tool based on 6 p

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Farhadi, Moslem, Amir Hossein Foruzan, Mina Esfandiarkhani, Yen-Wei Chen, and Hongjie Hu. "RECONSTRUCTION OF HIGH-RESOLUTION HEPATIC TUMOR CT IMAGES USING AN AUGMENTATION-BASED SUPER-RESOLUTION TECHNIQUE." Biomedical Engineering: Applications, Basis and Communications 33, no. 04 (2021): 2150026. http://dx.doi.org/10.4015/s1016237221500265.

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Improving the resolution of medical images is crucial in diagnosis, feature extraction, and data retrieval. A significant group of super-resolution algorithms is multi-frame techniques. However, they are not appropriate to medical data since they need several frames of the same scene, which bring a high risk of radiation or require a considerable acquisition time. We propose a new data augmentation technique and employ it in a multi-frame image reconstruction algorithm to improve the resolution of pathologic liver CT images. The input to our algorithm is a 3D CT-scan of the abdominal region. N

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Tiwari, Nitin, Nicola Baldo, Neelima Satyam, and Matteo Miani. "Mechanical Characterization of Industrial Waste Materials as Mineral Fillers in Asphalt Mixes: Integrated Experimental and Machine Learning Analysis." Sustainability 14, no. 10 (2022): 5946. http://dx.doi.org/10.3390/su14105946.

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In this study, the effect of seven industrial waste materials as mineral fillers in asphalt mixtures was investigated. Silica fume (SF), limestone dust (LSD), stone dust (SD), rice husk ash (RHA), fly ash (FA), brick dust (BD), and marble dust (MD) were used to prepare the asphalt mixtures. The obtained experimental results were compared with ordinary Portland cement (OPC), which is used as a conventional mineral filler. The physical, chemical, and morphological assessment of the fillers was performed to evaluate the suitability of industrial waste to replace the OPC. The volumetric, strength,

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Vu, Thanh, Baochen Sun, Bodi Yuan, Alex Ngai, Yueqi Li, and Jan-Michael Frahm. "Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5280–88. http://dx.doi.org/10.1609/aaai.v38i6.28335.

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The success of data mixing augmentations in image classification tasks has been well-received. However, these techniques cannot be readily applied to object detection due to challenges such as spatial misalignment, foreground/background distinction, and plurality of instances. To tackle these issues, we first introduce a novel conceptual framework called Supervision Interpolation (SI), which offers a fresh perspective on interpolation-based augmentations by relaxing and generalizing Mixup. Based on SI, we propose LossMix, a simple yet versatile and effective regularization that enhances the pe

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El Yadari, Manal, Fouad Jawab, Imane Moufad, and Jabir Arif. "Logistics Sprawl and Urban Congestion Dynamics Toward Sustainability: A Logistic Regression and Random-Forest-Based Model." Sustainability 17, no. 13 (2025): 5929. https://doi.org/10.3390/su17135929.

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Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in

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Liu, Xiaolin, Bo Wang, Shuanglong Jin, and Zongpeng Song. "Missing value interpolation algorithm for long-term temperature observation data based on data augmentation multiple interpolation method." Results in Engineering, July 2025, 106211. https://doi.org/10.1016/j.rineng.2025.106211.

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Ye, Mao, Haitao Wang, and Zheqian Chen. "MSMix: An Interpolation-Based Text Data Augmentation Method Manifold Swap Mixup." SSRN Electronic Journal, 2023. http://dx.doi.org/10.2139/ssrn.4471276.

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