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Effective Anomaly Detection Approach to Classify Noisy Data Using Robust Noise Detection and Removal Technique in IoT Healthcare Data
In IoT, it is a collection of connected devices or sensors that allows data to be collected and shared over the internet. Sensor data quality is...
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An acoustic feedback canceler based on probe noise and informative data for hearing aids
One of the important issues encountered in acoustic feedback canceler (AFC) systems is the bias occurring in the estimation of the acoustic feedback...
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Using topic-noise models to generate domain-specific topics across data sources
Domain-specific document collections, such as data sets about the COVID-19 pandemic, politics, and sports, have become more common as platforms grow...
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Recovering Clean Data with Low Rank Structure by Leveraging Pre-learned Dictionary for Structured Noise
In recent years, a series of methods have been proposed to recover clean data with low rank structure from noisy data, double robust principle...
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Autoencoder-Based Attribute Noise Handling Method for Medical Data
Medical datasets are particularly subject to attribute noise, that is, missing and erroneous values. Attribute noise is known to be largely...
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EcoLight+: a novel multi-modal data fusion for enhanced eco-friendly traffic signal control driven by urban traffic noise prediction
Urban traffic congestion is of utmost importance for modern societies due to population and economic growth. Thus, it contributes to environmental...
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Mitigating the impact of mislabeled data on deep predictive models: an empirical study of learning with noise approaches in software engineering tasks
Deep predictive models have been widely employed in software engineering (SE) tasks due to their remarkable success in artificial intelligence (AI)....
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Poisson noise and Gaussian noise separation through copula theory
This study explores an innovative optimization problem in copula denoising, aiming to distinguish and eliminate both Poisson noise and Gaussian...
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Abstract: Denoising of Home OCT Images using Noise-to-noise Trained on Artificial Eye Data
Optical coherence tomography (OCT) established as an essential part of the diagnosis, monitoring and treatment programs of patients suffering from...
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A hidden Markov model method for non-stationary noise reduction: case study on Sentinel data for mowing detection
We propose a method for reducing the non-stationary noise in signal time series of Sentinel data, based on a hidden Markov model. Our method is...
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Enhanced cluster detection and noise reduction for geospatial time series data of COVID-19
Spatial-temporal analysis of the COVID-19 cases is critical to find its transmitting behaviour and to detect the possible emerging clusters....
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A multi convolution pooling group fault diagnosis model with high generalization across data sets and large receptive field characteristics considering industrial environmental noise
Considering the noise impact in the bearing operating environment and the time-consuming and non-universal design of traditional diagnostic...
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Gaussian noise robust face hallucination via average filtering based data fidelity and locality regularization
In surveillance scenarios, the captured face images are often of low-resolution and contaminated by Gaussian noise. Noise creates problems in...
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NTDA: Noise-Tolerant Data Augmentation for Document-Level Event Argument Extraction
Event argument extraction (EAE), aiming at identifying event arguments over multiple sentences, mainly faces data sparsity problem. Cross-domain data...
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CCR-GSVM: A boundary data generation algorithm for support vector machine in imbalanced majority noise problem
In imbalanced data classification, training classification model with synthetic samples can effectively improve the performance in mining minority...
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Hybrid kernel approach to improving the numerical stability of machine learning for parametric equations with Gaussian processes in the noisy and noise-free data assumptions
In many applied sciences, the main aim is to learn the parameters of parametric operators which best fit the observed data. Raissi et al. (J Comput...
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Low-light image enhancement with joint illumination and noise data distribution transformation
Images captured in low-light environments often face two problems: low contrast and high noise, which are caused by the low number of photons in the...
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Noise4Denoise: Leveraging noise for unsupervised point cloud denoising
Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth...
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Robust enhanced collaborative filtering without explicit noise filtering
Graph convolutional neural networks have been successfully applied to collaborative filtering to capture high-quality user-item representations....
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A Belief Theory Based Instance Selection Scheme for Label Noise and Outlier Detection from Breast Cancer Data
In case of real datasets, the likelihood of the training data being corrupted with training label noise and outliers arises. Certain classification...