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Magnetic Resonance Imaging (MRI) is an essential tool for diagnosing and monitoring diseases under various conditions. However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional ...

Modelling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Emulator for fast chemical concentration modelling. We consider ...

• Motivation. Modelling atmospheric chemistry is complex and computationally intense. However, few studies have been conducted on large-scale atmospheric chemistry modelling or multiple-input-and-multiple-output chemical prediction. ...

Quickly obtaining and classifying relevant information about crisis events via social media platforms, such as Twitter and Weibo, plays a critical role in the subsequent rescue operations and post-disaster reconstruction. Current crisis event ...

The Rapid Serial Visual Presentation (RSVP) is a widely used paradigm for target detection tasks in Brain-Computer Interface (BCI) by decoding Electroencephalogram (EEG) signals. One major issue concerns the time-consuming calibration in cross-...

Adversarial attacks meticulously generate minuscule, imperceptible perturbations that add to images to deceive neural networks. Adversarial purification methods seek to remove perturbations using generative models to achieve defense. However, ...

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• Quantifies the relationship between residual perturbations and attack capability.
• Introduces information masking strategies to resist the perturbations.
• Proposes a simulated technique to defend against residual perturbations.
• ...

Significant progress has been made in the prediction methods of the remaining useful life (RUL) of machinery. Nevertheless, two major challenges still exist for the large-scale practical application of these methods. Firstly, most prediction ...

Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing essential information on electrical hearth activity. However, inherent noise and limited resolution can hinder an accurate interpretation of the recordings. In ...

• We defined a novel architecture based on autocencoders which is able to denoise and reconstruct high resolution copies of input low resolution ECG signals.
• This unique approach that has not been previously applied to ECG signals.
• ...

In this manuscript, we propose an innovative early warning Machine Learning-based model to identify potential threats to financial sustainability for non-financial companies. Unlike most state-of-the-art tools, whose outcomes are often difficult ...

• Popular models for corporate risk assessment often provide results that are difficult to understand.
• A novel visual-based approach to detect potential distress and safe zones is proposed.
• Balance sheet data are mapped into an ...

Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and modeling graph-structured data. In recent years, GNNs have gained significant attention in various domains. This review paper aims to provide an overview of the state-...

Single-cell RNA sequencing (scRNA-seq) analysis is capable of elucidating cell heterogeneity and diversity, making cell-level biological research possible. Cell type clusterization is one of the main goals of scRNA-seq analysis. However, existing ...

• Hierarchical graph structural features learned by GNN.
• Multiscale node attribute features learned by GAA.
• Heterogeneous intelligent fusion module.
• Multi-scale intelligent fusion module.
• Dual self-supervision module.

Surveillance systems are essential in computer vision, with video anomaly detection (VAD) critical for real-world security. Conventional approaches anticipate abnormal frames using normal patterns learned with normal training data and determine ...

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• Patch anomaly generation enforces normality learning, in terms of appearance and motion.
• Anomaly distance learning enlarges the feature distance of normal and abnormal frames.
• Context-aware shortcut widens the quality gap between ...

• Proposing an attention constrained low-rank and sparse autoencoder for hyperspectral anomaly detection.
• Designing two detectors, AClrAE and ACsAE, to focus more on global background reconstruction and anomaly reconstruction.
• ...

Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can ...

In aircraft, the braking system is a safety-critical and heavily used component of the landing gear, prone to significant wear. Anomalies arising in the wear dynamics can degrade the performance of the braking system and compromise the safety of ...

Clustering is essential for uncovering hidden patterns and relationships in complex datasets. Its importance reveals when labeled data is scarce, expensive, time-consuming to obtain. Real-world applications often exhibit heterogeneity due to the ...

Low-light images, characterized by inadequate illumination, pose challenges of diminished clarity, muted colors, and reduced details. Low-light image enhancement, an essential task in computer vision, aims to rectify these issues by improving ...

Anomaly detection and segmentation in industrial images are critical tasks requiring robust and precise methodologies. This paper presents the Multi-Scale Reverse Distillation (MSRD) methodology, an innovative improvement of the foundational ...

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• Introduce MSRD, which enhances anomaly detection in images.
• MSRD boosts RD with information at different levels.
• Novel decoder module for precise image reconstruction.
• The refined loss function improves the detection precision.

Cormputed tomography (CT) scanning is an effective medical imaging modality widely used in clinical medicine for diagnosing various conditions. CT can generate three-dimensional images, thus providing more information than traditional two-...

Innumerable approaches of deep learning-based COVID-19 detection systems have been suggested by researchers in the recent past, due to their ability to process high-dimensional, complex data, leading to more accurate prediction of the COVID-19 ...

The ability to accurately detect and classify falls is critical for ensuring timely medical intervention, especially for the elderly, who face a significantly higher risk of severe injuries, loss of independence, or fatal outcomes from falls. This ...

Machine learning and deep learning (DL)-based forecasting models have shown excellent predictive performances, but they require a large amount of data for model construction. Insufficient data can be augmented using generative adversarial ...