Reliable navigation is critical for UAVs operating in GNSS-denied environments, where conventional Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integrated navigation struggles to meet the requirements of high-reliability a...
Fake news often exhibits pronounced bias and misleading content. To foster a harmonious information environment, there is an urgent need for rapid fake news identification. Fake news detection can assess news authenticity by analyzing multidimensiona...
Background: Automated pain assessment aims to enable objective measurement of patients’ individual pain experiences for improving health care and conserving medical staff. This is particularly important for patients with a disability to communi...
Cross-domain emotion recognition based on electroencephalogram (EEG) is a challenging task, as EEG signals collected from different subjects or at different moments exhibit significant differences in distribution. How to enable deep learning model to...
Health monitoring of marine turbochargers under real-ship operation is complicated by operating-condition variability, recurrent online cleaning, and limited fault labels. This study presents a maintenance-aware temporal contrastive autoencoder (TCCL...
Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative a...
Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-dis...
Generative models, particularly autoencoders, often function as black boxes, making it challenging for non-expert users to effectively control the generation process and understand how inputs affect outputs. Existing methods for improving interpretab...
Robust image processing systems require input images that closely resemble real-world scenes. However, external factors, such as adverse environmental conditions or errors in data transmission, can alter the captured image, leading to information los...
As one of the most important components in rotating machinery, if bearings fail, serious disasters may occur. Therefore, the remaining useful life (RUL) prediction of bearings is of great significance. Health indicator (HI) construction and early fau...
Single-cell RNA sequencing (scRNA-seq) has revolutionized molecular biology and genomics by enabling the profiling of individual cell types, providing insights into cellular heterogeneity. Deep learning methods have become popular in single cell anal...
Element detection is an important step in the process of the non-destructive testing of printed circuit boards (PCB) based on computed tomography (CT). Compared with the traditional manual detection method, the image semantic segmentation method base...
In this paper, we present the idea of Self Supervised learning on the shape completion and classification of point clouds. Most 3D shape completion pipelines utilize AutoEncoders to extract features from point clouds used in downstream tasks such as...
Building change detection and building damage assessment are two essential tasks in post-disaster analysis. Building change detection focuses on identifying changed building areas between bi-temporal images, while building damage assessment involves...
Network intrusion data are characterized by high feature dimensionality, extreme category imbalance, and complex nonlinear relationships between features and categories. The actual detection accuracy of existing supervised intrusion-detection models...
Background: MicroRNAs (miRNAs) regulate biological processes and are associated with human diseases. Computational prediction can prioritize candidate associations for experimental investigation, but many existing methods emphasize nonlinear represen...
Specific emitter identification (SEI) is a core technology for wireless device security that plays a crucial role in protecting wireless communication systems from various security threats. However, current deep learning-based SEI methods heavily rel...
Public aquariums and similar institutions often use video as a method to monitor the behavior, health, and status of aquatic organisms in their environments. These video footages take up a sizeable amount of space and require the use of autoencoders...
Circular RNAs (circRNAs) participate in gene regulation and disease-associated processes. Computational prediction of circRNA–disease associations (CDAs) can help prioritize candidates for biological investigation. Here, we propose a multi-view...
Self-supervised learning has made significant progress in point cloud processing. Currently, the primary tasks of self-supervised learning, which include point cloud reconstruction and representation learning, are trained separately due to their stru...
The rapid proliferation of Internet of Things (IoT) devices has significantly expanded the attack surface of modern networks leading to a surge in IoT-based botnet attacks. Detecting such attacks remains challenging due to the high dimensionality and...
Class imbalance, where majority-class samples vastly outnumber minority-class samples, remains a persistent challenge in network intrusion detection systems (NIDS), often causing classifiers to overlook rare but critical attack types while yielding m...
Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq d...
Preserving long-tail, minority information during model compression has been linked to algorithmic fairness considerations. However, this assumes that large models capture long-tail information and smaller ones do not, which raises two questions. One...
With the advancement of 3D digital dentistry, accurate 3D tooth segmentation has become increasingly important in orthodontics and computer-aided diagnosis. However, existing supervised approaches heavily rely on exhaustive face-wise annotations and...
The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated...
We formulate a data-independent latent space regularization constraint for general unsupervised autoencoders. The regularization relies on sampling the autoencoder Jacobian at Legendre nodes, which are the centers of the Gauss–Legendre quadratu...
Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method f...
Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process ne...
Designing reliable fault detection and diagnosis (FDD) systems remains difficult when only limited fault-free data are available. Kolmogorov–Arnold Networks (KANs) have recently been proposed as parameter-efficient alternatives to multilayer pe...
Efficient monitoring of the blast furnace system is crucial for maintaining high production efficiency and ensuring product quality. This article introduces a hybrid cluster variational autoencoder model for monitoring the blast furnace ironmaking pr...
The question of molecular similarity is core in cheminformatics and is usually assessed via a pairwise comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chem...
This paper investigates the design of end-to-end (E2E) autoencoders within AI-enhanced communication systems. It emphasizes the advantages of transitioning from Fast Fourier Transform (FFT)-based Orthogonal Frequency Division Multiplexing (OFDM) to a...
This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced prop...
To address the phenomenon of color shift and low contrast in underwater images caused by wavelength- and distance-related attenuation and scattering when light propagates in water, we propose a method based on an attention mechanism and adversarial a...
Malware is becoming an effective support tool not only for professional hackers but also for amateur ones. Due to the support of free malware generators, anyone can easily create various types of malicious code. The increasing amount of novel malware...
The log messages generated in the system reflect the state of the system at all times. The realization of autonomous detection of abnormalities in log messages can help operators find abnormalities in time and provide a basis for analyzing the causes...
Crop growth modelling formalizes the biological processes at which genotype X environment interactions (GxE) are expressed. It has the potential to evaluate, in silico, the effect of elementary traits and related genetic factors on phenotype and yiel...
Electroencephalography is emerging as a promising conditioning modality for generative visual models. However, existing representation learning approaches often rely on high-capacity masked autoencoders and complex projection networks. When constrain...
Underwater images frequently suffer from severe quality degradation due to light attenuation and scattering effects, manifesting as color distortion, low contrast, and detail blurring. These issues significantly impair the performance of downstream t...
The possibility of securing textual image data sharing exponentially strengthens when it harnesses the potential of cryptography as well as deep learning methods. A review of the existing literature showcases some interesting and productive initiativ...
Recently, the methods based on the autoencoder reconstruction background have been applied to the area of hyperspectral image (HSI) anomaly detection (HSI-AD). However, the encoding mechanism of the autoencoder (AE) makes it possible to treat the ano...
Retinal vessel segmentation remains difficult along thin, low-contrast branches. We evaluate a conditional diffusion refiner for probability maps from a lightweight U-Net with reverse attention (LU-Net+RA). A frozen mask autoencoder produces a spatia...
Deploying text-to-image (T2I) models is challenging due to high computational demands, extensive data needs, and the persistent goal of enhancing generation quality and diversity, particularly on resource-constrained devices. We introduce a lightweig...
The accuracy of coal mine water inrush prediction models is affected mainly by the small number of samples and difficulty in feature extraction. A new data augmentation water inrush prediction method is proposed. This method uses the natural neighbor...
In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierar...
This article investigates and compares four unsupervised anomaly detection algorithms: the Autoencoder (AE), LSTM-Autoencoder (LSTM-AE), One-Class SVM (OCSVM), and the Isolation Forest (IF). The analysis focuses on SCADA telemetry data from an urban...
Graph contrastive learning (GCL) has gained considerable attention as a self-supervised learning technique that has been successfully employed in various applications, such as node classification, node clustering, and link prediction. Despite its ach...
In this study, we introduce an enhanced hybrid Autoencoder–Dense–Transformer Neural Network (AE-DTNN) model for developing an effective intrusion detection system (IDS) aimed at improving the performance and robustness of threat detection...
This paper provides an approach for recognizing human activities with wearable sensors. The continuous autoencoder (CAE) as a novel stochastic neural network model is proposed which improves the ability of model continuous data. CAE adds Gaussian ran...
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