Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources...
This study analyses the influence of the course of the disease of idiopathic cervical dystonia (CD) before botulinum toxin (BoNT) therapy on long-term outcomes. 74 CD-patients who were treated on a regular basis in the botulinum toxin outpatient depa...
In developing NoSQL databases, a major motivation is to achieve better efficient query performance compared with relational databases. The graph database is a NoSQL paradigm where navigation is based on links instead of joining tables. Links can be i...
Background/Objectives: Predicting blood–brain barrier (BBB) permeability from structure remains challenging. We evaluated whether graph neural networks (GNNs) improve experimental logBB prediction over descriptor-based machine learning and whet...
This study investigates the design, development, and practical exploitation of an educational ontology for classical guitar instruction, within a semantically driven and application-oriented framework. The proposed approach aims to bridge the gap bet...
With the boom in online information, knowledge graphs like Freebase, Wikidata, and YAGO have emerged, thanks to the introduction of the RDF (Resource Description Framework). As RDF data grew, more and more spatial data was incorporated into it. While...
A significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the m...
Geospatial information is indispensable for various real-world applications and is thus a prominent part of today’s data science landscape. Geospatial data is primarily maintained and disseminated through spatial data infrastructures (SDIs). Ho...
Background: The benefit of hearing rehabilitation is often measured using audiological tests or subjective questionnaires/interviews. It is important to consider both aspects in order to evaluate the overall benefits. Currently, there is no standardi...
With the progressive improvements in the power, effectiveness, and reliability of AI solutions, more and more critical human problems are being handled by automated AI-based tools and systems. For more complex or particularly critical applications, t...
With an ever-increasing production and registration of chemical substances, obtaining reliable and up to date information on their use types (UT) and chemical class (CC) is of crucial importance. We evaluated the current status of open access chemica...
The aim of this study was to detect clinical hints regarding the development of secondary treatment failure (STF) in patients with focal dystonia who were exclusively treated with incobotulinumtoxin/A (incoBoNT/A). In total, 33 outpatients (26 with i...
Aim of the study: To compare the course of severity of cervical dystonia (CD) before and after long-term botulinum toxin (BoNT) therapy to detect indicators for a good or poor clinical outcome. Patients and Methods: A total of 74 outpatients with idi...
Auditory brainstem response (ABR) threshold determination requires clinicians to identify the transition between waveforms with and without a detectable response across stimulus intensities. Automation remains challenging because responses near the t...
In recent years, the rapid development of deep learning technology has brought new opportunities for specific emitter identification and has greatly improved the performance of radar emitter identification. The most specific emitter identification me...
Radar unconventional active jamming, including unconventional deceptive jamming and barrage jamming, poses a serious threat to wideband radars. This paper proposes an unconventional-active-jamming recognition method for wideband radar. In this method...
Background: Repetitive intramuscular injections of botulinum neurotoxin (BoNT) have become the treatment of choice for a variety of disease entities. But with the onset of BoNT therapy, the natural course of a disease is obscured. Nevertheless, the p...
Incorrect sensor readings can cause serious problems in Wireless Sensor Networks (WSNs), potentially disrupting the operation of the entire system. As shown in the literature, they can arise from various reasons; therefore, addressing this issue has...
Multichannel vibration from a planetary gearbox exhibits operating-condition-dependent interactions across both time scale and lag. Static undirected graphs cannot represent ordered temporal dependence, while repeated graph aggregation can attenuate...
Which population does a block hold-out error average over? On a terrain graph, the hold-out is a block cluster sample trimmed by a held-out-side buffer (unscored strips beside training), with closed-form inclusion probabilities for most nodes. For a...
Cross-modality person re-identification (ReID) aims at searching a pedestrian image of RGB modality from infrared (IR) pedestrian images and vice versa. Recently, some approaches have constructed a graph to learn the relevance of pedestrian images of...
Industrial robot maintenance knowledge graphs often contain missing relationships, while semantic differences among maintenance text, graph structure, and operational condition signals complicate multimodal link prediction. Sparsely connected entitie...
High-Resolution Range Profile (HRRP) radar recognition suffers from data scarcity challenges in real-world applications. We present HRRPGraphNet++, a framework combining dynamic graph neural networks with meta-learning for few-shot HRRP recognition....
Background: Distributed graph databases are a promising method for storing and conducting complex pathway queries on large-scale drug knowledge graphs to support drug research. However, there is a research gap in evaluating drug knowledge graphs&rsqu...
Orthogonal time frequency space (OTFS) modulation is well suited to high-mobility communications, and its combination with multiple-input multiple-output (MIMO) transmission further improves spectral efficiency and link reliability. In practical MIMO...
Distributed databases are increasingly used in enterprise and cloud environments, but their distributed architecture introduces significant security challenges, including data leaks and insider threats. In the context of escalating cyber threats targ...
Observational and experimental evidence has linked chronotype to both psychological and cardiometabolic traits. Recent Mendelian randomization (MR) studies have investigated direct links between chronotype and several of these traits, often in isolat...
Knowledge representation learning is representing entities and relations in a knowledge graph as dense low-dimensional vectors in the continuous space, which explores the features and properties of the graph. Such a technique can facilitate the compu...
Automatic modulation recognition (AMR) remains challenging under low-signal-to-noise ratio (SNR) conditions, where severe noise can obscure weak modulation-specific waveform patterns. To address this issue, this paper proposes an Adaptive Holistic Fu...
The proliferation of UAVs operating in complex interference environments has intensified the demand for collaborative spectrum management to mitigate interference and maximize network capacity. This paper proposes a heterogeneous graph neural network...
Visible-Infrared Person Re-Identification (VI-ReID) is of crucial importance in applications such as monitoring and security. However, challenges faced from intra-class variations and cross-modal differences are often exacerbated by inaccurate infrar...
This paper proposes DB-LIO (database-driven LiDAR-inertial odometry), a simultaneous localization and mapping (SLAM) system that addresses memory scalability challenges in extended autonomous operation. Existing LiDAR-SLAM systems accumulate keyframe...
Automatic modulation recognition (AMR) is essential for cognitive radio and intelligent wireless communication, yet its performance degrades markedly under low signal-to-noise ratio (SNR) conditions because weak local structures are corrupted and cla...
Fault diagnosis in grid-connected grid-forming hybrid energy storage station (GFM-HESS) systems is challenging because fault transients are jointly affected by converter control dynamics, multi-source electrical couplings, operating-condition variati...
Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug. However, existing deep-learning DTA pr...
Low-slow-small (LSS) target recognition using multi-dimensional radar remains challenging due to weak signatures, similar kinematics, and overlapping short-term Doppler patterns. Digital-array radar provides continuous, complementary Doppler-spectrum...
Current Facial expression recognition methods typically extract facial features indiscriminately, incorporating expression-irrelevant information that compromises recognition accuracy. To overcome this, we propose Multi-stage Feature Sparse Constrain...
Cooperative automatic modulation classification (CAMC) using a swarm of sensors is intriguing nowadays as it would be much more robust than the conventional single-sensing-node automatic modulation classification (AMC) method. We propose a novel robu...
Among many machine learning models for analyzing the relationship between miRNAs and diseases, the prediction results are optimized by establishing different machine learning models, and less attention is paid to the feature information contained in...
Multimodal knowledge graph completion (MMKGC) aims to predict missing entities by exploiting structural, visual, and textual information and is important for semantic retrieval, recommendation, and intelligent question answering. Existing relation-aw...
The symmetry between different representation spaces plays a crucial role in effectively modeling complex multimodal data. To address the challenge of equipment knowledge graphs containing hierarchical relationships that cannot be fully represented i...
Knowledge graph embedding represents entities and relations as low-dimensional continuous vectors. Recently, researchers have attempted to leverage the potential semantic connections between entities with hierarchical relationships in the knowledge g...
With the increasing demand for person re-identification (Re-ID) tasks, the need for all-day retrieval has become an inevitable trend. Nevertheless, single-modal Re-ID is no longer sufficient to meet this requirement, making Multi-Modal Data crucial i...
Hyperspectral images (HSIs) are inevitably degraded by mixed noise, which hampers downstream interpretation. Recent graph-signal-processing denoisers encode the spatial–spectral structure of an HSI through a product graph over superpixel bodies...
The main goal of the present study is to explore the noise mitigation potential using an active control strategy based on rotor phase synchronization. This work is focused on the effects of the inflow velocity on the noise interference effect. The in...
A point cloud obtained by stereo matching algorithm or three-dimensional (3D) scanner generally contains much complex noise, which will affect the accuracy of subsequent surface reconstruction or visualization processing. To eliminate the complex noi...
Wind turbine bearings operate long-term under complex and variable operating conditions, where fault impulse characteristics are easily submerged by strong noise. Traditional graph signal processing-based bearing fault diagnosis methods are limited b...
Automatic modulation recognition (AMR) is a key enabling technique for intelligent spectrum sensing, non-cooperative wireless signal analysis, and communication monitoring. However, its reliability degrades significantly under low signal-to-noise rat...
The complexity and age of industrial plants have prompted a rapid increase in equipment maintenance and replacement activities in recent years. Consequently, plant owners are challenged to reduce the process and review time of equipment purchase orde...
HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon’s action, with t...
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