Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduce...
Polarization characteristics are significantly crucial for tasks in various fields, including the remote sensing of oceans and atmosphere, as well as the polarization LIDAR and polarimetric imaging in scattering media. Many polarimetric metrics (such...
Deep metric learning combines deep learning with metric learning to explore the deep spectral space and distinguish between the target and background. Current target detection methods typically fail to accurately distinguish local differences between...
The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically ver...
Ontologies are critical for organizing and interpreting complex domain-specific knowledge, with applications in data integration, functional prediction, and knowledge discovery. As the manual curation of ontology annotations becomes increasingly infe...
Objective: To describe a novel measure of EEG signal variability that distinguishes cognitive brain states. Method: We describe a novel characterization of amplitude variability in the EEG signal termed “High Variability Periods” or &ldqu...
Raman microspectroscopy is a powerful, label-free technique for the biochemical characterization of cells, but its complex spectral data require advanced computational methods for meaningful interpretation. Clustering analysis is widely used in spect...
Unsupervised domain adaptation (UDA) based on adversarial learning for remote-sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data. Existing methods train classifiers accord...
Nervousness results from variance and changes in the verdicts of supply and logistics networks and activities. Nervousness is considered a source of confusion in supply chain (SC) systems because it is associated with frequent decision changes. New S...
AI language models are increasingly transforming language research in various ways. How can language educators and researchers respond to the challenge posed by these AI models? Specifically, how can we embrace this technology to inform and enhance s...
An enhanced quantum-based image fidelity metric, the QIFM metric, is proposed as a tool to assess the “congruity” between two or more quantum images. The often confounding contrariety that distinguishes between classical and quantum information proce...
The Enhanced Flight Vision System (EFVS) plays a significant role in the Next-Generation low visibility aircraft landing technology, where the involvement of optical sensing systems increases the visual dimension for pilots. This paper focuses on dep...
Background: This study investigates the application of machine learning models to classify electrocardiogram signals, addressing challenges such as class imbalances and inter-class overlap. In this study, “normal” and “abnormal&rdqu...
KPC (KrasG12D:Trp53R172H:Pdx1-Cre) and CKS (KrasG12D:Smad4L/L:Ptf1a-Cre) mice are genetically engineered mouse (GEM) models that capture features of human pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasms (IPMN), re...
Background: The limited availability of cardiac MRI data significantly constrains deep learning applications in cardiovascular imaging, necessitating innovative approaches to address data scarcity while preserving critical cardiac anatomical features...
Loop closure detection (LCD), also known as place recognition, is a crucial component of visual simultaneous localization and mapping (vSLAM) systems, aiding in the reduction of cumulative localization errors on a global scale. However, changes in en...
Anomaly prediction in time series is crucial for ensuring the stability and security of data centers, especially in scientific contexts such as INFN-CNAF, the National Center for Research and Development in Information and Communication Technology of...
This study aims to develop and validate a multisequence MRI-based radiomics approach for distinguishing metaplastic breast cancer (MBC) from non-metaplastic triple-negative breast cancer (TNBC) at the initial diagnosis, which could facilitate optimal...
Interacting hand reconstruction presents significant opportunities in various applications. However, it currently faces challenges such as the difficulty in distinguishing the features of both hands, misalignment of hand meshes with input images, and...
Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lea...
This study introduces Protectbot, an innovative chatbot framework designed to improve safety in children’s online gaming environments. At its core, Protectbot incorporates DialoGPT, a conversational Artificial Intelligence (AI) model rooted in...
Grazing potential (GP, in % day−1) was estimated for the plankton communities of 13 Greek lakes covering the trophic spectrum, in order to examine its sensitiveness in discriminating different classes of ecological water quality. Lakes with hig...
Anomaly detection (AD) algorithms can be instrumental in industrial scenarios to enhance the detection of potentially serious problems at a very early stage. Of course, the “Industry 4.0” revolution is fostering the implementation of inte...
Decision Support Systems (DSSs) are increasingly shaping high-performance sport by translating complex time series data into actionable insights for coaches and practitioners. This paper outlines a structured, five-stage DSS development pipeline, gro...
Road detection from images has emerged as an important way to obtain road information, thereby gaining much attention in recent years. However, most existing methods only focus on extracting road information from single temporal intensity images, whi...
Deep learning-based hyperspectral target detection (HTD) methods often face the challenge of insufficient prior information and difficulty in distinguishing local and global spectral differences. To address these problems, we propose a self-supervise...
In high-dimensional space, most multi-objective optimization algorithms encounter difficulties in solving many-objective optimization problems because they cannot balance convergence and diversity. As the number of objectives increases, the non-domin...
Non-line-of-sight (NLOS) errors significantly impact the accuracy of ultra-wideband (UWB) indoor positioning, posing a major barrier to its advancement. This study addresses the challenge of effectively distinguishing line-of-sight (LOS) from NLOS si...
The availability of high-quality and ample synthetic aperture radar (SAR) image datasets is crucial for understanding and recognizing target characteristics. However, in practical applications, the limited availability of SAR target images significan...
Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplifica...
Diagnosing bipolar disorder (BD) and schizophrenia (SCH) presents significant challenges due to overlapping symptoms, reliance on subjective assessments, and the late-stage manifestation of many symptoms. Current methods using structural magnetic res...
Background/Objectives: Spontaneous intracranial hypotension (SIH), caused by spinal cerebrospinal fluid (CSF) leakage, commonly presents with orthostatic headache and CSF hypovolemia. While CSF dynamics in the cerebral aqueduct are well studied, alte...
This paper presents the development of a novel algorithm for unsupervised learning called RUN-ICON (Reduce UNcertainty and Increase CONfidence). The primary objective of the algorithm is to enhance the reliability and confidence of unsupervised clust...
Multi-object tracking (MOT) is one of the significant directions of computer vision. Though existing methods can solve simple tasks like pedestrian tracking well, some complex downstream tasks featuring uniform appearance and diverse motion remain di...
Power systems are generally designed to be reliable when faced with low-impact, high-probability, and expected power outages. By contrast, the probability of extreme event (extreme weather or natural disasters) occurrence is low, but may seriously af...
The study discloses the application of transformer-based deep learning models for the task of super-enhancers prediction in human tumor cell lines with a specific focus on sequence-only features within studied entities of super-enhancer and enhancer...
Wheat is one of the world’s essential crops, and the presence of foliar diseases significantly affects both the yield and quality of wheat. Accurate identification of wheat leaf diseases is crucial. However, traditional segmentation models face...
As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navi...
This research addresses the challenges of early detection of Acute Lymphoblastic Leukemia (ALL), a life-threatening blood cancer particularly prevalent in children. Manual diagnosis of ALL is often error-prone, time-consuming, and reliant on expert i...
(1) Objectives: Assessment of novel impedance parameters such as the post-reflux swallow-induced peristaltic wave (PSPW) index and mean nocturnal baseline impedance (MNBI) have been proposed to enhance the accuracy of gastroesophageal reflux disease...
Camouflaged object detection (COD) is a challenging task, aimed at segmenting objects that are similar in color and texture to their background. Sufficient multi-scale feature fusion is crucial for accurately segmenting object regions. However, most...
This survey explores the evolution of test scenario generation for autonomous vehicles (AVs), distinguishing between non-adaptive and adaptive scenario approaches. Non-adaptive scenarios, where dynamic objects follow predetermined scripts, provide re...
While many neural networks have been proposed for hyperspectral image classification, current backbones cannot achieve accurate results due to the insufficient representation by scalar features and always cause a cumbersome calculation burden. To sol...
Autonomous vehicles are expected to reduce traffic accident casualties, as driver distraction accounts for 90% of accidents. These vehicles rely on sensors and controllers to operate independently, requiring robust security mechanisms to prevent mali...
In this paper, we present an update to the ellipsoid profile algorithm (EP), a simple technique for the measurement of the globularity of protein structures without the calculation of molecular surfaces. The globularity property is understood in this...
In network security, intrusion detection systems (IDSs) are essential for maintaining network integrity. Traditional IDSs primarily use supervised learning, relying on extensive datasets for effective training, which limits their ability to address r...
In this study, we present an innovative approach to improve the prediction of protein–protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interacti...
Postural instability is a common complication in advanced Parkinson’s disease (PD) associated with recurrent falls and fall-related injuries. The test of retropulsion, consisting of a rapid balance perturbation by a pull in the backward directi...
27 June 2024
The existing body of research on dynamic customer segmentation has primarily focused on segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies for distinct customer groups. However, these approaches often lack the gr...
In order to detect weak underwater tonals, adaptive line enhancers (ALEs) have been widely applied in passive sonars. Unfortunately, conventional ALEs cannot perform well amid impulse noise generated by ice cracking, snapping shrimp or other factors....
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