This work outlines an approach for localizing anomalies in nuclear reactor cores during their steady state operation, employing deep, one-dimensional, convolutional neural networks. Anomalies are characterized by the application of perturbation diagn...
With applications in psychology, security, and human–computer interaction, facial expression recognition (FER) has become an essential tool for non-verbal communication. Current research often categorizes expressions into micro- and macro-types...
Meningiomas are the most prevalent benign intracranial life-threatening brain tumors, with a life expectancy of a few months in the later stages, so this type of tumor in the brain image should be recognized and detected efficiently. The source of me...
Background. Muscle depletion (MD) assessed by computed tomography (CT) has been shown to be a predictive marker in solid tumors, but has not been assessed in non-Hodgkin’s lymphomas. Despite software improvements, MD measurement remains highly time-c...
This paper considers the risk model perturbed by a diffusion process with a time delay in the arrival of the first two claims and takes into account dependence between claim amounts and the claim inter-occurrence times. Assuming that the time arrival...
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep...
Approximately 32–42% of very preterm infants develop minor motor abnormalities. Earlier diagnosis soon after birth is urgently needed because the first two years of life represent a critical window of opportunity for early neuroplasticity in in...
Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as &ld...
The surfaces of real objects can visually appear to be glossy, matte, or anywhere in between, but essentially, they display varying degrees of diffuse and specular reflectance. Diffuse and specular reflectance provides different clues for light estim...
29 August 2024
Background: Machine learning models can provide quick and reliable assessments in place of medical practitioners. With over 50 million adults in the United States suffering from osteoarthritis, there is a need for models capable of interpreting muscu...
This article develops an innovative and intelligent method for analysing the criminal community’s influence on risk-forming parameters based on an analysis of regional economic processes. The research motivation was the need to create an intell...
The generation of images from scene graphs is an important area in computer vision, where structured object relationships are used to create detailed visual representations. While recent methods, such as generative adversarial networks (GANs), transf...
In this paper, we shall give a concise review of recent numerical methods of some generalized fractional models. As is elucidated later, the generalized fractional models may arise from either mathematical point of view or application point of view....
Actual diffusion activity function is an important metric utilized to describe the diffusion activities of a vacancy defect substance. In this paper, we propose a deep learning three-dimensional convolutional CNN model (D3-CNN). A 3D convolution has...
A 1-D diffusion model of temperature is employed to understand important features of temperature response to the changes of surface heat flux (SHF) and vertical diffusivity shown in 3-D model simulations. Analytical results show that the temperature...
Translating Synthetic aperture radar (SAR) images into optical images is intrinsically ill-posed because microwave backscatter and optical reflectance describe different physical properties of the observed scene. Although frequency-domain modeling ha...
To address sparse meteorological data and the “smoothing effect” over complex terrain, this study proposes a spatiotemporal model based on a Diffusion Graph Convolutional Network (DG model). Focusing on Quanzhou, China, and using 2020&nda...
Automatic medical segmentation is crucial for assisting doctors in identifying disease regions effectively. As a state-of-the-art (SOTA) approach, generative AI models, particularly diffusion models, have surpassed GANs in generating high-quality ima...
We investigate the diffusion phenomenon of particles in the vicinity of a spherical colloidal particle where particles may be adsorbed/desorbed and react on the surface of the colloidal particle. The mathematical model comprises a generalized diffusi...
Automatic skin lesion segmentation is essential for early melanoma diagnosis, yet the scarcity and limited diversity of annotated training data hinder progress. We introduce a two-stage framework that first employs a denoising diffusion probabilistic...
When detecting micro-defects or defect samples with significant dimensional variations in textile defect detection, the localization accuracy of the detection model remains a critical bottleneck hindering the automation upgrade of textile production...
Fractional integrodifferential diffusion equations play a significant role in describing anomalous diffusion phenomena. In this paper, we study the existence and uniqueness of mild solutions to these equations. Firstly, we construct an appropriate re...
To address the issues of low resolution and missing edge details in current power imagery, this paper proposes a diffusion-based super-resolution method. By applying the iterative denoising process of diffusion models, the proposed method learns feat...
Accurate traffic state estimation (TSE) for port motorway networks is critical for mitigating congestion and improving operational efficiency in port cities. Port motorways have their specific traffic patterns, such as surges in container truck deman...
Directional room impulse responses (DRIR) measured with spherical microphone arrays (SMA) enable the reproduction of room reverberation effects on three-dimensional surround-sound systems (e.g., Higher-Order Ambisonics) through multichannel convoluti...
Face liveness detection is a critical preprocessing step in face recognition for avoiding face spoofing attacks, where an impostor can impersonate a valid user for authentication. While considerable research has been recently done in improving the ac...
Pediatric pneumonia remains a critical global health challenge requiring accurate and interpretable diagnostic solutions. Although deep learning has shown potential for pneumonia recognition on chest X-ray images, gaps persist in understanding model...
The diffusion coefficient of heavy quarks in a deconfined medium is examined in this research using a deep convolutional neural network (CNN) that is trained with data from relativistic heavy ion collisions involving heavy flavor hadrons. The CNN is...
In this paper, some new properties of the fundamental solution to the multi-dimensional space- and time-fractional diffusion-wave equation are deduced. We start with the Mellin-Barnes representation of the fundamental solution that was derived in the...
Universal image restoration (UIR) aims to accurately restore images with a variety of unknown degradation types and levels. Existing methods, including both learning-based and prior-based approaches, heavily rely on low-quality image features. Howeve...
By redesigning the definition of energy in the discrete sense, we construct structure-preserving alternating direction implicit (ADI) difference schemes to solve the space fractional nonlinear damped wave equation, where the temporal and spatial disc...
A three-dimensional mathematical model and simulation of the impurity diffusion process are developed under the given statistical characteristics of the system of internal stochastically disposed point sources of mass. These sources, possessing varyi...
Underwater image enhancement (UIE) is inherently challenging due to complex degradation effects such as light absorption and scattering, which result in color distortion and a loss of fine details. Most existing methods focus on spatial-domain proces...
Along with the recent advancements in video streaming, concerns over the security of transferred data have increased. Thus, the development of fast and reliable image encryption methodologies has become an emerging research area in the field of commu...
Accurate and real-time traffic speed prediction remains challenging due to the irregularity and asymmetry of real-traffic road networks. Existing models based on graph convolutional networks commonly use multi-layer graph convolution to extract an un...
In this work, the problem of eliminating a nonuniform rectilinear smearing of an image is considered, using a mathematical- and computer-based approach. An example of such a problem is a picture of several cars, moving with different speeds, taken wi...
Recommendation systems have become crucial tools for mitigating information overload and enhancing user experience. Among them, LightGCN has achieved remarkable results in both efficiency and performance by simplifying graph convolution operations. H...
Traffic sensors provide real-time measurements of current traffic conditions, whereas traffic management applications require forecasts of future traffic speed or flow. This study considers the incremental expansion of an operating sensor network, in...
Classifying low-grade glioma (LGG) patients from high-grade glioma (HGG) is one of the most challenging tasks in planning treatment strategies for brain tumor patients. Previous studies derived several handcrafted features based on the tumor’s...
An initial-boundary-value problem is considered for the one-dimensional diffusion equation with a general convolutional derivative in time and nonclassical boundary conditions. We are concerned with the inverse source problem of recovery of a space-d...
Facial image super-resolution seeks to reconstruct high-quality details from low-resolution inputs, yet traditional methods, such as interpolation, convolutional neural networks (CNNs), and generative adversarial networks (GANs), often fall short, su...
We study a reaction–diffusion model for population–toxin interactions on a two-dimensional torus, where avoidance is driven by a nonlocal average of toxin-related memory. The variables represent population density, toxin concentration, an...
In this paper, we consider a direct problem and an inverse problem involving a nonlinear fractional diffusion equation, which can be applied to many physical situations. The equation contains a Caputo fractional derivative, a symmetric uniformly elli...
Flood forecasting is crucial for predicting floods and facilitating timely evacuations. Machine learning and deep learning algorithms, such as artificial neural networks (ANN), recurrent neural networks (RNN), and Transformer models, have shown signi...
The fractional diffusion equation is one of the important recent models that can efficiently characterize various complex diffusion processes, such as in inhomogeneous or heterogeneous media or in porous media. This article provides a method for the...
The surface material of an object is a key factor that affects non-line-of-sight (NLOS) imaging. In this paper, we introduce the bidirectional reflectance distribution function (BRDF) into NLOS imaging to study how the target surface material influen...
The application of graph convolutional neural networks for traffic prediction is a standard procedure; however, this approach is rarely used under the assumption that the exact city plan is unknown and the prediction area is a city-sized region. This...
A color image encryption algorithm based on double fractional order chaotic neural network (CNN), interlaced dynamic deoxyribonucleic acid (DNA) encoding and decoding, zigzag confusion, bidirectional bit-level diffusion and convolution operation is p...
In this article, we consider how differing approaches that characterize biological microstructure with diffusion weighted magnetic resonance imaging intersect. Without geometrical boundary assumptions, there are techniques that make use of power law...
Forecasting human mobility during disasters is essential for emergency response but remains challenging because disasters cause abrupt, uncertain, and context-dependent deviations from regular mobility patterns. Deterministic models capture dominant...
of 9