Digital Geometry Algorithms

Brimkov, Valentin E.; Barneva, Reneta P.

doi:10.1007/978-94-007-4174-4

Cited by 8 publications

(26 citation statements)

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“…There is a vast quantity of numerical methods, and they can be defined and classified by three fundamental modules: the field approximation (or interpolation) function, the used formulation and the integration. 3 From this point of view, it is possible to define the Finite Element Method (FEM) and the Meshless Methods as numerical methods.…”

Section: Meshless Methodsmentioning

confidence: 99%

“…7 It was only in the middle 90s that meshless methods came into focus of interest for numerically solving partial differential equations, especially in the computational mechanics community. 3,8 Despite this fact, this methods have rapidly evolved, solving many of their initial problems, such as accuracy, imposition of essential boundary conditions, numerical integration, stability and many others. 9 The type of functions used initially for meshless methods were approximation functions, since the implementation of the influence-domain concept was easier and the background integration scheme was nodal independent.…”

Section: Meshless Methodsmentioning

confidence: 99%

“…9 The type of functions used initially for meshless methods were approximation functions, since the implementation of the influence-domain concept was easier and the background integration scheme was nodal independent. 3 One of the first meshless method is the Smooth Particle Hydrodynamics (SPH) method, which was created to solve problems in astrophysics and, later on, fluid dynamics. Although SPH and their corrected versions were based on a strong form, other methods were based on a weak form.…”

Section: Meshless Methodsmentioning

confidence: 99%

“…As a result, this technique evolved and originated the Radial Point Interpolation Method (RPIM). 3,11 In order to stabilize the procedure, the Radial Basis Function (RBF) was added in the construction process of the interpolation function. In addition to the benefit mentioned before, the RBF allowed the removal of the possible singularities existent in PIM.…”

Section: Radial Point Interpolation Methodsmentioning

confidence: 99%

“…Moreover, since the RPIM uses the concept of 'influence-domain', it creates sparse and banded stiffness matrices, which are more adequate to complex geometries problems. 3 Due to all of the aforementioned advantages, together with the high convergence of the method, it is still used nowadays.…”

Section: Radial Point Interpolation Methodsmentioning

confidence: 99%

See 4 more Smart Citations

A 2D stress analysis of zirconia dental implants: A comparison study

Piqueiro¹

2017

Biodental Engineering IV

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Throughout the years, the health of the mouth and the smile have an increasing importance, due to the fact that is one of the most important interactive communication skills of a person. In fact, the frequency of removable dental prosthesis among adults varied between 13 and 29%, while the frequency of fixed dental prosthesis was the highest in Sweden (45%) and Switzerland (34%). Yet, dental implant treatments offers a solution that may generate more satisfactory outcomes for patients who are not able to adapt to conventional dentures or who have already compromised local host bone. There are two types of dental implants, which are the removable dental prosthesis (RDPs) and the fixed dental prosthesis (FDPs). The aforementioned types of restorations have different names according to the fixation (or absence of fixation) to the bone. Fixed dental prostheses are the ones that will be mainly focused in this study. These prostheses are constituted by three key elements, which are the crown, the abutment and the dental implant itself. The biomechanical integrity of implants comprises the mechanical behavior of implant materials, especially zirconia. Zirconia restorations have found their indications for FPDs supported by teeth implants due to its mechanical reliability. It was used advanced discretization numerical techniques-meshless methods-more specifically, the Natural Neighbour Radial Point Interpolation Method (NNRPIM) and the Radial Point Interpolation Method (RPIM), and also the Finite Element Method (FEM)

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Section: Meshless Methodsmentioning

confidence: 99%

Section: Meshless Methodsmentioning

confidence: 99%

Section: Meshless Methodsmentioning

confidence: 99%

Section: Radial Point Interpolation Methodsmentioning

confidence: 99%

Section: Radial Point Interpolation Methodsmentioning

confidence: 99%

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A 2D stress analysis of zirconia dental implants: A comparison study

Piqueiro¹

2017

Biodental Engineering IV

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HS‐UNET‐ID: An approach for human skin classification integrating between UNET and improved dense convolutional network

Nguyen

Luong

Phan

et al. 2022

Int J Imaging Syst Tech

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The Sun's ultraviolet radiation, toxic chemicals from industry, and other factors can cause problems to human skin. These factors have boosted the risk of humans contracting skin cancer. In identifying and detecting signals of skin cancer diseases, recent advancements in deep learning techniques significantly improve the accuracy of diagnosis. This work delves into processing techniques for human skin images with MorphologyEx (blackhat), thresholding, semantic segmentation with UNET, and several famous convolutional neural network architectures to improve skin classification performance. If skin cancer can be stopped in the first stage, the survival rate of the patient could be more than 95%, while it drops to just 5% if the detection is carried lately in Stage IV4. Due to its seriousness, the healthcare and research community has received considerable attention, and its most important goal is to be able to diagnose it at an early stage. However, the challenge due to the similarity in melanocytic and non-melanoma skin lesions is inevitable. We achieved skin classification performances of 0.8560, 0.9672, 0.8063, and 0.8560 measured by overall accuracy, area under curve, Matthews correlation coefficient, and F1-score, respectively. The proposed approach has been deployed into applications on Mobile and Web platforms.

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Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques

Hameed

Shabut

Ghosh

et al. 2020

Expert Systems with Applications

151

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Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the "divide and conquer" rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions.

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Digital Geometry Algorithms

Cited by 8 publications

References 0 publications

A 2D stress analysis of zirconia dental implants: A comparison study

A 2D stress analysis of zirconia dental implants: A comparison study

HS‐UNET‐ID: An approach for human skin classification integrating between UNET and improved dense convolutional network

Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques

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