Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function

Hurtík, Petr; Molek, Vojtěch; Hůla, Jan

doi:10.1109/tfuzz.2019.2911494

Cited by 39 publications

(17 citation statements)

References 44 publications

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“…This has motivated the development of tools using fuzzy systems theory for data analysis (Hurtik et al. 2020 ; Lan et al. 2020 ; Pires and Serra 2020 ), mainly from the association of Kalman filters and type-2 fuzzy systems, which is the particular motivation of this paper in the sense of overcoming limitations of classic Kalman filtering to face high order nonlinearities, processing different types of uncertainties using interval fuzzy operation regions in non-stationary experimental dataset, and guaranteeing a set of possible solutions within a confidence region.…”

Section: Related Workmentioning

confidence: 99%

“…Although recent studies have addressed the processing of uncertainties in the formulation of data analysis methodologies in different application domains such as engineering (Ma and Ma 2020), health (Heintzman and Kleinberg 2016), epidemiology (Gilbert et al 2014), economics (Khairalla et al 2018), among others, the research in this issue is still open. This has motivated the development of tools using fuzzy systems theory for data analysis (Hurtik et al 2020;Lan et al 2020;Pires and Serra 2020), mainly from the association of Kalman filters and type-2 fuzzy systems, which is the particular motivation of this paper in the sense of overcoming limitations of classic Kalman filtering to face high order nonlinearities, processing different types of uncertainties using interval fuzzy operation regions in non-stationary experimental dataset, and guaranteeing a set of possible solutions within a confidence region.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

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A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data

Gomes

Serra

2021

Evolving Systems

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In this paper, a methodology for design of fuzzy Kalman filter, using interval type-2 fuzzy models, in discrete time domain, via spectral decomposition of experimental data, is proposed. The adopted methodology consists of recursive parametric estimation of local state space linear submodels of interval type-2 fuzzy Kalman filter for tracking and forecasting of the dynamics inherited to experimental data, using an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm. The partitioning of the experimental data is performed by interval type-2 fuzzy Gustafson–Kessel clustering algorithm. The interval Kalman gains in the consequent proposition of interval type-2 fuzzy Kalman filter are updated according to unobservable components computed by recursive spectral decomposition of experimental data. Computational results illustrate the efficiency of proposed methodology for filtering and tracking the time delayed state variables of Chen’s chaotic attractor in a noisy environment, and experimental results illustrate its applicability for adaptive and real time forecasting the dynamic spread behavior of novel Coronavirus 2019 (COVID-19) outbreak in Brazil.

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Section: Related Workmentioning

confidence: 99%

Section: Motivation and Contributionsmentioning

confidence: 99%

A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data

Gomes

Serra

2021

Evolving Systems

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“…The higher E(θ) value is, the higher the resolution of a reconstructed single-frame character image. The calculation equation of E(θ) is [24,25]:…”

Section: Super-resolution Image Reconstruction Based On Wavelet Neuramentioning

confidence: 99%

An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

Guo

Woźniak

2020

Mobile Netw Appl

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The application of the traditional single frame character image super-resolution reconstruction method has some problems, such as noise can not be removed completely and anti-interference performance is poor. A new method for the super-resolution reconstruction of single frame character image based on wavelet neural network is proposed. The structure and interface of image acquisition unit of solid state image sensor are designed. Combined with pinhole imaging model and camera self-calibration, image acquisition of Internet of Things is completed. An image degradation model was established to simulate the degradation process of ideal high-resolution image to low-resolution image. Wavelet threshold denoising method is used to remove the noise in a single frame character image and improve the anti-interference performance of the method. The wavelet neural network reflection model is used to reconstruct the single frame feature image and improve the resolution of the image. The experimental results show that the blur degree of the reconstructed image is always less than 5%. In the whole experiment, the accuracy of this method can be maintained at 80% ~ 90%. The image detail retention rate of the research method is relatively stable. With the increase of the number of experimental images, the retention rate of image details remains between 80% and 95%, indicating that the method is effective in practical application.

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“…Recently, state-of-the-art methods [22][23][24] have been utilized handcrafted features for tumor segmentation. These strategies are close to the inclusion of additional customized elements represented by a fuzzy set to a crisp input.…”

Section: Introductionmentioning

confidence: 99%

“…These handcrafted fuzzy elements are calculated based on a crisp input, which ultimately restricts their performance. In [23] derives the intensity of every pixel by a neighbor pixel membership value within a distance of 2 pixels, which are equal to adding a 5×5 kernel convolution to the input signal. Instead of using fuzzy data preprocessing approaches for cascading neural networks, extract features from smooth information, deeply incorporating fuzzy neighborhood learning at the level of network architecture to boost the efficiency of the proposed model.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation and Prediction using Fuzzy Neighborhood Learning Approach for 3D MRI Images

Pitchai¹,

Supraja

et al. 2021

Preprint

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Segmentation of brain tumors is a daunting process comprising the delineation of heterogeneous cancerous tissues and diffuse types in anatomical representations of the brain. Deep learning techniques have recently made important strides in the segmentation of brain tumors. However, owing to the irregularity of the tumor, most of the deep learning-based segmentation techniques are not used directly for tumor detection. Although recent studies are capable of addressing the irregularity issue and retaining permutation invariance, many approaches struggle to catch the valuable high-dimensional local features of finer resolution. Inspired by the fuzzy learning methods and an analysis of the shortcomings of existing methods, an automated fuzzy neighborhood learning-based 3D segmentation technique has been proposed for the detection of cerebrum tumors in 3D images. In this technique, the fuzzy neighborhood function is deeply integrated with the proposed network architecture. This technique has been evaluated on BRATS 2013dataset. The simulation results show that the proposed brain tumor detection technique is superior to other methods in the diagnosis of brain tumors with the dice coefficient of 0.85 and the Jaccard index of 0.74.

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Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function

Cited by 39 publications

References 44 publications

A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data

A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data

An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

Brain Tumor Segmentation and Prediction using Fuzzy Neighborhood Learning Approach for 3D MRI Images

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