Wyniki wyszukiwania - BazTech

1

Handling dimensionality of ambiguity using ensemble classification in social networks to detect multi-label sentiment polarity

Sonawane Sudarshan S., Kolhe Satish R.

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 1

7--26

EN

With ever-increasing demand, social media platforms are rapidly developing to enable users to express and share their opinions on a variety of topics. Twitter is one such social media site. This platform enables a comprehensive view of the social media target setting, which may include products, social events, political scenarios, and administrative resolutions. The accessible tweets expressing the target audience’s perspective are frequently impacted by ambiguity caused by natural language processing (NLP) limitations. By classifying tweets according to their sentiment polarity, we can determine whether they express a good or negative point of view, a neutral opinion, or an input tweet that is irrelevant to the sentiment polarity context. Categorizing tweets according to their sentiment can assist future activities within the target domain in constructively evaluating the sentiment polarity and enabling improved decision-making based on the observed sentiment polarity. In this study, tweets that were previously categorized with one of the sentiment polarities were used to conduct predictive analytics of the new tweet to determine its sentiment polarity. The ambiguity of the tweets corpus utilized in the training phase is a critical limitation of the sentiment categorization procedure. While several recent models proposed sentiment classification algorithms, they confined themselves to two labels: positive and negative opinion, oblivious to the plague of ambiguity in the training corpus. In this regard, a novel multi-label classification of sentiment polarity called handling dimensionality of ambiguity using ensemble classification (HAD-EC) method, which diffuses ambiguity and thus minimizes false alerts, is proposed. The experimental assessment validates the HAD-EC approach by comparing the suggested model’s performance to other two existing models.

2

First-arrival picking through fuzzy c-means and robust locally weighted regression

Gao Lei, Liu Dang, Luo Guan Feng, Song Guo Jie, Min Fan

Acta Geophysica

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2021

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Vol. 69, no. 5

1623--1636

EN

First-arrival picking is a crucial step in seismic data processing. Because of the diverse background noises and irregular near-surface conditions, it is difcult to pick frst arrivals. In addition, existing algorithms are usually sensitive to parameter settings. Therefore, this paper proposes the frst-arrival picking through fuzzy c-means and robust locally weighted regression (FPFR) algorithm consisting of two subroutines. The pre-picking subroutine obtains initial frst arrivals through fuzzy c-means clustering and adaptive cluster-selection techniques. The smoothing subroutine handles background noises and near-ground conditions through adaptive parameter regression technique. The experiment is conducted on six feld seismic datasets and one synthetic dataset. Results show that FPFR is more accurate than three state-of-the-art methods.

3

A hybrid approach for the delineation of brain lesion from CT images

Gautam A., Raman B., Raghuvanshi S.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

504--518

EN

Brain lesion segmentation from radiological images is the most important task in accurate diagnosis of patients. This paper presents a hybrid approach for the segmentation of brain lesion from computed tomography (CT) images based on the combination of fuzzy clustering using hyper tangent function as the robust kernel and distance regularized level set evolution (DRLSE) function as the edge based active contour method. Kernel based fuzzy clustering method divides the image into different regions. These regions can be used to find region of interest by using DRLSE algorithm to generate the optimal region boundary. The proposed method results in smooth boundary of the required regions with high accuracy of segmentation. In this paper, results are compared with standard fuzzy c-means (FCM) clustering, spatial FCM, robust kernel based fuzzy clustering (RFCM) and DRLSE algorithms. The performance of the proposed method is evaluated on CT scan images of hemorrhagic lesion, which shows that our method can segment brain lesion more accurately than the other conventional methods.

4

Fabric Defect Detection Using a Hybrid and Complementary Fractal Feature Vector and FCM-based Novelty Detector

Zhou J., Wang J., Bu H.

Fibres & Textiles in Eastern Europe

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2017

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Vol. 25, no. 6 (126)

46--52

EN

Automated detect detection in woven fabrics for quality control is still a challenging novelty detection problem. This work presents five novel fractal features based on the box-counting dimension to address the novelty detection of fabric defect. Making use of the formation of woven fabric, the fractal features are extracted in a one-dimension series obtained by projecting a fabric image along the warp and weft directions, where their complementarity in discriminating defects is taken into account. Furthermore a new novelty detector based on fuzzy c-means (FCM) is devised to deal with one-class classification of the features extracted. Finally, by jointly applying the features proposed and the FCM based novelty detector, we evaluate the method proposed for eight datasets with different defects and textures, where satisfying results are achieved with a low overall missing detection rate.

PL

Automatyczne wykrywanie defektów tkanin w celu kontroli ich jakości mimo wielu dotychczasowych badań nadal stanowi wyzwanie. Mając na celu opracowanie nowatorskiej metody wykrywaniem wad tkanin przedstawiono pięć cech fraktalnych. W celu klasyfikacji wyodrębnionych cech opracowano detektor wad tkanin oparty na zbiorze rozmytym wartości średnich (FCM). Poprzez wspólne zastosowanie proponowanych cech i opartego na FCM detektorze sprawdzono proponowaną metodę dla ośmiu zestawów danych z różnymi defektami i teksturami. Stwierdzono, że otrzymane wyniki są na satysfakcjonującym poziomie.

5

Fuzzy clustering based algorithm for determination of the two-phase gas-liquid flows similarity level

Fiderek P., Wajman R., Kucharski J.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 2

52--55

EN

In the article there is a description of the FCM based algorithm for the determination of the similarity level of the current investigated by the capacitance process tomography methods to the previously prepared pattern flows. Additionally readers can find a description of raw tomographic data collection method used, preparation of the most significant features vector routine and basic theoretical issues.

PL

Artykuł zawiera opis algorytmu opartego na klasyfikacji rozmytej mającego na celu wyznaczenie wartości podobieństwa badanych za pomocą metod pojemnościowej tomografii przemysłowej przepływów dwufazowych do wcześniej przygotowanych przepływów wzorcowych. W artykule można znaleźć opis wykorzystanych metod akwizycji danych, przygotowania wektora cech znaczących oraz podstawowych zagadnień teoretycznych.

6

Detection of Arrhythmia from ECG Signals by a Robust Approach to Outliers

Orhan U.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 7

81--85

EN

The study focuses on arrhythmia detection from ECG signals, and for this aim it uses Fuzzy C-means (FCM) and Single Neuron Perceptron (SNP). FCM clustering adapted to time-series transforms ECG signals into useful features, and then SNP classifies them. We use MITBIH Arrhythmia database. The database is utilized for two experiments in the study. In the first experiment, RR intervals trimmed from the database are prepared for training the model, and in the second one ECG segments are used for real time simulation. Obtained results are compared with some other studies. According to the results, the proposed approach is good at arrhythmia detection as well as at least the studies in the literature. Lastly we interpret the results and present some studies for the future.

PL

W artykule skoncentrowano się na detekcji arytmii na podstawie sygnału ECG przy wykorzystaniu pojedynczego perceptronu i algorytmu FCM. Do badań wykorzystano bazę danych MIT-BIH Arrhythmia. W artykule oceniono zastosowaną metodę, przedstawiono interpretację wyników i dalsze propozycje.

7

Badanie sieci społecznych z wykorzystaniem algorytmu grupowania rozmytego

Sosnowski Z. A., Rembowicz K.

Symulacja w Badaniach i Rozwoju

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2012

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Vol. 3, nr 3

169--174

PL

W pracy przedstawiono propozycje, uzyskane wyniki oraz wypływające z nich wnioski dotyczące zastosowania teorii zbiorów rozmytych do analizy sieci społecznych. Wyniki symulacji pokazują, że proponowane podejście wykorzystujące własności zbiorów rozmytych sprawdza się bardzo dobrze w analizie spójnych sieci społecznych z niedużą liczbą klastrów.

EN

The paper presents proposals, the obtained results and the resulting conclusions concerning the use of fuzzy set theory to the analysis of social networks. The simulation results show that the proposed approach using fuzzy property works very well in the analysis of social networks consistent with a small number of clusters.

8

Image segmentation based on fuzzy clustering with neighborhood information

Yang Y.

Optica Applicata

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2009

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Vol. 39, nr 1

135--147

EN

In this paper, an improved fuzzy c-means (IFCM) clustering algorithm for image segmentation is presented. The originality of this algorithm is based on the fact that the conventional FCM-based algorithm considers no spatial context information, which makes it sensitive to noise. The new algorithm is formulated by incorporating the spatial neighborhood information into the original FCM algorithm by a priori probability and initialized by a histogram based FCM algorithm. The probability in the algorithm that indicates the spatial influence of the neighboring pixels on the centre pixel plays a key role in this algorithm and can be automatically decided in the implementation of the algorithm by the fuzzy membership. To quantitatively evaluate and prove the performance of the proposed method, series of experiments and comparisons with many derivates of FCM algorithms are given in the paper. Experimental results show that the proposed method is effective and robust to noise. In this paper, an improved fuzzy c-means (IFCM) clustering algorithm for image segmentation is presented. The originality of this algorithm is based on the fact that the conventional FCM-based algorithm considers no spatial context information, which makes it sensitive to noise. The new algorithm is formulated by incorporating the spatial neighborhood information into the original FCM algorithm by a priori probability and initialized by a histogram based FCM algorithm. The probability in the algorithm that indicates the spatial influence of the neighboring pixels on the centre pixel plays a key role in this algorithm and can be automatically decided in the implementation of the algorithm by the fuzzy membership. To quantitatively evaluate and prove the performance of the proposed method, series of experiments and comparisons with many derivates of FCM algorithms are given in the paper. Experimental results show that the proposed method is effective and robust to noise.

9

Automatic Unsupervised Segmentation Methods for MRI Based on Modified Fuzzy C-Means

Xiao K., Ho S.H., Hassanien Aboul ella

Fundamenta Informaticae

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2008

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Vol. 87, nr 3-4

465-481

10

RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets

Maji P., Pal S.K.

Fundamenta Informaticae

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2007

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Vol. 80, nr 4

475-496

EN

A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed in this paper. It comprises a judicious integration of the principles of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. The concept of crisp lower bound and fuzzy boundary of a class, introduced in rough-fuzzy c-means, enables efficient selection of cluster prototypes. Several quantitative indices are introduced based on rough sets for evaluating the performance of the proposed c-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated on a set of real life data sets.

11

Fuzzy c-means clustering algorithm with a novel penalty term for image segmentation

Yang Y., Zheng C., Lin P.

Opto-Electronics Review

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2005

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Vol. 13, No. 4

309-315

EN

Fuzzy clustering techniques, especially fuzzy c-means (FCM) clustering algorithm, have been widely used in automated image segmentation. However, as the conventional FCM algorithm does not incorporate any information about spatial context, it is sensitive to noise. To overcome this drawback of FCM algorithm, a novel penalized fuzzy c-means (PFCM) algorithm for image segmentation is presented in this paper. The algorithm is formulated by incorporating the spatial neighbourhood information into the original FCM algorithm with a penalty term. The penalty term acts as a regularizer in this algorithm, which is inspired by the neighbourhood expectation maximization (NEM) algorithm and is modified in order to satisfy the criterion of the FCM algorithm. Experimental results on synthetic, simulated and real images indicate that the proposed algorithm is effective and more robust to noise and other artifacts than the standard FCM algorithm.

12

Fuzzy clustering with spatial constraints for image thresholding

Yang Y., Zheng C., Lin P.

Optica Applicata

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2005

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Vol. 35, nr 4

943-954

EN

Image thresholding plays an important role in image segmentation. This paper presents a novel fuzzy clustering based image thresholding technique, which incorporates the spatial neighborhood information into the standard fuzzy c-means (FCM) clustering algorithm. The prior spatial constraint, which is defined as weight in this paper, is inspired by the k-nearest neighbor (k-NN) algorithm and is modified from two aspects in order to improve the performance of image thresholding. The algorithm is initialized by a fast FCM algorithm, in which the iteration is carried out with the statistical gray level histogram of image instead of the conventional whole data of image; therefore its convergence is fast. Extensive experiment results and both qualitative and quantitative comparative studies with several existing methods on the thresholding of some synthetic and real images illustrate the effectiveness and robustness of the proposed algorithm.

13

A Novel Statistical Approach for Segmentation of Single-Channel Brain MRI Using an Improved EM algorithm

Yang Y., Zheng Ch., Lin P.

Journal of Applied Computer Science

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2005

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Vol. 13, nr 1

113-125

EN

This paper presents a novel statistical method for segmentation of single-channel brain magnetic resonance (MR) image data. The method based on an improved expectation maximization (EM) algorithm proposed in this paper involves three steps. Firstly, after pre-processing the image with the curvature anisotropic diffusion filter, the background (BG) and brain masks of the image are obtained by applying a combination approach of thresholding with morphology. Secondly, the connected threshold region growing technique is employed to get the preliminary results of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) on a brain MRI. Finally, the previous results are served as the priori knowledge for the improved EM algorithm to segment the brain MRI. The performance of the proposed method is compared with that of the popular used fuzzy-C means (FCM) segmentation. Experimental results show our approach is effective, robust and significantly faster than the conventional EM based method.

14

Exploiting color and topological features for region segmentation with recursive fuzzy C-means

Cucchiara R., Grana C., Seidenari S., Pellacani G.

Machine Graphics and Vision

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2002

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Vol. 11, No. 2/3

169-182

EN

In this paper we define a novel approach to images segmentation into regions which focuses on both visual and topologocal cues, namely color similarity, inclusion and spatial adjacency. Many color clustering algorithms have been proposed in the past for skin lesion images but mone exploits explicity the inclusion properties between regions. Our algorithm is based on a recursive version of fuzzy c-means (FCM) clustering algorithm in the 2D color histogram constructed by Principal Component Analysis (PCA) of the color space. The distinctive feature of the proposal is that recursion is guided by evaluation of adjacency and mutual inclusion properties of extracted regions; then, the recursive analysis addresses only included or regions with a non-negligible size. This approach allows a coarse-to-fine segmentation which focuses attention on the inner parts of the images, in order to highlight the internal structure of the object depiced in the image. This could be particulary useful in many applications, especially in biomedical image analysis. Inthis work we apply the technique to segmentation of skin lesions in dermatoscopic images. It could be a suitable support for diagnosis of skin melanoma, since dermatologists are interrested in analysis of spatial relations, symmetrical positions and inlusion of regions.

15

An varepsilon-Insensitive Approach to Fuzzy Clustering

Łęski J.

International Journal of Applied Mathematics and Computer Science

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2001

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Vol. 11, no 4

993-1007

EN

Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new varepsilon-insensitive Fuzzy C-Means (varepsilonFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.