Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

Zhang, Wenyuan; Guo, Xijuan; Huang, Tianyu; Liu, Jiale

doi:10.3390/sym11060753

Cited by 12 publications

(3 citation statements)

References 78 publications

(92 reference statements)

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“…The most basic to the most advanced feature extraction algorithms for diagnosing cervical cancer from Pap smear images were presented in the recent research. Some of the features have been extracted for the identification of lesion image of skin is Geometrical features such as Asymmetry, Diameter, Concavity, Area, perimeter, eccentricity and other features such as Shape, Size, Texture identification [24] GLCM and Haralick Features.…”

Section: Feature Extractionmentioning

confidence: 99%

A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification

Haridas

Jayamalar

2023

JMC

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One of the most common and curable types of cancer in women is cervical cancer, a common chronic condition. Pap smear images is a common way for screening the cervical cancer. It does not present with symptoms until the disease has advanced stages, cervical cancer cannot be detected in its early stages. Because of this, accurate staging will make it easier to give the patient the right amount of treatment. In this paper proposes the Anisotropic Diffusion Filter has been used to improve the Pap smear image by removing noise and preserving the image's edges. The contrast of a Pap smear image has been enhanced using Histogram Equalization. The enhanced image has been segmented using Improved Weighted Fuzzy C-means clustering to make it easier to identify the effective features. As a result, the effective features are extracted from the segmented region and used by a Restricted Boltzmann Machine classifier based on Deep Learning to classify the cancer. The performance of the proposed cervical cancer detection system can be measured in terms of sensitivity, specificity, F-measure and accuracy. The performance measures for the proposed system can be achieves 95.3% accuracy, 88.6% specificity, 89.13% precision, 88.56% recall, and 89.7% F-measure respectively. Based on simulation results, the proposed method performs better than conventional methods such as RDVLNN, Random Forest (RF), Extreme Learning Machine (ELM), and Support Vector Machine (SVM) for detecting cervical cancer.

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Section: Feature Extractionmentioning

confidence: 99%

A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification

Haridas

Jayamalar

2023

JMC

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“…Recommending hotel accommodation should firstly confirm the best matched tourist attractions for the tourists' interests, which is the precondition for the tourism activities. Construct the attribute vector based on the tourist attraction cells C T(i) in the urban cellular space (CS), which is used to build the CS-IDIANA clustering algorithm to cluster the tourist attractions in the CS [19][20][21].…”

Section: Modeling Of the Cs-idiana Clusteringmentioning

confidence: 99%

“…cellular space (CS), which is used to build the CS-IDIANA clustering algorithm to cluster the tourist attractions in the CS[19][20][21]. The feature attribute…”

mentioning

confidence: 99%

Tourism Hotel Accommodation Recommendation Algorithm Based on the Cellular Space-Improved Divisive Analysis (CS-IDIANA) Clustering Model

Zhou,

Peng,

Wen

et al. 2023

Electronics

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On the basis of analyzing the problems concerning hotel accommodation recommendation (HAR), this paper constructs a tourism HAR algorithm based on the CS-IDIANA clustering model (cellular space-improved divisive analysis). The algorithm integrates the cellular space model with DIANA, and takes the tourist attractions and the travel route costs as the research background and constraint conditions. Considering the feature attributes and spatial attributes of the tourist attractions, the tourist attraction recommendation algorithm based on the CS-IDIANA clustering model is established, then the HAR algorithm based on the spatial accessibility and route cost is constructed, with the constraints of the spatial accessibility field strength (SAFS) between the hotels and attractions and the travel route costs between the hotels and attractions. The experiment selects the tourism city Zhengzhou as the research object, and the experimental results are analyzed in four dimensions: the clustering results, the recommendation field strength of the tourist attractions, the hotel SAFS and the HAR results. The experiment proves that the proposed algorithm can find the best matched tourist attractions for tourists and the hotels with the lowest tour route cost based on the constraint conditions. Compared to the suboptimal hotels, the route costs are reduced by 5.67% and 9.63%, respectively. Compared to the hotel with the highest route cost, it reduces the travel costs by 29.23%. Compared with the two commonly used recommendation methods, the UCFR (user-based collaborative filtering recommendation) and ICFR (item-based collaborative filtering recommendation), the proposed CSIDR (CS-IDIANA recommendation) has a higher accuracy and recall rate.

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Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation

Wang

Chao

et al. 2020

Applied Soft Computing

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Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

Cited by 12 publications

References 78 publications

A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification

A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification

Tourism Hotel Accommodation Recommendation Algorithm Based on the Cellular Space-Improved Divisive Analysis (CS-IDIANA) Clustering Model

Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation

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