Modified Fuzzy C-Means Clustering Research Articles

Automated lung cancer diagnosis using swarm intelligence with deep learning

ABSTRACT In worldwide, lung cancer is amajor threatening issue for humans that increase the mortality rate. Here, the existing techniques are suffered from huge false-positiverates. Such kind of limitations encourages the researchers to designa novel automated lung cancer identification model for providing highly accurate outcome in the early phase. Thus, the major goal ofthis research work is to design and initiate the lung cancer identification framework using an enhanced deep learning model. The standard dataset attained for the analysis is offered to pre-processing part. Then, the Frangi filter is utilized for removingthe vessel from the image. The adoption of modified Fuzzy C-Means Clustering (FCM) with local thresholding is employed for the nodule segmentation, where parameter tuning is executed by Modified Dimension Range-based Cat Swarm Optimization (MDR-CSO). The resultant texture and shape features are subjected to the Enhanced Recurrent Neural Network (ERNN) for diagnosing lung cancer. Throughout theresult analysis, the accuracy and MCC rate of the developed model is95% and 91%. Thus, the result analysis of the offered method providesa better lung cancer detection rate than the classical techniquesthroughout the experimental analysis.

A Point Cloud Simplification Method Based on Modified Fuzzy C-Means Clustering Algorithm with Feature Information Reserved

To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed. Firstly, the normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. Thirdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm. Finally, the point cloud is simplified according to point cloud feature information and simplified parameters. The point cloud test data are simplified using the new algorithm and traditional algorithms; then, the results are compared and discussed. The results show that the new proposed algorithm can not only effectively improve the precision of point cloud simplification but also reserve the accuracy of part features.

Binarization of optical fringe patterns with intensity inhomogeneities based on modified FCM algorithm

Binarization of fringe patterns is a commonly used process in the fringe skeleton method for optical fringe pattern analysis. Most of the existing binarization algorithms of fringe patterns are based on the determination of gray level threshold. However, spatial intensity inhomogeneity induced by the uneven illumination makes the gray level threshold hard to determine. In this paper, we present a method for the binarization of fringe patterns based on a modified Fuzzy c-means clustering (FCM) algorithm. This method permits the clustering of pixels influenced by their immediate surroundings. The pixels in the fringe pattern are clustered into white fringes and black fringes without calculating thresholds. We evaluate the performance of this method by applying it to computer-simulated and real fringe patterns. Experimental results demonstrate that the proposed method can achieve the desired binarization results and perform better than the previous binarization methods, under non-uniform illumination conditions.

Deep Convolutional Neural Network with Modified Fuzzy C-Means Clustering Based Mobile Applications Offloading to Clouds

Deep Convolutional Neural Network with Modified Fuzzy C-Means Clustering Based Mobile Applications Offloading to Clouds

XML web quality analysis by employing MFCM clustering Technique and KNN classification

The great accomplishment of web search engine is keyword search which is the most trendy search representation for regular consumers. It is permits that the consumer can create the queries without the knowledge of query language and the database schema. So, it is also considered as a user friendly method. The quality of XML web has to be accurate if the exact queries have to be answered. Here we have proposed a method to access the quality of the XML web by analysing the keyword present in the XML web based on the respective keyword search. In our proposed method we collect number of XML documents and are clustered based on the keyword depending on the type of XML files. Modified fuzzy C means (MFCM) is used for clustering. Once the clustering based on the respective keyword is done, we classify the XML web based on quality of the data by utilising KNN classifier.

A Novel Content Based Medical Image Retrieval Technique with Aid of Modified Fuzzy C-Means Clustering (CBMIR-MFCM)

A Novel Content Based Medical Image Retrieval Technique with Aid of Modified Fuzzy C-Means Clustering (CBMIR-MFCM)

Automatic Fuzzy Rule Base Generation from Numerical Data: Cuckoo Search Algorithm

Classifiers are an powerful class of fuzzy systems. Evolving fuzzy classifiers from numerical data has assumed lot of remarks in the recent past. This paper proposes a method of evolving fuzzy classifiers using a three step technique. In the first step, a modified Fuzzy C-Means Clustering technique is applied to generate membership functions. In the next step, rule base are generated using cuckoo search algorithm. The third step was used to reduce the size of the generated rule base. By this method rule explosion issue was successfully tackled. The proposed method was applied using MATLAB. The approach was tested on a very well known multi dimensional classification data sets i.e. Iris Data. The performance of the proposed method was very encouraging. Further the algorithm is implemented on a Mamdani type control model for a battery charger data set. This integrated approach was able to evolve model quickly.

Road Sign Segmentation and Recognition under Bad Illumination Condition using Modified Fuzzy C-means Clustering

In this paper, we present a novel approach on road sign segmentation under bad lighting condition employing a modified fuzzy c-means clustering. Most of the cases accidents occur in bad weather or when the road signs cannot be recognized. The proposed system implements a system that segments the road sign using fuzzy c-means clustering. At first, the image is detected in RGB colour space and then converted into HSV colour model. After the conversion we detected the edge by applying Canny edge detector. Thus the proposed method is applied on the image. The method is based on the local similarity measure so that the noise created or the blurred image can easily be detected. But the traditional Fuzzy C-means clustering lack enough robustness to noise and preserving details of the image. Experimental results demonstrate that the system can segment the road signs successfully under bad illumination conditions. This system can be applied to typical type of traffic signs such as triangular, circle etc. The output result of the system is encouraging as the accuracy rate is 99%.

Automated Colorization of Grayscale Images Using Texture Descriptors and a Modified Fuzzy C-Means Clustering

A novel example-based process for Automated Colorization of grayscale images using Texture Descriptors (ACTD) without any human intervention is proposed. By analyzing a set of sample color images, coherent regions of homogeneous textures are extracted. A multi-channel filtering technique is used for texture-based image segmentation, combined with a modified Fuzzy C-means (FCM) clustering algorithm. This modified FCM clustering algorithm includes both the local spatial information from neighboring pixels, and the spatial Euclidian distance to the cluster’s center of gravity. For each area of interest, state-of-the-art texture descriptors are then computed and stored, along with corresponding color information. These texture descriptors and the color information are used for colorization of a grayscale image with similar textures. Given a grayscale image to be colorized, the segmentation and feature extraction processes are repeated. The texture descriptors are used to perform Content-Based Image Retrieval (CBIR). The colorization process is performed by Chroma replacement. This research finds numerous applications, ranging from classic film restoration and enhancement, to adding valuable information into medical and satellite imaging. Also, this can be used to enhance the detection of objects from x-ray images at the airports.

A Modified Fuzzy C-Means Clustering with Spatial Information for Image Segmentation

A traditional approach to segmentation of magnetic resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. However, the conventionally standard FCM algorithm is sensitive to noise. To overcome the above problem, a modified FCM algorithm (called MS-FCM later) for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster. In The proposed algorithm every point of the data set has a weight in relation to every cluster. Therefore this weight permits to have a better classification especially in the case of noise data. The proposed algorithm is applied to both artificial synthesized image and real image. Segmentation results demonstrate that the presented algorithm performs more robust to noise than the standard FCM algorithm.