Brain tumor is the irregular growth of cells in the brain that can develop into malignant or benign tumors. However, the prediction of brain tumors is the most difficult task in the medical field due to the anatomy of...
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Brain tumor is the irregular growth of cells in the brain that can develop into malignant or benign tumors. However, the prediction of brain tumors is the most difficult task in the medical field due to the anatomy of the tumor cells. In recent years, advances in deep learning access to medical diagnostic imaging have led to greater accuracy in short time segmenting brain tumors. In this work, a novel approach based on Segmentation-based kernelfuzzyc-Mean (SKFcM) with Penguin Search Optimization algorithm (PeSOA) with an Adaptive Dense Neural Network (ADNN) classifier was implemented. The MRI images are pre-processed using Bright-contrast Dynamic Histogram Equalization (BcDHE) with a weighted median filter and the multi features are extracted with the Modular linear discriminant analysis (MLDA). The Adaptive Dense Neural Network (ADNN) using a unique SKFcM with a machine learning-based Penguin Search Optimization algorithm was used to segment brain tumors (PeSOA). The effectiveness of the proposed method was estimated based on specificity, accuracy, sensitivity, and tumor area length in vertical and horizontal locations. The proposed approach progresses the overall accuracy of 1.11%, 4.44%, and 6.18% better than cNN, ANN-fuzzyc-means, and R-cNN, respectively.
An explicit mapping is generally unknown for kernel data analysis but their inner product should be known. Though kernel fuzzy c-means algorithm for data with tolerance has been proposed by the authors, the cluster ce...
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ISBN:
(纸本)9781424469208
An explicit mapping is generally unknown for kernel data analysis but their inner product should be known. Though kernel fuzzy c-means algorithm for data with tolerance has been proposed by the authors, the cluster centers and the tolerance in higher dimensional space have been unseen. contrary to this common assumption, an explicit mapping has been introduced by one of the authors and the situation of kernelfuzzyc-means in higher dimensional space has been described via kernel principal component analysis using the explicit mapping. In this paper, the cluster centers and the tolerance of kernelfuzzyc-means for data with tolerance are described via kernel principal component analysis using the explicit mapping.
Accurately understanding driving behaviour is of crucial importance for advanced driving assistant systems such as adaptive cruise control system and intelligent forward collision warning system. To understand differe...
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Accurately understanding driving behaviour is of crucial importance for advanced driving assistant systems such as adaptive cruise control system and intelligent forward collision warning system. To understand different driving styles, this study employs the clustering method and topic model to extract latent driving states, which can elaborate and analyse the commonness and individuality of driving behaviour characteristics with the longitudinal driving behaviour data collected by the instrumented vehicle. To handle the large set of data and discover the valuable knowledge, the data mining techniques including ensemble clustering method based on the kernel fuzzy c-means algorithm and the modified latent Dirichlet allocation model are employed in this study. The aggressive', cautious' and moderate' driving states are discovered and the underlying quantified structure is built for the driving style analysis.
To identify the difference between dynamic and static simulator modes, a novel data analyzing method was presented in this paper using flight data sampled from manual flight task. The proposed method combined diffusio...
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To identify the difference between dynamic and static simulator modes, a novel data analyzing method was presented in this paper using flight data sampled from manual flight task. The proposed method combined diffusion maps and kernel fuzzy c-means algorithm (KFcM) to identify types of flight data. Hybrid bacterial foraging (BF) and particle swarm optimization (PSO) algorithm (BF-PSO) was also introduced to optimize unknown parameters of the KFcM. This algorithm increased the possibility to find the optimal values avoided being trapped in local minima. The clustering accuracy of the proposed method applied in flight dataset demonstrated this method had the ability to recognize the types of flight state. The results of the paper indicated that the pilots movement sensing influenced pilot performance under the manual departure task. (c) 2016 Elsevier GmbH. All rights reserved.
Moving object tracking is an effective optimization procedure based on the impermanent relevant information associated with the original frames. Suggesting a method with efficient accuracy in convoluted atmospheres is...
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Moving object tracking is an effective optimization procedure based on the impermanent relevant information associated with the original frames. Suggesting a method with efficient accuracy in convoluted atmospheres is a difficulty for scientists in the area of research study. In this research, powerful object detection and movement tracking videos are proposed. Here, we are considering the input video sequence PETS and Hall monitor videos. Initially, the background and foreground separations are done by modified kernel fuzzy c-means algorithm. The object detection and tracking are done by gravitational search algorithm-based deep belief neural network. The implementation will be in MATLAB. The effectiveness of the recommended strategy is assessed with means of precision, recall, F-measure, FPR, FNR, PWc, FAR, similarity, specificity, and accuracy. From the experimental results, the proposed work outperforms the state of artwork. Here, the proposed method attains maximum precision and recall value for both PETS and Hall monitor video when compared to the existing algorithm.
Purpose - The purpose of this paper is to apply the Takagi-Sugeno (T-S) fuzzy model techniques in order to treat and classify textual data sets with and without noise. A comparative study is done in order to select th...
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Purpose - The purpose of this paper is to apply the Takagi-Sugeno (T-S) fuzzy model techniques in order to treat and classify textual data sets with and without noise. A comparative study is done in order to select the most accurate T-S algorithm in the textual data sets. Design/methodology/approach - From a survey about what has been termed the "Tunisian Revolution," the authors collect a textual data set from a questionnaire targeted at students. Five clustering algorithms are mainly applied: the Gath-Geva (G-G) algorithm, the modified G-G algorithm, the fuzzyc-meansalgorithm and the kernel fuzzy c-means algorithm. The authors examine the performances of the four clustering algorithms and select the most reliable one to cluster textual data. Findings - The proposed methodology was to cluster textual data based on the T-S fuzzy model. On one hand, the results obtained using the T-S models are in the form of numerical relationships between selected keywords and the rest of words constituting a text. consequently, it allows the authors to interpret these results not only qualitatively but also quantitatively. On the other hand, the proposed method is applied for clustering text taking into account the noise. Originality/value - The originality comes from the fact that the authors validate some economical results based on textual data, even if they have not been written by experts in the linguistic fields. In addition, the results obtained in this study are easy and simple to interpret by the analysts.
clustering accuracy of the kernelfuzzyc-means (KFcM) algorithm is affected by its equal partition trend for data sets. A Neighbor Sample Membership Weighted KFcM (NSM-WKFcM) algorithm is achieved by introducing the ...
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ISBN:
(纸本)9781467316842
clustering accuracy of the kernelfuzzyc-means (KFcM) algorithm is affected by its equal partition trend for data sets. A Neighbor Sample Membership Weighted KFcM (NSM-WKFcM) algorithm is achieved by introducing the weighted information of the neighbor sample membership into the standard KFcM algorithm in this paper. A set of Beijing-l micro-satellite's multispectral images is adopted to be classified by the KFcM and NSM-WKFcM algorithms. Experimental results indicate that the NSM-WKFcM algorithm significantly improve the unsupervised classification ability of remote sensing images compared with the KFcM algorithm.
The premise and basis of load modeling are substation load composition inquiries and cluster ***,the traditional kernelfuzzyc-means(KFcM)algorithm is limited by artificial clustering number selection and its converg...
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The premise and basis of load modeling are substation load composition inquiries and cluster ***,the traditional kernelfuzzyc-means(KFcM)algorithm is limited by artificial clustering number selection and its convergence to local optimal *** overcome these limitations,an improved KFcM algorithm with adaptive optimal clustering number selection is proposed in this *** algorithm optimizes the KFcM algorithm by combining the powerful global search ability of geneticalgorithm and the robust local search ability of simulated annealing *** improved KFcM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index *** with the traditional KFcM algorithm,the enhanced KFcM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.
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