Sensor networks are nowadays a popular wireless technology due their role in different applications. It becomes the fabric of our daily life, as these networks have the potential to enhance human life. In WSN major ap...
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ISBN:
(纸本)9781450353069
Sensor networks are nowadays a popular wireless technology due their role in different applications. It becomes the fabric of our daily life, as these networks have the potential to enhance human life. In WSN major applications, sensor nodes have limited power resource and typically powered by small and limited batteries whish replacement is very difficult and expensive in hostile environment. To prolong the sensor network's lifetime, many routing protocols have been proposed to achieve the energy efficiency in homogenous and heterogeneous WSN networks. In this paper work, we proposed a new hybrid routing protocol based on fuzzyc-means and DEEc protocol to form clusters and manage the transmission of data to the base station. The simulation results demonstrate the performance and prove the effectiveness of the proposed protocol. The energy consumption is minimized and the network lifetime of the sensor nodes is extended.
The effect of partial volume related to anatomical MRI and functional images limit the diagnostic potential of brain imaging. To remedy for this problem, we propose a fuzzy-genetic brain segmentation scheme for the as...
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ISBN:
(纸本)9781509055043
The effect of partial volume related to anatomical MRI and functional images limit the diagnostic potential of brain imaging. To remedy for this problem, we propose a fuzzy-genetic brain segmentation scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes, from brain images of Alzheimer patients from a real database. This clustering process based on Possibilisticc-means (PcM) algorithm, which allows modeling the degree of relationship between each voxels and a given tissue;and based on fuzzy genetic initialization for the centers of clusters by a fuzzyc-means (FcM) algorithm, and for which the result is optimized by genetic process. The visual results show a concordance between the ground truth segmentation and the hybrid algorithm results, which allows efficient tissue classification. The superiority was also proved with the quantitative results of the proposed method in comparison with the both conventional FcM and PcM algorithms.
Software engineering community often investigates the error concerning software development effort estimation as a part, and sometimes, as an improvement of an effort estimation technique. The aim of this paper is to ...
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ISBN:
(纸本)9789897582509
Software engineering community often investigates the error concerning software development effort estimation as a part, and sometimes, as an improvement of an effort estimation technique. The aim of this paper is to propose an approach dealing with both model and attributes measurement error sources whatever the effort estimation technique used. To do that, we explore the concepts of entropy and fuzzyclustering to propose a new framework to cope with both error sources. The proposed framework has been evaluated with the cOcOMO'81 dataset and the fuzzy Analogy effort estimation technique. The results are promising since the actual confidence interval percentages are closer to those proposed by the framework.
In this article, we have devised modified geneticalgorithm (MfGA) based fuzzy c-means algorithm, which segment magnetic resonance (MR) images. In FcM, local minimum point can be easily derived for not selecting the c...
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ISBN:
(纸本)9789811031533;9789811031526
In this article, we have devised modified geneticalgorithm (MfGA) based fuzzy c-means algorithm, which segment magnetic resonance (MR) images. In FcM, local minimum point can be easily derived for not selecting the centroids correctly. The proposed MfGA improves the population initialization and crossover parts of GA and generate the optimized class levels of the multilevel MR images. After that, the derived optimized class levels are applied as the initial input in FcM. An extensive performance comparison of the proposed method with the conventional FcM on two MR images establishes the superiority of the proposed approach.
In this paper, a fuzzyc-meansclustering algorithm is proposed to determine the optimum deployment of sensor nodes. It is for a given application space to improve energy efficiency and reduce cost. We performed simul...
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In this paper, a fuzzyc-meansclustering algorithm is proposed to determine the optimum deployment of sensor nodes. It is for a given application space to improve energy efficiency and reduce cost. We performed simulation for ‘L' shaped area to find minimum number and optimum location of sensor nodes.
fuzzyc-means (FcM) algorithm is a general method for clustering analysis. When there exitsts noise variables in the data, the error rate of the FcM algorithm relatively increases. Thus, how to choose the weight of th...
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fuzzyc-means (FcM) algorithm is a general method for clustering analysis. When there exitsts noise variables in the data, the error rate of the FcM algorithm relatively increases. Thus, how to choose the weight of the variable to reduce the error rate is an important issue. To solve this problem, this paper presents a new method of variable weight selection, called covariance Matrix (cM) method. The simulation results show that the proposed variable selection method can effectively reduce the error rate. Finally, the proposed cM method is applied to color image segmentation.
Using cruise observations and reanalysis data, this study analyzes the effects of wind, freshwater, and turbulent mixing on the two upwellings: one is off the eastern coast of Hainan Island (HEU) and the other is off ...
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Using cruise observations and reanalysis data, this study analyzes the effects of wind, freshwater, and turbulent mixing on the two upwellings: one is off the eastern coast of Hainan Island (HEU) and the other is off the northeastern coast of Hainan Island (HNEU). During the cruise in 2009, the HNEU occurred with southwesterly to southeasterly wind. The relative large values of turbulent kinetic energy dissipation rate and diffusivity estimated from the Thorpe scale indicate that the upwelling water is further uplifted to the surface by strong turbulent mixing in the HNEU region. But the HEU was not observed under the southeasterly wind. During the cruise in 2012, the HNEU disappeared in the upper layer with freshwater covered and southeasterly wind, while the apparent HEU only accompanied with southwesterly wind. To obtain the general characteristics, we define three types of upwelling patterns, i.e., the intensified HEU, the intensified HNEU, and both HEU and HNEU in one day, using the reanalysis data. The composites of sea surface temperature (SST), wind, and precipitate for each upwelling pattern identify that the HNEU is associated with the prevailing southeasterly wind and can be limited in the lower layer when it is covered by freshwater. But the HEU is mainly driven by southwesterly wind but is not remarkably affected by freshwater.
Suppressed fuzzyc-means (s-FcM) clustering was introduced in Fan et al. (Pattern Recogn Lett 24: 1607-1612, 2003) with the intention of combining the higher speed of hard c-means (HcM) clustering with the better clas...
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Suppressed fuzzyc-means (s-FcM) clustering was introduced in Fan et al. (Pattern Recogn Lett 24: 1607-1612, 2003) with the intention of combining the higher speed of hard c-means (HcM) clustering with the better classification properties of fuzzyc-means (FcM) algorithm. The authors modified the FcM iteration to create a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membership grew by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper, we clarify the view upon the optimality and the competitive behavior of s-FcM via analytical computations and numerical analysis. A quasi competitive learning rate (QLR) is introduced first, in order to quantify the effect of suppression. As the investigation of s-FcM's optimality did not provide a precise result, an alternative, optimally suppressed FcM (Os-FcM) algorithm is proposed as a hybridization of FcM and HcM. Both the suppressed and optimally suppressed FcM algorithms underwent the same analytical and numerical evaluations, their properties were analyzed using the QLR. We found the newly introduced Os-FcM algorithm quicker than s-FcM at any nontrivial suppression level. Os-FcM should also be favored because of its guaranteed optimality.
Wireless sensor networks are rapidly evolving technological platforms with tremendous applications in several domains. Since sensor nodes are battery powered and may be used in dangerous or inaccessible environments, ...
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Wireless sensor networks are rapidly evolving technological platforms with tremendous applications in several domains. Since sensor nodes are battery powered and may be used in dangerous or inaccessible environments, it is difficult to replace or recharge their power supplies. clustering is an effective approach to achieve energy efficiency in wireless sensor networks. In clustering-based routing protocols, cluster heads are selected among all sensor nodes within the network, and then clusters are formed by simply assigning each node to the nearest cluster head. The main drawback is that there is no control on the distribution of cluster heads over the network. In addition to the problem of generating unbalanced clusters, almost all routing protocols are designed for a certain application scope, and could not cover all applications. In this paper, we propose a swarm intelligence based fuzzy routing protocol (named SIF), in order to overcome the mentioned drawbacks. In SIF, fuzzyc-meansclustering algorithm is utilized to cluster all sensor nodes into balanced clusters, and then appropriate cluster heads are selected via Mamdani fuzzy inference system. This strategy not only guarantees to generate balanced clusters over the network, but also has the ability to determine the precise number of clusters. In fuzzy-based routing protocols in literature, the fuzzy rule base table is defined manually, which is not optimal for all applications. Since tuning the fuzzy rules very affects on the performance of the fuzzy system, we utilize a hybrid swarm intelligence algorithm based on firefly algorithm and simulated annealing to optimize the fuzzy rule base table of SIF. The fitness function can be defined according to the application specifications. Unlike other routing protocols which have been designed for a certain application scope, the main objective of our methodology is to prolong the network lifetime based on the application specifications. In other words, SIF not onl
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 fuzzy c-means algorithm 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.
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