The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Paper Currency

Guangul, Solomon Wondaya

doi:10.11591/ijeecs.v12.i3.pp1106-1110

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Consequently, when the clusters sizes are not similar, the situation will lead to an imbalanced in the energy consumption among the nodes, which will result in a reduction in the lifespan of the network. Although there are many studies that have investigated on which of these algorithms is more superior for clustering process in other fields [6,7,[9][10][11][12][13]. However, based on our knowledge, there is none that investigate which algorithm has a relatively better performance in terms forming a balanced size of clusters with the random distribution manner for nodes in the monitoring area.…”

Section: Introductionmentioning

confidence: 99%

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Hassan¹,

Shah²,

Othman³

et al. 2020

IJECE

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The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.

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Section: Introductionmentioning

confidence: 99%

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Hassan¹,

Shah²,

Othman³

et al. 2020

IJECE

View full text Add to dashboard Cite

show abstract

“…Through the utilization of suitable clustering algorithm, the formation of clusters with objects that have the same features into the same cluster as opposed to objects in differing clusters is enabled. This means, clustering entails the allocation of objects possessing certain similarities into the same cluster according to their characteristics [6][7][8]. One of the most important challenges faced by the clustering approach in WSN is how to improve the cluster structure and construct a balanced size of clusters.…”

Section: Introductionmentioning

confidence: 99%

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

et al. 2020

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The clustering approach is considered as a vital method for many fields such as machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others. Similarly, it has great significance in wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor's battery, that is considered as a major challenge in the WSNs. Fuzzy c-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network. To overcome this problem, a new clustering method called FCM-CM has been proposed by improving the FCM algorithm to form balanced clusters for random nodes deployment. The improvement is conducted by integrating the FCM with a centralized mechanism (CM). The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clusters in addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan.

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The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Paper Currency

Cited by 2 publications

References 5 publications

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

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