Long‐Term Selection in Plants in the Developing World
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Abstract In this paper, we propose two term selection methods for classifying nursing-care texts. In a term selection method based on GA, two objectives which are maximizing correctly classified texts and minimizing selected terms are optimized. The weighted sum of these two objectives was used as the evaluation function. Therefore, GA-based term selection is performed aiming at the improvement in classification performance on testing sets. In a NSGA-II based term selection method, non-dominated solutions are found. As the result, we can have a set of pareto-optimal solutions. These solutions are helpful to analyze classification results from the viewpoint of terms. From experimental results, we show effectiveness of our proposed term selection methods. Keywords: Nursing-care textsTerm selectionSupport vector machineGenetic algorithm
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Model Selection is a task selecting set of potential models.This method is capable of establishing hidden semantic relations among the observed features, using a number of latent variables.In this paper, the selection of the correct number of latent variables is critical.In the most of the previous researches, the number of latent topics was selected based on the number of invoked classes.This paper presents a method, based on backward elimination approach, which is capable of unsupervised order selection in PLSA.During the elimination process, proper selection of some latent variables which must be deleted is the most essential problem, and its relation to the final performance of the pruned model is straightforward.To treat this problem, we introduce a new combined pruning method which selects the best options for removal, has been used.The obtained results show that this algorithm leads to an optimized number of latent variables.In this paper, we propose a novel approach, namely DPMFS, to address this issue.
Document Clustering
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At present majority of research is on cluster analysis which is based on information retrieval from data that portrays the objects and their association among them.When there is a talk on good cluster formation, then selection of an optimal cluster core or center is the necessary criteria.This is because an inefficient center may result in unpredicted outcomes.Hence, a sincere attempt had been made to offer few suggestions for discovering the near optimal cluster centers.We have looked at few versatile approaches of data clustering like K-Means, TLBOC, FEKM, FECA and MCKM which differs in their initial center selection procedure.They have been implemented on diverse data sets and their inter and intra cluster formation efficiency were tested using different validity indices.The clustering accuracy was also conducted using Rand index criteria.All the algorithms computational complexity was analyzed and finally their computation time was also recorded.As expected, mostly FECA and to some extend FEKM and MCKM confers better clustering results as compared to K-Means and TLBOC as the former ones manages to obtain near optimal cluster centers.More specifically, the accuracy percentage of FECA is higher than the other techniques however, it's computational complexity and running time is moderately higher.
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To improve the effectiveness and QoS of service in a sensor network there are number of communication and localization architectures followed by sensor network.One of such architecture is Leader Selection Architecture.This architecture restrict the communication to short distances so that the energy consumption of a node is reduces.In this paper, an improved approach is defined to perform the selection of Leader.This leader selection architecture approach is defined under multiple parameters including the energy, connectivity analysis and the balancing over the network.The improvement is also performed to generate the safe communication over the network controlled by the leader node.The obtained results show that the work has improved the network communication as well as network life.
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Text Categorization
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Term selection is an important work of examining and approving the scientific and technological terms. Generally, term selecting should be based on the disciplinary framework, and the selected information should be scientific, representative and authoritative. In order to keep the term system balance, systematic and comprehensive, term selection should be divided into groups and levels, and basic terms and new terms should be paid enough attention. Moreover, the term system should be re-arranged and re-organized after new term inclusion, and duplicate terms should be processed.
Scientific literature
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