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Articles by Naomie Salim
Total Records ( 5 ) for Naomie Salim
  Mohammad Syafrullah and Naomie Salim
  Term extraction is one of the layers in the ontology development process, which has the task to extract all the terms contained in the input document automatically. The objective of this process is to generate a list of terms that are relevant to the domain of the input document. In this study, we present a hybrid method for improving the precision term extraction using a combination of Continuous Particle Swarm Optimization (PSO) and Discrete Binary PSO (BPSO) to optimize fuzzy systems. In this method, PSO and BPSO are used to optimize the membership functions and rule sets of fuzzy systems, respectively. The method was applied to the domain of tourism documents and compared with other term extraction methods: TFIDF, Weirdness, GlossaryExtraction and TermExtractor. From the experiment conducted, the combination of PSO-BPSO showed their capability to generate an optimal set of parameters for the fuzzy membership functions and rule sets automatic adjustment. Findings also showed that fuzzy system performance after optimization achieved significant improvements compared with that before optimization and gave better results compared to those four algorithms.
  Akram Osman Yousaf Osman , Naomie Salim and Faisal Saeed
  Text web forum summarization assists users to manage the vast amount of available information online, by condensing thread contents and extracting the most relevant facts or topics. Accordingly, the text web forum has become a valuable source of knowledge. Forums contain testimonies from people who have had both positive and negative experiences. Regrettably, the text web forum suffers from unavoidable problems including irrelevance of posts, repeated text and non-quality information values. Furthermore, a user needs to read quality information from text web forums according to his/her needs. This study reviews the respective summarization of the text web forum and quality dimensions of the text web forum. And how quality can be improved the text web forum summarization. Therefore, the studies of summary evaluation methods are also conducted in this study. So, the evaluation can be later used to evaluate whether incorporating quality can improve text web forum summarization.
  Iskandar Ishak and Naomie Salim
  Problem statement: Efficient searching is a fundamental problem for unstructured peer to peer networks. Flooding requires a lot of resources in the network and thus will increase the search cost. Searching approach that utilizes minimum network resources is required to produce efficient searching in the robust and dynamic peer-to-peer network. Approach: This study addressed the need for efficient flood-based searching in unstructured peer-to-peer network by considering the content of query and only selecting peers that were most related to the query given. We used minimum information to perform efficient peer selection by utilizing the past queries data and the query message. We exploited the nearest-neighbor concept on our query similarity and query hits space metrics for selecting the most relevant peers for efficient searching. Results: As demonstrated by extensive simulations, our searching scheme achieved better retrieval and low messages consumption. Conclusion: This study suggested that, in an unstructured peer-to-peer network, flooding that was based on the selection of relevant peers, can improve searching efficiency.
  Mohammed Salem Binwahlan , Naomie Salim and Ladda Suanmali
  Problem statement: The aim of automatic text summarization systems is to select the most relevant information from an abundance of text sources. A daily rapid growth of data on the internet makes the achieve events of such aim a big challenge. Approach: In this study, we incorporated fuzzy logic with swarm intelligence; so that risks, uncertainty, ambiguity and imprecise values of choosing the features weights (scores) could be flexibly tolerated. The weights obtained from the swarm experiment were used to adjust the text features scores and then the features scores were used as inputs for the fuzzy inference system to produce the final sentence score. The sentences were ranked in descending order based on their scores and then the top n sentences were selected as final summary. Results: The experiments showed that the incorporation of fuzzy logic with swarm intelligence could play an important role in the selection process of the most important sentences to be included in the final summary. Also the results showed that the proposed method got a good performance outperforming the swarm model and the benchmark methods. Conclusion: Incorporating more than one technique for dealing with the sentence scoring proved to be an effective mechanism. The PSO was employed for producing the text features weights. The purpose of this process was to emphasize on dealing with the text features fairly based on their importance and to differentiate between more and less important features. The fuzzy inference system was employed to determine the final sentence score, on which the decision was made to include the sentence in the summary or not.
  Mohammad Syafrullah and Naomie Salim
  Problem statement: Term extraction is one of the layers in the ontology development process which has the task to extract all the terms contained in the input document automatically. The purpose of this process is to generate list of terms that are relevant to the domain of the input document. In the literature there are many approaches, techniques and algorithms used for term extraction where each of approaches, techniques and algorithms has the objective to improve the precision of the extracted terms. Approach: We proposed a new approach using particle swarm optimization techniques in order to improve the precision of term extraction results. We choose five features to represent the term score. Results: The approach had been applied to the domain of Islamic documents. We compare our term extraction method with TFIDF, Weirdness, GlossaryExtraction and TermExtractor. Conclusion: The experimental results showed that our proposed approach achieves better precision than those four algorithms.
 
 
 
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