Frequent itemset-based feature selection and Rider Moth Search Algorithm for document clustering

Yarlagadda, Madhulika; Rao, K. Gangadhara; Srikrishna, A.

doi:10.1016/j.jksuci.2019.09.002

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…For optimal server selection process, in this paper rider optimization algorithm [21] is utilized (ROA). The rider optimization algorithm has several applications such as document clustering, enhanced video super-resolution, webpage re-raking and resource allocation [22,23]. The proposed methodology can be implemented for NPTEL and also proposed methodology diagram is exposed in Fig.…”

Section: Rider Optimization Algorithm Based Cloud Server Selectionmentioning

confidence: 99%

Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

Pandian¹,

Columbus²

2023

Computer Systems Science and Engineering

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Currently, e-learning is one of the most prevalent educational methods because of its need in today's world. Virtual classrooms and web-based learning are becoming the new method of teaching remotely. The students experience a lack of access to resources commonly the educational material. In remote locations, educational institutions face significant challenges in accessing various web-based materials due to bandwidth and network infrastructure limitations. The objective of this study is to demonstrate an optimization and queueing technique for allocating optimal servers and slots for users to access cloud-based elearning applications. The proposed method provides the optimization and queueing algorithm for multi-server and multi-city constraints and considers where to locate the best servers. For optimal server selection, the Rider Optimization Algorithm (ROA) is utilized. A performance analysis based on time, memory and delay was carried out for the proposed methodology in comparison with the existing techniques. The proposed Rider Optimization Algorithm is compared to Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Firefly Algorithm (FFA), the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user requests. Thus the proposed method outweighs the conventional techniques by its enhanced performance over them.

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Section: Rider Optimization Algorithm Based Cloud Server Selectionmentioning

confidence: 99%

Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

Pandian¹,

Columbus²

2023

Computer Systems Science and Engineering

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“…The speed of information retrieval (IR) can greatly be enhanced through the use of a good document clustering approach. A Modsup-based term frequency and Rider Optimization-based Moth Search Algorithm (Rn-MSA) [10] is presented. It serves as a good document clustering approach that enhances the efficiency of corpus navigation.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to News Archiving from Newswires

Muhammad-Bello

Lukman

Salim

2021

Communications in Computer and Information Science

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A news archive is the core operational tool a media relations team depends on in order to effectively feed a data-hungry organization. An ingrained approach to news archiving in existence is the use of a relational database. As a consequence, integrating search engines that support full-text search is practically impossible due to the strict data schema that is defined in relational database systems. Therefore, there is a need for news archives that support full-text search with relevance ranking of news. In this paper, an approach that supports full-text search is proposed. The process is started by crawling newswire websites for news that are relevant with respect to some predefined keywords and extracting them. Then, they are stored in a data structure known as an inverted-index which supports full-text search, aggregation, and relevance ranking of search results. Search results are ranked and returned to a user in the order of decreasing relevance to the search term. We were able to provide a software solution written in java, the jsoup library for HTML parsing, and an elasticsearch implementation of a search engine. We tested our solution on nine newswires using ten keywords and were able to retrieve a total of 42 relevant news matching seven keywords. The approach proposed in this paper when compared to the manual approach performed better in terms of retrieval speed and accuracy. We conclude that three main components are important in a good digital archive: relevance, extraction, and search. This work is an integration of a good relevance marking technique, an extraction method, and a search engine.

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“…Grey wolf optimization algorithm depends on how the wolf pack behaves (the hierarchy and hunting) [35], [36]. The behavior of genuine moths in looking for light sources is mimicked by moth flame optimization algorithms [37]- [40].…”

Section: Introductionmentioning

confidence: 99%

Cuckoo algorithm with great deluge local-search for feature selection problems

Alsmadi

Alzaqebah

Jawarneh

et al. 2022

IJECE

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<p class="Abstract"><span lang="EN-US">Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.</span></p>

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Frequent itemset-based feature selection and Rider Moth Search Algorithm for document clustering

Cited by 13 publications

References 10 publications

Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

A Novel Approach to News Archiving from Newswires

Cuckoo algorithm with great deluge local-search for feature selection problems

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