Efficient text document clustering approach using multi-search Arithmetic Optimization Algorithm

Abualigah, Laith; Almotairi, Khaled H.; Al-qaness, Mohammed A. A.; Ewees, Ahmed A.; Yousri, Dalia; Elaziz, Mohamed Abd; Nadimi-Shahraki, Mohammad H.

doi:10.1016/j.knosys.2022.108833

Cited by 28 publications

(9 citation statements)

References 72 publications

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“…In this work, we utilize both algorithms obtaining the same results. In [38], an efficient text document clustering approach using a multi-search arithmetic optimization algorithm was introduced. From [47], we have the following definition.…”

Section: Methodsmentioning

confidence: 99%

“…We evaluate the effect of social media on academic performance when employing WhatsApp during academic activities. Although the use of WhatsApp was investigated in [16] for academic purposes, our study focusses on different domains and contexts, employing an efficient text document clustering approach by means of a multi-search arithmetic optimization algorithm instead of how it was utilized in [38].…”

Section: Objective Contribution and Description Of Sectionsmentioning

confidence: 99%

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Modern Multivariate Statistical Methods for Evaluating the Impact of WhatsApp on Academic Performance: Methodology and Case Study in India

Sharma¹,

Singh²,

Leiva

et al. 2022

Applied Sciences

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Despite the increasing amount of research on social media, there are few studies on the use of WhatsApp to assess academic performance. Surprisingly, students use social media during lectures, causing a problem. According to the literature, students utilize WhatsApp throughout academic activities where it is prohibited. Researchers and policymakers must pay attention to this problem to understand its impact on academic achievement. In this paper, by using multivariate statistical methods, we investigate the impact of WhatsApp use on academic performance. We construct a questionnaire for this investigation and apply it to a case study based on a sample of 258 students of management from India. We determine the prevalence of WhatsApp employment among these students and note that many of them utilize the app for academic purposes. We found a positive association between time spent on WhatsApp and students’ grade point average (GPA) based on factor, principal component, correlation, and chi-square analyses. Answering questions on the app in class is related to the program’s ability to help students learn. More use of the app in class leads to a lower GPA. However, sometimes, using the app might help students learn and perform better. We identify that many students disseminate materials through the app for academic purposes, contributing to their academic performance. Furthermore, this app is utilized for communicating with their teachers. Computations were carried out with the R and SPSS software.

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Section: Methodsmentioning

confidence: 99%

Section: Objective Contribution and Description Of Sectionsmentioning

confidence: 99%

Modern Multivariate Statistical Methods for Evaluating the Impact of WhatsApp on Academic Performance: Methodology and Case Study in India

Sharma¹,

Singh²,

Leiva

et al. 2022

Applied Sciences

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“…Automatic keyphrase extraction is used in many domains dealing with textual data, such as text classification [5], document clustering [6], document summarization [7], and search engines [8]. Although some studies have attempted to limit these domains like [9], which limited their use to five domains, due to importance of the information provided by the keyphrases, the AKE can also be exploited in many other domains such as recommender systems [10], web mining [11], bibliometric analysis [12], and sentiment analysis [13].…”

Section: Applicationsmentioning

confidence: 99%

A Systematic Literature Review of Keyphrases Extraction Approaches

Ajallouda

Fagroud

Zellou

et al. 2022

Int. J. Interact. Mob. Technol.

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The keyphrases of a document are the textual units that characterize its content such as the topics it addresses, its ideas, their field, etc. Thousands of books, articles and web pages are published every day. Manually extracting keyphrases is a tedious task and takes a lot of time. Automatic keyphrases extraction is an area of text mining that aims to identify the most useful and important phrases that give meaning to the content of a document. Keyphrases can be used in many Natural Language Processing (NLP) applications, such as text summarization, text clustering and text classification. This article provides a Systematic Literature Review (SLR) to investigate, analyze, and discuss existing relevant contributions and efforts that use new concepts and tools to improve keyphrase extraction. We have studied the supervised and unsupervised approaches to extracting keyphrases published in the period 2015-2022. We have also identified the steps most commonly used by the different approaches. Additionally, we looked at the criteria that should be evaluated to improve the accuracy of keyphrases extraction. Each selected approach was evaluated for its ability to extract keyphrases. Our findings highlight the importance of keyphrase extraction, and provide researchers and practitioners with information about proposed solutions and their limitations, which contributes to extract keyphrases in a powerful and meaningful way effective.

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“…Researchers have also combined different algorithms to produce hybrid versions possessing improved performance, e.g. ANGEL [15], PSOGSA [16], SFLA-IWO [17], GGWO [18], WOA [19], AOA [20], m-SCBOA [21], FRCSA [22]. The present study focuses on the Genetic Algorithm and the Particle Swarm Optimization, and their combination into hybrid algorithms.…”

Section: Introductionmentioning

confidence: 99%

Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization

Aivaliotis-Apostolopoulos

Loukidis

2022

PLoS ONE

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Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration, and as a result it often converges rapidly to local optima other than the global optimum. The genetic algorithm has the ability to overcome local extrema throughout the optimization process, but it often suffers from slow convergence rates. This paper proposes a new hybrid algorithm that nests particle swarm optimization operations in the genetic algorithm, providing the general population with the exploitation prowess of the genetic algorithm and a sub-population with the high exploitation capabilities of particle swarm optimization. The effectiveness of the proposed algorithm is demonstrated through solutions of several continuous optimization problems, as well as discrete (traveling salesman) problems. It is found that the new hybrid algorithm provides a better balance between exploration and exploitation compared to both parent algorithms, as well as existing hybrid algorithms, achieving consistently accurate results with relatively small computational cost.

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Efficient text document clustering approach using multi-search Arithmetic Optimization Algorithm

Cited by 28 publications

References 72 publications

Modern Multivariate Statistical Methods for Evaluating the Impact of WhatsApp on Academic Performance: Methodology and Case Study in India

Modern Multivariate Statistical Methods for Evaluating the Impact of WhatsApp on Academic Performance: Methodology and Case Study in India

A Systematic Literature Review of Keyphrases Extraction Approaches

Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization

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