A modified bond energy algorithm with fuzzy merging and its application to Arabic text document clustering

Mahmoud, Rana Husni Al; Hammo, Bassam; Faris, Hossam

doi:10.1016/j.eswa.2020.113598

Cited by 15 publications

(8 citation statements)

References 53 publications

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“…Still, not all of the issues can be resolved by these techniques [23,24]. Heuristic algorithms are currently used to address optimization problems in many different disciplines, including optimal power flow problems and parameter optimization of photovoltaic models [25,26]. Therefore, in the face of complex difficulties, we must provide algorithms with more efficiency [27].…”

Section: Refmentioning

confidence: 99%

Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems

Hijjawi,

Alshinwan,

Khashan

et al. 2023

Processes

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Several metaheuristic algorithms have been implemented to solve global optimization issues. Nevertheless, these approaches require more enhancement to strike a suitable harmony between exploration and exploitation. Consequently, this paper proposes improving the arithmetic optimization algorithm (AOA) to solve engineering optimization issues based on the cuckoo search algorithm called AOACS. The developed approach uses cuckoo search algorithm operators to improve the ability of the exploitation operations of AOA. AOACS enhances the convergence ratio of the presented technique to find the optimum solution. The performance of the AOACS is examined using 23 benchmark functions and CEC-2019 functions to show the ability of the proposed work to solve different numerical optimization problems. The proposed AOACS is evaluated using four engineering design problems: the welded beam, the three-bar truss, the stepped cantilever beam, and the speed reducer design. Finally, the results of the proposed approach are compared with state-of-the-art approaches to prove the performance of the proposed AOACS approach. The results illustrated an outperformance of AOACS compared to other methods of performance measurement.

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Section: Refmentioning

confidence: 99%

Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems

Hijjawi,

Alshinwan,

Khashan

et al. 2023

Processes

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“…(2019), Khalifa and Elnagar (2020), Nassif et al (2021b), Farha and Magdy (2021), question and answering systems, Romeo et al . (2019), Einea and Elnagar (2019), clustering, AlMahmoud, Hammo, and Faris (2020), and text classification, Abbas et al (2019), Orabi, El Rifai, and Elnagar (2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The use of word embedding models is quite popular in Arabic computational linguistic applications, AL-Smadi et al (2017), Alkhatlan, Kalita, and Alhaddad (2018), Mohamed and Shokry (2022), Bounhas, Soudani, and Slimani (2020). Therefore, measuring the robustness of such embeddings is essential for producing effective, Arabic sentiment analysis, Altowayan and Elnagar (2017), Al-Smadi et al (2019, Khalifa and Elnagar (2020), Nassif et al (2021b), Farha and Magdy (2021), question and answering systems, Romeo et al (2019), Elnagar (2019), clustering, AlMahmoud, Hammo, andFaris (2020), and text classification, Abbas et al (2019), Orabi, El Rifai, and.…”

Section: Literature Reviewmentioning

confidence: 99%

A benchmark for evaluating Arabic word embedding models

2022

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Modelling the distributional semantics of such a morphologically rich language as Arabic needs to take into account its introflexive, fusional, and inflectional nature attributes that make up its combinatorial sequences and substitutional paradigms. To evaluate such word distributional models, the benchmarks that have been used thus far in Arabic have mimicked those in English. This paper reports on a benchmark that we designed to reflect linguistic patterns in both Contemporary Arabic and Classical Arabic, the first being a cover term for written and spoken Modern Standard Arabic, while the second for pre-modern Arabic. The analogy items we included in this benchmark are chosen in a transparent manner such that they would capture the major features of nouns and verbs; derivational and inflectional morphology; high-, middle-, and low-frequency patterns and lexical items; and morphosemantic, morphosyntactic, and semantic dimensions of the language. All categories included in this benchmark are carefully selected to ensure proper representation of the language. The benchmark consists of 45 roots of the trilateral, all-consonantal, and semivowel-inclusive types; six morphosemantic patterns (’af‘ala; ifta‘ala; infa‘ala; istaf‘ala; tafa‘‘ala; and tafā‘ala); five derivations (the verbal noun, active participle, and the contrasts in Masculine-Feminine; Feminine-Singular-Plural; Masculine-Singular-Plural); and morphosyntactic transformations (perfect and imperfect verbs conjugated for all pronouns); and lexical semantics (synonyms, antonyms, and hyponyms of nouns, verbs, and adjectives), as well as capital cities and currencies. All categories include an equal proportion of high-, medium-, and low-frequency items. For the purpose of validating the proposed benchmark, we developed a set of embedding models from different textual sources. Then, we tested them intrinsically using the proposed benchmark and extrinsically using two natural language processing tasks: Arabic Named Entity Recognition and Text Classification. The evaluation leads to the conclusion that the proposed benchmark is truly reflective of this morphologically rich language and discriminatory of word embeddings.

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“…Credit represents a class of marketable securities [1], offering holders stable cash flow returns at specific future times [2]. Various factors influence bonds, encompassing both micro-entities and the macro-environment [3].…”

Section: Introductionmentioning

confidence: 99%

Credit rating algorithm of corporate bonds based on Gaussian process mixture model and improved K-means

Xia

2023

J. Comb. Math. Comb. Comput.

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The primary challenge in credit analysis revolves around uncovering the correlation between repayment terms and yield to maturity, constituting the interest rate term structure-an essential model for corporate credit term evaluation. Presently, interest rate term structures are predominantly examined through economic theoretical models and quantitative models. However, predicting treasury bond yields remains a challenging task for both approaches. Leveraging the clustering analysis algorithm theory and the attributes of an insurance company’s customer database, this paper enhances the K-means clustering algorithm, specifically addressing the selection of initial cluster centers in extensive sample environments. Utilizing the robust data fitting and analytical capabilities of the Gaussian process mixture model, the study applies this methodology to model and forecast Treasury yields. Additionally, the research incorporates customer credit data from a property insurance company to investigate the application of clustering algorithms in the analysis of insurance customer credit.

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A modified bond energy algorithm with fuzzy merging and its application to Arabic text document clustering

Cited by 15 publications

References 53 publications

Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems

Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems

A benchmark for evaluating Arabic word embedding models

Credit rating algorithm of corporate bonds based on Gaussian process mixture model and improved K-means

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