Enhance the Performance of Independent Component Analysis for Text Classification by Using Particle Swarm Optimization

Shabat, Hafedh Ali; Abbas, Nidaa A.

doi:10.1109/icoase51841.2020.9436547

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…In this research focuses on the development of crime text clustering in English-language crime documents. ICA is unsupervised machine learning model that plays an important role in several domains such as biomedical engineering [4], text mining [5,6]. Also has been extensively utilized in other various fields such as mobile communications, signal processing, audio signal separation, multispectral image demixing, and seismic signal processing.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Performances of Two Unsupervised Methods for ‎Classification Crime Text

Abdali,

Hashim,

Al-Obaidi

2024

IJSSE

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Crime is a pervasive societal issue that has a negative impact on both a community's economic growth and overall quality of life. The bulk of crimes committed in everyday times are documented online by the wherewithal of news reports, blogs, and social networking sites. To improve crime analytics and community protection in response to rising crime. Law enforcement agencies continue to promote effective electronic information systems and crime data mining. Consequently, the aim of this study is to design a system of crime that depends on unsupervised machine-learning techniques that categorize five types of crime text. Two famous unsupervised algorithms: Independent Component Analysis based on natural gradient (NG-ICA) and Fast Independent Component Analysis (Fast-ICA) were used, to recover the latent components from observations. In order to evaluate the proposed system, the BERNAMA dataset, which had been manually annotated was used. Two experiments were conducted, and the results showed that the approaches that were employed satisfied promising results. Where the NG-ICA achieved an average F-measure of 83.3% and the Fast-ICA achieved 87.1%. This outcome demonstrates the appropriateness of these techniques in the implementation of text mining.

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Section: Introductionmentioning

confidence: 99%

Evaluating Performances of Two Unsupervised Methods for ‎Classification Crime Text

Abdali,

Hashim,

Al-Obaidi

2024

IJSSE

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“…The majority of Metaheuristic algorithms are inspired by nature [3], [4], for instance, ant colony optimization (ACO) [5], PSO [6], and cuckoo search algorithms [7]. Several new Metaheuristic algorithms have been produced after the coming to light of swarm intelligence approaches like the PSO that appear in the 1995s, and these techniques have been used in practically different fields of optimization, text mining [8], scheduling and planning [9], and machine intelligence [10].…”

Section: Introductionmentioning

confidence: 99%

Analysis study of the bee algorithms as a mechanism for solving combinatorial problems

Shabat

Raheem

2023

IJEECS

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Combinatorial optimization problems are problems that have a large number of discrete solutions and a cost function for evaluating those solutions in comparison to one another. With the vital need of solving the combinatorial problem, several research efforts have been concentrated on the biological entities behaviors to utilize such behaviors in population-based metaheuristic. This paper presents bee colony algorithms which is one of the sophisticated biological nature life. A brief detail of the nature of bee life has been presented with further classification of its behaviors. Furthermore, an illustration of the algorithms that have been derived from bee colony which are bee colony optimization, and artificial bee colony. Finally, a comparative analysis has been conducted between these algorithms according to the results of the traveling salesman problem solution. Where the bee colony optimization (BCO) rendered the best performance in terms of computing time and results.

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“…It's interesting to note that the number of suggested nature-inspired optimization algorithms has increased exponentially. These algorithms deal with many data types, including text data [23] and image data [24]. Recently researchers have proposed a variety of techniques to speed up image thresholding, including swarm intelligence optimization algorithms such as the particle swarm algorithm [25], ant colony algorithm [26], and fruit fly optimization algorithm [27].…”

Section: Introductionmentioning

confidence: 99%

An Otsu thresholding for images based on a nature-inspired optimization algorithm

Raheem¹,

Shabat²

2023

IJEECS

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Thresholding is a type of image segmentation, where the pixels change to make the image easier to analyze. In bi-level thresholding, the image in grayscale format is transformed into a binary format. The traditional methods for image thresholding may be inefficient in finding the best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied for optimization in different applications to find optimal solutions with minimum computational time. The proposed work aims to optimize the fitness function obtained by the Otsu algorithm using a metaheuristic swarm-based algorithm called the bat algorithm. As a result, the optimal threshold value for bi-level images in cloud detection was obtained. Also, one of the trajectory-based algorithms called hill climbing was applied to optimize the fitness function taken from the Otsu algorithm. The HYTA dataset was used to evaluate the work, which was later confirmed through testing. The findings of experiments indicated that the developed algorithm is promising and the performance of the metaheuristic population-based algorithm is better than the trajectory-based algorithm in terms of efficiency and computational time for image thresholding.

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Enhance the Performance of Independent Component Analysis for Text Classification by Using Particle Swarm Optimization

Cited by 3 publications

References 26 publications

Evaluating Performances of Two Unsupervised Methods for ‎Classification Crime Text

Evaluating Performances of Two Unsupervised Methods for ‎Classification Crime Text

Analysis study of the bee algorithms as a mechanism for solving combinatorial problems

An Otsu thresholding for images based on a nature-inspired optimization algorithm

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