Metaheuristic algorithms in text clustering

Hassan, Ibrahim Hayatu; Abdullahi, Mohammed; Ali, Yusuf Sahabi; Jeremiah, Isuwa; Abdulraheem, Silifat Adaramaja

doi:10.1016/b978-0-323-91781-0.00007-7

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Metaheuristic algorithms are widely used in many fields to obtain the most effective solutions for various problems. Some of these fields are; image processing [2,3], control techniques [4,5], deep learning models [6,7], machine learning algorithms [8], optimal filter design [9], text clustering [10], feature selection [11], etc. Segmentation is the first and most important step in image processing [12].…”

Section: Introductionmentioning

confidence: 99%

Improved PSO With Visit Table and Multiple Direction Search Strategies for Skin Cancer Image Segmentation

Olmez,

Koca,

Sengür

et al. 2024

IEEE Access

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Automated screening is employed to assist skin specialists in accurately detecting skin lesions at an early stage. Multilevel thresholding is a widely popular and efficient technique for enhancing the classification of skin cancer images. This paper proposes improved PSO with a novel visit table and multiple directions search strategies to develop the performance of the multilevel thresholding. A visit table strategy has been developed that prevents unnecessary searches of the original particle swarm optimization (PSO) algorithm by allowing the discovery of new points by making fewer visits to frequently visited points and their neighbors. Besides, a multiple directions search strategy has been introduced for the PSO to increase the diversity of the population and overcome the stuck at the local optimum by enhancing exploration ability. The qualitative, quantitative, and scalability analyzes of the improved PSO (IPSO) method were carried out on 50 benchmark functions and the highest performance was achieved with the proposed method in most of these functions. Secondly, a multilevel image segmentation application is presented on skin cancer images using two-dimensional (2D) non-local means histograms, improved PSO and Renyi's entropy. In this work, the ISIC 2017 skin cancer image dataset is used for segmentation application and various performance evaluation metrics are used. The obtained results are compared with seven state-of-the-art approaches to show the efficiency of the proposed approach. It can be noted from the obtained results that, the proposed method outperforms the compared method based on the average of evaluation metrics for all skin cancer images. The best results in SSIM value of 0.8285, FSIM value of 0.7332, and PSNR value of 19.0576 are achieved by using the proposed method in skin cancer image segmentation. Hence, our proposed method is ready to be tested with huge databases and can aid skin specialists in making an accurate diagnosis. INDEX TERMSMultilevel thresholding, multiple directions search strategy, PSO, skin cancer, visit table strategy.

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Section: Introductionmentioning

confidence: 99%

Improved PSO With Visit Table and Multiple Direction Search Strategies for Skin Cancer Image Segmentation

Olmez,

Koca,

Sengür

et al. 2024

IEEE Access

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“…of TDC [10]. Based on the various solutions controlled in every iteration, these algorithms are classified as local search or population-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Text Document Clustering Using Chaotic Northern Goshawk Optimization with K-means Algorithm

2024

IJIES

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Text document clustering (TDC) is becoming the most famous technique in various real-world applications like information retrieval, data mining, and so on. The TDC poses a significant challenge in these applications as the volume of data collection continues to grow daily. In clustering, the metaheuristics algorithms are used for solving the NP-hard problem and to enhance the model performance. Once the solution space is maximum, traditional approaches are incapable of identifying the solution in minimal time. In this research, the Chaotic Northern Goshawk Optimization (CNGO) with K-means Clustering algorithm is proposed for TDC. Initially, three benchmark datasets such as Reuters-21578, 20-Newsgroup and BBC Sport are used to estimate the performance. Then, the pre-processing techniques such as tokenization, stop word removal, stemming and lemmatization are used for data denoising. The word2vec feature extraction technique is used for converting the document into numerical vectors and finally, the CNGO with K-means is used for TDC. The effectiveness of the proposed method is estimated by utilizing various metrics and it attains the accuracy of 98.36%, 96.26% and 97.36% by using 20-Newsgroup, Reuters-21578 and BBC datasets when compared to competitor approaches like Salp Swarm Algorithm (SSA) and Rider Optimization-based Moth Search Algorithm (Rn-MSA).

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Optimizing microarray cancer gene selection using swarm intelligence: Recent developments and an exploratory study

Isuwa,

Abdullahi,

Ali

et al. 2023

Egyptian Informatics Journal

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Metaheuristic algorithms in text clustering

Cited by 4 publications

References 16 publications

Improved PSO With Visit Table and Multiple Direction Search Strategies for Skin Cancer Image Segmentation

Improved PSO With Visit Table and Multiple Direction Search Strategies for Skin Cancer Image Segmentation

Text Document Clustering Using Chaotic Northern Goshawk Optimization with K-means Algorithm

Optimizing microarray cancer gene selection using swarm intelligence: Recent developments and an exploratory study

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