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Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis
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Author(s): Janmenjoy Nayak (Aditya Institute of Technology and Management, India), Bighnaraj Naik (Veer Surendra Sai University of Technology (VSSUT), Odisha, India), Pandit Byomakesha Dash (Veer Surendra Sai University of Technology, Burla, India)and Danilo Pelusi (University of Teramo, Italy)
Copyright: 2024
Pages: 18
Source title:
Research Anthology on Bioinformatics, Genomics, and Computational Biology
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/979-8-3693-3026-5.ch055
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Abstract
Biomedical data is often more unstructured in nature, and biomedical data processing task is becoming more complex day by day. Thus, biomedical informatics requires competent data analysis and data mining techniques for designing decision support system's framework to solve clinical and heathcare-related issues. Due to increasingly large and complex data sets and demand of biomedical informatics research, researchers are attracted towards automated machine learning models. This paper is proposed to design an efficient machine learning model based on fuzzy c-means with meta-heuristic optimizations for biomedical data analysis and clustering. The main contributions of this paper are 1) projecting an efficient machine learning model based on fuzzy c-means and meta-heuristic optimization for biomedical data classification, 2) employing benchmark validation techniques and critical hypothesises testing, and 3) providing a background for biomedical data processing with a view of data processing and mining.
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