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Bio-Inspired Algorithms for Ecosystem Data Analysis

Bio-Inspired Algorithms for Ecosystem Data Analysis

Mohamed Elhadi Rahmani
Copyright: © 2018 |Pages: 26
ISBN13: 9781522530046|ISBN10: 1522530045|EISBN13: 9781522530053
DOI: 10.4018/978-1-5225-3004-6.ch013
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MLA

Rahmani, Mohamed Elhadi. "Bio-Inspired Algorithms for Ecosystem Data Analysis." Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management, edited by Reda Mohamed Hamou, IGI Global, 2018, pp. 225-250. https://doi.org/10.4018/978-1-5225-3004-6.ch013

APA

Rahmani, M. E. (2018). Bio-Inspired Algorithms for Ecosystem Data Analysis. In R. Hamou (Ed.), Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management (pp. 225-250). IGI Global. https://doi.org/10.4018/978-1-5225-3004-6.ch013

Chicago

Rahmani, Mohamed Elhadi. "Bio-Inspired Algorithms for Ecosystem Data Analysis." In Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management, edited by Reda Mohamed Hamou, 225-250. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3004-6.ch013

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Abstract

Ecological systems are known by their relationships with the environment. They affect and are affected by various external factors such as climate and the basic materials that form the soil. Good distinctions of relationships is the first important point in the modeling of ecosystems. The diversity of these systems caused a large amount of data that became hard to analyze, which made researchers classify it as NP-Hard problems. This chapter presents a study of application of bio-inspired algorithms for ecosystem data analysis. The chapter contains application of four different approaches that were inspired by authors of the paper from four different phenomena, and they were applied for analysis of four different ecosystem data collected from real life cases. Results showed a very high accuracy and proved the efficiency of bio-inspired algorithms for supervised classification of real ecosystem data.

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