Modified Multi-Grey Wolf Pack for Vital Sign-Based Disease Identification
| Autor: | Nabanita Banerjee, Sumitra Mukhopadhyay |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 020209 energy 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pattern recognition Identification (biology) 02 engineering and technology Artificial intelligence business Sign (mathematics) |
| DOI: | 10.4018/978-1-7998-1718-5.ch002 |
| Popis: | Noninvasive process of vital sign identification and design of low-cost decision-making system for the betterment of rural health care support is a prime facet of research. The identification of bio-signals from different sensors, noise removal, signal processing, and decision making requires the use of sophisticated expert system. In this chapter, the authors propose a modified multi grey wolf pack optimization technique (MMGWO) for better generalization and diversification. The basic model has been modified using net energy gain of the individual wolf in the process of hunting. Here multiple packs of wolves are considered with simultaneous sharing and conflict among them. The performance of the proposed technique is tested on 23 well known classical benchmark functions, CEC 2014 benchmark problem set along with classical real-life applications. The experimental results and related analysis show that the proposed MMGWO is significantly superior to other existing techniques. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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