International Journal of Mathematical Research

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Multivariate Analysis of EEG Data: Some Aspects of Diagnostic of MANOVA Model

Pages: 1-17
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Multivariate Analysis of EEG Data: Some Aspects of Diagnostic of MANOVA Model

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DOI: 10.18488/journal.24.2018.71.1.17

Md Rokonuzzaman

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Md Rokonuzzaman (2018). Multivariate Analysis of EEG Data: Some Aspects of Diagnostic of MANOVA Model. International Journal of Mathematical Research, 7(1): 1-17. DOI: 10.18488/journal.24.2018.71.1.17
The main focus of this study is the multivariate analysis of Electroencephalogram data which included multivariate analysis of variance. The multivariate model diagnostics comprise checking number of assumptions of MANOVA model such as multivariate normality, homogeneity of covariance matrices. In this paper, the model X=BC+E is used and estimate the different parameters. Also by using a general form, H0: GBF=0 to test the different types of null hypothesis. Here G and F is known matrices and obtained from the hypothesis. This study gives mathematical ideas from multivariate statistical analysis to find a solution or a good approximation of a complex scientific problem.
Contribution/ Originality
This study gives mathematical ideas from multivariate statistical analysis to find a solution or a good approximation of a complex scientific problem.

An Artificial Human Optimization Algorithm Titled Human Thinking Particle Swarm Optimization

Pages: 18-25
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An Artificial Human Optimization Algorithm Titled Human Thinking Particle Swarm Optimization

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DOI: 10.18488/journal.24.2018.71.18.25

Satish Gajawada , Hassan M. H. Mustafa

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Satish Gajawada , Hassan M. H. Mustafa (2018). An Artificial Human Optimization Algorithm Titled Human Thinking Particle Swarm Optimization. International Journal of Mathematical Research, 7(1): 18-25. DOI: 10.18488/journal.24.2018.71.18.25
Artificial Human Optimization is a latest field proposed in December 2016. Just like artificial Chromosomes are agents for Genetic Algorithms, similarly artificial Humans are agents for Artificial Human Optimization Algorithms. Particle Swarm Optimization is very popular algorithm for solving complex optimization problems in various domains. In this paper, Human Thinking Particle Swarm Optimization (HTPSO) is proposed by applying the concept of thinking of Humans into Particle Swarm Optimization. The proposed HTPSO algorithm is tested by applying it on various benchmark functions. Results obtained by HTPSO algorithm are compared with Particle Swarm Optimization algorithm.
Contribution/ Originality
The paper contributes a new algorithm to the Artificial Human Optimization Field. All the optimization algorithms which were proposed based on Artificial Humans will come under Artificial Human Optimization Field. The concept of Human Thinking is introduced into the Particle Swarm Optimization (PSO) to create new algorithm titled HTPSO.