Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi’s T-Method

Taguchi’s T-Method is one of the Mahalanobis Taguchi System- (MTS-) ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. The prediction model’s complexity aspect can be further enhanced by removing features that do not provide valuable information on the overall prediction. In order to accomplish this, a matrix called orthogonal array (OA) is used within the existing Taguchi’s T-Method. However, OA’s fixed-scheme matrix and its drawback in coping with the high-dimensionality factor led to a suboptimal solution. On the contrary, the usage of SNR (dB) as its objective function was a reliable measure. The application of Binary Bitwise Artificial Bee Colony (BitABC) has been adopted as the novel search engine that helps cater to OA’s limitation within Taguchi’s T-Method. The generalization aspect using bootstrap was a fundamental addition incorporated in this research to control the effect of overfitting in the analysis. The adoption of BitABC has been tested on eight (8) case studies, including large and small sample datasets. The result shows improved predictive accuracy ranging between 13.99% and 32.86% depending on cases. This study proved that incorporating BitABC techniques into Taguchi’s T-Method methodology effectively improved its prediction accuracy.

The Poisson-Lognormal Model as a Versatile Framework for the Joint Analysis of Species Abundances

Joint Species Distribution Models (JSDM) provide a general multivariate framework to study the joint abundances of all species from a community. JSDM account for both structuring factors (environmental characteristics or gradients, such as habitat type or nutrient availability) and potential interactions between the species (competition, mutualism, parasitism, etc.), which is instrumental in disentangling meaningful ecological interactions from mere statistical associations. Modeling the dependency between the species is challenging because of the count-valued nature of abundance data and most JSDM rely on Gaussian latent layer to encode the dependencies between species in a covariance matrix. The multivariate Poisson-lognormal (PLN) model is one such model, which can be viewed as a multivariate mixed Poisson regression model. Inferring such models raises both statistical and computational issues, many of which were solved in recent contributions using variational techniques and convex optimization tools. The PLN model turns out to be a versatile framework, within which a variety of analyses can be performed, including multivariate sample comparison, clustering of sites or samples, dimension reduction (ordination) for visualization purposes, or inferring interaction networks. This paper presents the general PLN framework and illustrates its use on a series a typical experimental datasets. All the models and methods are implemented in the R package PLNmodels, available from cran.r-project.org.

Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

Various driver’s vigilance estimation techniques currently exist in literature. But none of them detects the vigilance of driver in complexity domain. As a result, we have proposed the recently introduced multivariate multiscale entropy (MMSE) method to fill this research gap. In this research, we have applied the MMSE technique to differential entropy features of electroencephalogram (EEG) and electrooculogram (EOG) signals for detecting vigilance of driver in complexity domain. The MMSE has also been employed to PERCLOS (Percentage of Eye Closure) values to analyse cognitive states (awake, tired and drowsy) in complexity domain. The contribution of this research is to show how a new feature called MMSE can efficiently classify the awake, tired and drowsy state of the driver in complexity domain. Another contribution is to demonstrate the distinguishing ability of the MMSE by validating it with applying multivariate sample entropy feature of cognitive states to support vector machine (SVM). The experimental MMSE analysis curves show statistically significant differences (p < 0.01) in terms of complexity among brain EEG signals, forehead EEG signals and EOG signals. Moreover, the difference in the multivariate sample entropy across all scales in awake (1.0828 ± 0.4664), tired (0.7841 ± 0.3183) and drowsy (0.2938 ± 0.1664) states are statistically significant (p <0.01). Also, the SVM, a machine learning technique, has discriminated the cognitive states with the promising classification accuracy of 76.2%. As a result, the MMSE analysis of cognitive states can be implemented practically for vigilance detection by building a programmable vigilance detection system.

The Poisson-lognormal model as a versatile framework for the joint analysis of species abundances

Joint Species Abundance Models (JSDM) provide a general multivariate framework to study the joint abundances of all species from a community. JSDM account for both structuring factors (environmental characteristics or gradients, such as habitat type or nutrient availability) and potential interactions between the species (competition, mutualism, parasitism, etc.), which is instrumental in disentangling meaningful ecological interactions from mere statistical associations.Modeling the dependency between the species is challenging because of the count-valued nature of abundance data and most JSDM rely on Gaussian latent layer to encode the dependencies between species in a covariance matrix. The multivariate Poisson-lognormal (PLN) model is one such model, which can be viewed as a multivariate mixed Poisson regression model. The inference of such models raises both statistical and computational issues, many of which were solved in recent contributions using variational techniques and convex optimization.The PLN model turns out to be a versatile framework, within which a variety of analyses can be performed, including multivariate sample comparison, clustering of sites or samples, dimension reduction (ordination) for visualization purposes, or inference of interaction networks. This paper presents the general PLN framework and illustrates its use on a series a typical experimental datasets. All the models and methods are implemented in the R package PLNmodels, available from cran.r-project.org.