Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.

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Linear discriminant analysis (LDA) and logistic regression (LR) are often used for the purpose of classifying populations or groups using a set of predictor variables. Assumptions of multivariate normality and equal variancecovariance matrices across groups are required before proceeding with LDA, but such assumptions are not required for LR and hence LR is considered to be much more robust than LDA. In this paper, several real datasets which are different in terms of normality, number of independent variables and sample size are used to study the performance of both methods. The methods are compared based on the percentage of correct classification and B index. The results show that overall, LR performs better regardless of the distribution of the data is normal or nonnormal. However, LR needs longer computing time than LDA with the increase in sample size. The performance of LDA was also tested by using various prior probabilities. The results show that the average percentage of correct classification and the B index are higher when the prior probability is set based on the group size rather than using equal probabilities for all groups.

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The subject of cursive handwritten character recognition is still open to be studied because of its complex nature. Recognition of Arabic handwritten and its variants such as Farsi (Persian) and Urdu had been receiving considerable attention in recent years.Numerous methods for recognizing Arabic characters have been proposed and applied to various types of images. These research highlights have not yet been given to another variant of Arabic which is the Jawi script. This paper provides a comprehensive review of existing works in handwritten Jawi character recognition. It includes the history and writing of Jawi, and challenges, current works, and future direction of Jawi character recognition system.