An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach

Abstract: New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique

An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results

A large number of classification algorithms have been proposed in the machine learning literature. These algorithms have different pros and cons, and no algorithm is the best for all datasets. Hence, a challenging problem consists of choosing the best classification algorithm with its best hyper-parameter settings for a given input dataset. In the last few years, Automated Machine Learning (Auto-ML) has emerged as a promising approach for tackling this problem, by doing a heuristic search in a large space of candidate classification algorithms and their hyper-parameter settings. In this work we propose an improved version of our previous Evolutionary Algorithm (EA) – more precisely, an Estimation of Distribution Algorithm – for the Auto-ML task of automatically selecting the best classifier ensemble and its best hyper-parameter settings for an input dataset. The new version of this EA was compared against its previous version, as well as against a random forest algorithm (a strong ensemble algorithm) and a version of the well-known Auto-ML method Auto-WEKA adapted to search in the same space of classifier ensembles as the proposed EA. In general, in experiments with 21 datasets, the new EA version obtained the best results among all methods in terms of four popular predictive accuracy measures: error rate, precision, recall and F-measure

Development and testing of a composite index to monitor the continuum of maternal health service delivery at provincial and district level in South Africa

The continuum of care is a recommended framework for comprehensive health service delivery for maternal health, and it integrates health system and social determinants of health. There is a current lack of knowledge on a measurement approach to monitor performance on the framework. In this study we aim to develop and test a composite index for assessing the maternal health continuum in a province in South Africa with the possibility of nationwide use

Prevalence, associated factors and predictors of anxiety: a community survey in Selangor, Malaysia

Background: Anxiety is the most common mental health disorders in the general population. This study aimed to determine the prevalence of anxiety, its associated factors and the predictors of anxiety among adults in the community of Selangor, Malaysia. Methods: A cross sectional study was carried out in three districts in Selangor, Malaysia. The inclusion criteria of this study were Malaysian citizens, adults aged 18 years and above, and living in the selected living quarters based on the list provided by the Department of Statistics Malaysia (DOS). Participants completed a set of questionnaires, including the validated Malay version of Generalized Anxiety Disorder 7 (GAD 7) to detect anxiety. Results: Of the 2512 participants who were approached, 1556 of them participated in the study (61.90 %). Based on the cut-off point of 8 and above in the GAD-7, the prevalence of anxiety was 8.2 %. Based on the initial multiple logistic regression analysis, the predictors of anxiety were depression, serious problems at work, domestic violence and high perceived stress. When reanalyzed again after removing depression, low self-esteem and high perceived stress, six predictors that were identified are cancer, serious problems at work, domestic violence, unhappy relationship with family, non-organizational religious activity and intrinsic religiosity. Conclusion: This study reports the prevalence of anxiety among adults in the community of Selangor, Malaysia and also the magnitude of the associations between various factors and anxiety

T.B.: EFuNNs ensembles construction using a clustering method and a coevolutionary genetic algorithm (to appear

Abstract. This paper presents the experiments which where made with the Clustering and Coevolution to Construct Neural Network Ensemble (CONE) approach on two classification problems and two time series prediction problems. This approach was used to create a particular type of Evolving Fuzzy Neural Network (EFuNN) ensemble and optimize its parameters using a Coevolutionary Multi-objective Genetic Algorithm. The results of the experiments reinforce some previous results which have shown that the approach is able to generate EFuNN ensembles with accuracy either better or equal to the accuracy of single EFuNNs generated without using coevolution. Besides, the execution time of CONE to generate EFuNN ensembles is lower than the execution time to produce single EFuNNs without coevolution.