Exploratory simulation of an Intelligent Iris Verifier Distributed System

This paper discusses some topics related to the latest trends in the field of evolutionary approaches to iris recognition. It presents the results of an exploratory experimental simulation whose goal was to analyze the possibility of establishing an Interchange Protocol for Digital Identities evolved in different geographic locations interconnected through and into an Intelligent Iris Verifier Distributed System (IIVDS) based on multi-enrollment. Finding a logically consistent model for the Interchange Protocol is the key factor in designing the future large-scale iris biometric networks. Therefore, the logical model of such a protocol is also investigated here. All tests are made on Bath Iris Database and prove that outstanding power of discrimination between the intra- and the inter-class comparisons can be achieved by an IIVDS, even when practicing 52.759.182 inter-class and 10.991.943 intra-class comparisons. Still, the test results confirm that inconsistent enrollment can change the logic of recognition from a fuzzified 2-valent consistent logic of biometric certitudes to a fuzzified 3-valent inconsistent possibilistic logic of biometric beliefs justified through experimentally determined probabilities, or to a fuzzified 8-valent logic which is almost consistent as a biometric theory - this quality being counterbalanced by an absolutely reasonable loss in the user comfort level.Comment: 4 pages, 2 figures, latest version: http://fmi.spiruharet.ro/bodorin

A Novel Fuzzy Scoring Approach of Behavioural Interviews in Personnel Selection

The need for a behavioural interview scoring strategy is a critical element in order to ensure an optimal organizational human capital. Behavioural interview based on storytelling approach is a technique through which career seekers are required to provide clear details of how they have handled such workloads in the past. The whole literature assumes the existence of strong correlations between the score received on the selection interview and subsequent job performance, so in this paper we intend to highlight the relationship between these two assessments as well as the modelling using fuzzy logic of a CAR alternative system for scoring the selection interview. The results demonstrated that there is a very significant association between the classic interview score and work performance (r=0.894 to p<0.01). Furthermore, there is also a significant correlation coefficient of r=0.925 at a p<0.01, between the fuzzy CAR score and job performance, thus the validity and the optimization of the procedure are fully proven

Alzheimer’s Disease under the Purview of Graph Theory Centric Genetic Networks

Notice that the synapsis of brain is a form of communication. As communication demands connectivity, it is not a surprise that "graph theory" is a fastest growing area of research in the life sciences. It attempts to explain the connections and communication between networks of neurons. Alzheimer’s disease (AD) progression in brain is due to a deposition and development of amyloid plaque and the loss of communication between nerve cells. Graph/network theory can provide incredible insights into the incorrect wiring leading to memory loss in a progressive manner. Network in AD is slanted towards investigating the intricate patterns of interconnections found in the pathogenesis of brain. Here, we see how the notions of graph/network theory can be prudently exploited to comprehend the Alzheimer’s disease. We begin with introducing concepts of graph/network theory as a model for specific genetic hubs of the brain regions and cellular signalling. We begin with a brief introduction of prevalence and causes of AD followed by outlining its genetic and signalling pathogenesis. We then present some of the network-applied outcome in assessing the disease-signalling interactions, signal transduction of protein-protein interaction, disturbed genetics and signalling pathways as compelling targets of pathogenesis of the disease.</em

A New Approach to Nonlinear Tracking Control Based on Fuzzy Approximation

The problem of tracking control is addressed for a class of nonlinear systems with uncertainties. The original nonlinear systems are approximated by a fuzzy T-S model based on which a state-feedback controller is constructed by using the linear matrix inequalities. The approximating error is eliminated by an adaptive compensator based on fuzzy logic systems. The effectiveness of the proposed control scheme is demonstrated by a simulation example. The main advantage is that the designer makes milder constraint assumption for the approximation error and the uncertainties in nonlinear systems