Function approximation and documentation of sampling data using artificial neural networks

Ecuadorian infant mortality linked to socioeconomic factors during the last 30 years

Abstract Objectives: to analyze the difference among geographical units and the evolution of infant mortality rate (IMR) based on Ecuadorian censuses (1990-2001-2010). Methods: artificial Neural Network analyzed the impact of sociodemographic factors over the variability of IMR. Poisson regression analyzed the variation of the standardized IMR (sIMR). Results: the decrease in the national IMR was 63.8%; however, 42.8% provinces showed an increase in 2001-2010. The variability was explained mainly by illiteracy decrease. The adjusted RR between provincial sIMR with illiteracy and poverty revealed a trend towards the unit. Conclusions: the variation of IMR reflects a complex interaction of the sociodemographic factors.

Artificial neural networks: a novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R(2)) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R(2) in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies.

Spatial succession modeling of biological communities: a multi-model approach

Strong spatial correlation may exist in the spatial succession of biological communities, and the spatial succession can be mathematically described. It was confirmed by our study on spatial succession of both plant and arthropod communities along a linear transect of natural grassland. Both auto-correlation and cross-correlation analyses revealed that the succession of plant and arthropod communities exhibited a significant spatial correlation, and the spatial correlation for plant community succession was stronger than arthropod community succession. Theoretically it should be reasonable to infer a site's community composition from the last site in the linear transect. An artificial neural network for state space modeling (ANNSSM) was developed in present study. An algorithm (i.e., Importance Detection Method (IDM)) for determining the relative importance of input variables was proposed. The relative importance for plant families Gramineae, Compositae and Leguminosae, and arthropod orders Homoptera, Diptera and Orthoptera, were detected and analyzed using IDM. ANNSSM performed better than multivariate linear regression and ordinary differential equation, while ordinary differential equation exhibited the worst performance in the simulation and prediction of spatial succession of biological communities. A state transition probability model (STPM) was proposed to simulate the state transition process of biological communities. STPM performed better than multinomial logistic regression in the state transition modeling. We suggested a novel multi-model framework, i.e., the joint use of ANNSSM and STPM, to predict the spatial succession of biological communities. In this framework, ANNSSM and STPM can be separately used to simulate the continuous and discrete dynamics.

Comparison between two PCR-based bacterial identification methods through artificial neural network data analysis

The 16S ribosomal ribonucleic acid (rRNA) and 16S-23S rRNA spacer region genes are commonly used as taxonomic and phylogenetic tools. In this study, two pairs of fluorescent-labeled primers for 16S rRNA genes and one pair of primers for 16S-23S rRNA spacer region genes were selected to amplify target sequences of 317 isolates from positive blood cultures. The polymerase chain reaction (PCR) products of both were then subjected to restriction fragment length polymorphism (RFLP) analysis by capillary electrophoresis after incomplete digestion by Hae III. For products of 16S rRNA genes, single-strand conformation polymorphism (SSCP) analysis was also performed directly. When the data were processed by artificial neural network (ANN), the accuracy of prediction based on 16S-23S rRNA spacer region gene RFLP data was much higher than that of prediction based on 16S rRNA gene SSCP analysis data (98.0% vs. 79.6%). This study proved that the utilization of ANN as a pattern recognition method was a valuable strategy to simplify bacterial identification when relatively complex data were encountered.

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.