Carmen Paz Suárez Araujo

Recent Advances in Intelligent Engineering Systems

This volume is a collection of 19 chapters on intelligent engineering systems written by respectable experts of the fields. The book consists of three parts. The first part is devoted to the foundational aspects of computational intelligence. It consists of 8 chapters that include studies in genetic algorithms, fuzzy logic connectives, enhanced intelligence in product models, nature-inspired optimization technologies, particle swarm optimization, evolution algorithms, model complexity of neural networks, and fitness landscape analysis. The second part contains contributions to intelligent computation in networks, presented in 5 chapters. The covered subjects include the application of self-organizing maps for early detection of denial of service attacks, combating security threats via immunity and adaptability in cognitive radio networks, novel modifications in WSN network design for improved SNR and reliability, a conceptual framework for the design of audio based cognitive infocommunication channels, and a case study on the advantages of fuzzy and anytime signal- and image processing techniques. Computational intelligence represents a widely spread interdisciplinary research area with many applications in various disciplines including engineering, medicine, technology, environment, among others. Therefore, third part of this book consists of 6 chapters on applications. This is a very important part of the volume because the reader can find in it a wide range of fields where computational intelligence plays a significant role.

Novel Neural Network Models for Computing HomotheticInvariances: An Image Algebra Notation

In this paper we propose a theoretical approach toinvariant perception. Invariant perception is an importantaspect in both natural and artificial perception systems, and itremains an important unsolved problem in heuristically basedpattern recognition. Our approach is based on a general theoryof neural networks and studies of invariant perception by thecortex. The neural structures that we propose uphold both thearchitecture and functionality of the cortex as currentlyunderstood.The formulation of the proposed neural structuresis in the language of image algebra, a mathematical environmentfor expressing image processing algorithms. Thus, an additionalbenefit of our study is the implication that image algebraprovides an excellent environment for expressing and developingartificial perception systems.The focus of our study is oninvariances that are expressible in terms of affinetransformations, specifically, homothetic transformations. Ourdiscussion will include both one-dimensional andtwo-dimensional signal patterns. The main contribution of thispaper is the formulation of several novel morphological neuralnetworks that compute homothetic auditory and visualinvariances. With respect to the latter, we employ the theoryand trends of currently popular artificial vision systems.

Morphological neural networks and image algebra in artificial perception systems

Image algebra as a mathematical structure provides a much broader framework of neural computing. The matrix product in the basic equations of the current linear-based neural networks are furnished by the generalized matrix product obtaining new computational models as morphological neural networks (MNN). In this paper we propose a theoretic approach on the invariant perception. We also show that image algebra can be used not only in the field of image processing but in other areas related to artificial perception systems. Our study is based on both a general theory of neural network and the invariant perception by the cortex theory. The neural structures that we propose uphold both the architecture and functionality of the cortex. We present a neural network model for computing auditory homothetic invariances in accordance with a general framework in image algebra. The neuronal synthesis of this model is obtain using MNN theory with the binary operations the maximum and the multiplication in the neural network formulation. We also propose a second model which is achieved introducing a simple logarithmic transformation in the current model. In addition we propose an alternative MNN for computing homothetic invariances which arise from how the problems are formulated in the systems of artificial vision. This last neural network is appropriate to compute visual invariances when we process patterns defined in two dimension spaces.

Artificial Intelligent Systems Based on Supervised HUMANN for Differential Diagnosis of Cognitive Impairment: Towards a 4P-HCDS

Differential and early diagnosis of cognitive impairment (CI) continues being one of the crucial points to which clinical medicine faces at every level of attention, and a significant public health concern. This work proposes new CI diagnostic tools based on a data fusion scheme, artificial neural networks and ensemble systems. Concretely we have designed a supervised HUMANN [1] with capacity of missing data processing (HUMANN-S) and a HUMANN-S ensemble system. These intelligent diagnostic systems are inside EDEVITALZH, a clinical virtual environment to assist the diagnosis and prognosis of CI, Alzheimers disease and other dementias. Our proposal is a personalized, predictive, preventive, and participatory-healthcare delivery system (4P-HCDS) and is an optimal solution for an e-health framework. We explore their ability presenting preliminary results on differential diagnosis of CI using neuropsychological tests from 267 consultations on 30 patients by the Alzheimers Patient Association of Gran Canaria.

A parametric study of HUMANN in relation to the noise: application for the identification of compounds of environmental interest

In this paper we present a parametric study of a hierarchical unsupervised modular adaptive neural network (HUMANN), in dealing with noise. HUMANN is a biologically plausible feedforward neural architecture which has the capacity for working in domains with noise and overlapping classes, with no priori information of the number of different classes in the data, with highly non-linear boundary class and with high dimensionality data vectors. It is appropriate for classification processes performing blind clustering. The study has been accomplished round the two most noise-dependent HUMANN parameters, λ and ρ, using synthesized databases (sinusoidal signals with Gaussian noise). We show that HUMANN is highly resistant to noise, improving the performance of different neural architectures such as ART2 and DIGNET. We also present the application of HUMANN for the identification of pollutants in the environment. Specifically it has been tested with Polychlorinated dibenzofurans (PCDFs), some of the most hazardous pollutants of the environment.

Turing Universality of Neural Nets (Revisited)

We show how to use recursive function theory to prove Turing universality of finite analog recurrent neural nets, with a piecewise linear sigmoid function as activation function. We emphasize the modular construction of nets within nets, a relevant issue from the software engineering point of view.

Automatic prognostic determination and evolution of cognitive decline using artificial neural networks

This work tries to go a step further in the development of methods based on automatic learning techniques to parse and interpret data relating to cognitive decline (CD). There have been studied the neuropsychological tests of 267 consultations made over 30 patients by the Alzheimers Patient Association of Gran Canaria in 2005. The Sanger neural network adaptation for missing values treatment has allowed making a Principal Components Analysis (PCA) on the successfully obtained data. The results show that the first three obtained principal components are able to extract information relating to functional, cognitive and instrumental sintomatology, respectively, from the test. By means of these techniques, it is possible to develop tools that allow physicians to quantify, view and make a better pursuit of the sintomatology associated to the cognitive decline processes, contributing to a better knowledge of these ones.

HUMANN-based system to identify benzimidazole fungicides using multi-synchronous fluorescence spectra: An ensemble approach

AbstractIn this paper, we approach, using neural computation and ensemble systems, a pattern classification problem in fluorescence spectrometry, the resolution of difficult multi-fungicide mixtures (overlapping), specifically the benzimidazole fungicides, benomyl, carbendazim, thiabendazole and fuberidazole. These fungicides are compounds of an important environmental interest. Because of this, from an analytical point of view, it is interesting to develop sensitive, selective and simple methods for their determination. Fluorescence spectrometry has proven to be a sensitive and selective technique for determination of many compounds of environmental interest, but in some cases it is not enough. HUMANN is a hierarchical, unsupervised, modular, adaptive neural net with high biological plausibility, which has shown to be suitable for identification of these fungicides and organochlorinated compounds of environmental interest. We propose two modular artificial intelligent systems, with a structure of pre-processing and processing stage, a multi-input HUMANN-based system, using multi-fluorescence spectra as input to the system, and a HUMANN-ensemble system. We analyze the optimal configuration of inputs and the ensemble in order to obtain better results. We study such figures as precision and sensitivity of the method. Our proposal is a smart, flexible and effective complementary method, which allows reducing the analytical and/or computational complexity of the analysis. FigureStages in identification of benzimidazole fungicides

GaNEn: A new gating neural ensemble for automatic assessment of the Severity Level of Dementia using neuropsychological tests

Dementia is one of the associated diseases to aging most prevalents. An important issue about this neuropathology, as of yet unsolved, is the absence of therapeutic tools that manage or stop its progression and symptoms in a constant and supported way. In the present study, we propose a new computational intelligent tool to diagnose the Severity Level of Dementia (SLD) using gating neural network and neural ensemble approaches. We present a gating neural ensemble (GaNEn). This system is a new formulation of a neural network ensemble, where the gating neural network takes part in the combination strategy of ensemble system, and the main expert module in its construction is a HUMANN-S (Supervised HUMANN (Hybrid Unsupervised Modular Artificial Neural Network)) architecture. GaNEn is characterized by an incremental capacity concerning missing data management and their influence in the final diagnosis. It improves previous computational solutions and obtains higher accuracy diagnosis. The GaNEn system is a significant achievement in the medical diagnosis of neurological disorders because it could aid in the design of pharmaco-therapeutic strategies to contain dementia. It is also capable of supplying the best neuropsychological scales for dementia severity grades. We have explored its ability using a battery of neuropsychological tests from people with Alzheimer type dementia (AD), Vascular type dementia (VD) and other dementia type (OD) like Trauma, Subcortical, Parkinson and Infectious, from the Alzheimers Association of Gran Canaria.

An Ensemble Approach for the Diagnosis of Cognitive Decline with Missing Data

This work applies new techniques of automatic learning to diagnose neuro decline processes usually related to aging. Early detection of cognitive decline (CD) is an advisable practice under multiple perspectives. A study of neuropsychological tests from 267 consultations on 30 patients by the Alzheimers Patient Association of Gran Canaria is carried out. We designed neural computational CD diagnosis systems, using a multi-net and ensemble structure that is applied to the treatment of missing data present in consultations. The results show significant improvements over simple classifiers. These systems would allow applying policies of early detection of dementias in primary care centers where specialized professionals are not present.