Mario Aldape-Pérez

An associative memory approach to medical decision support systems

Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets.

Collaborative learning in postgraduate level courses

Pedagogical experiences in postgraduate level courses are presented.Students use computational tools and repositories in collaborative learning.Problems in medical environments, pollutants, and concept lattices are tackled.The enhancement of teaching-learning processes by collaborative learning is shown. Nowadays, we are immersed in the social and mobile networks era. As a positive consequence of this, collaborative and mobile learning in educational environments have been encouraged thanks to the use of computing for human learning. By coupling the advantages of collaborative and mobile learning, the teaching-learning processes involved in postgraduate courses may be greatly enhanced. The pedagogical experiences in this regard lived by the authors in the Alpha-Beta Research Group when coupling collaborative and mobile learning in the context of postgraduate level courses, are presented in this paper.

Collaborative learning based on associative models

We address social networking and collaborative learning in the medical domain.We examine social networking between computer experts and postgraduate students.We assess collaborative learning between expert and less experienced physicians. This paper addresses social networking and collaborative learning in the medical domain by focusing on two main objectives: the first one concerns about social networking between computer science experts and postgraduate students, while the second concerns about collaborative learning between medical experts and less experienced physicians. The tasks of algorithms testing and performance evaluation were assigned to computer science postgraduate students. They made extensive use of social networking in order to implement associative models to perform pattern classification tasks in medical datasets and share performance results. Associative memories have a number of properties, including a rapid, compute efficient best-match and intrinsic noise tolerance that make them ideal for diagnostic hypothesis-generation processes in the medical domain. Using supervised machine learning algorithms allows less experienced physicians to compare their diagnostic results between workgroups and verify whether their knowledge is consistent with the results delivered by computational tools. Throughout the experimental phase the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of five different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of DAM against the performance achieved by other twenty well known algorithms. Experimental results have shown that DAM achieved the best performance in three of the five pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets. Experimental results confirm that the proposed algorithm can be a valuable tool for promoting collaborative learning among less experienced physicians.

Pattern classification using smallest normalized difference associative memory

Abstract In this paper a new associative classification algorithm is presented. The proposed algorithm overcomes the limitations of the original Alpha-Beta associative memory, while maintaining the fundamental set recalling capacity. This algorithm has two phases. The first phase is based on an Alpha-Beta auto-associative memory, which works in the domain of real numbers, unlike the traditional Alpha-Beta associative memories. In the second phase, normalized difference between the results of first phase and every pattern of the fundamental set is calculated. In order to demonstrate the behaviour and accuracy of the algorithm, multiple well known datasets and classification algorithms have been used. Experimental results have shown that our proposal achieved the best performance in three of the eight pattern classification problems in the medical field, using Stratified 10 Fold cross-validation. Our proposal achieved the best classification accuracy averaged over the all datasets addressed in the present work. Experimental results and statistical significance tests, allow us to affirm that the proposed model is an efficient alternative to perform pattern classification tasks.

Optimized Associative Memories for Feature Selection

Performance in most pattern classifiers is improved when redundant or irrelevant features are removed, however, this is mainly achieved by high demanding computational methods or successive classifiers construction. This paper shows how Associative Memories can be used to get a mask value which represents a subset of features that clearly identifies irrelevant or redundant information for classification purposes, therefore, classification accuracy is improved while significant computational costs in the learning phase are reduced. An optimal subset of features allows register size optimization, which contributes not only to significant power savings but to a smaller amount of synthesized logic, furthermore, improved hardware architectures are achieved due to functional units size reduction, as a result, it is possible to implement parallel and cascade schemes for pattern classifiers on the same ASIC.

FPGA Implementation of Parallel Alpha-Beta Associative Memories

Associative memories have a number of properties, including a rapid, compute efficient best-match and intrinsic noise tolerance that make them ideal for many applications. However, a significant bottleneck to the use of associative memories in real-time systems is the amount of data that requires processing. Notwithstanding, Alpha-Beta Associative Memories have been widely used for color matching in industrial processes [1], text translation [2] and image retrieval applications [3]. The aim of this paper is to present the work that produced a dedicated hardware design, implemented on a field programmable gate array (FPGA) that applies the Alpha-Beta Associative Memories model for fingerprint verification tasks. Along the experimental phase, performance of the proposed associative memory architecture is measured by learning large sequences of symbols and recalling them successfully. As a result, a simple but efficient embedded processing architecture that overcomes various challenges involved in pattern recognition tasks is implemented on a Xilinx Spartan3 FPGA.

Thresholded Learning Matrix for Efficient Pattern Recalling

The Lernmatrix, which is the first known model of associative memory, is a heteroassociative memory that can easily work as a binary pattern classifier if output patterns are appropriately chosen. However, this mathematical model undergoes fundamental patterns misclassification whenever crossbars saturation occurs. In this paper, a novel algorithm that overcomes Lernmatrixweaknesses is proposed. The crossbars saturation occurrence is solved by means of a dynamic threshold value which is computed for each recalled pattern. The algorithm applies the dynamic threshold value over the ambiguously recalled class vector in order to obtain a sentinel vector which is used for uncertainty elimination purposes. The efficiency and effectiveness of our approach is demonstrated through comparisons with other methods using real-world data.

Alpha-Beta Weightless Neural Networks

A novel weightless neural network model is presented, based on the known operations Alpha and Beta, and three original operations proposed. The new model of weightless neural network has been called CAINN --- Computing Artificial Intelligent Neural Network. The experimental aspect is presented by applying the CAINN model to several known databases. Also, comparative studies about the performance of the CAINN model concerning ADAM weightless neural network and other models are reported. Results exhibit the superiority of the CAINN model over the ADAM model, its counterpart as a weightless neural network; and over other models immersed in the state of the art of neural networks; taking into account the No Free Lunch theorem.

An Associative Memory Approach to Healthcare Monitoring and Decision Making

The rapid proliferation of connectivity, availability of ubiquitous computing, miniaturization of sensors and communication technology, have changed healthcare in all its areas, creating the well-known healthcare paradigm of e-Health. In this paper, an embedded system capable of monitoring, learning and classifying biometric signals is presented. The machine learning model is based on associative memories to predict the presence or absence of coronary artery disease in patients. Classification accuracy, sensitivity and specificity results show that the performance of our proposal exceeds the performance achieved by each of the fifty widely known algorithms against which it was compared.

Theoretical Foundations for the Alpha-Beta Associative Memories: 10 Years of Derived Extensions, Models, and Applications

The current paper contains the theoretical foundation for the off-the-mainstream model known as Alpha-Beta associative memories (