Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

Case-based reasoning (CBR) is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CB

Electromiographic Signal Processing Using Embedded Artificial Intelligence: An Adaptive Filtering Approach

In recent times, Artificial Intelligence (AI) has become ubiquitous in technological fields, mainly due to its ability to perform computations in distributed systems or the cloud. Nevertheless, for some applications -as the case of EMG signal processing- it may be highly advisable or even mandatory an on-the-edge processing, i.e., an embedded processing methodology. On the other hand, sEMG signals have been traditionally processed using LTI techniques for simplicity in computing. However, making this strong assumption leads to information loss and spurious results. Considering the current advances in silicon technology and increasing computer power, it is possible to process these biosignals with AI-based techniques correctly. This paper presents an embedded-processing-based adaptive filtering system (here termed edge AI) being an outstanding alternative in contrast to a sensor-computer- actuator system and a classical digital signal processor (DSP) device. Specifically, a PYNQ-Z1 embedded system is used. For experimental purposes, three methodologies on similar processing scenarios are compared. The results show that the edge AI methodology is superior to benchmark approaches by reducing the processing time compared to classical DSPs and general standards while maintaining the signal integrity and processing it, considering that the EMG system is not LTI. Likewise, due to the nature of the proposed architecture, handling information exhibits no leakages. Findings suggest that edge computing is suitable for EMG signal processing when an on-device analysis is required

Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

Case-based reasoning (CBR) is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CB

Analysis of OM-Based Literature Reviews on Facility Layout Planning

972-981This work consists of a systematized review of the state of the art of reviews for the problem of Facility Layout Planning (FLP) within the Operations Management (OM) field to support the decisions taken for the improvement of the manufacturing and logistics in a factory environment. The first phase begins by defining the search strategies for obtaining the scientific literature, for which we used ten databases. With these, a base of 112 articles was obtained, but after the systematized process was reduced to 32 directly related articles. In the second phase, we executed a Dimensional analysis of these literature review articles employing a quantitative analysis of the sections and subsections of the selected articles. The third phase comprises the identification of gaps and future research lines. Finally, the conclusions obtained from the systematized review process are presented

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

562732

Kernel-based framework for spectral dimensionality reduction and clustering formulation: A theoretical study

This work outlines a unified formulation to represent spectral approaches for both dimensionality reduction and clustering. Proposed formulation starts with a generic latent variable model in terms of the projected input data matrix.Particularly, such a projection maps data onto a unknown high-dimensional space. Regarding this model, a generalized optimization problem is stated using quadratic formulations and a least-squares support vector machine.The solution of the optimization is addressed through a primal-dual scheme.Once latent variables and parameters are determined, the resultant model outputs a versatile projected matrix able to represent data in a low-dimensional space, as well as to provide information about clusters. Particularly, proposedformulation yields solutions for kernel spectral clustering and weighted-kernel principal component analysis

Structural capital model for universities based on JDL data fusion model and information quality [Modelo de capital estructural para universidades basado en el modelo de fusión de datos JDL y la calidad de la información]

Intellectual capital is one of the most critical intangible active assets for universities, and there are multiple models to value it through the human, structural, and relational components. However, this is an open field of research that still demands new solutions to assess it effectively from each of its components. For the assessment of the structural component in higher education institutions, this study proposes a model that combines the assessment of the quality of information and the JDL data fusion model (joint directors of laboratories), which has been used in applications military. The proposed model is original in the methods used and their association, distributed in six levels that execute the pre-processing of the information, valuation of objects, valuation of the situation and the risk, and the refinement of the process. Besides, it evaluates the quality of the information, its traceability, and context to refine the process and obtain a more objective assessment taking into account the imperfection of the information for decision-making in the management of impact and risk. The model not only allows the assessment of structural capital, but also supports decision-making based on the quality of information and its impact. The functionality of the model is described by levels. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved

Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)

This work presents a wireless sensor network (WSN) system able to determine the water quality of rivers. Particularly, we consider the Tahuando River from Ibarra, Ecuador, as a case study. The main goal of this research is to determine the river’s status throughout its route, by generating data reports into an interactive user interface. To this end, we use an array of sensors collecting several measures such as: turbidity, temperature, water quality, pH, and temperature. Subsequently, from the information collected on an Internet-of-Things (IoT) server, we develop a data analysis scheme with both data representation and supervised classification. As an important result, our system outputs a map that shows the contamination levels of the river at different regions. Furthermore, in terms of data analysis performance, the proposed system reduces the data matrix by 97% from its original size, while it reaches a classification performance over 90%. Furthermore, as an additional remarkable result, we here introduce the so-called quantitative metric of balance (QMB), which measures the balance or ratio between performance and power consumption