Brenda L. Flores-Rios

Tracking the Evolution of the Internet of Things Concept Across Different Application Domains

Both the idea and technology for connecting sensors and actuators to a network to remotely monitor and control physical systems have been known for many years and developed accordingly. However, a little more than a decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This paper presents the changes that the IoT has undertaken since its conception and research on how technological advances have shaped it and fostered the arising of derived names suitable to specific domains. A two-step literature review through major publishers and indexing databases was conducted; first by searching for proposals on the Internet of Things concept and analyzing them to find similarities, differences, and technological features that allow us to create a timeline showing its development; in the second step the most mentioned names given to the IoT for specific domains, as well as closely related concepts were identified and briefly analyzed. The study confirms the claim that a consensus on the IoT definition has not yet been reached, as enabling technology keeps evolving and new application domains are being proposed. However, recent changes have been relatively moderated, and its variations on application domains are clearly differentiated, with data and data technologies playing an important role in the IoT landscape.

Explicit and tacit knowledge conversion effects, in software engineering undergraduate students

This study evaluates the effect of conversion between tacit and explicit knowledge, and its influence on Software engineering and Software Process Improvement in the context of a small school software company in which undergraduate students participate as personnel. A survey measurement instrument was applied to the last three generations of students. The effect was measured from an interaction of the four modes of the SECI model knowledge conversion using regression analysis associated with four hypotheses study. The findings show that students are able to generate tacit and explicit knowledge in a similar way to software organizations. This study is considered a contribution of both academia and software industry that encourages this type of experiences in undergraduate students and prepares them as intellectual capital with an organizational culture that shares knowledge.

Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

Feature Selection in Spectroscopy Brain Cancer Data

In cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of different tumor types provides better treatment and toxicity minimization on patients. Predicting cancer types using non-invasive information –e.g. \(^1\)H-MRS data– could avoid patients to suffer collateral problems derived from exploration techniques that require surgery. Two Feature Selection Algorithms specially designed to be use in \(^1\)H-MRS Proton Magnetic Resonance Spectroscopy data of brain tumors are presented. These two algorithms take advantage of two distinctive aspects: first, metabolite levels are quite different between types of tumors and two, \(^{1}\)H-MRS data possess a quasi-temporal series shape. Experimental readings on an international data set show highly competitive models in terms of accuracy, complexity and medical interpretability.

Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques

Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic strategies are known to halt disease progression or reverse muscle weakness or atrophy. Many genes may be incorrectly regulated in affected muscle tissue, but the mechanisms responsible for the progressive muscle weakness remain largely unknown. Although machine learning (ML) has made significant inroads in biomedical disciplines such as cancer research, no reports have yet addressed FSHD analysis using ML techniques. This study explores a specific FSHD data set from a ML perspective. We report results showing a very promising small group of genes that clearly separates FSHD samples from healthy samples. In addition to numerical prediction figures, we show data visualizations and biological evidence illustrating the potential usefulness of these results.

Glucose Oxidase Biosensor Modeling by Machine Learning Methods

Biosensors are small analytical devices incorporating a biological element for signal detection. The main function of a biosensor is to generate an electrical signal which is proportional to a specific analyte i.e. to translate a biological signal into an electrical reading. Nowadays its technological attractiveness resides in its fast performance, and its highly sensitivity and continuous measuring capabilities; however, its understanding is still under research. This paper focuses to contribute to the state of the art of this growing field of biotechnology specially on Glucose Oxidase Biosensors (GOB) modeling through statistical learning methods from a regression perspective. It models the amperometric response of a GOB with dependent variables under different conditions such as temperature, benzoquinone, PH and glucose, by means of well known machine learning algorithms. Support Vector Machines(SVM), Artificial Neural Networks (ANN) and Partial least squares (PLS) are the algorithms selected to do the regression task.