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Advances in Artificial Intelligence: Selected Papers from MICAI 2013, 2014 and 2015—12th, 13th and 14th Mexican International Conferences on Artificial Intelligence

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 May 2016) | Viewed by 51821

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Dr. Miguel González-Mendoza

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Guest Editor

School of Engineering and Sciences, Tecnologico de Monterrey, Campus Estado de Mexico, Mexico City 52926, Mexico
Interests: machine learning and soft computing; web ontologies and designing of mobile applications; business intelligence framework; ambient intelligence framework
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Dr. Lourdes Martínez-Villaseñor

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Facultad de Ingeniería, Universidad Panamericana, Mexico City 03920, Mexico
Interests: ubiquitous user modeling interoperability; wearable sensors; machine learning
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Prof. Dr. Hiram Ponce

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Guest Editor

Facultad de Ingeniería, Universidad Panamericana, Mexico City 03920, Mexico
Interests: machine learning; nature-inspired computing; robotics; ambient assisted living; sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mexican International Conference on Artificial Intelligence (MICAI) was characterized as a premier conference in artificial intelligence. It is a high-level, peer-reviewed international conference covering all areas of artificial intelligence, traditionally held in Mexico. The scientific program includes keynote lectures, paper presentations, tutorials, panels, and workshops.

12^th, 13^th and 14^th Mexican International Conferences on Artificial Intelligence (MICAI 2014 and MICAI 2015) were organized by the Mexican Society for Artificial Intelligence (SMIA) and hosted by the Universidad Autónoma Metropolitana Unidad Azcapotzalco, Instituto Tecnológico de Tuxtla Gutiérrez, and the Electric Research Institute, respectively.

Selected papers are invited to submit the extended versions of their original contributions regarding to the following topics, but not limited to, with special emphasis on sensing and ubiquitous technologies:

Sensor Applications with Artificial Intelligence Approaches
Expert Systems and Knowledge-Based Systems
Knowledge Acquisition ,Representation and Management
Multi-agent Systems and Distributed AI
Natural Language Processing
Ontologies
Intelligent Interfaces: Multimedia, Virtual Reality
Machine Learning and Pattern Recognition
Soft Computing including Neural Networks, Fuzzy Logic, Genetic Algorithms
Model-Based Reasoning
Non-monotonic Reasoning
Common Sense Reasoning
Case-Based Reasoning
Spatial and Temporal Reasoning
Constraint Programming
Robotics
Planning and Scheduling
Hybrid Intelligent Systems
Bioinformatics and Medical Applications
Data Mining

Dr. Miguel González-Mendoza
Dr. Ma. Lourdes Martínez-Villaseñor
Dr. Hiram Ponce
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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3969 KiB

Open AccessArticle

Emotion-Bracelet: A Web Service for Expressing Emotions through an Electronic Interface

by Alicia Martinez, Hugo Estrada, Alejandra Molina, Manuel Mejia and Joaquin Perez

Sensors 2016, 16(12), 1980; https://doi.org/10.3390/s16121980 - 24 Nov 2016

Cited by 2 | Viewed by 6002

Abstract

The mechanisms to communicate emotions have dramatically changed in the last 10 years with social networks, where users massively communicate their emotional states by using the Internet. However, people with socialization problems have difficulty expressing their emotions verbally or interpreting the environment and providing an appropriate emotional response. In this paper, a novel solution called the Emotion-Bracelet is presented that combines a hardware device and a software system. The proposed approach identifies the polarity and emotional intensity of texts published on a social network site by performing real-time processing using a web service. It also shows emotions with a LED matrix using five emoticons that represent positive, very positive, negative, very negative, and neutral states. The Emotion-Bracelet is designed to help people express their emotions in a non-intrusive way, thereby expanding the social aspect of human emotions. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence: Selected Papers from MICAI 2013, 2014 and 2015—12th, 13th and 14th Mexican International Conferences on Artificial Intelligence)

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1707 KiB

Open AccessArticle

Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

by Felix F. Gonzalez-Navarro, Margarita Stilianova-Stoytcheva, Livier Renteria-Gutierrez, Lluís A. Belanche-Muñoz, Brenda L. Flores-Rios and Jorge E. Ibarra-Esquer

Sensors 2016, 16(11), 1483; https://doi.org/10.3390/s16111483 - 26 Oct 2016

Cited by 30 | Viewed by 7219

Abstract

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. Full article

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659 KiB

Open AccessArticle

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

by Hiram Ponce, Luis Miralles-Pechuán and María De Lourdes Martínez-Villaseñor

Sensors 2016, 16(11), 1715; https://doi.org/10.3390/s16111715 - 25 Oct 2016

Cited by 34 | Viewed by 6891

Abstract

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. Full article

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1021 KiB

Open AccessArticle

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients

by Andrzej W. Przybyszewski, Mark Kon, Stanislaw Szlufik, Artur Szymanski, Piotr Habela and Dariusz M. Koziorowski

Sensors 2016, 16(9), 1498; https://doi.org/10.3390/s16091498 - 14 Sep 2016

Cited by 29 | Viewed by 5183

Abstract

We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease’s symptoms, with the help of various therapies. In the case of Parkinson’s disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist’s diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient ‘well-being’ scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naïve Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD. Full article

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2958 KiB

Open AccessArticle

Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs

by Helena Gómez-Adorno, Grigori Sidorov, David Pinto, Darnes Vilariño and Alexander Gelbukh

Sensors 2016, 16(9), 1374; https://doi.org/10.3390/s16091374 - 29 Aug 2016

Cited by 24 | Viewed by 6050

Abstract

We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution. Full article

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Open AccessArticle

Mining IP to Domain Name Interactions to Detect DNS Flood Attacks on Recursive DNS Servers

by Roberto Alonso, Raúl Monroy and Luis A. Trejo

Sensors 2016, 16(8), 1311; https://doi.org/10.3390/s16081311 - 17 Aug 2016

Cited by 12 | Viewed by 8387

Abstract

The Domain Name System (DNS) is a critical infrastructure of any network, and, not surprisingly a common target of cybercrime. There are numerous works that analyse higher level DNS traffic to detect anomalies in the DNS or any other network service. By contrast, few efforts have been made to study and protect the recursive DNS level. In this paper, we introduce a novel abstraction of the recursive DNS traffic to detect a flooding attack, a kind of Distributed Denial of Service (DDoS). The crux of our abstraction lies on a simple observation: Recursive DNS queries, from IP addresses to domain names, form social groups; hence, a DDoS attack should result in drastic changes on DNS social structure. We have built an anomaly-based detection mechanism, which, given a time window of DNS usage, makes use of features that attempt to capture the DNS social structure, including a heuristic that estimates group composition. Our detection mechanism has been successfully validated (in a simulated and controlled setting) and with it the suitability of our abstraction to detect flooding attacks. To the best of our knowledge, this is the first time that work is successful in using this abstraction to detect these kinds of attacks at the recursive level. Before concluding the paper, we motivate further research directions considering this new abstraction, so we have designed and tested two additional experiments which exhibit promising results to detect other types of anomalies in recursive DNS servers. Full article

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8096 KiB

Open AccessArticle

A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors

by Ricardo Acevedo-Avila, Miguel Gonzalez-Mendoza and Andres Garcia-Garcia

Sensors 2016, 16(6), 782; https://doi.org/10.3390/s16060782 - 28 May 2016

Cited by 12 | Viewed by 11022

Abstract

Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms. Full article

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Advances in Artificial Intelligence: Selected Papers from MICAI 2013, 2014 and 2015—12th, 13th and 14th Mexican International Conferences on Artificial Intelligence

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