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

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

Deadline for manuscript submissions: closed (31 May 2016)

Special Issue Editors

Guest Editor
Dr. Miguel González-Mendoza

School of Engineering and Sciences, Tecnologico de Monterrey, Campus Estado de Mexico, 52926 Mexico City, Mexico
Website | E-Mail
Phone: +52 55 58645875
Interests: machine learning and soft computing; Web ontologies and designing of mobile applications; business intelligence framework; ambient intelligence framework
Guest Editor
Dr. Ma. Lourdes Martínez-Villaseñor

Faculty of Engineering, Universidad Panamericana, 03920 Mexico City, Mexico
E-Mail
Fax: +52 55 54821600 Ext 5227
Interests: ubiquitous user modeling interoperability; wearable sensors; machine learning
Guest Editor
Dr. Hiram Ponce

Faculty of Engineering, Universidad Panamericana, 03920 Mexico City, Mexico
Website | E-Mail
Phone: +52 55 54821600 Ext 5254
Interests: machine learning and soft computing; nature-inspired computing; control systems; sensors and mechatronics

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.

12th, 13th and 14th 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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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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Research

Open AccessArticle Emotion-Bracelet: A Web Service for Expressing Emotions through an Electronic Interface
Sensors 2016, 16(12), 1980; doi:10.3390/s16121980
Received: 1 June 2016 / Revised: 23 October 2016 / Accepted: 9 November 2016 / Published: 24 November 2016
PDF Full-text (3969 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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
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Open AccessArticle Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods
Sensors 2016, 16(11), 1483; doi:10.3390/s16111483
Received: 30 May 2016 / Revised: 29 July 2016 / Accepted: 9 August 2016 / Published: 26 October 2016
PDF Full-text (1707 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
[...] Read more.
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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Open AccessArticle A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks
Sensors 2016, 16(11), 1715; doi:10.3390/s16111715
Received: 1 June 2016 / Revised: 6 October 2016 / Accepted: 7 October 2016 / Published: 25 October 2016
Cited by 3 | PDF Full-text (659 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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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Open AccessArticle Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients
Sensors 2016, 16(9), 1498; doi:10.3390/s16091498
Received: 8 July 2016 / Revised: 29 August 2016 / Accepted: 31 August 2016 / Published: 14 September 2016
Cited by 4 | PDF Full-text (1021 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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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Open AccessArticle Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs
Sensors 2016, 16(9), 1374; doi:10.3390/s16091374
Received: 31 May 2016 / Revised: 31 July 2016 / Accepted: 19 August 2016 / Published: 29 August 2016
Cited by 3 | PDF Full-text (2958 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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
Sensors 2016, 16(8), 1311; doi:10.3390/s16081311
Received: 1 June 2016 / Revised: 9 August 2016 / Accepted: 13 August 2016 / Published: 17 August 2016
Cited by 1 | PDF Full-text (1077 KB) | HTML Full-text | XML Full-text
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,
[...] Read more.
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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Open AccessArticle A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors
Sensors 2016, 16(6), 782; doi:10.3390/s16060782
Received: 5 February 2016 / Revised: 17 May 2016 / Accepted: 25 May 2016 / Published: 28 May 2016
Cited by 2 | PDF Full-text (8096 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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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