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Advances in Computational Intelligence : Preface
(Springer Verlag, 2018)
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The Mexican International Conference on Artificial Intelligence (MICAI) is a yearly international conference series that has been organized by the Mexican Society of Artificial Intelligence (SMIA) since 2000. MICAI is a major international artificial intelligence forum and the main event in the academic life of the country’s growing artificial intelligence community. MICAI conferences publish high-quality papers in all areas of artificial intelligence and its applications. The proceedings of the previous MICAI events have been published by Springer in its Lecture Notes in Artificial Intelligence series, vol. 1793, 2313, 2972, 3789, 4293, 4827, 5317, 5845, 6437, 6438, 7094, 7095, 7629, 7630, 8265, 8266, 8856, 8857, 9413, 9414, 10061, 10062, 10632, and 10633. Since its foundation in 2000, the conference has been growing in popularity and improving in quality. ©2018 Springer Verlag.
26 1
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Predicting climate conditions using Internet-of- Things and artificial hydrocarbon networks
(2017)
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Gutiérrez, Sebastián
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Montoya Pacheco, Alejandro
The prediction and understanding of environmental conditions is of great importance to prevent and analyze changes in environment, supporting meteorological based sectors, such as agriculture. In that sense, this paper presents an Internet of Things (IoT) system for predicting climate conditions, i.e. temperature, using artificial intelligence by means of a supervised learning method, the artificial hydrocarbon networks model. It allows predicting the temperature of remote locations using information from a web service comparing it with a field temperature sensor. Experimental results of the supervised learning model are presented in two modes: offline training to detect the suitable parameters of the model and testing to validate the model with new data retrieval from the web service. Preliminary results conclude that artificial hydrocarbon networks model predicts remote temperature with mean error of 0.05°c in testing mode. © 2018 IMEKO-International Measurement Federation Secretariat. All rights reserved.
12 2
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A Hierarchical Reinforcement Learning Based Artificial Intelligence for Non-Player Characters in Video Games
(2014)
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Padilla, Ricardo
Nowadays, video games conforms a huge industry that is always developing new technology. In particular, artificial intelligence techniques have been used broadly in the well-known non-player characters (NPC) given the opportunity to users to feel video games more real. This paper proposes the usage of the MaxQ-Q hierarchical reinforcement learning algorithm in non-player characters in order to increase the experience of the user in terms of naturalness. A case study of an NPC with the proposed artificial intelligence based algorithm in a first personal shooter video game was developed. Experimental results show that this implementation improves naturalness from the user’s point of view. In addition, the proposed MaxQ-Q based algorithm in NPCs allow to programmers a robust way to give artificial intelligence to them.
Scopus© Citations 5 40 1
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Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition
(2015)
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Miralles-Pechuán, Luis
In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In context-aware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.
Scopus© Citations 5 14 1
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Artificial Hydrocarbon Networks for Online Sales Prediction
(2015)
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Miralles-Pechuán, Luis
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Online retail sales have been growing worldwide in the last decade. In order to cope with this high dynamicity and market share competition, online retail sales prediction and online advertising have become very important to answer questions of pricing decisions, advertising responsiveness, and product demand. To make adequate investment in products and channels it is necessary to have a model that relates certain features of the product with the number of sales that will occur in the future. In this paper we describe a comparative analysis of machine learning techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). This method is a new type of machine learning that have proved to adapt very well to a wide spectrum of problems of regression and classification. Thus, we use artificial hydrocarbon networks for predicting the number of online sales, and then we compare their performance with other ten well-known methods of machine learning regression, obtaining promising results.
Scopus© Citations 9 14 1
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Motion magnification using the Hermite transform
(2015)
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Gomez-Coronel, Sandra L.
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Escalante-Ramírez, Boris
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Mora Esquivel, Juan I.
We present an Eulerian motion magnification technique with a spatial decomposition based on the Hermite Transform (HT). We compare our results to the approach presented in. We test our method in one sequence of the breathing of a newborn baby and on an MRI left ventricle sequence. Methods are compared using quantitative and qualitative metrics after the application of the motion magnification algorithm.
Scopus© Citations 9 32 1
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The development of an artificial organic networks toolkit for LabVIEW
(2015)
Ponce, Pedro
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Molina, Arturo
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Two of the most challenging problems that scientists and researchers face when they want to experiment with new cutting-edge algorithms are the time-consuming for encoding and the difficulties for linking them with other technologies and devices. In that sense, this article introduces the artificial organic networks toolkit for LabVIEW™ (AON-TL) from the implementation point of view. The toolkit is based on the framework provided by the artificial organic networks technique, giving it the potential to add new algorithms in the future based on this technique. Moreover, the toolkit inherits both the rapid prototyping and the easy-to-use characteristics of the LabVIEW™ software (e.g., graphical programming, transparent usage of other softwares and devices, built-in programming event-driven for user interfaces), to make it simple for the end-user. In fact, the article describes the global architecture of the toolkit, with particular emphasis in the software implementation of the so-called artificial hydrocarbon networks algorithm. Lastly, the article includes two case studies for engineering purposes (i.e., sensor characterization) and chemistry applications (i.e., blood–brain barrier partitioning data model) to show the usage of the toolkit and the potential scalability of the artificial organic networks technique. © 2015 Wiley Periodicals, Inc.
Scopus© Citations 22 19 1
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Bio-inspired Training Algorithms for Artificial Hydrocarbon Networks: A Comparative Study
(2014)
Artificial hydrocarbon networks (AHN) is a supervised learning algorithm inspired on chemical organic compounds. Its first implementation occupied the well-known least squares estimates (LSE) as part of the training algorithm. Unsurprisingly, AHN cannot converge to suitable solutions when dealing with high dimensional data, falling into the curse of dimensionality. In that sense, this paper proposes two hybrid training algorithms for AHN using bio-inspired algorithms, i.e. Simulated annealing and particle swarm optimization, and compares them against the LSE-based method. Experimental results show that these bio-inspired algorithms improve the performance of artificial hydrocarbon networks, concluding that these hybrid algorithms can be used as alternative learning algorithms for high dimensional data.
Scopus© Citations 3 12 1
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A novel robust liquid level controller for coupled-tanks systems using artificial hydrocarbon networks
(2015)
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Ponce, Pedro
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Bastida, Héctor
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Molina, Arturo
This paper proposes a robust liquid-level controller for coupled-tanks systems when dealing with variable discharge rates at the secondary tank, based on a hybrid fuzzy inference system that uses artificial hydrocarbon networks at the defuzzification step, so-called fuzzy-molecular control. The design methodology of the proposed controller is presented and discussed. In addition, a case study was run over the CE105 TecQuipment coupled-tanks system in order to implement and validate the fuzzy-molecular controller proposed in that work. A comparative evaluation with the proposed controller, a conventional PID controller specifically designed for this system and a QFT robust controller, was done. Also, a performance evaluation in terms of robustness, reference-tracking in a fixed operating point and reference-tracking in a variable operating point on-the-fly was run and analyzed. Results conclude that the proposed fuzzy-molecular controller deals with uncertainty and noise, can handle dynamics in operating point, a model of the plant is not required, and it is easy and simple to implement in comparison with other controllers in literature. To this end, the proposed fuzzy-molecular liquid-level controller inherits characteristics from fuzzy controllers and artificial hydrocarbon networks in order to implement an advanced robust and intelligent control system.
Scopus© Citations 24 8 2
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The Power of Natural Inspiration in Control Systems
(2015)
Ayala-Solares, José Roberto
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Throughout history, nature has always been an inspiration for mankind. It is not an exaggeration to say that almost every human invention, from engineering to social sciences, has been an attempt to replicate nature. In fact, nature continues to play an important roll in different human activities. From a scientific perspective, nature-inspired methods have proven to be an efficient tool for tackling real-life problems that are difficult to solve because of their high complexity or the limitation of resources to analyze them. The core idea is the fact that several natural phenomena, from simple to complex, always try to optimize certain parameters. Thus, this chapter gives an overview of nature-inspired methods from computational point of view, and summarizes key contributions of this book that focuses on methods that can simulate natural phenomena using computers, and the benefits of applying this methodology to the analysis and design of engineering control systems. © Springer International Publishing Switzerland 2016.
Scopus© Citations 4 9 1

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