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.
{"title":"Bio-inspired Training Algorithms for Artificial Hydrocarbon Networks: A Comparative Study","authors":"Hiram Ponce","doi":"10.1109/MICAI.2014.31","DOIUrl":"https://doi.org/10.1109/MICAI.2014.31","url":null,"abstract":"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.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126239826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. J. S. García, C. P. Herrero, I. H. P. Torres, Jaime A. Hernandez, Maya Carrillo, Sergio O. Zamorano, Ismael Mena
In this paper a process of creating ontologies system based on other existing ontologies is described, in order to response biomedical spatial queries on the Web. GeOntoMex is a Mexican spatial ontology, which is structured according to its political-administrative division, in addition, axioms are defined to represent the spatial relationships between geographic entities. Moreover, the Health Onto Mex ontology, whose structure corresponds to the INEGI's taxonomy (National Institute of Statistics and Geography) health services, is presented. Later, a system based on the aforementioned ontologies is shown. The system named Geo Health Onto Mex, could lead to more accurate user queries that requires a specific medical service in a given geographical area.
本文描述了在已有本体的基础上创建本体系统的过程，以响应Web上的生物医学空间查询。GeOntoMex是墨西哥的一种空间本体，它根据其政治-行政区划进行结构，并定义公理来表示地理实体之间的空间关系。此外，还提出了卫生到墨西哥本体，其结构与国家统计和地理研究所的卫生服务分类相对应。稍后，将展示一个基于上述本体的系统。这个名为Geo Health Onto Mex的系统可以为用户提供更准确的查询，这些查询需要在给定的地理区域提供特定的医疗服务。
{"title":"Development of an Ontologies System for Spatial Biomedical Applications","authors":"M. J. S. García, C. P. Herrero, I. H. P. Torres, Jaime A. Hernandez, Maya Carrillo, Sergio O. Zamorano, Ismael Mena","doi":"10.1109/MICAI.2014.10","DOIUrl":"https://doi.org/10.1109/MICAI.2014.10","url":null,"abstract":"In this paper a process of creating ontologies system based on other existing ontologies is described, in order to response biomedical spatial queries on the Web. GeOntoMex is a Mexican spatial ontology, which is structured according to its political-administrative division, in addition, axioms are defined to represent the spatial relationships between geographic entities. Moreover, the Health Onto Mex ontology, whose structure corresponds to the INEGI's taxonomy (National Institute of Statistics and Geography) health services, is presented. Later, a system based on the aforementioned ontologies is shown. The system named Geo Health Onto Mex, could lead to more accurate user queries that requires a specific medical service in a given geographical area.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121340504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The dimensionality reduction by feature selection is one of the fundamental steps in the pre-processing data stage in the intelligent data analysis. Feature selection (FS) literature embodies a wide spectrum of algorithms, methods and strategies, but mostly all fall into two classes, the well known wrappers and filters. The decision of which feature or variable is selected or discarded from the best current subset is still subject of research nowadays. In this paper, an experimental study about non-deterministic local search methods as main engine to this decision making is presented. The Simulated Annealing Algorithm, the Genetic Algorithm, the Tabu Search and the Threshold Accepting Algorithm are analyzed. They are used to select subset of features on several real and artificial data sets with different configurations -- i.e. Continuous and discrete data, high-low number of cases/features -- in a wrapper fashion. The Nearest Neighbor Classifier, the Linear and Quadratic Discriminant Classifier, the Naive Bayes classifier and the Support Vector Machine are evaluated as the performance function in the wrapper scheme.
{"title":"Non-deterministic Local Search Methods for Feature Selection: An Experimental Study","authors":"Marina P. Fernandez-Perez, F. F. González-Navarro","doi":"10.1109/MICAI.2014.16","DOIUrl":"https://doi.org/10.1109/MICAI.2014.16","url":null,"abstract":"The dimensionality reduction by feature selection is one of the fundamental steps in the pre-processing data stage in the intelligent data analysis. Feature selection (FS) literature embodies a wide spectrum of algorithms, methods and strategies, but mostly all fall into two classes, the well known wrappers and filters. The decision of which feature or variable is selected or discarded from the best current subset is still subject of research nowadays. In this paper, an experimental study about non-deterministic local search methods as main engine to this decision making is presented. The Simulated Annealing Algorithm, the Genetic Algorithm, the Tabu Search and the Threshold Accepting Algorithm are analyzed. They are used to select subset of features on several real and artificial data sets with different configurations -- i.e. Continuous and discrete data, high-low number of cases/features -- in a wrapper fashion. The Nearest Neighbor Classifier, the Linear and Quadratic Discriminant Classifier, the Naive Bayes classifier and the Support Vector Machine are evaluated as the performance function in the wrapper scheme.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134237132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using EMG signals as control signals has been a widely accepted option in the last decades. Using a wide array of techniques, EMG signals can be used in a variety of practical ways, from prostethics to exoesqueletons, however a concrete functional relationship between EMG signals and the dynamic and kinematic aspects of the upper limbs has not been established. Nowadays, almost every device that uses EMG signals uses them for classification purposes. In this work, we employ Fourier analysis in conjunction with other signal processing tools to treat the EMG signal, the treated signal is then used as an input of an artificial neural network in order to establish a simplified functional relationship between EMG and the upper limbs. We also employed other traditional signal processing methods for comparison purposes.
{"title":"Establishing a Simplified Functional Relationship between EMG Signals and Actuation Signals Using Artificial Neural Networks","authors":"Raul Almada-Aguilar, L. Torres-Treviño, G. Quiroz","doi":"10.1109/MICAI.2014.26","DOIUrl":"https://doi.org/10.1109/MICAI.2014.26","url":null,"abstract":"Using EMG signals as control signals has been a widely accepted option in the last decades. Using a wide array of techniques, EMG signals can be used in a variety of practical ways, from prostethics to exoesqueletons, however a concrete functional relationship between EMG signals and the dynamic and kinematic aspects of the upper limbs has not been established. Nowadays, almost every device that uses EMG signals uses them for classification purposes. In this work, we employ Fourier analysis in conjunction with other signal processing tools to treat the EMG signal, the treated signal is then used as an input of an artificial neural network in order to establish a simplified functional relationship between EMG and the upper limbs. We also employed other traditional signal processing methods for comparison purposes.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123847657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juan Carlos Espinosa Ceniceros, S. E. Schaeffer, S. Villarreal
Augmented-reality (AR) interfaces are receiving growing attention due to their versatility and usefulness in numerous application areas. In this paper, we tackle the problem of environmental awareness in consumers at the time of purchase: we design, implement, and evaluate a novel interface for overlaying product ecological information in the consumer's field of vision. The identification of the product is done by computer-vision techniques that detect logotypes of brands as well as ecological labels (such as recycling symbols) when the user holds a product package. The recognition is performed with feature-detection algorithms. We evaluate the interface in terms of computational load for image processing and usability, reporting favorable results in terms of computation time, effect on the ecological consciousness of the users, and the usability.
{"title":"Augmented Reality for Green Consumption: Using Computer Vision to Inform the Consumers at Time of Purchase","authors":"Juan Carlos Espinosa Ceniceros, S. E. Schaeffer, S. Villarreal","doi":"10.1109/MICAI.2014.13","DOIUrl":"https://doi.org/10.1109/MICAI.2014.13","url":null,"abstract":"Augmented-reality (AR) interfaces are receiving growing attention due to their versatility and usefulness in numerous application areas. In this paper, we tackle the problem of environmental awareness in consumers at the time of purchase: we design, implement, and evaluate a novel interface for overlaying product ecological information in the consumer's field of vision. The identification of the product is done by computer-vision techniques that detect logotypes of brands as well as ecological labels (such as recycling symbols) when the user holds a product package. The recognition is performed with feature-detection algorithms. We evaluate the interface in terms of computational load for image processing and usability, reporting favorable results in terms of computation time, effect on the ecological consciousness of the users, and the usability.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129925857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the present article was implemented a maximum sensibility neural network in a reconfigurable logical electronic structure (cell) in which different basic logical functions and combinational logic circuits as comparators, multiplexers and encoders are obtained. This neural network has advantages like easy implementation and a quick learning based on manipulation of the information in place of a gradient algorithm. The reconfiguration of the cell it will realized by modifying one specific input that will change de logical function.
{"title":"Reconfigurable Logical Cells Using a Maximum Sensibility Neural Network","authors":"Manuel Ortiz Salazar, L. Torres-Treviño","doi":"10.1109/MICAI.2014.23","DOIUrl":"https://doi.org/10.1109/MICAI.2014.23","url":null,"abstract":"In the present article was implemented a maximum sensibility neural network in a reconfigurable logical electronic structure (cell) in which different basic logical functions and combinational logic circuits as comparators, multiplexers and encoders are obtained. This neural network has advantages like easy implementation and a quick learning based on manipulation of the information in place of a gradient algorithm. The reconfiguration of the cell it will realized by modifying one specific input that will change de logical function.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114614205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mario Aguilera-Ruiz, L. Torres-Treviño, A. Rodríguez-Liñán
In this paper, a maximum sensibility neural network is proposed to make an online learning system of a inverse controller of a plant. This neural network is trained to learn the response of the plant to different random inputs. Once the network is trained, it can be used to control the plant to a desired output.
{"title":"Control by Online Learning Using a Maximum Sensibility Neural Network","authors":"Mario Aguilera-Ruiz, L. Torres-Treviño, A. Rodríguez-Liñán","doi":"10.1109/MICAI.2014.24","DOIUrl":"https://doi.org/10.1109/MICAI.2014.24","url":null,"abstract":"In this paper, a maximum sensibility neural network is proposed to make an online learning system of a inverse controller of a plant. This neural network is trained to learn the response of the plant to different random inputs. Once the network is trained, it can be used to control the plant to a desired output.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124714208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The popularity of internet along with the huge number of reviews posted daily via social media, blogs and review sites invokes the research challenges on topic or aspect based analysis. In the recent years, it also has become a challenging task to mine opinions with respect to the aspects from the available unstructured and noisy data. In this paper, we present a novel approach to identify the key terms and its sentiments from the reviews of Restaurants and Laptops with the help of different features and Conditional Random Field based machine learning algorithm. The supervised method achieves F-score of 0.7493380 and 0.6858054 for aspect term identification whereas 0.68982 and 0.6041 of accuracy for aspect based sentiment classification on Restaurant and Laptop reviews, respectively.
{"title":"Identifying Aspects and Analyzing Their Sentiments from Reviews","authors":"Braja Gopal Patra, Niloy J. Mukherjee, Arijit Das, Soumik Mandal, Dipankar Das, Sivaji Bandyopadhyay","doi":"10.1109/MICAI.2014.8","DOIUrl":"https://doi.org/10.1109/MICAI.2014.8","url":null,"abstract":"The popularity of internet along with the huge number of reviews posted daily via social media, blogs and review sites invokes the research challenges on topic or aspect based analysis. In the recent years, it also has become a challenging task to mine opinions with respect to the aspects from the available unstructured and noisy data. In this paper, we present a novel approach to identify the key terms and its sentiments from the reviews of Restaurants and Laptops with the help of different features and Conditional Random Field based machine learning algorithm. The supervised method achieves F-score of 0.7493380 and 0.6858054 for aspect term identification whereas 0.68982 and 0.6041 of accuracy for aspect based sentiment classification on Restaurant and Laptop reviews, respectively.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132018250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knowing in which activities users are involved is an essential part of their context, which become more and more important in modern context-aware applications, but determining these activities could be a daunting task. Many sensors have been used as information source for guessing human activity, such as accelerometers, video cameras, etc., but recently the availability of a sophisticated sensor designed specifically for tracking humans, as is the Microsoft Kinect has opened new opportunities. The aim of this paper is to determine some human activities, such as eating, reading, drinking, etc., while the person is seated, using the Kinect skeleton structure as input. In this paper we take an unsupervised approach based on K-means for clustering activities, and Hidden Markov Models (HMM) to recognize the activities captured with the Microsoft Kinect's skeleton tracking feature. We show also how the number of clusters affects the performance of the HMM, and that after reaching a certain number of clusters, the performance of the HMM models to recognize activities does not improve anymore.
{"title":"Recognizing Activities Using a Kinect Skeleton Tracking and Hidden Markov Models","authors":"Armando Nava, Leonardo Garrido, R. Brena","doi":"10.1109/MICAI.2014.18","DOIUrl":"https://doi.org/10.1109/MICAI.2014.18","url":null,"abstract":"Knowing in which activities users are involved is an essential part of their context, which become more and more important in modern context-aware applications, but determining these activities could be a daunting task. Many sensors have been used as information source for guessing human activity, such as accelerometers, video cameras, etc., but recently the availability of a sophisticated sensor designed specifically for tracking humans, as is the Microsoft Kinect has opened new opportunities. The aim of this paper is to determine some human activities, such as eating, reading, drinking, etc., while the person is seated, using the Kinect skeleton structure as input. In this paper we take an unsupervised approach based on K-means for clustering activities, and Hidden Markov Models (HMM) to recognize the activities captured with the Microsoft Kinect's skeleton tracking feature. We show also how the number of clusters affects the performance of the HMM, and that after reaching a certain number of clusters, the performance of the HMM models to recognize activities does not improve anymore.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131370518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Equations or symbolic models of analog circuits increase designers' quantitative and qualitative understanding of a circuit, leading to a better decision-making. In this work symbolic regression is defined as white-box modeling, as opposed to other, more opaque, modeling types. This paper presents an approach to generate data-driven white box models. Our approach consists of two steps: firstly, the Pareto-optimal performance sizes of the Unity Gain Cell are obtained. For this work, unity gain and bandwidth have been simultaneously optimized using the NSGA-II algorithms. Secondly, the resulting Pareto Optimal front is used as data for the construction of white box models of performance as a function of the MOSFET design variables using Multigene genetic programming, which is a modified symbolic regression technique. Experiments were carried out using data obtained by SPICE simulation from the optimization of a voltage follower and a current follower, a set of nine functions (including operators), RMSE as precision measure, and a number of nodes as complexity measure. Among the symbolic models obtained, the simplest in terms of interpretability were sums of polynomials of the design variables. It was found that Multigene Genetic Programming can extract interpretable expressions even where the original design space was not sampled uniformly.
{"title":"White Box Model of Feasible Solutions of Unity Gain Cells","authors":"S. Polanco-Martagón, José Ruíz Ascencio","doi":"10.1109/MICAI.2014.32","DOIUrl":"https://doi.org/10.1109/MICAI.2014.32","url":null,"abstract":"Equations or symbolic models of analog circuits increase designers' quantitative and qualitative understanding of a circuit, leading to a better decision-making. In this work symbolic regression is defined as white-box modeling, as opposed to other, more opaque, modeling types. This paper presents an approach to generate data-driven white box models. Our approach consists of two steps: firstly, the Pareto-optimal performance sizes of the Unity Gain Cell are obtained. For this work, unity gain and bandwidth have been simultaneously optimized using the NSGA-II algorithms. Secondly, the resulting Pareto Optimal front is used as data for the construction of white box models of performance as a function of the MOSFET design variables using Multigene genetic programming, which is a modified symbolic regression technique. Experiments were carried out using data obtained by SPICE simulation from the optimization of a voltage follower and a current follower, a set of nine functions (including operators), RMSE as precision measure, and a number of nodes as complexity measure. Among the symbolic models obtained, the simplest in terms of interpretability were sums of polynomials of the design variables. It was found that Multigene Genetic Programming can extract interpretable expressions even where the original design space was not sampled uniformly.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127268722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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