Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975862
Erick Castellanos, F. Ramos, M. Ramos
Plant's simulation through Lindenmayer Systems is a well know field, but most of the work in the area focus on the growth part of the developmental process. From an artificial life perspective, it is desired to have a simulation that includes all the stages of the cycle of life of a plant. That is the reason why this paper target the last stage and propose a strategy to include the concept of death through Lindenmayer systems. By using parametric and context-sensitive Lindenmayer systems in the modeling and simulation, the semantics of the mentioned concept can be captured and, thereby, with the proper interpretation, a graphic result, at a morphological level, can be displayed. A proof of concept that includes most of the concepts covered is also given.
{"title":"Semantic death in plant's simulation using Lindenmayer systems","authors":"Erick Castellanos, F. Ramos, M. Ramos","doi":"10.1109/ICNC.2014.6975862","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975862","url":null,"abstract":"Plant's simulation through Lindenmayer Systems is a well know field, but most of the work in the area focus on the growth part of the developmental process. From an artificial life perspective, it is desired to have a simulation that includes all the stages of the cycle of life of a plant. That is the reason why this paper target the last stage and propose a strategy to include the concept of death through Lindenmayer systems. By using parametric and context-sensitive Lindenmayer systems in the modeling and simulation, the semantics of the mentioned concept can be captured and, thereby, with the proper interpretation, a graphic result, at a morphological level, can be displayed. A proof of concept that includes most of the concepts covered is also given.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"13 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131419263","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}
Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975939
Donglin Cao, Dazhen Lin, Yanping Lv
ECG is a kind of high dimensional dataset and the useful information of illness only exists in few heartbeats. To achieve a good classification performance, most existing approaches used features proposed by human experts, and there is no approach for automatic useful feature extraction. To solve that problem, we propose an ECG Codebook Model (ECGCM) which automatically builds a small number of codes to represent the high dimension ECG data. ECGCM not only greatly reduces the dimension of ECG, but also contains more meaningful semantic information for Myocardial Infarction detection. Our experiment results show that ECGCM achieves 2% and 20.5% improvement in sensitivity and specificity respectively in Myocardial Infarction detection.
{"title":"ECG codebook model for Myocardial Infarction detection","authors":"Donglin Cao, Dazhen Lin, Yanping Lv","doi":"10.1109/ICNC.2014.6975939","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975939","url":null,"abstract":"ECG is a kind of high dimensional dataset and the useful information of illness only exists in few heartbeats. To achieve a good classification performance, most existing approaches used features proposed by human experts, and there is no approach for automatic useful feature extraction. To solve that problem, we propose an ECG Codebook Model (ECGCM) which automatically builds a small number of codes to represent the high dimension ECG data. ECGCM not only greatly reduces the dimension of ECG, but also contains more meaningful semantic information for Myocardial Infarction detection. Our experiment results show that ECGCM achieves 2% and 20.5% improvement in sensitivity and specificity respectively in Myocardial Infarction detection.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131313545","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}
Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975884
Y. Yuan, Yuanguo Zhu
Artificial bee colony (ABC) algorithm introduced by D. Karaboga was inspired by the behaviors of real honey bee colonies. The routes of the swarm are exploited according to the neighbor information by employed bees and onlookers in the ABC algorithm. The classic artificial bee colony algorithm as a swarm optimization method is sometimes trapped in local optima. In this paper we propose a hybrid algorithm based on ABC algorithm and genetic algorithm. In the hybrid procedure, the crossover operator and mutation operator of genetic algorithm are introduced to improve the ABC algorithm in solving complex optimization problems. In the paper, the experiments for Traveling Salesman Problem and function optimization problems show that the proposed algorithm is more efficient compared with other techniques in recent literature.
{"title":"A hybrid artificial bee colony optimization algorithm","authors":"Y. Yuan, Yuanguo Zhu","doi":"10.1109/ICNC.2014.6975884","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975884","url":null,"abstract":"Artificial bee colony (ABC) algorithm introduced by D. Karaboga was inspired by the behaviors of real honey bee colonies. The routes of the swarm are exploited according to the neighbor information by employed bees and onlookers in the ABC algorithm. The classic artificial bee colony algorithm as a swarm optimization method is sometimes trapped in local optima. In this paper we propose a hybrid algorithm based on ABC algorithm and genetic algorithm. In the hybrid procedure, the crossover operator and mutation operator of genetic algorithm are introduced to improve the ABC algorithm in solving complex optimization problems. In the paper, the experiments for Traveling Salesman Problem and function optimization problems show that the proposed algorithm is more efficient compared with other techniques in recent literature.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122690018","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}
Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975816
P. Hájek, V. Olej
This paper develops a methodology to extract concepts containing qualitative information from corporate annual reports. The concepts are extracted from the corpus of U.S. corporate annual reports using WordNet ontology and singular value decomposition, and further visualized using self-organizing maps. The methodology makes it possible to easily compare the concepts with future financial performance. The results suggest that annual reports differ in terms of the concepts emphasized reflecting future financial performance.
{"title":"Comparing corporate financial performance and qualitative information from annual reports using self-organizing maps","authors":"P. Hájek, V. Olej","doi":"10.1109/ICNC.2014.6975816","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975816","url":null,"abstract":"This paper develops a methodology to extract concepts containing qualitative information from corporate annual reports. The concepts are extracted from the corpus of U.S. corporate annual reports using WordNet ontology and singular value decomposition, and further visualized using self-organizing maps. The methodology makes it possible to easily compare the concepts with future financial performance. The results suggest that annual reports differ in terms of the concepts emphasized reflecting future financial performance.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122960513","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}
Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975859
Rong-Qiang Zeng, Ming-Sheng Shang
This paper presents a multi-objective path relinking algorithm for solving bi-objective flow shop problem, where we aim to minimize the total completion time and total tardiness. In this algorithm, we integrate path relinking techniques into hypervolume-based multi-objective optimization. We propose a method to construct a path and select a set of non-dominated solutions from the path for further improvements. Experimental results show the proposed algorithm is very effective in comparison with the original multi-objective local search algorithms.
{"title":"Solving bi-objective flow shop problem with multi-objective path relinking algorithm","authors":"Rong-Qiang Zeng, Ming-Sheng Shang","doi":"10.1109/ICNC.2014.6975859","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975859","url":null,"abstract":"This paper presents a multi-objective path relinking algorithm for solving bi-objective flow shop problem, where we aim to minimize the total completion time and total tardiness. In this algorithm, we integrate path relinking techniques into hypervolume-based multi-objective optimization. We propose a method to construct a path and select a set of non-dominated solutions from the path for further improvements. Experimental results show the proposed algorithm is very effective in comparison with the original multi-objective local search algorithms.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128750829","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}
Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975926
Yanshan He, Min Yue
Owing to the widely used of data stream, frequent itemset mining on data stream have received more attention. Data stream is fast changing, massive, and potentially infinite. Therefore, we have to establish new data structure and algorithm to mine it. On the base of our previous work, we propose a new paralleled frequent itemset mining algorithm for data stream based on sliding window, which is called PFIMSD. The algorithm compresses whole data in current window into PSD-trees on paralleled processor only by one-scan. Increment method is used to append or delete related branch on PSD-tree when window is sliding. The experiment shows PFIMSD algorithm has good performance on efficiency and expansibility.
{"title":"Parallel frequent itemset mining on streaming data","authors":"Yanshan He, Min Yue","doi":"10.1109/ICNC.2014.6975926","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975926","url":null,"abstract":"Owing to the widely used of data stream, frequent itemset mining on data stream have received more attention. Data stream is fast changing, massive, and potentially infinite. Therefore, we have to establish new data structure and algorithm to mine it. On the base of our previous work, we propose a new paralleled frequent itemset mining algorithm for data stream based on sliding window, which is called PFIMSD. The algorithm compresses whole data in current window into PSD-trees on paralleled processor only by one-scan. Increment method is used to append or delete related branch on PSD-tree when window is sliding. The experiment shows PFIMSD algorithm has good performance on efficiency and expansibility.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116886547","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}
Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975911
Xiaoxu He, C. Shao, Y. Xiong
Cluster analysis is an important and challenging subject in time series data mining. It has a very important application prospect in many areas, such as medical images, atmosphere, finance, etc. Many current clustering techniques have still many problems, for example, k-means is a very effective method in finding different shapes and tolerating noise, but its result severely depends on the suitable choice of parameters. Inspired by nuclear field in physics, we propose a new dynamic clustering method based on nuclear force and interaction. Basically, each data point in data space is considered as a material particle with a spherically symmetric field around it and the interaction of all data points forms a nuclear field. Through the interaction of nuclear force, the initial clusters are iteratively merged and a hierarchy of clusters are generated. Experimental results show that compared with the typical clustering method k-means, the proposed approach enjoys favorite clustering quality and requires no careful parameters tuning.
{"title":"A new dynamic clustering method based on nuclear field","authors":"Xiaoxu He, C. Shao, Y. Xiong","doi":"10.1109/ICNC.2014.6975911","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975911","url":null,"abstract":"Cluster analysis is an important and challenging subject in time series data mining. It has a very important application prospect in many areas, such as medical images, atmosphere, finance, etc. Many current clustering techniques have still many problems, for example, k-means is a very effective method in finding different shapes and tolerating noise, but its result severely depends on the suitable choice of parameters. Inspired by nuclear field in physics, we propose a new dynamic clustering method based on nuclear force and interaction. Basically, each data point in data space is considered as a material particle with a spherically symmetric field around it and the interaction of all data points forms a nuclear field. Through the interaction of nuclear force, the initial clusters are iteratively merged and a hierarchy of clusters are generated. Experimental results show that compared with the typical clustering method k-means, the proposed approach enjoys favorite clustering quality and requires no careful parameters tuning.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"40 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114118302","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}
Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975852
Yuxin Liu, Yuxiao Lu, Chao Gao, Z. Zhang, Li Tao
Multi-objective traveling salesman problem (MOTSP) is an important field in operations research, which has wide applications in the real world. Multi-objective ant colony optimization (MOACO) as one of the most effective algorithms has gained popularity for solving a MOTSP. However, there exists the problem of premature convergence in most of MOACO algorithms. With this observation in mind, an improved multi-objective network ant colony optimization, denoted as PM-MONACO, is proposed, which employs the unique feature of critical tubes reserved in the network evolution process of the Physarum-inspired mathematical model (PMM). By considering both pheromones deposited by ants and flowing in the Physarum network, PM-MONACO uses an optimized pheromone matrix updating strategy. Experimental results in benchmark networks show that PM-MONACO can achieve a better compromise solution than the original MOACO algorithm for solving MOTSPs.
{"title":"A multi-objective ant colony optimization algorithm based on the Physarum-inspired mathematical model","authors":"Yuxin Liu, Yuxiao Lu, Chao Gao, Z. Zhang, Li Tao","doi":"10.1109/ICNC.2014.6975852","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975852","url":null,"abstract":"Multi-objective traveling salesman problem (MOTSP) is an important field in operations research, which has wide applications in the real world. Multi-objective ant colony optimization (MOACO) as one of the most effective algorithms has gained popularity for solving a MOTSP. However, there exists the problem of premature convergence in most of MOACO algorithms. With this observation in mind, an improved multi-objective network ant colony optimization, denoted as PM-MONACO, is proposed, which employs the unique feature of critical tubes reserved in the network evolution process of the Physarum-inspired mathematical model (PMM). By considering both pheromones deposited by ants and flowing in the Physarum network, PM-MONACO uses an optimized pheromone matrix updating strategy. Experimental results in benchmark networks show that PM-MONACO can achieve a better compromise solution than the original MOACO algorithm for solving MOTSPs.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"8 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114122382","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}
Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975979
X. Ge, Lili Li, Hui Li
Epidemic spreading and immunization are influenced by network structure measured via metrics. Degree mixing is a common property of network reflecting links in regards of node degree. In this paper we study epidemics spreading and immunization on degree mixing using empirical network data, analytic models, and numerical simulation. We demonstrate that assortative (or disassortative) degree mixing indeed influence spreading and effect of immunization. In the point of epidemic spreading, assortativity decreases speed and stable infected ratio, resulting in a better result, but decrease epidemic threshold. In the point of immunization, strategy that targets hub nodes has better effect on disassortative network.
{"title":"Epidemic spreading and immunization on assortative degree mixing networks","authors":"X. Ge, Lili Li, Hui Li","doi":"10.1109/ICNC.2014.6975979","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975979","url":null,"abstract":"Epidemic spreading and immunization are influenced by network structure measured via metrics. Degree mixing is a common property of network reflecting links in regards of node degree. In this paper we study epidemics spreading and immunization on degree mixing using empirical network data, analytic models, and numerical simulation. We demonstrate that assortative (or disassortative) degree mixing indeed influence spreading and effect of immunization. In the point of epidemic spreading, assortativity decreases speed and stable infected ratio, resulting in a better result, but decrease epidemic threshold. In the point of immunization, strategy that targets hub nodes has better effect on disassortative network.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116169959","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}
Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975919
Yue Chen, Changchun Pan, Gen-ke Yang, Jie Bai
With the promotion of Early Admission (EA) among the universities in China, its prediction accuracy of the potential of the students with regard to their academic performance is highly concerned. In this study, the statistical methods and the artificial intelligence technologies were used comparatively to build the prediction models. According to our best knowledge, this is the first time that a model is established to evaluate student candidates for admission to the university. We carried out a comparison of the current EA system based on the real admission data from a reputed university with typical EA procedures. The results show that prediction capability of EA is improved significantly with the help of the models. Afterwards, the impact of predictors was discussed and presented.
{"title":"Intelligent decision system for accessing academic performance of candidates for early admission to university","authors":"Yue Chen, Changchun Pan, Gen-ke Yang, Jie Bai","doi":"10.1109/ICNC.2014.6975919","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975919","url":null,"abstract":"With the promotion of Early Admission (EA) among the universities in China, its prediction accuracy of the potential of the students with regard to their academic performance is highly concerned. In this study, the statistical methods and the artificial intelligence technologies were used comparatively to build the prediction models. According to our best knowledge, this is the first time that a model is established to evaluate student candidates for admission to the university. We carried out a comparison of the current EA system based on the real admission data from a reputed university with typical EA procedures. The results show that prediction capability of EA is improved significantly with the help of the models. Afterwards, the impact of predictors was discussed and presented.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116215991","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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