Pub Date : 2014-10-16DOI: 10.1109/NaBIC.2014.6921898
E. Pires, J. Machado, P. Oliveira
Multi-objective optimization inspired on genetic algorithms are population based search methods. The population elements, chromosomes, evolve using inheritance, mutation, selection and crossover mechanisms. The aim of these algorithms is to obtain a representative non-dominated Pareto front from a given problem. Several approaches to study the convergence and performance of algorithm variants have been proposed, particularly by accessing the final population. In this work, a novel approach by analyzing multi-objective algorithm dynamics during the algorithm execution is considered. The results indicate that Shannon entropy can be used as an algorithm indicator of diversity and convergence.
{"title":"Diversity study of multi-objective genetic algorithm based on Shannon entropy","authors":"E. Pires, J. Machado, P. Oliveira","doi":"10.1109/NaBIC.2014.6921898","DOIUrl":"https://doi.org/10.1109/NaBIC.2014.6921898","url":null,"abstract":"Multi-objective optimization inspired on genetic algorithms are population based search methods. The population elements, chromosomes, evolve using inheritance, mutation, selection and crossover mechanisms. The aim of these algorithms is to obtain a representative non-dominated Pareto front from a given problem. Several approaches to study the convergence and performance of algorithm variants have been proposed, particularly by accessing the final population. In this work, a novel approach by analyzing multi-objective algorithm dynamics during the algorithm execution is considered. The results indicate that Shannon entropy can be used as an algorithm indicator of diversity and convergence.","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127631184","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 : 2010-12-01DOI: 10.1109/NABIC.2010.5716377
Wang Kejun
Ant-based clustering is a biologically-inspired computational heuristic that has been used in various domains for general clustering tasks. In this paper we propose its use as the tool for clustering high-dimensional vectors (visual words) which are descriptive features for human actions extracted from video sequences. This codebook generation stage is critical in the popular ‘Bag-of-Words’ framework in which a visual codebook is constructed on the statistics of various features in images or videos. K-means algorithm is widely used in this process but this has two major shortcomings namely: it requires user specification of input parameter k which can bias the algorithm and make it converge at a sub-optimal number of clusters. Also, optimal value of k needs to be determined empirically. Our method generates a codebook of highly descriptive spatio-temporal ‘words’ using ant-based clustering to determine the optimal number of clusters in the dataset. The number of clusters generated was set as the number of codewords for the vocabulary. The limitations of k-means were overcome with the robustness of ant-based clustering heuristic. This idea when applied to the benchmark KTH database produced codewords that produced a compact representation of human actions which gave the desired recognition result when compared to similar approach based on k-means clustering.
{"title":"Ant-based clustering of visual-words for unsupervised human action recognition","authors":"Wang Kejun","doi":"10.1109/NABIC.2010.5716377","DOIUrl":"https://doi.org/10.1109/NABIC.2010.5716377","url":null,"abstract":"Ant-based clustering is a biologically-inspired computational heuristic that has been used in various domains for general clustering tasks. In this paper we propose its use as the tool for clustering high-dimensional vectors (visual words) which are descriptive features for human actions extracted from video sequences. This codebook generation stage is critical in the popular ‘Bag-of-Words’ framework in which a visual codebook is constructed on the statistics of various features in images or videos. K-means algorithm is widely used in this process but this has two major shortcomings namely: it requires user specification of input parameter k which can bias the algorithm and make it converge at a sub-optimal number of clusters. Also, optimal value of k needs to be determined empirically. Our method generates a codebook of highly descriptive spatio-temporal ‘words’ using ant-based clustering to determine the optimal number of clusters in the dataset. The number of clusters generated was set as the number of codewords for the vocabulary. The limitations of k-means were overcome with the robustness of ant-based clustering heuristic. This idea when applied to the benchmark KTH database produced codewords that produced a compact representation of human actions which gave the desired recognition result when compared to similar approach based on k-means clustering.","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116967974","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 : 2009-12-01DOI: 10.1109/NABIC.2009.5393604
Vinod S. S. Chandra, Reshmi Girijadevi
MicroRNAs (miRNAs) have been found in diverse organisms and play critical role in gene expression regulations of many essential cellular processes. Discovery of miRNAs and identification of their target genes are fundamental to the study of such regulatory circuits. To distinguish the real pre-miRNA from other stem loop hairpins with similar stem loop (pseudo pre-miRNA) is an important task in molecular biology. From the analysis of experimentally proved pre-miRNAs, we identified 17 parameters for miRNA formation. These parameters are grouped into two categories: structural and thermodynamic properties of the pre-miRNAs. A set of feature vector was formed from the pre-miRNA-like hairpins of human, mouse and rat. A feed forward multi layer perceptron Artificial Neural Network (ANN) classifier is trained by these feature vectors. This classifier is an application program, that decide whether a given sequence is a pre-miRNA like hairpin sequence or not. If the sequence is a pre-miRNA like hairpin, then the ANN classifier will predict whether it is a real pre-miRNA or a pseudo premiRNA. The approach can classify correctly the precursors of Human Mouse and Rat, with an average sensitivity of 97.40% and specificity of 95.85%. When compared with previous approaches, MiPred, mR-abela, ProMiR and Triplet SVM classifier, current approach was greater in total accuracy.
{"title":"A Pre-microRNA Classifier by Structural and Thermodynamic Motifs","authors":"Vinod S. S. Chandra, Reshmi Girijadevi","doi":"10.1109/NABIC.2009.5393604","DOIUrl":"https://doi.org/10.1109/NABIC.2009.5393604","url":null,"abstract":"MicroRNAs (miRNAs) have been found in diverse organisms and play critical role in gene expression regulations of many essential cellular processes. Discovery of miRNAs and identification of their target genes are fundamental to the study of such regulatory circuits. To distinguish the real pre-miRNA from other stem loop hairpins with similar stem loop (pseudo pre-miRNA) is an important task in molecular biology. From the analysis of experimentally proved pre-miRNAs, we identified 17 parameters for miRNA formation. These parameters are grouped into two categories: structural and thermodynamic properties of the pre-miRNAs. A set of feature vector was formed from the pre-miRNA-like hairpins of human, mouse and rat. A feed forward multi layer perceptron Artificial Neural Network (ANN) classifier is trained by these feature vectors. This classifier is an application program, that decide whether a given sequence is a pre-miRNA like hairpin sequence or not. If the sequence is a pre-miRNA like hairpin, then the ANN classifier will predict whether it is a real pre-miRNA or a pseudo premiRNA. The approach can classify correctly the precursors of Human Mouse and Rat, with an average sensitivity of 97.40% and specificity of 95.85%. When compared with previous approaches, MiPred, mR-abela, ProMiR and Triplet SVM classifier, current approach was greater in total accuracy.","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"124 1 Suppl 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131261416","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 : 1900-01-01DOI: 10.1007/978-3-319-27400-3_19
E. Abera, Ayalew Belay Habtie, A. Abraham
{"title":"Real-Time Vehicle Emission Monitoring and Location Tracking Framework","authors":"E. Abera, Ayalew Belay Habtie, A. Abraham","doi":"10.1007/978-3-319-27400-3_19","DOIUrl":"https://doi.org/10.1007/978-3-319-27400-3_19","url":null,"abstract":"","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122288303","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 : 1900-01-01DOI: 10.1007/978-3-319-27400-3_27
Ayalew Belay Habtie, A. Abraham, Dida Midekso
{"title":"A Neural Network Model for Road Traffic Flow Estimation","authors":"Ayalew Belay Habtie, A. Abraham, Dida Midekso","doi":"10.1007/978-3-319-27400-3_27","DOIUrl":"https://doi.org/10.1007/978-3-319-27400-3_27","url":null,"abstract":"","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127753044","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 : 1900-01-01DOI: 10.1007/978-3-319-27400-3_20
Ayalew Belay Habtie, A. Abraham, Dida Midekso
{"title":"Applying Design Science Research to Design and Evaluate Real-Time Road Traffic State Estimation Framework","authors":"Ayalew Belay Habtie, A. Abraham, Dida Midekso","doi":"10.1007/978-3-319-27400-3_20","DOIUrl":"https://doi.org/10.1007/978-3-319-27400-3_20","url":null,"abstract":"","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"120 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126229812","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 : 1900-01-01DOI: 10.1007/978-3-319-27400-3_25
E. Adetiba, O. Olugbara, Tunmike B. Taiwo
{"title":"Identification of Pathogenic Viruses Using Genomic Cepstral Coefficients with Radial Basis Function Neural Network","authors":"E. Adetiba, O. Olugbara, Tunmike B. Taiwo","doi":"10.1007/978-3-319-27400-3_25","DOIUrl":"https://doi.org/10.1007/978-3-319-27400-3_25","url":null,"abstract":"","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128478392","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 : 1900-01-01DOI: 10.1007/978-3-319-27400-3_18
Ahmed Hassan, N. Pillay
{"title":"A Hyper-Heuristic Approach to Solving the Ski-Lodge Problem","authors":"Ahmed Hassan, N. Pillay","doi":"10.1007/978-3-319-27400-3_18","DOIUrl":"https://doi.org/10.1007/978-3-319-27400-3_18","url":null,"abstract":"","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116047954","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 : 1900-01-01DOI: 10.1007/978-3-319-27400-3_15
Clive Frankland, N. Pillay
{"title":"Evolving Heuristic Based Game Playing Strategies for Checkers Incorporating Reinforcement Learning","authors":"Clive Frankland, N. Pillay","doi":"10.1007/978-3-319-27400-3_15","DOIUrl":"https://doi.org/10.1007/978-3-319-27400-3_15","url":null,"abstract":"","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"44 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114122211","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 : 1900-01-01DOI: 10.1007/978-3-319-27400-3_35
Mardé Helbig
{"title":"Updating the Global Best and Archive Solutions of the Dynamic Vector-Evaluated PSO Algorithm Using epsilon ϵ -dominance","authors":"Mardé Helbig","doi":"10.1007/978-3-319-27400-3_35","DOIUrl":"https://doi.org/10.1007/978-3-319-27400-3_35","url":null,"abstract":"","PeriodicalId":129539,"journal":{"name":"World Congress on Nature and Biologically Inspired Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134308705","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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