Pub Date : 2015-07-09DOI: 10.1109/ReTIS.2015.7232846
A. M. Rather
A new and robust hybrid model is presented here for the purpose of forecasting currency exchange rate. Initially forecasts are obtained from three different models: linear-trend model, autoregressive moving average model as well as from artificial neural network. Because of its non-linear features, results obtained from artificial neural network outperform rest of the two linear models. With the goal to further improve the performance of forecasting models, forecasts obtained from three models are merged together so as to form a hybrid model. In order to do so, optimal weights are required which are generated using an optimization model and solved using genetic algorithms. The proposed hybrid model has been tested on real-world data; the results confirm that this approach can be a promising method in forecasting currency exchange rate.
{"title":"Computational intelligence based hybrid approach for forecasting currency exchange rate","authors":"A. M. Rather","doi":"10.1109/ReTIS.2015.7232846","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232846","url":null,"abstract":"A new and robust hybrid model is presented here for the purpose of forecasting currency exchange rate. Initially forecasts are obtained from three different models: linear-trend model, autoregressive moving average model as well as from artificial neural network. Because of its non-linear features, results obtained from artificial neural network outperform rest of the two linear models. With the goal to further improve the performance of forecasting models, forecasts obtained from three models are merged together so as to form a hybrid model. In order to do so, optimal weights are required which are generated using an optimization model and solved using genetic algorithms. The proposed hybrid model has been tested on real-world data; the results confirm that this approach can be a promising method in forecasting currency exchange rate.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127815463","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232869
Abolfazl Ghavidel, Amin Rezaeian, M. Rezaee
In order to infer evolutionary relationships as well as reconstruct phylogenetic trees, evolutionists often employ two general approaches: character-based and distance-based. Inasmuch as character based methods could be inordinately expensive in computational process, researchers have to use some estimation methods with practical run time. In this context, distance based methods are exceedingly quicker due to the utilizing of distance matrices. In Computational Biology, sequence comparison is of fundamental importance which tries to find similar sequences. Many different techniques have been developed to calculate the right distance measure among DNA sequences, however, they are almost only used for making distance matrix; additionally, they usually work in the absence of using models of evolution too. In this paper, a novel technique, based on mathematical variance calculation, is proposed to show how much gene sequences in a group are all to be similar. In this strategy, we use mathematical formula of variance to acquire the average of differences amongst all sequences of a specific set (called cluster). Eventually, all sequences with variation lower than the predefined variance will be clustered into some groups while each group contains a phylogenetic tree. We are of the idea that our method, in spite of simplicity in design, could be used as a logical criterion to cluster sequences of DNA and it also could prove useful as a simple technique to build phylogenetic networks based on distance, especially when there are a large number of input sequences.
{"title":"A species clustering method based on variation of molecular data with the aid of variance proportion","authors":"Abolfazl Ghavidel, Amin Rezaeian, M. Rezaee","doi":"10.1109/ReTIS.2015.7232869","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232869","url":null,"abstract":"In order to infer evolutionary relationships as well as reconstruct phylogenetic trees, evolutionists often employ two general approaches: character-based and distance-based. Inasmuch as character based methods could be inordinately expensive in computational process, researchers have to use some estimation methods with practical run time. In this context, distance based methods are exceedingly quicker due to the utilizing of distance matrices. In Computational Biology, sequence comparison is of fundamental importance which tries to find similar sequences. Many different techniques have been developed to calculate the right distance measure among DNA sequences, however, they are almost only used for making distance matrix; additionally, they usually work in the absence of using models of evolution too. In this paper, a novel technique, based on mathematical variance calculation, is proposed to show how much gene sequences in a group are all to be similar. In this strategy, we use mathematical formula of variance to acquire the average of differences amongst all sequences of a specific set (called cluster). Eventually, all sequences with variation lower than the predefined variance will be clustered into some groups while each group contains a phylogenetic tree. We are of the idea that our method, in spite of simplicity in design, could be used as a logical criterion to cluster sequences of DNA and it also could prove useful as a simple technique to build phylogenetic networks based on distance, especially when there are a large number of input sequences.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132279685","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232875
A. Marouf, Shaumic Shondipon, Md. Kamrul Hasan, H. Mahmud
In Human Computer Interaction (HCI), one of the recent research areas is Hand Gesture Recognition (HGR). In hand gesture recognition, finger identification and fingertip detection is a challenging work. Because of the enormous applications like sign language, human robot interaction, gesture based applications this area is gaining researchers' attention. In this paper, a novel approach of finger identification named as 4Y model, is proposed. This model is based on geometric calculations and general biometric features. The experimental result for the model gives up to 92% accuracy based on its inputs.
{"title":"4Y model: A novel approach for finger identification using KINECT","authors":"A. Marouf, Shaumic Shondipon, Md. Kamrul Hasan, H. Mahmud","doi":"10.1109/ReTIS.2015.7232875","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232875","url":null,"abstract":"In Human Computer Interaction (HCI), one of the recent research areas is Hand Gesture Recognition (HGR). In hand gesture recognition, finger identification and fingertip detection is a challenging work. Because of the enormous applications like sign language, human robot interaction, gesture based applications this area is gaining researchers' attention. In this paper, a novel approach of finger identification named as 4Y model, is proposed. This model is based on geometric calculations and general biometric features. The experimental result for the model gives up to 92% accuracy based on its inputs.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129648128","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 : 2015-07-01DOI: 10.1109/ReTIS.2015.7232900
D. Mohan, Dipankar Das, Sivaji Bandyopadhyay
Argumentation, constituting of major component of human intelligence is considered as a process where the arguments are constructed as well as tackled. Argumentation is a collection of propositions called “Premises” except one which is termed as “Conclusion”. If we identify argumentation from the perspectives of emotions, it means to examine whether consistency is conveyed from a set of premises to its corresponding conclusion or not. In the present task, we have developed a rule based baseline system followed by a machine learning frame work. Two types of different corpora, ECHR (European Court of Human Rights) and the Araucaria Database were used for experiments. We used the Bayes' theorem to find the effects of various emotions in identifying conclusion from the set of given premises with the help of argumentation. We have employed the Naïve Bayes, Sequential Minimal Optimization (SMO) and Decision Tree classifiers in our machine learning frame work and evaluated the results of the rule based system by manual experts. The evaluation achieves the maximum F-Score of 0.874 and 0.649 for premises and conclusion in case of rule based system whereas 0.958 and 0.815 for the Naïve Bayes, 0.893 and 0.458 for the SMO and 0.951 and 0.957 for the Decision Tree classifiers, respectively.
{"title":"Emotion argumentation","authors":"D. Mohan, Dipankar Das, Sivaji Bandyopadhyay","doi":"10.1109/ReTIS.2015.7232900","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232900","url":null,"abstract":"Argumentation, constituting of major component of human intelligence is considered as a process where the arguments are constructed as well as tackled. Argumentation is a collection of propositions called “Premises” except one which is termed as “Conclusion”. If we identify argumentation from the perspectives of emotions, it means to examine whether consistency is conveyed from a set of premises to its corresponding conclusion or not. In the present task, we have developed a rule based baseline system followed by a machine learning frame work. Two types of different corpora, ECHR (European Court of Human Rights) and the Araucaria Database were used for experiments. We used the Bayes' theorem to find the effects of various emotions in identifying conclusion from the set of given premises with the help of argumentation. We have employed the Naïve Bayes, Sequential Minimal Optimization (SMO) and Decision Tree classifiers in our machine learning frame work and evaluated the results of the rule based system by manual experts. The evaluation achieves the maximum F-Score of 0.874 and 0.649 for premises and conclusion in case of rule based system whereas 0.958 and 0.815 for the Naïve Bayes, 0.893 and 0.458 for the SMO and 0.951 and 0.957 for the Decision Tree classifiers, respectively.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115928324","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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