Pub Date : 2017-12-01DOI: 10.1109/PDCAT.2017.00032
Nadia Abd-Alsabour
Local search plays a significant role when added to search algorithms as it prompts getting better solutions. Nevertheless, there are numerous situations in which including the local search algorithms does not contribute to the search process and hence should not be incorporated. This paper investigates this issue by performing two types of experiments using one of the most established search algorithms which is genetic algorithms. The obtained results are detailed and discussed in sections five and six respectively.
{"title":"Investigating the Influence of Adding Local Search to Search Algorithms","authors":"Nadia Abd-Alsabour","doi":"10.1109/PDCAT.2017.00032","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00032","url":null,"abstract":"Local search plays a significant role when added to search algorithms as it prompts getting better solutions. Nevertheless, there are numerous situations in which including the local search algorithms does not contribute to the search process and hence should not be incorporated. This paper investigates this issue by performing two types of experiments using one of the most established search algorithms which is genetic algorithms. The obtained results are detailed and discussed in sections five and six respectively.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123436568","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}
As e-learning becomes a basic need for several universities, a variety of Learning Management Systems (LMS) is proposed on the market. However, available LMSs do not satisfy all the needs of different institutions, which push them to develop their own systems. Since developing and maintaining new software are cost, time and effort consuming, and with the increasing demand on e-Learning systems, it becomes necessary to find an efficient solution that allows the fast development of systems and overcomes the afore-mentioned issues. We strongly believe that adopting a software product line approach in e-Learning domain can bring important benefits. We propose a general model for collaborative learning processes and we present the development process of an e-Learning software product line. Throughout the development process, we demonstrate how this approach allows us to satisfy the variable needs of universities/learners and benefit from the systematic large-scale reuse at the same time. In order to help organizations in providing similar services without the need to structure each of them separately, this paper presents how to support variability in learning process modeling.
{"title":"Towards Supporting Modeling Variability in E-Learning Application: A Case Study","authors":"Sameh Azouzi, Sonia Ayachi Ghannouchi, Zaki Brahmi","doi":"10.1109/PDCAT.2017.00083","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00083","url":null,"abstract":"As e-learning becomes a basic need for several universities, a variety of Learning Management Systems (LMS) is proposed on the market. However, available LMSs do not satisfy all the needs of different institutions, which push them to develop their own systems. Since developing and maintaining new software are cost, time and effort consuming, and with the increasing demand on e-Learning systems, it becomes necessary to find an efficient solution that allows the fast development of systems and overcomes the afore-mentioned issues. We strongly believe that adopting a software product line approach in e-Learning domain can bring important benefits. We propose a general model for collaborative learning processes and we present the development process of an e-Learning software product line. Throughout the development process, we demonstrate how this approach allows us to satisfy the variable needs of universities/learners and benefit from the systematic large-scale reuse at the same time. In order to help organizations in providing similar services without the need to structure each of them separately, this paper presents how to support variability in learning process modeling.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"227 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120934127","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00031
Nandika Liyanage
Data can be store as structured, semi-structured or unstructured formats in various distributed environments. Extraction of data from multiple data sources or data warehouse and convert to a proper order is quite time consuming task even using the latest hardware and software technologies. Sorting is one of the key concepts that helps to improve the efficiency of various computational process. Unlike the early single processor or single server operations using monolithic applications, multi-core distributed environments required more advanced computational theories and algorithms. Existing sorting theories are basically derived from linear algorithms and enhanced to support for distributed processing. This research discuss the characteristics of existing parallel sorting algorithms, techniques, limitation. Aim and objective is to introduce a new pure parallel sorting algorithm and sorting architecture that pointing to execute under latest distributed environments. The proposed pattern and the sorting architecture can be used as rule based parallel sorting technique and algorithm to support for any type of distributed environment to sort infinite dataset.
{"title":"ProjectileSort - Rule Based Parallel Sorting Algorithm - Architecture for Reconfigurable Multi-Partition Object Arrays","authors":"Nandika Liyanage","doi":"10.1109/PDCAT.2017.00031","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00031","url":null,"abstract":"Data can be store as structured, semi-structured or unstructured formats in various distributed environments. Extraction of data from multiple data sources or data warehouse and convert to a proper order is quite time consuming task even using the latest hardware and software technologies. Sorting is one of the key concepts that helps to improve the efficiency of various computational process. Unlike the early single processor or single server operations using monolithic applications, multi-core distributed environments required more advanced computational theories and algorithms. Existing sorting theories are basically derived from linear algorithms and enhanced to support for distributed processing. This research discuss the characteristics of existing parallel sorting algorithms, techniques, limitation. Aim and objective is to introduce a new pure parallel sorting algorithm and sorting architecture that pointing to execute under latest distributed environments. The proposed pattern and the sorting architecture can be used as rule based parallel sorting technique and algorithm to support for any type of distributed environment to sort infinite dataset.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122404844","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00012
I. Liu, Tay-Jiun Fang, Jung-Shian Li, Meng-Wei Sun, Chuan-Gang Liu
Abstract—Nowadays, network virtualization has been widely investigated in order to prevent Internet ossification, and develop future emerging network applications flexibly. However, prior work by Pignolet et al. shows the possible attacking methodology with which the attackers can disclose the whole cloud topology while deploying virtual networks in cloud named “Topology Disclosure Attack”. In this attack model, the attacker pretends to deploy virtual networks in cloud by issuing the graph requests to service provider. And the service provider responds the requests to the attacker after examining his/her topology resources. With this request/reply model, Pignolet et al. believe this attack eventually infers the targeted topology. However, one vital reason leads this attack to the failure- too many virtual requests from one adversary in a time. This paper tries to provide a new topology disclosure attack model, which multiple attackers launch attacks at the same time with the assistance of proposed Query-Trie and network tomography technique. Hence, in this paper, we propose much more possible attack model in cloud and this topic also encourages the network researchers to develop resistance mechanism against it in the future.
{"title":"A New Colluded Adversarial VNet Embeddings Attack in Cloud","authors":"I. Liu, Tay-Jiun Fang, Jung-Shian Li, Meng-Wei Sun, Chuan-Gang Liu","doi":"10.1109/PDCAT.2017.00012","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00012","url":null,"abstract":"Abstract—Nowadays, network virtualization has been widely investigated in order to prevent Internet ossification, and develop future emerging network applications flexibly. However, prior work by Pignolet et al. shows the possible attacking methodology with which the attackers can disclose the whole cloud topology while deploying virtual networks in cloud named “Topology Disclosure Attack”. In this attack model, the attacker pretends to deploy virtual networks in cloud by issuing the graph requests to service provider. And the service provider responds the requests to the attacker after examining his/her topology resources. With this request/reply model, Pignolet et al. believe this attack eventually infers the targeted topology. However, one vital reason leads this attack to the failure- too many virtual requests from one adversary in a time. This paper tries to provide a new topology disclosure attack model, which multiple attackers launch attacks at the same time with the assistance of proposed Query-Trie and network tomography technique. Hence, in this paper, we propose much more possible attack model in cloud and this topic also encourages the network researchers to develop resistance mechanism against it in the future.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116539370","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00063
Min-Chun Huang, Chi-He Chang, Chao-Wei Tseng, Ru-Jen Lee
Video-on-demand (VoD) service has become the important service in Internet Protocol Television (IPTV) based on Content-delivery-network (CDN). There has been a large amount of media in recently years. Combined with growing users, how to quickly address the video requests from users with low delay is important for industry. In other words, to allocate resource (i.e. media) appropriately according to different situations is critical. This paper proposes strategies which make video replica and put it nearby users (set-up boxes) previously. The proposed strategies not only consider the popularity of the media but also consider its hot time, i.e. the time slots which most of the users access it. The experiment result shows that with the proposed strategies, user requests can be quickly addressed and the overall network traffic load can be reduced.
{"title":"Content Delivery Based on Popularity and Time Slot","authors":"Min-Chun Huang, Chi-He Chang, Chao-Wei Tseng, Ru-Jen Lee","doi":"10.1109/PDCAT.2017.00063","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00063","url":null,"abstract":"Video-on-demand (VoD) service has become the important service in Internet Protocol Television (IPTV) based on Content-delivery-network (CDN). There has been a large amount of media in recently years. Combined with growing users, how to quickly address the video requests from users with low delay is important for industry. In other words, to allocate resource (i.e. media) appropriately according to different situations is critical. This paper proposes strategies which make video replica and put it nearby users (set-up boxes) previously. The proposed strategies not only consider the popularity of the media but also consider its hot time, i.e. the time slots which most of the users access it. The experiment result shows that with the proposed strategies, user requests can be quickly addressed and the overall network traffic load can be reduced.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116131119","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00024
Lu Zhang, Xiaopeng Fan, Chengzhong Xu
To predicate the future with high accuracy is a holy grail in financial market. However, the volatility of chaotic financial market challenges new technologies from computer science to economic science all the time. Recently, Recurrent Neural Network (RNN) plays a new role in financial market prediction. However, results from RNN are restricted by sample size of training datasets, and show predication accuracy can hardly be guaranteed in a long term. On the other hand, Representative Pattern Discovery (RPD) is an effective way in long-term prediction while it is ineffective in short-term prediction. In this paper, we define a representative pattern for time series, and propose a fusion financial prediction strategy based on RNN and RPD. We take the advantages of both RNN and RPD, in the way that the proposed strategy is stateful to keep the short-term trend and it rectifies the predication by a time-dependent incremental factor in a long-term way. Compared with RNN and pattern discovery respectively, our experimental results demonstrate that our proposed strategy performs much better than that of others. It can increase the prediction accuracy by 6% on the basis of RNN at most, but at a cost of higher Mean Squared Error.
{"title":"A Fusion Financial Prediction Strategy Based on RNN and Representative Pattern Discovery","authors":"Lu Zhang, Xiaopeng Fan, Chengzhong Xu","doi":"10.1109/PDCAT.2017.00024","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00024","url":null,"abstract":"To predicate the future with high accuracy is a holy grail in financial market. However, the volatility of chaotic financial market challenges new technologies from computer science to economic science all the time. Recently, Recurrent Neural Network (RNN) plays a new role in financial market prediction. However, results from RNN are restricted by sample size of training datasets, and show predication accuracy can hardly be guaranteed in a long term. On the other hand, Representative Pattern Discovery (RPD) is an effective way in long-term prediction while it is ineffective in short-term prediction. In this paper, we define a representative pattern for time series, and propose a fusion financial prediction strategy based on RNN and RPD. We take the advantages of both RNN and RPD, in the way that the proposed strategy is stateful to keep the short-term trend and it rectifies the predication by a time-dependent incremental factor in a long-term way. Compared with RNN and pattern discovery respectively, our experimental results demonstrate that our proposed strategy performs much better than that of others. It can increase the prediction accuracy by 6% on the basis of RNN at most, but at a cost of higher Mean Squared Error.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127282236","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00075
N. Haddar, M. Tmar
The paper studies the principal emerged data-centric business process models and workflow systems. Especially, the paper presents a comparative study based on substantial criteria that must be meet in a data-centric process model and its run-time system. This study allows to identify several recommended enhancement in further researches.
{"title":"Models and Run-Time Systems for Data Intensive Workflow Applications","authors":"N. Haddar, M. Tmar","doi":"10.1109/PDCAT.2017.00075","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00075","url":null,"abstract":"The paper studies the principal emerged data-centric business process models and workflow systems. Especially, the paper presents a comparative study based on substantial criteria that must be meet in a data-centric process model and its run-time system. This study allows to identify several recommended enhancement in further researches.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131653245","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00087
Yumeng Guo, Wenke Wang, Sikun Li
With the increasing size of flow field, a challenge in streamline visualization arises that the memory of calculation node cannot accommodate the entire required data. To solve this problem, out-of-core technique divides the flow field into blocks and read block on demand of computing. Data prefetching is a frequent out-of core method to reduce the affection of the gap between I/O and calculation speed, while the performance is coherent with prefetching hit rate. In this paper, we focus on how to improve the prefetching hit rate to increase the data prefetching efficiency by changing the style of flow field partitioning, and present a novel feature-based dynamic block partition method that divides data to blocks of different sizes. The key of our method is first to compute the feature attributes of the field, and then determine the partitioning points by specific operations to divide feature regions more finely. It is easy to apply our approach to replace block partition part of all state-of-the-art prefetching algorithms. Experimental results show that the major quality measurement of our partitioning strategy for prefetching is much better than the traditional methods, with an increase of about 10%in both prefetch hit rate and effective rate.
{"title":"Feature-Based Adaptive Block Partition Method for Data Prefetching in Streamline Visualization","authors":"Yumeng Guo, Wenke Wang, Sikun Li","doi":"10.1109/PDCAT.2017.00087","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00087","url":null,"abstract":"With the increasing size of flow field, a challenge in streamline visualization arises that the memory of calculation node cannot accommodate the entire required data. To solve this problem, out-of-core technique divides the flow field into blocks and read block on demand of computing. Data prefetching is a frequent out-of core method to reduce the affection of the gap between I/O and calculation speed, while the performance is coherent with prefetching hit rate. In this paper, we focus on how to improve the prefetching hit rate to increase the data prefetching efficiency by changing the style of flow field partitioning, and present a novel feature-based dynamic block partition method that divides data to blocks of different sizes. The key of our method is first to compute the feature attributes of the field, and then determine the partitioning points by specific operations to divide feature regions more finely. It is easy to apply our approach to replace block partition part of all state-of-the-art prefetching algorithms. Experimental results show that the major quality measurement of our partitioning strategy for prefetching is much better than the traditional methods, with an increase of about 10%in both prefetch hit rate and effective rate.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115497601","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00030
C. R. Valêncio, José Carlos De Freitas, Rogéria Cristiane Gratão de Souza, L. A. Neves, G. F. D. Zafalon, A. Colombini, William Tenório
Co-authorship analysis in science and technology partnerships provides a vision of cooperation patterns between individuals and organizations and is still widely used to understand and assess scientific collaboration patterns. This analysis is conducted by means of bibliometry, which is the quantitative study of scientific production. However, with the evolution of database management systems, there was a significant increase in the volume of stored data, which could difficult the analysis. In this context, the developed work presents an efficient parallel optimization of bibliometric information, in order to allow this scientific analysis in a Big Data environment. Our results show that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the number of nodes tends to infinity; the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the number of nodes tends to infinity. Also, the results found present good values of speed up and efficiency.
{"title":"An Efficient Parallel Optimization for Co-Authorship Network Analysis","authors":"C. R. Valêncio, José Carlos De Freitas, Rogéria Cristiane Gratão de Souza, L. A. Neves, G. F. D. Zafalon, A. Colombini, William Tenório","doi":"10.1109/PDCAT.2017.00030","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00030","url":null,"abstract":"Co-authorship analysis in science and technology partnerships provides a vision of cooperation patterns between individuals and organizations and is still widely used to understand and assess scientific collaboration patterns. This analysis is conducted by means of bibliometry, which is the quantitative study of scientific production. However, with the evolution of database management systems, there was a significant increase in the volume of stored data, which could difficult the analysis. In this context, the developed work presents an efficient parallel optimization of bibliometric information, in order to allow this scientific analysis in a Big Data environment. Our results show that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the number of nodes tends to infinity; the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the number of nodes tends to infinity. Also, the results found present good values of speed up and efficiency.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"73 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121811361","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00082
Khine Zar Thwe, Nu War
Audio feature extraction and classification are important tool for audio signal analysis in many applications, such as home care system, security surveillance, meeting room sounds and music classification and so on. This paper presents sound classification by combining of image processing and signal processing to classify the data accurately. Firstly, audio signal is converted into time-frequency representation same as texture image in image processing. And then local tetra pattern (LTrP) text feature is used to extract features from this image. Finally, audio signal is classified by using one-vs-one SVM classifiers. Evaluation is tested on ESC-10 dataset.
{"title":"Local Tetra Pattern Texture Features for Environmental Sound Event Classification","authors":"Khine Zar Thwe, Nu War","doi":"10.1109/PDCAT.2017.00082","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00082","url":null,"abstract":"Audio feature extraction and classification are important tool for audio signal analysis in many applications, such as home care system, security surveillance, meeting room sounds and music classification and so on. This paper presents sound classification by combining of image processing and signal processing to classify the data accurately. Firstly, audio signal is converted into time-frequency representation same as texture image in image processing. And then local tetra pattern (LTrP) text feature is used to extract features from this image. Finally, audio signal is classified by using one-vs-one SVM classifiers. Evaluation is tested on ESC-10 dataset.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133391802","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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