The Experts below are selected from a list of 3267 Experts worldwide ranked by ideXlab platform
Holger Arndt - One of the best experts on this subject based on the ideXlab platform.
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The java Data Mining Package - A Data processing library for java
Proceedings - International Computer Software and Applications Conference, 2009Co-Authors: Holger ArndtAbstract:This paper describes a novel and innovative Java library for machine learning and \nData processing.
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COMPSAC (1) - The Java Data Mining Package - A Data Processing Library for Java
2009 33rd Annual IEEE International Computer Software and Applications Conference, 2009Co-Authors: Holger ArndtAbstract:This paper describes a novel and innovative Java library for machine learning and Data processing.
Daniel Bourget - One of the best experts on this subject based on the ideXlab platform.
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Integrating Asterisk with InRule to Detect Suspicious Calls
2010Co-Authors: Ahmad Hammoud, Daniel BourgetAbstract:Monitoring telecommunications systems is of a crucial importance. Nowadays, PBX software Packages can be configured to allow the storage of Call Detail Records in a Database. There is a permanent need to analyze those records and allow business owners to detect PBX misuse from inside and outside the company. The word "misuse" covers an employee making too many personal phone calls, a salesman making fewer phone calls than expected, a client making an excessive number of phone calls, and other suspicious calls. A Data-Mining Package or any other analytical tool would not be as efficient because it would report what happened when it is too late to take action. In this article, we integrate Asterisk with a rule-based engine called InRule. Asterisk will consult InRule whenever a call is about to be made and thus take appropriate actions.
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AICT - Integrating Asterisk with InRule to Detect Suspicious Calls
2010 Sixth Advanced International Conference on Telecommunications, 2010Co-Authors: Ahmad Hammoud, Daniel BourgetAbstract:Monitoring telecommunications systems is of a crucial importance. Nowadays, PBX software Packages can be configured to allow the storage of Call Detail Records in a Database. There is a permanent need to analyze those records and allow business owners to detect PBX misuse from inside and outside the company. The word "misuse" covers an employee making too many personal phone calls, a salesman making fewer phone calls than expected, a client making an excessive number of phone calls, and other suspicious calls. A Data-Mining Package or any other analytical tool would not be as efficient because it would report what happened when it is too late to take action. In this article, we integrate Asterisk with a rule-based engine called InRule. Asterisk will consult InRule whenever a call is about to be made and thus take appropriate actions.
Paolo Gamba - One of the best experts on this subject based on the ideXlab platform.
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on the architecture of a big Data classification tool based on a map reduce approach for hyperspectral image analysis
International Geoscience and Remote Sensing Symposium, 2015Co-Authors: V. A. Ayma, P. Happ, R.s. Ferreira, Dario Augusto Borges Oliveira, G A O P Costa, Raul Queiroz Feitosa, Antonio Plaza, Paolo GambaAbstract:Advances in remote sensors are providing exceptional quantities of large-scale Data with increasing spatial, spectral and temporal resolutions, raising new challenges in its analysis, e.g. those presents in classification processes. This work presents the architecture of the InterIMAGE Cloud Platform (ICP): Data Mining Package; a tool able to perform supervised classification procedures on huge amounts of Data, on a distributed infrastructure. The architecture is implemented on top of the MapReduce framework. The tool has four classification algorithms implemented taken from WEKA's machine learning library, namely: Decision Trees, Naive Bayes, Random Forest and Support Vector Machines. The SVM classifier was applied on Datasets of different sizes (2 GB, 4 GB and 10 GB) for different cluster configurations (5, 10, 20, 50 nodes). The results show the tool as a potential approach to parallelize classification processes on big Data.
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Classification algorithms for big Data analysis, a map reduce approach
International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2015Co-Authors: V. A. Ayma, P. Happ, R. Feitosa, Antonio J. Plaza, R.s. Ferreira, G Costa, D De Oliveira, Paolo GambaAbstract:Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of Data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of Data, usually referred as big Data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA's machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on Data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.
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IGARSS - On the architecture of a big Data classification tool based on a map reduce approach for hyperspectral image analysis
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015Co-Authors: V. A. Ayma, P. Happ, R.s. Ferreira, Dario Augusto Borges Oliveira, G A O P Costa, Raul Queiroz Feitosa, Antonio Plaza, Paolo GambaAbstract:Advances in remote sensors are providing exceptional quantities of large-scale Data with increasing spatial, spectral and temporal resolutions, raising new challenges in its analysis, e.g. those presents in classification processes. This work presents the architecture of the InterIMAGE Cloud Platform (ICP): Data Mining Package; a tool able to perform supervised classification procedures on huge amounts of Data, on a distributed infrastructure. The architecture is implemented on top of the MapReduce framework. The tool has four classification algorithms implemented taken from WEKA's machine learning library, namely: Decision Trees, Naive Bayes, Random Forest and Support Vector Machines. The SVM classifier was applied on Datasets of different sizes (2 GB, 4 GB and 10 GB) for different cluster configurations (5, 10, 20, 50 nodes). The results show the tool as a potential approach to parallelize classification processes on big Data.
Ahmad Hammoud - One of the best experts on this subject based on the ideXlab platform.
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Integrating Asterisk with InRule to Detect Suspicious Calls
2010Co-Authors: Ahmad Hammoud, Daniel BourgetAbstract:Monitoring telecommunications systems is of a crucial importance. Nowadays, PBX software Packages can be configured to allow the storage of Call Detail Records in a Database. There is a permanent need to analyze those records and allow business owners to detect PBX misuse from inside and outside the company. The word "misuse" covers an employee making too many personal phone calls, a salesman making fewer phone calls than expected, a client making an excessive number of phone calls, and other suspicious calls. A Data-Mining Package or any other analytical tool would not be as efficient because it would report what happened when it is too late to take action. In this article, we integrate Asterisk with a rule-based engine called InRule. Asterisk will consult InRule whenever a call is about to be made and thus take appropriate actions.
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AICT - Integrating Asterisk with InRule to Detect Suspicious Calls
2010 Sixth Advanced International Conference on Telecommunications, 2010Co-Authors: Ahmad Hammoud, Daniel BourgetAbstract:Monitoring telecommunications systems is of a crucial importance. Nowadays, PBX software Packages can be configured to allow the storage of Call Detail Records in a Database. There is a permanent need to analyze those records and allow business owners to detect PBX misuse from inside and outside the company. The word "misuse" covers an employee making too many personal phone calls, a salesman making fewer phone calls than expected, a client making an excessive number of phone calls, and other suspicious calls. A Data-Mining Package or any other analytical tool would not be as efficient because it would report what happened when it is too late to take action. In this article, we integrate Asterisk with a rule-based engine called InRule. Asterisk will consult InRule whenever a call is about to be made and thus take appropriate actions.
V. A. Ayma - One of the best experts on this subject based on the ideXlab platform.
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on the architecture of a big Data classification tool based on a map reduce approach for hyperspectral image analysis
International Geoscience and Remote Sensing Symposium, 2015Co-Authors: V. A. Ayma, P. Happ, R.s. Ferreira, Dario Augusto Borges Oliveira, G A O P Costa, Raul Queiroz Feitosa, Antonio Plaza, Paolo GambaAbstract:Advances in remote sensors are providing exceptional quantities of large-scale Data with increasing spatial, spectral and temporal resolutions, raising new challenges in its analysis, e.g. those presents in classification processes. This work presents the architecture of the InterIMAGE Cloud Platform (ICP): Data Mining Package; a tool able to perform supervised classification procedures on huge amounts of Data, on a distributed infrastructure. The architecture is implemented on top of the MapReduce framework. The tool has four classification algorithms implemented taken from WEKA's machine learning library, namely: Decision Trees, Naive Bayes, Random Forest and Support Vector Machines. The SVM classifier was applied on Datasets of different sizes (2 GB, 4 GB and 10 GB) for different cluster configurations (5, 10, 20, 50 nodes). The results show the tool as a potential approach to parallelize classification processes on big Data.
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Classification algorithms for big Data analysis, a map reduce approach
International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2015Co-Authors: V. A. Ayma, P. Happ, R. Feitosa, Antonio J. Plaza, R.s. Ferreira, G Costa, D De Oliveira, Paolo GambaAbstract:Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of Data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of Data, usually referred as big Data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA's machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on Data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.
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IGARSS - On the architecture of a big Data classification tool based on a map reduce approach for hyperspectral image analysis
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015Co-Authors: V. A. Ayma, P. Happ, R.s. Ferreira, Dario Augusto Borges Oliveira, G A O P Costa, Raul Queiroz Feitosa, Antonio Plaza, Paolo GambaAbstract:Advances in remote sensors are providing exceptional quantities of large-scale Data with increasing spatial, spectral and temporal resolutions, raising new challenges in its analysis, e.g. those presents in classification processes. This work presents the architecture of the InterIMAGE Cloud Platform (ICP): Data Mining Package; a tool able to perform supervised classification procedures on huge amounts of Data, on a distributed infrastructure. The architecture is implemented on top of the MapReduce framework. The tool has four classification algorithms implemented taken from WEKA's machine learning library, namely: Decision Trees, Naive Bayes, Random Forest and Support Vector Machines. The SVM classifier was applied on Datasets of different sizes (2 GB, 4 GB and 10 GB) for different cluster configurations (5, 10, 20, 50 nodes). The results show the tool as a potential approach to parallelize classification processes on big Data.
