S. A. Oyewole, O. Olugbara, Emmanuel Adetiba, T. Nepal
Support Vector Machine (SVM) is widely recognized as a potent data mining technique for solving supervised learning problems. The technique has practical applications in many domains such as e-commerce product classification. However, data sets of large sizes in this application domain often present a negative repercussion for SVM coverage because its training complexity is highly dependent on input size. Moreover, a single kernel may not adequately produce an optimal division between product classes, thereby inhibiting its performance. The literature recommends using multiple kernels to achieve flexibility in the applications of SVM. In addition, color features of product images have been found to improve classification performance of a learning technique, but choosing the right color model is particularly challenging because different color models have varying properties. In this paper, we propose color image classification framework that integrates linear and radial basis function (LaRBF) kernels for SVM. Experiments were performed in five different color models to validate the performance of SVM based LaRBF in classifying 100 classes of e-commerce product images obtained from the PI 100 Microsoft corpus. Classification accuracy of 83.5% was realized with the LaRBF in RGB color model, which is an improvement over an existing method.
{"title":"Classification of Product Images in Different Color Models with Customized Kernel for Support Vector Machine","authors":"S. A. Oyewole, O. Olugbara, Emmanuel Adetiba, T. Nepal","doi":"10.1109/AIMS.2015.33","DOIUrl":"https://doi.org/10.1109/AIMS.2015.33","url":null,"abstract":"Support Vector Machine (SVM) is widely recognized as a potent data mining technique for solving supervised learning problems. The technique has practical applications in many domains such as e-commerce product classification. However, data sets of large sizes in this application domain often present a negative repercussion for SVM coverage because its training complexity is highly dependent on input size. Moreover, a single kernel may not adequately produce an optimal division between product classes, thereby inhibiting its performance. The literature recommends using multiple kernels to achieve flexibility in the applications of SVM. In addition, color features of product images have been found to improve classification performance of a learning technique, but choosing the right color model is particularly challenging because different color models have varying properties. In this paper, we propose color image classification framework that integrates linear and radial basis function (LaRBF) kernels for SVM. Experiments were performed in five different color models to validate the performance of SVM based LaRBF in classifying 100 classes of e-commerce product images obtained from the PI 100 Microsoft corpus. Classification accuracy of 83.5% was realized with the LaRBF in RGB color model, which is an improvement over an existing method.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121347789","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}
General voice based access control systems are based on voice biometrics. This process enables an unauthorized access by recording the voice of the authorized person. So there is a requirement to prevent unauthorized access through recording speech. Other than voice biometrics, here we have two challenges. (i) To extract the authentication information. (ii) To find the unauthorized source. The speech goes through DA-AD-DA conversion, while it is recorded and used for access control. The watermarking method which will use for this purpose must be robust to DA-AD conversion attack, which is usually involved in recordings. In this work, we propose a method based on casting Log Co-ordinate Mapping (LCM), in which embedding two watermark segments in two different frequency regions, one for authentication information purpose and other for finding unauthorized source. The LCM method has approving performance against DA-AD conversion attacks [1]. The modifications made for this does not impact the perceptible auditory quality and the embedding capacity improved by selecting the appropriate frequency regions in the log scale. Our results show that our method robustly extracts the source identification information while detecting the malicious source if the audio is being recorded and played back by unauthorized source.
{"title":"Tamper Detection in Speech Based Access Control Systems Using Watermarking","authors":"B. Garlapati, S. Chalamala, K. Kakkirala","doi":"10.1109/AIMS.2015.59","DOIUrl":"https://doi.org/10.1109/AIMS.2015.59","url":null,"abstract":"General voice based access control systems are based on voice biometrics. This process enables an unauthorized access by recording the voice of the authorized person. So there is a requirement to prevent unauthorized access through recording speech. Other than voice biometrics, here we have two challenges. (i) To extract the authentication information. (ii) To find the unauthorized source. The speech goes through DA-AD-DA conversion, while it is recorded and used for access control. The watermarking method which will use for this purpose must be robust to DA-AD conversion attack, which is usually involved in recordings. In this work, we propose a method based on casting Log Co-ordinate Mapping (LCM), in which embedding two watermark segments in two different frequency regions, one for authentication information purpose and other for finding unauthorized source. The LCM method has approving performance against DA-AD conversion attacks [1]. The modifications made for this does not impact the perceptible auditory quality and the embedding capacity improved by selecting the appropriate frequency regions in the log scale. Our results show that our method robustly extracts the source identification information while detecting the malicious source if the audio is being recorded and played back by unauthorized source.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114110527","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}
A data bank can provide very useful information while mined properly.[27] In order to be optimally extracted, data mining can be done by observing capacity and characteristics of the data; so it can generates Knowledge Discovery in Databases as expected. For instance in Gene Bank, every single record of DNA, there are at least ten thousand sequences recorded. If the data is more than a hundred records, it will be a big sequence of data to be processed. Hepatitis C Virus (HCV) is a liver disease which can infect humans through blood. HCV infection can be asymptomatic, or it can be hepatitis acute, chronic, furthermore cirrhosis. Hepatitis C is generally does not show symptoms in the early stages. About 75 percent people with hepatitis C did not realize that they had infected until liver damage years later. Therefore needed a sequences DNA Mining is needed to analyse the DNA history whether it is infected by HCV or not. This study compares several methods of string matching to discover which methods have the best performance in processing DNA mining. In addition, this study also analyzed DNA HCV genetic mutations trend as a Knowledege Discovery in Database in DNA mining.
{"title":"Pattern Matching Performance Comparisons as Big Data Analysis Recommendations for Hepatitis C Virus (HCV) Sequence DNA","authors":"Berlian Al Kindhi, T. A. Sardjono","doi":"10.1109/AIMS.2015.27","DOIUrl":"https://doi.org/10.1109/AIMS.2015.27","url":null,"abstract":"A data bank can provide very useful information while mined properly.[27] In order to be optimally extracted, data mining can be done by observing capacity and characteristics of the data; so it can generates Knowledge Discovery in Databases as expected. For instance in Gene Bank, every single record of DNA, there are at least ten thousand sequences recorded. If the data is more than a hundred records, it will be a big sequence of data to be processed. Hepatitis C Virus (HCV) is a liver disease which can infect humans through blood. HCV infection can be asymptomatic, or it can be hepatitis acute, chronic, furthermore cirrhosis. Hepatitis C is generally does not show symptoms in the early stages. About 75 percent people with hepatitis C did not realize that they had infected until liver damage years later. Therefore needed a sequences DNA Mining is needed to analyse the DNA history whether it is infected by HCV or not. This study compares several methods of string matching to discover which methods have the best performance in processing DNA mining. In addition, this study also analyzed DNA HCV genetic mutations trend as a Knowledege Discovery in Database in DNA mining.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116145164","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}
This paper examines a 2-stage supply chain that features a buyback contract between manufacturer and retailer under uncertain demand and consumer returns policy with partial refund amount. The supply chain is optimized using the utility of profit that includes the mean and variance of profit. The optimal values of buyback price, wholesale price, and retailer's order quantity are determined for the coordination situation of the decentralized supply chain when its members are risk averse. Through a computational study, the impacts of the supply chain members' risk attitudes and refund amount on the optimal decisions are investigated for the uncoordinated supply chain where one of the agents makes off-optimal decision.
{"title":"A Computational Study of Risk-Averse Parameter Effects on a 2-Stage Supply Chain Coordination under Refund-Dependent Demand","authors":"N. T. Loi, T. Duc, J. Buddhakulsomsiri","doi":"10.1109/AIMS.2015.51","DOIUrl":"https://doi.org/10.1109/AIMS.2015.51","url":null,"abstract":"This paper examines a 2-stage supply chain that features a buyback contract between manufacturer and retailer under uncertain demand and consumer returns policy with partial refund amount. The supply chain is optimized using the utility of profit that includes the mean and variance of profit. The optimal values of buyback price, wholesale price, and retailer's order quantity are determined for the coordination situation of the decentralized supply chain when its members are risk averse. Through a computational study, the impacts of the supply chain members' risk attitudes and refund amount on the optimal decisions are investigated for the uncoordinated supply chain where one of the agents makes off-optimal decision.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125148776","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}
H. Alyasiri, A. K. Al-Samarrie, Aseel H. Al-Nakkash
The powerful of WiMAX technique for providing the Subscribers (SSs) with a flexible interface to share resources is based on an Orthogonal Frequency Division Multiple Access (OFDMA). Conventional access schemes base on fixed parameters for all SSs in the networks without considering the effects of various channel characteristics among them. This motivates the authors to propose a new Adaptive Resource Allocation Scheme (ARAS) to construct the OFDMA based frame. The proposed ARAS integrates two approaches, the adaptive Cyclic Prefix (CP) length and dynamic frequency allocation. These two approaches are implemented, analyzed and evaluated based on the simulation of WiMAX frames in a dynamic manner resulting in a new frame pattern within each down link connection. The resulting frame shows the contribution of the time domain approach which represented by adaptive CP in mitigation ISI and ICI which improves the network performance in term of BER, where enhancement of 7 dB in SNR was gained at BER equals to 10-3 compared with the network which adopts fixed guard interval equals to 1/8. From the other side, the frequency domain approach, which represented by the dynamic frequency allocation proves its effectiveness in supporting the QoS requirements in term of data rate.
{"title":"Performance Enhancement by Adaptive Resource Allocation in WiMAX Networks","authors":"H. Alyasiri, A. K. Al-Samarrie, Aseel H. Al-Nakkash","doi":"10.1109/AIMS.2015.75","DOIUrl":"https://doi.org/10.1109/AIMS.2015.75","url":null,"abstract":"The powerful of WiMAX technique for providing the Subscribers (SSs) with a flexible interface to share resources is based on an Orthogonal Frequency Division Multiple Access (OFDMA). Conventional access schemes base on fixed parameters for all SSs in the networks without considering the effects of various channel characteristics among them. This motivates the authors to propose a new Adaptive Resource Allocation Scheme (ARAS) to construct the OFDMA based frame. The proposed ARAS integrates two approaches, the adaptive Cyclic Prefix (CP) length and dynamic frequency allocation. These two approaches are implemented, analyzed and evaluated based on the simulation of WiMAX frames in a dynamic manner resulting in a new frame pattern within each down link connection. The resulting frame shows the contribution of the time domain approach which represented by adaptive CP in mitigation ISI and ICI which improves the network performance in term of BER, where enhancement of 7 dB in SNR was gained at BER equals to 10-3 compared with the network which adopts fixed guard interval equals to 1/8. From the other side, the frequency domain approach, which represented by the dynamic frequency allocation proves its effectiveness in supporting the QoS requirements in term of data rate.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122458327","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}
The implementation of CPU-Scheduling algorithms such as Shortest-Job-First (SJF) and Shortest Remaining Time First (SRTF) is relying on knowing the length of the CPU-bursts for processes in the ready queue. There are several methods to predict the length of the CPU-bursts, such as exponential averaging method, however these methods may not give an accurate or reliable predicted values. In this paper, we will propose a Machine Learning (ML) based approach to estimate the length of the CPU-bursts for processes. The proposed approach aims to select the most significant attributes of the process using feature selection techniques and then predicts the CPU-burst for the process in the grid. ML techniques such as Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN), Artificial Neural Networks (ANN) and Decision Trees (DT) are used to test and evaluate the proposed approach using a grid workload dataset named "GWA-T-4 Auver Grid". The experimental results show that there is a strength linear relationship between the process attributes and the burst CPU time. Moreover, K-NN performs better in nearly all approaches in terms of CC and RAE. Furthermore, applying attribute selection techniques improves the performance in terms of space, time and estimation.
{"title":"A Machine Learning-Based Approach to Estimate the CPU-Burst Time for Processes in the Computational Grids","authors":"T. Helmy, Sadam Al-Azani, Omar Bin-Obaidellah","doi":"10.1109/AIMS.2015.11","DOIUrl":"https://doi.org/10.1109/AIMS.2015.11","url":null,"abstract":"The implementation of CPU-Scheduling algorithms such as Shortest-Job-First (SJF) and Shortest Remaining Time First (SRTF) is relying on knowing the length of the CPU-bursts for processes in the ready queue. There are several methods to predict the length of the CPU-bursts, such as exponential averaging method, however these methods may not give an accurate or reliable predicted values. In this paper, we will propose a Machine Learning (ML) based approach to estimate the length of the CPU-bursts for processes. The proposed approach aims to select the most significant attributes of the process using feature selection techniques and then predicts the CPU-burst for the process in the grid. ML techniques such as Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN), Artificial Neural Networks (ANN) and Decision Trees (DT) are used to test and evaluate the proposed approach using a grid workload dataset named \"GWA-T-4 Auver Grid\". The experimental results show that there is a strength linear relationship between the process attributes and the burst CPU time. Moreover, K-NN performs better in nearly all approaches in terms of CC and RAE. Furthermore, applying attribute selection techniques improves the performance in terms of space, time and estimation.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134404311","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}
K. Nwizege, Agbeb N. Stephen, Shedrack Mmeah, Michael MacMammah, I. P. Gibson
Below the Media Access Control (MAC) layer is the Physical (PHY) layer which deals with the actual transmission of the bits received from the MAC layer above into electromagnetic signals. This layer is optimized to implore power management in wireless networks. Power management is a crucial issue in wireless and mobile networks. In this paper, we propose an Adaptive Context-Aware Rate Selection (ACARS) algorithm to handle the issue of power consumption in wireless networks. This algorithm is implemented by optimizing the PHY layer to transmit efficiently as the number of nodes changes and we estimate the Signal-to-Noise Ratio (SNR) to the PHY layer. Results show that by using the appropriate power management technique, ACARS is reliable and efficient for power consumption in wireless networks which is a high demand for vehicular networks.
{"title":"Improving Network Performance with Rate Adaptation Algorithms for Vehicular Simulations","authors":"K. Nwizege, Agbeb N. Stephen, Shedrack Mmeah, Michael MacMammah, I. P. Gibson","doi":"10.1109/AIMS.2015.74","DOIUrl":"https://doi.org/10.1109/AIMS.2015.74","url":null,"abstract":"Below the Media Access Control (MAC) layer is the Physical (PHY) layer which deals with the actual transmission of the bits received from the MAC layer above into electromagnetic signals. This layer is optimized to implore power management in wireless networks. Power management is a crucial issue in wireless and mobile networks. In this paper, we propose an Adaptive Context-Aware Rate Selection (ACARS) algorithm to handle the issue of power consumption in wireless networks. This algorithm is implemented by optimizing the PHY layer to transmit efficiently as the number of nodes changes and we estimate the Signal-to-Noise Ratio (SNR) to the PHY layer. Results show that by using the appropriate power management technique, ACARS is reliable and efficient for power consumption in wireless networks which is a high demand for vehicular networks.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133119002","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}
Temporal information plays a substantial role in accessing Granger Causality. However, new technology limits the availability of data by simultaneously analyzing high dimensional data. Recent studies suggest that this problem can be resolved by reusing the data after reversing the timestamp. Based on this idea, we are proposing a new method called Forward Backward Pair wise Granger Causality that can deal with high dimensional data and can extract more causal data. We have used simulated data to compare our proposed method with the existing method and later, we have applied the proposed approach to control mice data to map the protein map involved in studying the fear.
{"title":"Protein Map of Control Mice Exposed to Context Fear Using a Novel Implementation of Granger Causality","authors":"M. Furqan, M. Y. Siyal","doi":"10.1109/AIMS.2015.26","DOIUrl":"https://doi.org/10.1109/AIMS.2015.26","url":null,"abstract":"Temporal information plays a substantial role in accessing Granger Causality. However, new technology limits the availability of data by simultaneously analyzing high dimensional data. Recent studies suggest that this problem can be resolved by reusing the data after reversing the timestamp. Based on this idea, we are proposing a new method called Forward Backward Pair wise Granger Causality that can deal with high dimensional data and can extract more causal data. We have used simulated data to compare our proposed method with the existing method and later, we have applied the proposed approach to control mice data to map the protein map involved in studying the fear.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114779073","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}
Jerne's Idiotypic Network theory features autonomous network formation, adaptation, learning and self-stabilization, all of which find extensive applications in computational realm. Researchers have used this model in a myriad of applications, however, the use of this model in real networked environments has hardly been addressed. This paper describes an Idiotypic Sieve to filter out the optimal solutions from a set of available solutions for a set of heterogeneous problems that could occur asynchronously or concurrently across a real network. The Idiotypic Sieve described herein, is conceived by emulating an Idiotypic network wherein antibodies (solutions) within a real physical network asynchronously interact with one another and also with the antigens (problems) in a distributed and decentralized manner and stimulate and suppress one another consequently changing their respective global populations across the network. The antibodies (solutions) are provided the much required mobility across the network by a set of mobile agents that autonomously patrol and migrate to nodes that are invaded by the antigens (problems). Emulation results carried out on a real network portrayed in this paper, show the effectiveness of the Idiotypic Sieve in generating and controlling the populations of both optimal and generic solutions to the heterogeneous set of problems.
{"title":"An Idiotypic Solution Sieve for Selecting the Best Performing Solutions in Real-World Distributed Intelligence","authors":"S. S. Jha, S. B. Nair","doi":"10.1109/AIMS.2015.22","DOIUrl":"https://doi.org/10.1109/AIMS.2015.22","url":null,"abstract":"Jerne's Idiotypic Network theory features autonomous network formation, adaptation, learning and self-stabilization, all of which find extensive applications in computational realm. Researchers have used this model in a myriad of applications, however, the use of this model in real networked environments has hardly been addressed. This paper describes an Idiotypic Sieve to filter out the optimal solutions from a set of available solutions for a set of heterogeneous problems that could occur asynchronously or concurrently across a real network. The Idiotypic Sieve described herein, is conceived by emulating an Idiotypic network wherein antibodies (solutions) within a real physical network asynchronously interact with one another and also with the antigens (problems) in a distributed and decentralized manner and stimulate and suppress one another consequently changing their respective global populations across the network. The antibodies (solutions) are provided the much required mobility across the network by a set of mobile agents that autonomously patrol and migrate to nodes that are invaded by the antigens (problems). Emulation results carried out on a real network portrayed in this paper, show the effectiveness of the Idiotypic Sieve in generating and controlling the populations of both optimal and generic solutions to the heterogeneous set of problems.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"151 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131124267","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}
Discovery of temporal dependence is the basic idea for evaluating gene networks using Granger causality. However, with the advancement of technology, now we can analyze multiple genes simultaneously that result in high dimensional data. Recent studies suggest that more causal information can be retrieved if we reverse the time stamp of time series data along with standard time series data. Based on these findings, we are proposing a new method called Forward Backward Pair wise Granger Causality. The results how that our method can handle high dimensional data and can extract more causal information compared to the standard ordinary least squares method. We have performed a comparison of proposed and existing method using simulated data and then used the proposed method on real Hela cell data and mapped the 19 genes that are commonly present in cancer.
{"title":"Gene Network Inference Using Forward Backward Pairwise Granger Causality","authors":"M. Furqan, M. Y. Siyal","doi":"10.1109/AIMS.2015.58","DOIUrl":"https://doi.org/10.1109/AIMS.2015.58","url":null,"abstract":"Discovery of temporal dependence is the basic idea for evaluating gene networks using Granger causality. However, with the advancement of technology, now we can analyze multiple genes simultaneously that result in high dimensional data. Recent studies suggest that more causal information can be retrieved if we reverse the time stamp of time series data along with standard time series data. Based on these findings, we are proposing a new method called Forward Backward Pair wise Granger Causality. The results how that our method can handle high dimensional data and can extract more causal information compared to the standard ordinary least squares method. We have performed a comparison of proposed and existing method using simulated data and then used the proposed method on real Hela cell data and mapped the 19 genes that are commonly present in cancer.","PeriodicalId":121874,"journal":{"name":"2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127843778","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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