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Sundaram Suresh - One of the best experts on this subject based on the ideXlab platform.
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Extended metaCognitive neuro-fuzzy inference system for biometric identification
Studies in Computational Intelligence, 2015Co-Authors: Bindu Madhavi Padmanabhuni, Kartick Subramanian, Sundaram SureshAbstract:Biometrics are increasingly being used as security measures in online as well as offline systems, giving rise to more reliable and unique authentication techniques. In these systems, false positive minimization is one of the crucial requirements, which is especially critical in security sensitive applications. In this chapter, we present an Extended MetaCognitive Neuro-Fuzzy Inference System (eMcFIS) based biometric identification system. eMcFIS consists of a Cognitive Component and a metaCognitive Component. The Cognitive Component, which is a neuro-fuzzy inference system, learns the input-output relationship efficiently. The metaCognitive Component is a self-regulatory learning mechanism, which actively regulates the learning in the Cognitive Component such that the network avoids over-fitting the training samples. Further, the learning strategies are chosen such that the network minimizes false-positive prediction. The proposed eMcFIS is first benchmarked on a set of medical datasets from machine learning databases. eMcFIS is then employed in detection of two real-world biometric security applications, signature verification and fingerprint recognition. The performance comparison with other state-of-the-art authentication systems clearly highlights the advantages of the proposed approach. © Springer International Publishing Switzerland 2016.
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A MetaCognitive Complex-Valued Interval Type-2 Fuzzy Inference System
IEEE Transactions on Neural Networks and Learning Systems, 2014Co-Authors: Kartick Subramanian, Ramasamy Savitha, Sundaram SureshAbstract:This paper presents a complex-valued interval type-2 neuro-fuzzy inference system (CIT2FIS) and derive its metaCognitive projection-based learning (PBL) algorithm. MetaCognitive CIT2FIS (Mc-CIT2FIS) consists of a CIT2FIS, which realizes Takagi-Sugeno-Kang type inference mechanism, as its Cognitive Component. A PBL with self-regulation is its metaCognitive Component. The rules of CIT2FIS employ interval type-\(2~q\) -Gaussian membership functions that can represent different radial basis functions for different values of \(q\) . As each sample is presented to the network, the metaCognitive Component monitors the hinge-loss error and class-specific knowledge potential of the current sample to efficiently decide on what-to-learn, when-to-learn, and how-to-learn it. When a new rule is added or existing rules are updated, the optimal parameters of CIT2FIS corresponding to the minimum of the hinge-loss error function are computed using a PBL algorithm derived using the Wirtinger calculus. The performance of Mc-CIT2FIS is evaluated on a set of benchmark real-valued classification problems from the UCI machine learning repository. A circular transformation is used to convert the real-valued features to the complex-valued features in these problems. The performance comparison and statistical study clearly show the superior classification ability of Mc-CIT2FIS. Finally, the proposed complex-valued network is used to solve a practical human action recognition problem that is represented by complex-valued optical flow-based feature set, and a human emotion recognition problem represented using complex-valued Gabor filter-based features. The performance results on these problems substantiate the superior classification ability of Mc-CIT2FIS.
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A Projection Based Learning Algorithm for Meta-Cognitive Neuro-Fuzzy Inference System
2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013), 2013Co-Authors: Kartick Subramanian, Sundaram SureshAbstract:In this paper, we propose a Projection Based Learning (PBL) algorithm for a Meta-Cognitive Neuro-Fuzzy Inference (McFIS) together referred to as PBL-McFIS. McFIS consists of a Cognitive Component, which is a zero-order Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system, and a meta-Cognitive Component, which is a self-regulatory learning mechanism for the neuro-fuzzy inference system. The learning in the Cognitive Component begins with zero rules, and as new samples are presented to the network, the meta-Cognitive Component monitors the hinge-loss error and spherical potential of the current sample to efficiently decide on what-to-learn, when-to-learn and how-to-learn. In this work we employ PBL-McFIS to solve classification problems and hence the monitory signals employ class-specific self-adaptive thresholds to decide on efficient learning strategies. These thresholds are self-adapted such that the trained network is compact and avoids over-fitting. During addition of new rules or updating of existing rules, the optimal output weights corresponding to the minimum hinge-loss error is computed using PBL algorithm. The learning algorithm considers class-specific as well as class overlap factors during training. The performance of PBL-McFIS is evaluated on a set of benchmark classification problems. The statistical performance analysis with other state-of-the-art neuro-fuzzy inference systems and SVM indicate improved classification ability of the proposed algorithm.
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Meta-Cognitive Neural Network for classification problems in a sequential learning framework
Neurocomputing, 2012Co-Authors: G. Sateesh Babu, Sundaram SureshAbstract:In this paper, we propose a sequential learning algorithm for a neural network classifier based on human meta-Cognitive learning principles. The network, referred to as Meta-Cognitive Neural Network (McNN). McNN has two Components, namely the Cognitive Component and the meta-Cognitive Component. A radial basis function network is the fundamental building block of the Cognitive Component. The meta-Cognitive Component controls the learning process in the Cognitive Component by deciding what-to-learn, when-to-learn and how-to-learn. When a sample is presented at the Cognitive Component of McNN, the meta-Cognitive Component chooses the best learning strategy for the sample using estimated class label, maximum hinge error, confidence of classifier and class-wise significance. Also sample overlapping conditions are considered in growth strategy for proper initialization of new hidden neurons. The performance of McNN classifier is evaluated using a set of benchmark classification problems from the UCI machine learning repository and two practical problems, viz., the acoustic emission for signal classification and a mammogram data set for cancer classification. The statistical comparison clearly indicates the superior performance of McNN over reported results in the literature.
Kartick Subramanian - One of the best experts on this subject based on the ideXlab platform.
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Extended metaCognitive neuro-fuzzy inference system for biometric identification
Studies in Computational Intelligence, 2015Co-Authors: Bindu Madhavi Padmanabhuni, Kartick Subramanian, Sundaram SureshAbstract:Biometrics are increasingly being used as security measures in online as well as offline systems, giving rise to more reliable and unique authentication techniques. In these systems, false positive minimization is one of the crucial requirements, which is especially critical in security sensitive applications. In this chapter, we present an Extended MetaCognitive Neuro-Fuzzy Inference System (eMcFIS) based biometric identification system. eMcFIS consists of a Cognitive Component and a metaCognitive Component. The Cognitive Component, which is a neuro-fuzzy inference system, learns the input-output relationship efficiently. The metaCognitive Component is a self-regulatory learning mechanism, which actively regulates the learning in the Cognitive Component such that the network avoids over-fitting the training samples. Further, the learning strategies are chosen such that the network minimizes false-positive prediction. The proposed eMcFIS is first benchmarked on a set of medical datasets from machine learning databases. eMcFIS is then employed in detection of two real-world biometric security applications, signature verification and fingerprint recognition. The performance comparison with other state-of-the-art authentication systems clearly highlights the advantages of the proposed approach. © Springer International Publishing Switzerland 2016.
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A MetaCognitive Complex-Valued Interval Type-2 Fuzzy Inference System
IEEE Transactions on Neural Networks and Learning Systems, 2014Co-Authors: Kartick Subramanian, Ramasamy Savitha, Sundaram SureshAbstract:This paper presents a complex-valued interval type-2 neuro-fuzzy inference system (CIT2FIS) and derive its metaCognitive projection-based learning (PBL) algorithm. MetaCognitive CIT2FIS (Mc-CIT2FIS) consists of a CIT2FIS, which realizes Takagi-Sugeno-Kang type inference mechanism, as its Cognitive Component. A PBL with self-regulation is its metaCognitive Component. The rules of CIT2FIS employ interval type-\(2~q\) -Gaussian membership functions that can represent different radial basis functions for different values of \(q\) . As each sample is presented to the network, the metaCognitive Component monitors the hinge-loss error and class-specific knowledge potential of the current sample to efficiently decide on what-to-learn, when-to-learn, and how-to-learn it. When a new rule is added or existing rules are updated, the optimal parameters of CIT2FIS corresponding to the minimum of the hinge-loss error function are computed using a PBL algorithm derived using the Wirtinger calculus. The performance of Mc-CIT2FIS is evaluated on a set of benchmark real-valued classification problems from the UCI machine learning repository. A circular transformation is used to convert the real-valued features to the complex-valued features in these problems. The performance comparison and statistical study clearly show the superior classification ability of Mc-CIT2FIS. Finally, the proposed complex-valued network is used to solve a practical human action recognition problem that is represented by complex-valued optical flow-based feature set, and a human emotion recognition problem represented using complex-valued Gabor filter-based features. The performance results on these problems substantiate the superior classification ability of Mc-CIT2FIS.
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A Projection Based Learning Algorithm for Meta-Cognitive Neuro-Fuzzy Inference System
2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013), 2013Co-Authors: Kartick Subramanian, Sundaram SureshAbstract:In this paper, we propose a Projection Based Learning (PBL) algorithm for a Meta-Cognitive Neuro-Fuzzy Inference (McFIS) together referred to as PBL-McFIS. McFIS consists of a Cognitive Component, which is a zero-order Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system, and a meta-Cognitive Component, which is a self-regulatory learning mechanism for the neuro-fuzzy inference system. The learning in the Cognitive Component begins with zero rules, and as new samples are presented to the network, the meta-Cognitive Component monitors the hinge-loss error and spherical potential of the current sample to efficiently decide on what-to-learn, when-to-learn and how-to-learn. In this work we employ PBL-McFIS to solve classification problems and hence the monitory signals employ class-specific self-adaptive thresholds to decide on efficient learning strategies. These thresholds are self-adapted such that the trained network is compact and avoids over-fitting. During addition of new rules or updating of existing rules, the optimal output weights corresponding to the minimum hinge-loss error is computed using PBL algorithm. The learning algorithm considers class-specific as well as class overlap factors during training. The performance of PBL-McFIS is evaluated on a set of benchmark classification problems. The statistical performance analysis with other state-of-the-art neuro-fuzzy inference systems and SVM indicate improved classification ability of the proposed algorithm.
S Suresh - One of the best experts on this subject based on the ideXlab platform.
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meta Cognitive rbf network and its projection based learning algorithm for classification problems
Applied Soft Computing, 2013Co-Authors: Sateesh G Babu, S SureshAbstract:'Meta-Cognitive Radial Basis Function Network' (McRBFN) and its 'Projection Based Learning' (PBL) algorithm for classification problems in sequential framework is proposed in this paper and is referred to as PBL-McRBFN. McRBFN is inspired by human meta-Cognitive learning principles. McRBFN has two Components, namely the Cognitive Component and the meta-Cognitive Component. The Cognitive Component is a single hidden layer radial basis function network with evolving architecture. In the Cognitive Component, the PBL algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. The meta-Cognitive Component controls the learning process in the Cognitive Component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions are considered for proper initialization of new hidden neurons, thus minimizes the misclassification. The interaction of Cognitive Component and meta-Cognitive Component address the what-to-learn, when-to-learn and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from UCI machine learning repository and two practical problems, viz., the acoustic emission signal classification and the mammogram for cancer classification. The statistical performance evaluation on these problems has proven the superior performance of PBL-McRBFN classifier over results reported in the literature.
M Vogelsprott - One of the best experts on this subject based on the ideXlab platform.
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osp parameters and the Cognitive Component of reaction time to a missing stimulus linking brain and behavior
Brain and Cognition, 2009Co-Authors: Oscar H Hernandez, M VogelsprottAbstract:Abstract This within-subjects experiment tested the relationship between the premotor (Cognitive) Component of reaction time (RT) to a missing stimulus and parameters of the omitted stimulus potential (OSP) brain wave. Healthy young men (N = 28) completed trials with an auditory stimulus that recurred at 2 s intervals and ceased unpredictably. Premotor RT and Motor RT were measured on active trials that required an immediate response to a missing stimulus. Passive trials required no response in order to measure the complete set of OSP parameters (i.e., onset, rate of rise, amplitude and peak latency). The results showed that faster Premotor RT was strongly associated with a faster rate of rise in the OSP wave. Motor RT was unrelated the OSP parameters. This new evidence is consistent with the occurrence of some common Cognitive processes generating behavioral and brain reactions to a missing stimulus.
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the omitted stimulus potential is related to the Cognitive Component of reaction time
International Journal of Neuroscience, 2008Co-Authors: Oscar H Hernandez, M VogelsprottAbstract:Omitted stimulus potentials (OSP) are waves that are considered to involve moderately high-level processing, but their relation to the Cognitive, premotor Component of reaction time (PMRT) to an omitted stimulus has not been examined. This relationship was tested in 20 participants who responded to an auditory omitted stimulus occurring in fast (7 Hz) and slow (.5 Hz) frequency trains while electrophysiological recordings provided measures of the OSP and EMG. In accord with the hypothesis, the time between the onset of the OSP and the EMG was strongly correlated to PMRT under both stimulus frequency conditions.
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alcohol impairs the Cognitive Component of reaction time to an omitted stimulus a replication and an extension
Journal of Studies on Alcohol and Drugs, 2007Co-Authors: Oscar H Hernandez, M Vogelsprott, Vanessa I KeaznarAbstract:Objective: Research from a recent study indicates that Cognitive performance is impaired by an acute dose of alcohol at blood alcohol concentrations (BACs) that do not affect motor performance. That study measured reaction time (RT) to the omission of a recurring stimulus and used behavioral criteria to fractionate premotor (Cognitive) and motor Components of RT when stimuli occurred at slow, 2-second intervals (0.5 Hz). The present experiment tested the generality of the evidence when stimuli occurred at slow or fast, 0.143-second intervals (7 Hz). Using muscle potential to fractionate RT, we tested the reproducibility of the findings obtained by a behavioral fractionation procedure. Method: Thirty male social drinkers were randomly assigned to two groups (n = 15 each) that received 0.8 g/kg alcohol or a placebo (0 g/kg). All participants performed a drug-free baseline test and a test during rising BACs. A test presented fast and slow frequency auditory stimuli in counterbalanced order within groups. Res...
Oscar H Hernandez - One of the best experts on this subject based on the ideXlab platform.
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osp parameters and the Cognitive Component of reaction time to a missing stimulus linking brain and behavior
Brain and Cognition, 2009Co-Authors: Oscar H Hernandez, M VogelsprottAbstract:Abstract This within-subjects experiment tested the relationship between the premotor (Cognitive) Component of reaction time (RT) to a missing stimulus and parameters of the omitted stimulus potential (OSP) brain wave. Healthy young men (N = 28) completed trials with an auditory stimulus that recurred at 2 s intervals and ceased unpredictably. Premotor RT and Motor RT were measured on active trials that required an immediate response to a missing stimulus. Passive trials required no response in order to measure the complete set of OSP parameters (i.e., onset, rate of rise, amplitude and peak latency). The results showed that faster Premotor RT was strongly associated with a faster rate of rise in the OSP wave. Motor RT was unrelated the OSP parameters. This new evidence is consistent with the occurrence of some common Cognitive processes generating behavioral and brain reactions to a missing stimulus.
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the omitted stimulus potential is related to the Cognitive Component of reaction time
International Journal of Neuroscience, 2008Co-Authors: Oscar H Hernandez, M VogelsprottAbstract:Omitted stimulus potentials (OSP) are waves that are considered to involve moderately high-level processing, but their relation to the Cognitive, premotor Component of reaction time (PMRT) to an omitted stimulus has not been examined. This relationship was tested in 20 participants who responded to an auditory omitted stimulus occurring in fast (7 Hz) and slow (.5 Hz) frequency trains while electrophysiological recordings provided measures of the OSP and EMG. In accord with the hypothesis, the time between the onset of the OSP and the EMG was strongly correlated to PMRT under both stimulus frequency conditions.
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alcohol impairs the Cognitive Component of reaction time to an omitted stimulus a replication and an extension
Journal of Studies on Alcohol and Drugs, 2007Co-Authors: Oscar H Hernandez, M Vogelsprott, Vanessa I KeaznarAbstract:Objective: Research from a recent study indicates that Cognitive performance is impaired by an acute dose of alcohol at blood alcohol concentrations (BACs) that do not affect motor performance. That study measured reaction time (RT) to the omission of a recurring stimulus and used behavioral criteria to fractionate premotor (Cognitive) and motor Components of RT when stimuli occurred at slow, 2-second intervals (0.5 Hz). The present experiment tested the generality of the evidence when stimuli occurred at slow or fast, 0.143-second intervals (7 Hz). Using muscle potential to fractionate RT, we tested the reproducibility of the findings obtained by a behavioral fractionation procedure. Method: Thirty male social drinkers were randomly assigned to two groups (n = 15 each) that received 0.8 g/kg alcohol or a placebo (0 g/kg). All participants performed a drug-free baseline test and a test during rising BACs. A test presented fast and slow frequency auditory stimuli in counterbalanced order within groups. Res...