Savitha Ramasamy

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A Meta-cognitive Interval Type-2 Neuro-Fuzzy Inference System (McIT2FIS) based classifier and its projection based learning algorithm is presented in this paper. McIT2FIS consists of two components, namely, a cognitive component and a meta-cognitive component. The cognitive component is an Interval Type-2 Neuro-Fuzzy Inference System (IT2FIS) represented as(More)
This paper presents a fast learning fully complex-valued classifier to solve real-valued classification problems, called the ‘Fast Learning Complex-valued Neural Classifier’ (FLCNC). The FLCNC is a single hidden layer network with a non-linear, real to complex transformed input layer, a hidden layer with a fully complex activation function and(More)
In this paper, we propose a risk-sensitive hinge loss function-based cognitive ensemble of extreme learning machine (ELM) classifiers for JPEG steganalysis. ELM is a single hidden-layer feed-forward network that chooses the input parameters randomly and estimates the output weights analytically. For steganalysis, we have extracted 548-dimensional merge(More)
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(More)
Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition.(More)
Prediction of post-surgery survival of breast cancer patients is critical for long term medical care. In this paper, we study the performances of several complex-valued classifiers in predicting the post-surgical survival, based on the real world Haber data set available in the UCI machine learning repository. The complex-valued classifiers used in the(More)
This paper presents a novel procedure involving waveatom transform and Circular Complex-valued Extreme Learning Machine (CC-ELM) for automatic characterization of mammographic microcalcifications into benign or malignant. Waveatom transform is used to transform the mammogram image into multi-frequency domain features. The best feature set is obtained by(More)
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