Harsh Vazirani

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Diagnosis of diseases is well known problem in the medical field. Past research shows that medical database of disease can be train by using various neural network models. Many medical problems face the problem of curse of dimensionality due to the excessively large number of input attributes. Breast cancer is one such problem. We propose the use of modular(More)
Biometric Identification is a very old field where we try to identify people by their biometric identities. The field shifted to bi-modal systems where more than one modality was used for the identification purposes. The bimodal systems face problem related to high dimensionality that may many times result in problems. The individual modules already have(More)
Handwriting Recognition enables a person to scribble something on a piece of paper and then convert it into text. If we look into the practical reality there are enumerable styles in which a character may be written. These styles can be self combined to generate more styles. Even if a small child knows the basic styles a character can be written, he would(More)
Classification has been a major problem of study whose application includes speaker recognition, character recognition, etc. In this paper we first adapt the Radial Basis Function Network (RBFN) for classification problems and then use customized Evolutionary Algorithms to evolve the RBFN. The neurons of the RBFN correspond to some class out of the(More)
Intelligent systems have been extensively used in the area of biomedical engineering for assisting the doctors in medical diagnosis. The inability of simple neural networks to solve the diagnosis problem, due to extensively large data size as well as complex mapping of inputs to outputs, has resulted in the growth of modular neural networks that try to(More)
The statistical and soft computing methods have been used to predict time series data from different fields. The different methods have been applied on same or different time series data. One method has been selected among the applied methods based error analysis. In this paper, an algorithm has been proposed to forecast the time series data based on(More)
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