Ravindra Nath

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Many complex problems like Speech Recognition, Bioinformatics, Climatology, Control and Communication are solved using Hidden Markov Models (HMM). Mostly, optimization problems are modeled as HMM learning problem in which HMM parameters are either maximized or minimized. In general, Baum-Welch Method (BW) is used to solve HMM learning problem giving only(More)
Hidden Markov model (HMM) is a stochastic method which has been used in various application like speech processing, signal processing and character recognition. It has three main problems. Third problem of HMM is the one in which we optimize the model parameters so as to describe how a given observation sequence comes about. The observation sequence is
Diabetic patients need to take insulin before every meal. The doctors have to decide insulin doses for every patient according to the patient's previous records of doses and sugar levels measured at regular intervals. This paper proposes a Hidden Markov Model to predict the insulin chart for a patient and uses Simulated Annealing search algorithm to(More)
The purpose of our on going research would be to track entities, which enter their field of vision over the sensor network. Based on their sightings, they maintain a dynamic cache that can be queried by a base station-a stationary or fixed node. Since transmitting messages consumes a lot of energy, as against local processing and moreover the limited energy(More)
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