Intelligent Neural Network For Bacteria Classification: An Innovation In Artificial Neural Network

The work focused on reliable outcome from next generation artificial neural network (ANN). ANN was efficiently used for decision making on labeled and unlabeled data but problem was that it was always generated as a result though the short input data. The conventional ANN model is being used in some financial sectors for prediction and analysis of financial data, but it would not make an outcome due to less applicable data. Our objective is to design a neural network which will have the intelligence by which it can generate most prominent decision. A mathematical model of new generation artificial neural network called Intelligent Neural Network (INN) has been proposed, which would solve that problem and would make the decision like a human. The INN model has been designed with two layers of fully connected neurons, where the first layer neurons has taken input as the features of bacteria and produced input for hidden neurons; and in the second layer the output from hidden neurons provided as input of decision neurons and the output of decision neurons was the expected result. This model was trained by back propagation process by reducing Sum Squared Error(SSE) through Stochastic Gradient Descent(SGD) technique. Prediction accuracy of this model was 97.11% to distinguish medically important bacteria. This study would help to laboratory users to identify medically important bacteria in an easy way.

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