The deep leaning has been the prominent and interesting field for researchers for last few years. There are several studies are being done on real problems in several streams of Science. In the deep leaning there are huge resources and labeled dataset are required to train the model. In convolutional neural network (CNN) training with random weights take a considerable amount of time to optimize the weights. To minimize efforts and other resources and deal with large, challenging dataset; which generally very complexed to use in the conventional training methods. New concept of deep learning has been evolved and emerge as prominent methods called transfer learning in last few years. In the transfer leaning, pre-trained model VGG16, which had been trained using 1000 different classes of Images. The VGG16 architecture having 16 CCN layers with ReLu activation function. In this study we took the Honey Bee image dataset consists of two type of the images of honey bee, some images are bearing pollen honey bees and remaining are non-bearing pollen honey bees. We got learn the model with dataset and classified problems using three optimization functions: Adaptive Moment Estimation Algorithm (ADAM), Root Mean Square Propagation (RMSprop) and Stochastic gradient descent (SGD). we compared the results, and found that Adaptive Moment Estimation (ADAM) function performed best, and got 91.94 percent Accuracy and 0.2027 losses.
An Improved Clutter Filtering and Spectral Moment Estimation Algorithm for Staggered PRT Sequences
