On Going Activities
Development of Optimization based CNN algorithm for glaucoma detection. The proposed algorithm will be able to learn the network with fewer sample size. The research aslo focused on developing neuaral network architecture that can solve various limitations of existing CNN models.
Development of a new evolutionary algorithm based on the Bryophyllum tree. This will be a new Optimization techinique the simulates formation of Bryophyllum forest. The algorithm is going to keep track of both maximization and minimization value of the objective function simultaniously.
Development of a one-pass CNN learning approch that needs no hyper-parameter tuning. The main objective of this research is to develop a novel learning algorithm for CNN that learns the covolutional and fully connected layer kernels in one epoch.
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