“Learn from yesterday, live for today, hope for tomorrow. The important thing is not to stop questioning.” ~Albert Einstein

Personal Information

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.


Synopsis of the latest publication

Meta-heuristic optimization schemes can facilitate feature learning even with small amount of training data. This paper presents a new feature learning mechanism called multi-objective Jaya convolutional network (MJCN) that attempts to learn meaningful features directly from the images. The proposed scheme, unlike the convolutional neural networks, comprises a convolution layer, a multiplication layer, an activation layer and an optimizer known as multi-objective Jaya optimizer (MJO). The convolution layer searches meaningful patterns in an image through the local neighborhood connections and the multiplication layer projects the convolutional response to a more compact feature space. (Read more...)