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Abstractʊ Research in efficient automated solutions for signature verification has increased in recent years. In this paper we present a parallel computation method for off-line signature verification. A signature image is divided into some blocks. Individual threads will work on each block. The approach is based on feature extraction of every block derived from the image. Every block is represented by a set of features like number of pixels in each block, block-center and distance from the image center. Each type feature has a different impact for assigning some weightage to a block. Experimental result shows that the method eliminates random and simple forgeries and also reduces skilled forgery to a great extent.

Machine Learning (ML) such as Artificial Neural Network (ANN), Deep learning, Recurrent Neural Networks (RNN), Alex Net, and ResNet can be considered as a broad research direction in the identification and classification of critical diseases. CNN and its particular variant, usually named U-Net Segmentation, has made a revolutionary advancement in the classification of medical diseases, specifically retinal diseases. However, because of the feature extraction complexity, U-Net has a significant flaw in high memory and CPU consumption while moving the whole feature map to the corresponding decoder. Furthermore, it can be concatenated to the unsampled decoder feature map avoids reusing pooling indices. In this research work, a convolutional neural network (CNN) model is proposed for multi-class classification problems with the efficient use of memory consumption. The proposed model has been evaluated on a standard benchmark dataset of Eye Net, having 32 classes of retinal diseases. From experimental evaluation, it has been concluded that the proposed model performs better regarding memory management and accuracy. The overall comparison has been performed based on precision, recall, and accuracy with different numbers of epochs and time consumption by each step. The proposed technique achieved an accuracy of 95% on the Eye-net dataset.