Digit Recognition Using Single Layer Neural Network with Principal Component Analysis

This paper presents an approach to digit recognition using single layer neural network classifier with Principal Component Analysis (PCA). The handwritten digit recognition is an important area of research as there are so many applications which are using handwritten recognition and it can also be applied to new application. There are many algorithms applied to this computer vision problem and many more algorithms are continuously developed on this to make the handwritten recognition classify digits more accurately with less computation involved. The proposed model in this paper aims to reduce the features to reduce computation requirements and successfully classify the digit into 10 categories (0 to 9). The system designed consists of backward propagation (BP) neural network and is trained and tested on the MNIST dataset of handwritten digit. The proposed system was able to obtain 98.39% accuracy on the MNIST 10,000 test dataset. The Principal Component Analysis (PCA) is used for feature extraction to curtail the computational and training time and at the same time produce high accuracy. It was clearly observed that the training time is reduced by up to 80% depending on the number of principal component selected. We will consider not only the accuracy, but also the training time, recognition time and memory requirements for entire process. Further, we identified the digits which were misclassified by the algorithm. Finally, we generate our own test dataset and predict the labels using this system

Evolutionary Distributed Control of a Biologically Inspired Modular Robot

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On the zeros of polynomials and analytic functions

For a polynomial of degree n, we have obtained some results, which generalize and improve upon the earlier well known results (under certain conditions). A similar result is also obtained for analytic function

Detecting TCP SYN Flood Attack in the Cloud

In this paper, an approach to protecting virtual machines (VMs) against TCP SYN flood attack in a cloud environment is proposed. An open source cloud platform Eucalyptus is deployed and experimentation is carried out on this setup. We investigate attacks emanating from one VM to another in a multi-tenancy cloud environment. Various scenarios of the attack are executed on a webserver VM. To detect such attacks from a cloud provider’s perspective, a security mechanism involving a packet sniffer, feature extraction process, a classifier and an alerting component is proposed and implemented. We experiment with k-nearest neighbor and artificial neural network for classification of the attack. The dataset obtained from the attacks on the webserver VM is passed through the classifiers. The artificial neural network produced a F1 score of 1 with the test cases implying a 100% detection accuracy of the malicious attack traffic from legitimate traffic. The proposed security mechanism shows promising results in detecting TCP SYN flood attack behaviors in the cloud

Some results on k-hypergeometric function

In this paper, we establish integral representation and differentiation formulas for k-Gauss hypergeometric function 2F1,k(a, b; c; z) and develops a relationship with k-Confluent hypergeometric function 1F1,k(a, b; c; z), which are based properties defined by Rao and Shukla. Our study is to identify the integral as well differential representation of 2F1,k(a, b; c; z) and also find the inverse Laplace transform on it

Cytoplasmic localization of the ORF2 protein of hepatitis E virus is dependent on its ability to undergo retrotranslocation from the endoplasmic reticulum

Hepatitis E virus (HEV) is a positive-strand RNA virus that is prevalent in much of the developing world. ORF2 is the major capsid protein of HEV. Although ORF2 is an N-linked glycoprotein, it is abundantly located in the cytoplasm in addition to having membrane and surface localization. The mechanism by which ORF2 protein obtains access to the cytoplasm is unknown. In this report, we prove that initially all ORF2 protein is present in the endoplasmic reticulum and a fraction of it becomes retrotranslocated to the cytoplasm. The ability of ORF2 to be retrotranslocated is dependent on its glycosylation status and follows the canonical dislocation pathway. However, in contrast to general substrates of the dislocation pathway, retrotranslocated ORF2 protein is not a substrate of the 26S proteasome complex and is readily detectable in the cytoplasm in the absence of any protease inhibitor, suggesting that the retrotranslocated protein is stable in the cytoplasm. This study thus defines the pathway by which ORF2 obtains access to the cytoplasm

Enhanced α1 Microglobulin Secretion from Hepatitis E Virus ORF3-expressing Human Hepatoma Cells Is Mediated by the Tumor Susceptibility Gene 101

Viruses are known to exploit the host cell machinery for their benefit during different stages of their life cycle within the infected host. One of the major challenges for a virus during the early stages of infection is to escape recognition by the host immune system. Viruses have adopted many novel strategies to evade the host immune response or to create an immune suppressed environment. An earlier study in our laboratory has demonstrated that the ORF3 protein of the hepatitis E virus expedites the secretion of alpha1 microglobulin, an immunosuppressant molecule. Based on this observation, we proposed that enhanced secretion of alpha1 microglobulin may help maintain an immunosuppressed milieu around the infected hepatocyte (Tyagi, S., Surjit, M., Roy, A. K., Jameel, S., and Lal, S. K. (2004) J. Biol. Chem. 279, 29308-29319). In the present study, we discovered that the ability of the ORF3 protein to expedite alpha1 microglobulin secretion is attributed to the PSAP motif present at the C terminus of the former. The ORF3 protein was able to associate with the tumor susceptibility gene 101 (TSG101) through the PSAP motif. Further, a PSAP motif-mutated ORF3 protein was unable to associate with TSG101 and also lost its ability to enhance the secretion of alpha1 microglobulin. In addition, the ORF3 protein was found to associate simultaneously with TSG101 and alpha1 microglobulin because all three of them were co-precipitated as a ternary complex. Finally, a dominant negative mutant of the VPS4 protein was shown to block the enhanced alpha1 microglobulin secretion in ORF3-expressing hepatocytes. These results suggest a mechanism by which the ORF3 protein exploits the endosomal sorting machinery to enhance the secretion of an immunosuppressant molecule (alpha1 microglobulin) from the cultured hepatocytes