Cryptographic Interweaving of Messages

During the past several decades, the information and communication technology sector has advanced significantly, enabling extensive information interchange over the internet, including message sharing and electronic transactions. These days, the main issue is how to transmit information securely. From ancient times, there has been interest in the field of cryptography research. A masterwork of cryptography is Muni Kumudendu's original work, Siribhoovalaya. His study served as the basis for the method suggested in this publication. Several messages can be sent using different keys utilising a single matrix. Encryption uses a variety of matrix traversal techniques, making it challenging for cryptanalysis to map the plaintext and ciphertext

Performance Comparison of Radial Basis Function Networks and Probabilistic Neural Networks for Telugu Character Recognition

The research on recognition of hand written scanned images of documents has witnessed several problems, some of which include recognition of almost similar characters. Therefore it received attention from the fields of image processing and pattern recognition. The system of pattern recognition comprises a two step process. The first stage is the feature extraction and the second stage is the classification. In this paper, the authors propose two classification methods, both of which are based on artificial neural networks as a means to recognize hand written characters of Telugu, a language spoken by more than 100 million people of south India(Negi et al. ,2001). In this model, the authors used Radial Basis Function (RBF) networks and Probabilistic Neural Networks (PNN) for classification. These classifiers were further evaluated using performance metrics such as accuracy, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and F measure. This paper is a comparison of results obtained with both the methods. The values of F measure are quite satisfactory and this is a good indication of the suitability of the methods for classification of characters. The values of F-Measure for both the methods approach the value of 1, which is a good indication and out of the two, RBF is a better method than PNN

Evaluation of the wound healing effect of Jasminum grandiflorum by wound contraction studies and wound breaking strength

Background: Wound is a common process leading to inflammation and healing process. Jasminum grandiflorum is a plant widely grown in Karnataka and is mention in ancient medical systems to promote healing. So the study was taken up to confirm the anti-inflammatory effect of the plant.Methods: Excision and incision wound models in albino rats were used to study the wound healing effect. It was mainly studied by the degree of epithelisation at various intervals and compared with control group. The strength of the wound was assessed by its wound breaking strength at the end.Results: It was found that jasmine extract increased the degree of epethelisation and thus promoting wound healing. It was also found that the wound breaking strength was more in the drug treated group compared to normal.Conclusions: Jasminum grandiflorum leaf extract is known to have wound healing property.   

Evaluation of wound healing effect of Jasminum grandiflorum in albino rats by histopathological studies

Background: Wound healing is an important process in regeneration of the lost tissue, it involves various steps. Varieties of substances are known to interact in the healing process, some of the plant products are found to be beneficial. Jasmine leaves are mentioned to have healing effect in ancient literature. Hence the study was carried out to evaluate the wound healing effect by assessing the histopathological parameters.Methods: The study was done in the albino rats which were divided into various groups. The histopathological parameters were studied at regular intervals.Results: It was found that jasmine paste improved the wound healing process at all the stages.Conclusion: Jasminum grandiflorum has wound healing effect in albino rats

NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets

This paper presents the model submitted by the NIT_COVID-19 team for identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared task addresses the problem of automatically identifying whether an English tweet related to informative (novel coronavirus) or not. These informative tweets provide information about recovered, confirmed, suspected, and death cases as well as the location or travel history of the cases. The proposed approach includes pre-processing techniques and pre-trained RoBERTa with suitable hyperparameters for English coronavirus tweet classification. The performance achieved by the proposed model for shared task WNUT 2020 Task2 is 89.14% in the F1-score metric.Comment: 5 pages, one figures, conferenc

Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing

We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.Comment: Published at 27th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2018

Biochemical studies on the effect of curcumin and embelin during N-nitrosodiethylamine/phenobarbital induced-hepatocarcinogenesis in wistar rats

The biochemical effects of administration of embelin a benzoquinone derivative from Embelia ribes and curcumin (diferuloyl methane) isolated from Curcuma longa against a 2 step hepatocarcinogenic regimen comprising of N-nitrosodiethylamine (DENA)/phenobarbital (PB) was studied in wistar strain male albino rats with respect to lipid profile, renal function tests and levels of blood glucose. Rats administered DENA/PB showed hyper cholesterolaemia, hyper triglyceridaemia, elevated low-density lipoproteins (LDL), free fatty acids (FFA), very-low-density lipoproteins (VLDL) levels and decreased urea levels. Pre- and co-treatment with embelin and curcumin for 14 weeks significantly prevented the biochemical alterations induced by DENA/PB. Results of our study suggest the protective and hypolipidemic effects of embelin and curcumin during chemically- induced hepatocarcinogenesis in wistar rats. © 2009 Academic Journals