Establishment of Public Key Infrastructure for Digital Signatures

Open Security Socket Layer (SSL) is a cryptographic library that uses appropriate security systems such as encryption, digital signatures, digital certificates, public/private key pairs, non-repudiation and time-stamping to participate in the cryptography. A Public Key Infrastructure (PKI) comprises a system of certificates, certificate authorities, subjects, relying partners, registration authorities and key repositories that provide for safe and reliable communications. In this paper, open SSL has been implemented to provide an alternative to the Transmission Control Protocol (TCP). Open SSL is a real time protocol in which the parties negotiate interactively to authenticate each other and establish a session key, in contrast to a protocol such as email in which one party prepares a message encrypt and send, that can later be decrypted and authenticated by the intended recipient. Keywords: Open SSL, Public Key Infrastructure, Digital signature

Psoriasis Skin Disease Classification based on Clinical Images

Psoriasis is an autoimmune skin disorder that causes skin plaques to develop into red and scaly patches. It affects millions of people globally. Dermatologists currently employ visual and haptic methods to determine a medical issue's severity. Intelligent medical imaging-based diagnosis systems are now a possibility because of the relatively recent development of deep learning technologies for medical image processing. These systems can help a human expert make better decisions about a patient's health. Convolutional neural networks, or CNNs, on the other hand, have achieved imaging performance levels comparable to, if not better than, those of humans. In the paper, a Dermnet dataset is used. Image preprocessing, fuzzy c-mean-based segmentation, MobileNet-based feature extraction, and a support vector machine (SVM) classification are used for skin disease classification. Dermnet's dataset was investigated for images of skin conditions using three classes Psoriasis, Dermatofibroma, and Melanoma are studied. The performance metrics such as accuracy, precision-recall, and f1-score are evaluated and compared for three classes of skin diseases. Despite working with a smaller dataset, MobileNet with Support Vector Machine outperforms ResNet in terms of accuracy (99.12%), precision (98.65%), and recall (99.66%)

Prediction of Chronic Kidney Disease using SVM and CNN

Chronic kidney disease is one of the deadliest diseases today and it is vital to have a good diagnosis as soon as possible. In medical treatment, machine learning has been reported to be effective. A doctor can diagnose the disease early by using machine learning classifier algorithms. This study investigated the chronic disease prognosis of this concept. Disease data was taken from the University of California, Irvine. Other measurement algorithms used in this study include C5.0, Chi-square automatic interaction detector, line extraction, SVM line with L1 and L2 flap, and neural network random tree. The database was also submitted to a feature selection program that merited the database. Scores are computer generated for each category segment using the following methods: Full Version, (ii) Link-Based Feature Selection, (iii) Folder Feature Selection, (iv) Minimal Collapse and Selected Optional Retrospective Features, (v) integrated small oversampling method with very small reduction features and selected bias on the selected operator, and (vi) how to do multiple samples combined with full functions. In the full multi-sample processing process, the findings show that L2-loaded LSVM has a very high accuracy of 96.86 percent. The graph shows the results of different methods, as well as precision, precision, recall, F-score, area under the curve, and GINI coefficient. The minimum absolute reduction and selection regression operation selected features using the synthetic minority oversampling approach produced the best results after using the synthetic minority oversampling method with full features. The support vector machine achieved a high accuracy of 96.46 percent in the process of making very large samples with very small turndowns and selected operator features. Machine learning methods used with convolutional neural networks and SVM classifier models on the same database, with 96.7 percent of high-definition support machine models and networks are used

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Not AvailableFarmers need information on improved technologies at every stage of the crop cultivation. In order to provide timely information to the cotton farmers of Andhra Pradesh and Telanagana State, mobile based voice advisories were disseminated to the farmers in regional language (Telugu). The impact of these advisories was evaluated and presented in this paperNot Availabl

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Not AvailableThe information on the frequently asked questions on cotton was provided.CICR, Nagpu

Synthesis and conformational studies of α/β2,3-peptides derived from alternating β2,3-amino acids and L-Ala repeats

International audienc

Antioxidant and Free Radical Scavenging Activities of Zingiberofficinale

Abstract: The present study was carried out to evaluate the antioxidant and free radical scavenging activity of ethanolic extract of ZingiberofficinaleRhizomes (Zingiberaceae) in various systems. DPPH radical, superoxide anion radical, nitric oxide radical and hydroxyl radicalscavenging assays were carried out to evaluate the antioxidant potential of the extract. The antioxidant activity of ethanolic extract increased in a dose dependent manner. About 50,100, 250 and 500 μg of ethanol extract of Zingiberofficinale(EZO) showed 61.44,66.25, 72.01 and 76.85% inhibition respectively on peroxidation of linoleic acid emulsion. Like antioxidant activity, the effect of EZO on reducing power increases in a dose dependentmanner. In DPPH radical scavenging assay the IC50 value of the extract was found to be 168.09μg/ml. EZO was found to inhibit the nitric oxide radicals generated from sodiumnitroprusside. The IC50 value was found to be 83.365 μg/ml, whereas the IC50 value of curcumin was 20.34 μg/ml. Moreover, the EZO was found to scavenge the superoxide generated byPMS/NADH-NBT system. EZO was also found to inhibit the hydroxyl radical generated byFenton's reaction, where the IC50 value of EZO was found to be more than 1000 μg/ml and for catechin the IC50 value was found to be 5 μg/ml, which indicates the prooxidant activity of EZO. The amounts of total phenolic compounds were also determined in this study. Theresults obtained in the present study indicate that the EZO can be a potential source of naturalantioxidant