In response to the coronavirus (COVID-19) pandemic, Government and public health authorities around the world are developing contact tracing apps as a way to trace and slow the unfold of the virus. There is major divergence among nations, however, between a "privacy-first" approach that protects citizens' information at the price of very restricted access for public health authorities and a "data-first" approach that stores massive amounts of knowledge that, whereas of immeasurable price to epidemiologists. Contact tracing apps work by gathering information from people who have tested positive for the virus and so locating and notifying individuals with whom those people are in shut contact, oftentimes by use of GPS, Bluetooth, or wireless technology. All of the user's information is employed and picked up, the study found that users' information would be created anonymous, encrypted, secured, and can be transmitted on-line and stored solely in an aggregated format. Contact tracing apps use either a centralized or a decentralized approach to work the user's information. Apps that use a centralized approach have high privacy risks. In this paper, the researcher's contributions related to the security and privacy of Contact tracing apps have been discussed and, later research gaps have been identified with proposed solutions.
Cloud computing is a continuously evolving technology that can enhance agility, availability, collaboration and scalability of data. Blockchain has a secure, immutable ledger which maintains all the transactions along with the timestamp. The blockchain framework and cloud computing technology jointly provides different ways of computational cost reduction. The existing methods help to identify the anonymous documents which are given in the form of requests from the cloud server. If the anonymized document requests are from the authorized users, then cloud provides better security and hence documents are not available for unauthorized users. But the main issue is access rights available for authorized users on sensitive data of the owner. To maintain the privacy the sensitive data are hidden using cryptographic techniques even for authorized users. The method adopted is Linear Elliptical Curve Digital Signature (LECDS) with Hyperledger blockchain, to prevent private data loss. The Linear regression method is used to classify the user information into two classes namely sensitive and non-sensitive. The nonsensitive data is encrypted using RSA and sensitive data is encrypted using LECC method. Modified Spider optimization search Algorithm (MSOA) is used to verify the integrity of outsourced data and search user query information in a cloud server. The hyper ledger blockchain verifies the user policy to create a private network through which the user communicates the cloud. In the analysis of the proposed method, the results are evaluated using various performance metrics such as security, throughput, classification accuracy and error rate.
With increase in population, improving the quality and quantity of food is essential. Paddy is a vital food crop serving numerous people in various continents of the world. The yield of paddy is affected by numerous factors. Early diagnosis of disease is needed to prevent the plants from successive stage of disease. Manual diagnosis by naked eye is the traditional method widely adopted by farmers to identify leaf diseases. However, when the task involves manual disease diagnosis, problems like the hiring of domain experts, time consumption, and inaccurate results will arise. Inconsistent results may lead to improper treatment of plants. To overcome this problem, automatic disease diagnosis is proposed by researchers. This will help the farmers to accurately diagnose the disease swiftly without the need for expert. This manuscript develops model to classify four types of paddy leaf diseases bacterial blight, blast, tungro and brown spot. To begin with, the image is preprocessed by resizing and conversion to RGB Red, Green and Blue (RGB) and Hue, Saturation and Value (HSV) color space. Segmentation is done. Global features namely: hu moments, Haralick and color histogram are extracted and concatenated. Data is split in to training part and testing part in 70:30 ratios. Images are trained using multiple classifiers like Logistic Regression, Random Forest Classifier, Decision Tree Classifier, K-Nearest Neighbor (KNN) Classifier, Linear Discriminant Analysis (LDA),Support Vector Machine (SVM) and Gaussian Naive Bayes. This study reports Random Forest classifier as the best classifier. The Accuracy of the proposed model gained 92.84% after validation and 97.62% after testing using paddy disordered samples. 10 fold cross validation is performed. Performance of classification algorithms is measured using confusion matrix with precision, recall, F1- score and support as parameters.
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