No abstract

Data mining algorithms are widely used to extract latent trends or patterns from databases. They are used to predict unknown class labels in order to help in accurate decision making process. The machine learning techniques are very useful in applications of healthcare domain. In this domain, the techniques are employed to diagnose diseases and even predicting the probabilities related to health and other aspects. Since diabetes is one of the diseases that prevail in Indians now and it causes other diseases as well, in this paper we studied the kidney disease diagnosis in diabetes patients. Kidney disease dataset is used to explore four different data mining algorithms. They are known as Naive Bayes, decision table, J48 and random forest. The dataset is used to have both training and testing sets and produce prediction details. These algorithms are evaluated by using measures like accuracy, mean absolute error (MAE) and root-mean-squared error (RMSE). The results revealed that all the machine learning algorithms are able to predict the disease. However, they differ in accuracy levels. With tenfold cross-validation, it is understood that random forest showed the highest accuracy, while Naive Bayes showed least MAE and RMSE. We built prototype application to demonstrate proof of the concept. Our experimental results revealed that the proposed framework to evaluate machine learning algorithms is useful.

This article represents a dynamic grid system (DGS), a privacy grid system defined by the user. This is the primary allencompassing secure and spatial data satisfying basic essential necessities for confidentiality-securing snapshot and location-based services (LBSs). First, secure and spatial data are responsible for achieving simple matching operation using a semi-trusted third party. The semi-trusted third party has no information about the location of the user. Second, under the defined adversary model, we can provide a secured snapshot and uninterrupted location-based services. Third, not beyond the proximity of the user's area, the communication cost does not rely on others ideal confidentiality location; it depends on the number of pertinent salient activities. Fourth, despite these things, it has only been targeted on the range and our system that can effectively support different spatial queries without altering the algorithms that are kept running by the semi-reliable third parties and the database servers, given that the spatial query is abstracted into spatial regions within the desired search area. The experimental assessment shows a more efficient approach towards the dynamic grid structure than the progressive confidentiality technique for uninterrupted location-based services. We offer a dual spatial data transformation and encryption scheme in which encrypted requests are executed completely on the encrypted database by the provider, and the user gets encrypted results. To attain services found on their location, location-based services want users to consistently report their locations to a potentially unreliable server which may open them to security risks. Lamentably, there were many restrictions in the existing confidentiality-securing methods for location-based services, such as the trustworthy third party requirement, confidentiality restrictions and high overhead communication.

No abstract

Cloud Computing is the rising generation key platform for sharing the resources like software as a service, infrastructure as a service, and platform as a service. In future all IT enterprises migrate into cloud platform. Cloud server exchanges the messages for remote location users with the help of multi cloud architectures. Security issues are generated in data transmission. Day by day new vulnerabilities are discovered in cloud computing. Previous cloud development provides the security in limited dimensions with the help of application logic. It not sufficient for control the all different attackers. This is not efficient and scalable environment. It's not optimal approach [1]. Increase the range of cloud computing security and detect the attacks in deep degree manner. It provides highly safe results with good data protection. Here we concentrate on two dimensions. Those dimensions are application logic view and regulatory framework. Its have excellent security properties. This approach provides the excellent security compare to previous approaches. We proved different properties like good integrity and confidentiality compare to previous approaches [1][2].