<p>One of a powerful application in the age of cloud computing is the outsourcing of scientific computations to cloud computing which makes cloud computing a very powerful computing paradigm, where the customers with limited computing resource and storage devices can outsource the sophisticated computation workloads into powerful service providers. One of scientific computations problem is Two-Point Boundary Value Problems(BVP) is a basic engineering and scientific problem, which has application in various domains. In this paper, we propose a privacy-preserving, verifiable and efficient algorithm for Two-Point Boundary Value Problems in outsourcing paradigm. We implement the proposed schema on the customer side laptop and using AWS compute domain elastic compute cloud (EC2) for the cloud side.</p>
The rapid advancement of mobile technologies over the past decade has had a significant impact on the appearance of M-learning applications. The research proposes the fast learning network model to investigate and identify the factors that affect student satisfaction in M-learning for the University of Tikrit students. The research model is conducted utilizing a questionnaire of 300 participating students based on variables. This research showed that the proposed model's perfor mance was superior to artificial neural network, <br /> k-nearest neighbors, and multilayer perceptron algorithms. The accuracy and specificity of predicting the student satisfaction coefficients in M-learning were 91.6% and 92.85%, respectively. The proposed findings demonstrate that diversity in the evaluation, teacher attitude and response, and quality of technology are key operators of student satisfaction.
Outsourcing of scientic computations is attracting increasing attention since it enables the customers with limited computing resource and storage devices to outsource the sophisticated computation workloads into powerful service providers. However, it also comes up with some security and privacy concerns and challenges, such as the input and output privacy of the customers, and cheating behaviors of the cloud. Motivated by these issues, this paper focused on privacy-preserving Linear Fractional Programming (LFP) as a typical and practically relevant case for veriable secure multiparty computation. We will investigate the secure and veriable schema with correctness guarantees, by using normal multiparty techniques to compute the result of a computation and then using veriable techniques only to verify that this result was correct.
Diabetes is a disease caused by an increase in blood glucose levels due to insulin secretion deficiency (type I diabetes) or impaired insulin activity (type II diabetes). More than 90% of people with this condition are diagnosed with type II diabetes. Due to the sharply prevalence of type 2 diabetes in recent years, the prognosis and early diagnosis of the disease have become even more important. In this study, a model for diagnosis of type II diabetes was developed using Artificial Neural Network (ANN) method. The execution of the Frequent Pattern Growth algorithm on medical data is difficult. Association rule-based classification is an interesting area focused that can be utilized for early diagnosis. The discretization phase is necessary to transform numerical characteristics. Pima Indians Diabetes Data Set is taken as an input. The execution time, a number of rules generation and the detection of outlier percentage are analyzed. The CFP-growth algorithm utilizes for finding frequent patterns where constructing the Minimum Item Support (MIS)-tree, CFP-array and producing frequent patterns from the MIS-tree. From the set of frequent itemsets found, create all the association rules that have confidence exceeding the minimum confidence. In this study, we aim to build a model that helps Physicians in predicting Diabetes early and accurately. Data were collected from the PIMA Indian data set. Consisted of 768 samples (268 diabetic and 500 non-diabetics). Levenberg-Marquardt back-propagation algorithm was used to train the network, and the accuracy of the prediction of whether a person is diabetics or not was 88.8%.
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