Secure multi-party protocols have been proposed for entities (organizations or individuals) that don't fully trust each other to share sensitive information. Many types of entities need to collect, analyze, and disseminate data rapidly and accurately, without exposing sensitive information to unauthorized or untrusted parties. Solutions based on secure multiparty computation guarantee privacy and correctness, at an extra communication (too costly in communication to be practical) and computation cost. The high overhead motivates us to extend this SMC to cloud environment which provides large computation and communication capacity which makes SMC to be used between multiple clouds (i.e., it may between private or public or hybrid clouds).Cloud may encompass many high capacity servers which acts as a hosts which participate in computation (IaaS and PaaS) for final result, which is controlled by Cloud Trusted Authority (CTA) for secret sharing within the cloud. The communication between two clouds is controlled by High Level Trusted Authority (HLTA) which is one of the hosts in a cloud which provides MgaaS (Management as a Service). Due to high risk for security in clouds, HLTA generates and distributes public keys and private keys by using Carmichael-R-Prime- RSA algorithm for exchange of private data in SMC between itself and clouds. In cloud, CTA creates Group key for Secure communication between the hosts in cloud based on keys sent by HLTA for exchange of Intermediate values and shares for computation of final result. Since this scheme is extended to be used in clouds( due to high availability and scalability to increase computation power) it is possible to implement SMC practically for privacy preserving in data mining at low cost for the clients.
For modeling and analyzing several variables, many techniques are available among which in statistical modeling, regression analysis is one. Regression Analysis (RA) is utilized for prediction and determination, where its utilization has generous cover with the field of Artificial Intelligence. RA is a measurable procedure’s for assessing the relationship among variables (one dependent and one or more independent). Its helps us to predict and that is why it is also called as predictive analysis model. In this study, we had used vehicle data like velocity with which traffic move’s, gradient, actual velocity to predict the velocity profile of the vehicle. Also, we had analyzed various regression models like linear regression, multivariate linear regression and nonlinear regression. The outcome of this work is to write a function for every model that everyone can reuse that without using pre-defined functions in languages and plotting the given data to best fit for analyzing.
Total Hip Arthroplasty (THA) using roentgenogram helps to detect and treat the loosening and misalignment of femoral component. Segmentation of the femoral and acetabular components is the most important process in computer aided diagnosis. This paper presents a fully automated loosening detection system for X-ray images. Anterior pelvic plane (APP) coordinates are used to measure the inclination and anteversion angle between the femoral and acetabular components. The proposed approach consists of the following modules. First we detect the edges of femoral and acetabular components. Secondly we find the center of the femoral component. Thirdly the pelvic coordinates are matched with it. Finally we measure the anteversion angle and loosening gap between the femoral and acetabular components. The proposed method is analyzed using a data set consisting of 108 images. Preliminary results show that the proposed method is computationally efficient and fast.
Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment.There is a need to find any deviation that can be acquired in position of artificial femur after the log time of surgery, well in advance thereby overcome the adverse socio economic and psychological burden to both the patient as well as the surgeon.The aim of the study is to develop a noninvasive, ultrasound-based, method of diagnosing acetabular cup loosening at an early stage before any major bone erosion has taken place.The proposed study will build on a previously successful technique for the diagnosis of loosing of the femoral stem component of a THR.This paper highlights the steps like box filter section, mainly first order and second order, followed by key points selection, key points extraction, key point matching and finally finding the deviation through motion vector analysis.The data for this research has been collected from different hospitals in Andhra Pradesh and Tamil Nadu.
<p>This research paper presents a ground-breaking approach to enhancing mobile healthcare applications through the design of a dynamic task offloading method in multi-cloud mobile edge computing (MEC) environments, leveraging the capabilities of deep learning. The primary objective is to address the limitations of existing systems, notably the constraints in computational resources and power efficiency in mobile devices, while ensuring data privacy and high accuracy in tasks like ECG analysis and brain tumor segmentation. The methodology introduces a novel hybrid task offloading (HTO) framework, ingeniously designed to dynamically allocate computation-intensive tasks between edge and cloud servers. This approach optimizes task distribution based on real-time analysis of workload and resource availability, ensuring efficient utilization of computational power. The deep learning aspect of the study utilizes advanced neural network algorithms to process complex datasets with high precision. Findings from the research reveal significant improvements in various performance metrics. Notably, there is a marked reduction in latency and energy consumption, which are critical in mobile healthcare applications. The HTO method demonstrated an enhanced efficiency in task offloading, achieving a balance between power consumption and computational speed. This balance is crucial for real-time data processing in healthcare scenarios. The achievement of this research lies in its potential to revolutionize mobile healthcare services. By reducing the latency by up to 30% and enhancing energy efficiency significantly, the HTO framework paves the way for more responsive and sustainable healthcare applications. These improvements are vital for real-time health monitoring and emergency response scenarios, where every second counts. Overall, this study contributes a significant advancement in the field of mobile healthcare, proposing a scalable and efficient solution for handling the increasing demands of computation in healthcare applications.</p>
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