S. Maheswari

Bio: S. Maheswari is an academic researcher from VIT University. The author has contributed to research in topics: Web service & Lagrangian relaxation. The author has an hindex of 5, co-authored 19 publications receiving 60 citations. Previous affiliations of S. Maheswari include Sri Padmavati Mahila Visvavidyalayam & National Engineering College.

Heart Disease Prediction System Using Decision Tree and Naive Bayes Algorithm

Abstract: The huge information of healthcare data is collected from the healthcare industry which is not "mined" unfortunately to make effective decision making for the identification of hidden information. The end user support system is used as the prediction application for the heart disease and this paper proposes windows through the intelligent prediction system the instance guidance for the heart disease is given to the user. Various symptoms of the heart diseases are fed into the application. The user precedes the processes by checking the specific detail and symptoms of the heart disease. The decision tree (ID3) and navie Bayes techniques in data mining are used to retrieve the details associated with each patient. Based on the accurate result prediction, the performance of the system is analyzed.

Secure sharing of personal health records in Jelastic cloud by attribute based encryption

Abstract: Secure sharing of patient health records in Jelastic cloud provides the more benefits to the data owners and end users. We know that building a specialized data center's is very difficult task and maintenance cost also very high. Sharing the PHR Application in the third party server raises the security and privacy risks. In this paper we introduce ABE highly secure encryption algorithm for providing the security and good privacy policies to the PHR information, which is stored in the cloud server. Not only this, for providing the Scalability, Load balancing and for easy maintenance to the application, we are deploying the Personal health record's application into Jelastic cloud by the use of Servint server. Nginx is the Load balancer to provide the load balance to our PHR application. Jelastic cloud is successfully providing the both Horizontal and Vertical Scaling to our PHR Application.

Comparative Analysis of Semantic Web Service Selection Methods

Abstract: The objective is to compare semantic web service selection methods based on Quality of Service (QoS) attributes. The investigation is carried out in three phases namely: Preprocessing, discovery and QoS based selection phase. Preprocessing phase deals with registration of services in the repository. Discovery phase deals with retrieval of functionally similar services using matchmaking techniques. The third phase focuses on QoS based selection using three methods namely Analytical Hierarchical Processing (AHP), Logical Scoring Preference with Ordered Weighted Averaging (LSP and OWA) and Fuzzy with Technique for Order Preference by Similarity to an Ideal Solution (Fuzzy Topsis). Further this paper analyzes the selection methods based on range of user preference criteria and compares them using Analysis of variance (ANOVA). It is a statistical hypothesis testing used for making decisions using data. Another objective of this paper is to propose a new approach to improve the performance of service selection using Iterative MapReduce. QWS dataset has been used for analyzing above mentioned three methods. Experimental results show that the Fuzzy Topsis scheme outperforms the other web service selection methods.

Optimization Model for Electricity Distribution System Control using Communication System by Lagrangian Relaxation Technique

Abstract: This paper mainly deals with the analysis of communication network for electricity distribution using Distribution Dispatch Centre (DDC) which is monitored and controlled by Supervising Control And Data Acquisition / Distribution Management System (SCADA / DMS). DDC controls the computer based substation control systems. Access of the control channel can be formulated as a Non-Linear Integer Programming (NLIP) which can be decomposed by Lagrangian Relaxation (LR) technique. The objective of the model is to maximize the inputs of the control parameters for the feeders and to obtain the optimal scheduling decision for each power flow. The optimal scheduling decision is obtained from the Lagrangian dual optimization problem. A duality gap exists between the lower bound and the objective function value provided by the Non-Linear Integer Programming feasibility heuristic. The optimum value is obtained by the scheduling algorithm. Based on numerical calculations and graphical representations, the feeder route losses are minimized with respect to control parameters.

R²PEDS : A Recoverable and Revocable Privacy-Preserving Edge Data Sharing Scheme

Abstract: Edge servers (ESs) are utilized to achieve the storage and sharing of IoT data. However, even if ES brings us much benefit, it also leads to many serious privacy leakage issues because users’ data in ESs are out of control. Moreover, ES providers may also disclose user’s private-sensitive data. Hence, in this article, we present a privacy-preserving, recoverable, and revocable edge data sharing scheme. In this scheme, we propose a novel attribute revocation chain based on the blockchain technology to achieve attribute revocation in ciphertext-policy attribute-based encryption (CP-ABE). Meanwhile, a secret sharing scheme (SSS) is introduced to assist the data recovery. Especially, for the situation that a single ES is hijacked, we also propose a corresponding efficient detection mechanism and key updating policy to promise the subsequent security of the whole system. Moreover, this scheme also resists Economic Denial-of-Sustainability (EDoS) attacks which are launched by some malicious users. The analysis shows that the proposed scheme can protect user’s privacy and resist many attacks. Additionally, relevant experimental results demonstrate that our scheme has low computational overhead on the user side.

Privacy Provision in Collaborative Ehealth With Attribute-Based Encryption: Survey, Challenges and Future Directions

Abstract: Collaborative eHealth that enables the collection and sharing of patients' data is eliminating the location and accessibility barriers in healthcare service delivery. With advances in technology, patients' data can be collected from anywhere, at any time, stored in a central location, and shared across multiple service providers to improve the quality of healthcare service delivery. Maintaining patients' data privacy during the storage and sharing process is critical for collaborative eHealth success. The attribute-based encryption, which has shown potential in data privacy protection in the cloud, has the necessary ingredients to be used in guarding against privacy violations in collaborative eHealth. In this paper, we survey the different attribute-based encryption schemes for usage in collaborative eHealth. We discuss some challenges associated with the use of attribute-based encryption schemes in collaborative eHealth and point out some future research directions. Also, we perform a comparative analysis of the surveyed schemes in terms of security, revocation ability, and efficiency.