S. Chandramathi

Bio: S. Chandramathi is an academic researcher from Pondicherry Engineering College. The author has contributed to research in topics: Quality of service & Fuzzy logic. The author has an hindex of 8, co-authored 25 publications receiving 244 citations. Previous affiliations of S. Chandramathi include Anna University & Sri Krishna College of Engineering & Technology.

A Review of various k-Nearest Neighbor Query Processing Techniques

Abstract: the queried object, from a large volume of given uncertain dataset, is a tedious task which involves time complexity and computational complexity. To solve these complexities, various research techniques were proposed. Among these, the simple, highly efficient and effective technique is, finding the K-Nearest Neighbor (kNN) algorithm. It is a technique which has applications in various fields such as pattern recognition, text categorization, moving object recognition etc. Different kNN techniques are proposed by various researchers under various situations. In this paper, we classified these techniques into two ways: (1) structure based (2) non-structure based kNN techniques. The aim of this paper is to analyze the key idea, merits, demerits and target data behind each kNN techniques. The structure based kNN techniques such as Ball Tree, k-d Tree, Principal Axis Tree (PAT), Orthogonal Structure Tree (OST), Nearest Feature Line (NFL), Center Line (CL) and Non-structured kNN techniques such as Weighted kNN, Condensed NN, Model based k-NN, Ranked NN (RNN), Pseudo/Generalized NN, Clustered k- NN(CkNN), Mutual kNN (MkNN), Constrained RkNN etc., are analyzed in this paper. It is observed that the structure based kNN techniques suffer due to memory limit whereas the Non-structure based kNN techniques suffer due to computation complexity. Hence, structure based kNN techniques can be applied to small volume of data whereas Non-structure kNN techniques can be applied to large volume of data.

Scalability Improved DSR Protocol for MANETs

Abstract: The reactive dynamic source routing (DSR) protocol is a commonly applied protocol in mobile ad hoc networks ( MANETs) When the network size is increased, it is observed that the overhead is also getting increased due to the source routing nature of DSR and this in turn reduces the efficiency of DSR protocol In order to improve the scalability of DSR,in this paper, a modification is proposed for DSR Simulation results show that the modified DSR (MDSR) has less overhead and delay compared to conventional DSR irrespective of network size

An overview of visual cryptography

Abstract: Security has become an inseparable issue as information technology is ruling the world now. Cryptography is the study of mathematical techniques related aspects of Information Security such as confidentiality, data security, entity authentication and data origin authentication, but it is not the only means of providing information security, rather one of the techniques. Visual cryptography is a new technique which provides information security which uses simple algorithm unlike the complex, computationally intensive algorithms used in other techniques like traditional cryptography. This technique allows Visual information (pictures, text, etc) to be encrypted in such a way that their decryption can be performed by the human visual system, without any complex cryptographic algorithms. This technique encrypts a secret image into shares such that stacking a sufficient number of shares reveals the secret image. Shares are usually presented in transparencies. In this paper we provide an overview of the emerging Visual Cryptography (VC) and related security research work done in this area.

A Fuzzy-based Firefly Algorithm for Dynamic Load Balancing in Cloud Computing Environment

Abstract: Cloud computing has become the rapidly growing area in industry today with the advancements in the field of science and technology. There are cloud service providers who provide large scale computing infrastructures as services in a supple manner which the users can scale up or down based on the requirements. In present day scenario of the network cascaded with task consummation, and the aspect of heterogeneity along with platform divergence, dynamic load balancing plays a vital role in optimizing the performance of the server in the cloud computing environment. Developing a proficient load balancing algorithm and efficient usage of resources is the ultimate goal for the lead of optimized performance in a cloud network. Taking these factors into consideration, the proposed system applies Firefly Load balancing algorithm for the cloud in a partitioned cloud environment to balance the load across the variety of partitions. The central idea of the proposed work is to prepare the nodes capable to conceive the arriving load and attract the workload after analyzing various parameters that characterize the load and the node. A balance factor is calculated, which probabilistically defines the extent of balancing that ensues. Fuzzy logic is applied in accretion, to resolve the time uncertainties following the initial stage of load balancing. By applying this logic it is observed that, a better performance is achieved in terms of computational time, load arrived, task migration and the cost incurred.

An evaluation of airline service quality using the fuzzy weighted SERVQUAL method

Abstract: The airline service quality is an important issue in the international air travel transportation industry. Although a number of studies focus on the subject of airline service quality evaluation in the past, most of these studies applied the SERVQUAL method to evaluate the airline service quality. But only few have attempted to evaluate the airline service quality using the weighted SERVQUAL method. Furthermore, human judgments are often vague and it is not easy for passengers to express the weights of evaluation criteria and the satisfaction of airline service quality using an exact numerical value. It is more realistic to use linguistic terms to describe the expectation value, perception value and important weight of evaluation criteria. Due to this type of existing fuzziness in the airline service quality evaluation, fuzzy set theory is an appropriate method for dealing with uncertainty. The subjective evaluation data can be more adequately expressed in linguistic variables. Thus this article attempts to fill this gap in the current literature by establishing a fuzzy weighted SERVQUAL model for evaluating the airline service quality. A case study of Taiwanese airline is conduced to demonstrate the effectiveness of the fuzzy weighted SERVQUAL model. Finally, some interesting conclusions and useful suggestions are given to airlines to improve the service quality.

Big data analytics for preventive medicine

Abstract: Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.