Bio: B. Balamurugan is an academic researcher from VIT University. The author has contributed to research in topics: Cloud computing & Quality of service. The author has an hindex of 3, co-authored 8 publications receiving 29 citations.
••01 Jan 2017
TL;DR: This chapter’s main objective is to broaden the understanding of healthcare services provided by social media to understand the means by which medical information is exchanged online and how to interpret this information with some specific examples.
Abstract: Ever since the launch of Facebook and Twitter Social Networking has been booming. Healthcare is improving mainly due to services provided by social media websites such as Sermo, Ozmosis and MomMD. This chapter’s main objective is to broaden the understanding of healthcare services provided by social media. The reader will be able to understand the means by which medical information is exchanged online and how to interpret this information with some specific examples. In addition to describing the architecture behind social media further insights to mobile applications has been given. Big data in healthcare and risks involved in using social media for healthcare have been discussed to caution its usage.
TL;DR: The ultimate aim of this study was to propose a stochastic queueing model and to yield results through probability generating functions so as to design its service system to offer satisfactory quality of service for a medical E-commerce firm facing customer impatience.
••01 May 2016
TL;DR: This work suggests using two-way caching scheme where local-cache is stored on client system and cloud- cache is present on network cloud, where the authors store virus definitions and behaviors according to collective intelligence techniques, to increase the optimality of virus definition search.
Abstract: Antivirus is most widely used to detect and stop malware and other unwanted files. Cloud antivirus is a malware detector architecture where virus definitions and other behaviors of suspicious files is analyzed on cloud and controlled by a light weight Agent on client system. We suggest using two-way caching scheme where local-cache is stored on client system and cloud-cache is present on network cloud, where we store virus definitions and behaviors according to collective intelligence techniques. Local-cache is used to detect the virus and other malware files while offline and cloud-cache uses the Artificial Intelligence Techniques for whole client base to get the most susceptible and prone virus and malware definitions thus increasing the optimality of virus definition search and hence the speed of the whole process gets increased.
TL;DR: A stochastic queueing model is proposed to study a single virtual machine queue with batch arrivals and two types of different services (registration and appointment) one after the other in series to help the management to calculate the service level of the web portal and to improve the performance.
Abstract: Online registration system (ORS) is an Indian government digital initiative under the policy of e-Kranti. This sector needs high quality of service (QoS) as it is crowded with numerous patients for various requisitions. In this paper, a stochastic queueing model is proposed to study a single virtual machine queue with batch arrivals and two types of different services (registration and appointment) one after the other in series. After getting this service, a certain batch of patient's may need an additional service from the ORS portal or they may quit. We also considered patient's impatient behaviour wherein batch of patients decide not to take any service in the ORS portal. We attain steady-state results and probability generating functions and the properties of several parameters on the virtual machine performance are analysed numerically. These results can help the management to calculate the service level of the web portal and to improve the performance.
TL;DR: This paper presents the working of the automated framework for the construction of semantic rich ontology structures and store and the effectiveness of the experimental results is high when compared to other Graph Derivation Representation methods.
••01 May 1975
TL;DR: The Fundamentals of Queueing Theory, Fourth Edition as discussed by the authors provides a comprehensive overview of simple and more advanced queuing models, with a self-contained presentation of key concepts and formulae.
Abstract: Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.
TL;DR: The authors identify nine themes, organized by predicted imminence (i.e., the immediate, near, and far futures), that they believe will meaningfully shape the future of social media through three lenses: consumer, industry, and public policy.
Abstract: Social media allows people to freely interact with others and offers multiple ways for marketers to reach and engage with consumers. Considering the numerous ways social media affects individuals and businesses alike, in this article, the authors focus on where they believe the future of social media lies when considering marketing-related topics and issues. Drawing on academic research, discussions with industry leaders, and popular discourse, the authors identify nine themes, organized by predicted imminence (i.e., the immediate, near, and far futures), that they believe will meaningfully shape the future of social media through three lenses: consumer, industry, and public policy. Within each theme, the authors describe the digital landscape, present and discuss their predictions, and identify relevant future research directions for academics and practitioners.
TL;DR: Experimental results prove that the efficiency of the proposed energy efficient node selection algorithm in IoT healthcare environment is superior than other node selection algorithms used in similar environments.
Abstract: Internet of vehicles (IoV) is an improved version of internet of things to resolve a number of issues in urban traffic environment. In this paper IoV technology is used to select the best ambulance based on a novel node selection algorithm. The proposed IoT healthcare monitoring system consists of number of mobile doctors, patient and mobile ambulance. Performance rank (PR) index is calculated for each mobile ambulance based on the medical capacity (b) of the mobile ambulance, the number of patients currently using the mobile ambulance (n), and the Euclidean distance from a neighboring mobile ambulance. The minimum PR index is considered as best ambulance to provide a service to the patient. Random waypoint mobility model is used to simulate the proposed IoT based healthcare monitoring system. The proposed energy efficient node selection algorithm is compared with various node selection algorithms such as cluster based routing protocol, workload-aware channel assignment algorithm and scenario-based clustering algorithm for performance evaluation. The packet delivery fraction, normalized routing load and average end-to-end delay are calculated to evaluate the performance of the proposed energy efficient node selection algorithm. We have used NS-2 simulator for the node simulation to show the performance of the energy efficient node selection framework. Experimental results prove that the efficiency of the proposed energy efficient node selection algorithm in IoT healthcare environment.
TL;DR: The architecture of a typical mobile healthcare application is discussed, in which customized privacy levels are defined for the individuals participating in the system, and how the communication across a social network in a multi-cloud environment can be made more secure and private.
Abstract: Social media has enabled information-sharing across massively large networks of people without spending much financial resources and time that are otherwise required in the print and electronic media. Mobile-based social media applications have overwhelmingly changed the information-sharing perspective. However, with the advent of such applications at an unprecedented scale, the privacy of the information is compromised to a larger extent if breach mitigation is not adequate. Since healthcare applications are also being developed for mobile devices so that they also benefit from the power of social media, cybersecurity privacy concerns for such sensitive applications have become critical. This article discusses the architecture of a typical mobile healthcare application, in which customized privacy levels are defined for the individuals participating in the system. It then elaborates on how the communication across a social network in a multi-cloud environment can be made more secure and private, especially for healthcare applications.
TL;DR: A CBIR system based on transfer learning from a CNN trained on a vast image database, thus exploiting the generic image representation that it has already learned, and different strategies to exploit the updated CNN for returning a novel set of images that are expected to be relevant to the user’s needs are suggested.
Abstract: Given the great success of Convolutional Neural Network (CNN) for image representation and classification tasks, we argue that Content-Based Image Retrieval (CBIR) systems could also leverage on CNN capabilities, mainly when Relevance Feedback (RF) mechanisms are employed. On the one hand, to improve the performances of CBIRs, that are strictly related to the effectiveness of the descriptors used to represent an image, as they aim at providing the user with images similar to an initial query image. On the other hand, to reduce the semantic gap between the similarity perceived by the user and the similarity computed by the machine, by exploiting an RF mechanism where the user labels the returned images as being relevant or not concerning her interests. Consequently, in this work, we propose a CBIR system based on transfer learning from a CNN trained on a vast image database, thus exploiting the generic image representation that it has already learned. Then, the pre-trained CNN is also fine-tuned exploiting the RF supplied by the user to reduce the semantic gap. In particular, after the user’s feedback, we propose to tune and then re-train the CNN according to the labelled set of relevant and non-relevant images. Then, we suggest different strategies to exploit the updated CNN for returning a novel set of images that are expected to be relevant to the user’s needs. Experimental results on different data sets show the effectiveness of the proposed mechanisms in improving the representation power of the CNN with respect to the user concept of image similarity. Moreover, the pros and cons of the different approaches can be clearly pointed out, thus providing clear guidelines for the implementation in production environments.