A Mobile Geo-Communication Dataset for Physiology-Aware DASH in Rural Ambulance Transport

Use of telecommunication technologies for remote, continuous monitoring of patients can enhance effectiveness of emergency ambulance care during transport from rural areas to a regional center hospital. However, the communication along the various routes in rural areas may have wide bandwidth ranges from 2G to 4G; some regions may have only lower satellite bandwidth available. Bandwidth fluctuation together with real-time communication of various clinical multimedia pose a major challenge during rural patient ambulance transport.; AB@The availability of a pre-transport route-dependent communication bandwidth database is an important resource in remote monitoring and clinical multimedia transmission in rural ambulance transport. Here, we present a geo-communication dataset from extensive profiling of 4 major US mobile carriers in Illinois, from the rural location of Hoopeston to the central referral hospital center at Urbana. In collaboration with Carle Foundation Hospital, we developed a profiler, and collected various geographical and communication traces for realistic emergency rural ambulance transport scenarios. Our dataset is to support our ongoing work of proposing "physiology-aware DASH", which is particularly useful for adaptive remote monitoring of critically ill patients in emergency rural ambulance transport. It provides insights on ensuring higher Quality of Service (QoS) for most critical clinical multimedia in response to changes in patients' physiological states and bandwidth conditions. Our dataset is available online for research community.Comment: Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys'17), Pages 158-163, Taipei, Taiwan, June 20 - 23, 201

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Machine learning applications in operations management and digital marketing

In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2

Social networks : service selection and recommendation

University of Technology, Sydney. Faculty of Engineering and Information Technology.The Service-Oriented Computing paradigm is widely acknowledged for its potential to revolutionize the world of computing through the utilization of Web services. It is expected that Web services will fully leverage the Semantic Web to outsource some of their functionalities to other Web services that provide value-added services, and by integrating the business logic of Web services in the form of business to business and business to consumer e-commerce applications. In the Service Web, Web services and Web-Based Social Networks are emerging in which a wide range of similar functionalities are expected to be offered by a vast number of Web services, and applications can search and compose services according to users’ needs in a seamless and an automatic fashion. Web services are expected to outsource some of their functionalities to other Web services. In such situations, some services may be new to the service market, and some may act maliciously in order to be selected. A key requirement is to provide mechanisms for quality selection and recommendation of relevant Web services with perceived risk considerations. Although the future of Web service selection and recommendation looks promising, there are challenging issues related to user knowledge and behavior, as well as issues related to recommendation approaches. This dissertation addresses the demanding issues in Web service selection and recommendation from theory and practice perspectives. These challenges include cold-start users, who represent more than 50% of the social network population, the capture of users’ preferences, risk mitigation in service selection, customers’ privacy and application scalability. This dissertation proposes a novel approach to automate social-based Web service selection and recommendation in a dynamic environment. It utilizes Web-Based Social Networks and the “Follow the Leader” strategy, for a Credibility-based framework that includes two credibility models: the user Credibility model which is used to qualify consumers as either leaders or followers based on their credibility, and the service Credibility model which is used to identify the best services that act as market leaders. Experimental evaluation results demonstrate that the social network service selection and recommendation approach utilizing the credibility-based framework and “Follow the Leader” strategy provides an efficient, effective and scalable provision of credible services, especially for cold-start users. The research results take a further step towards developing a social-based automated and dynamically adaptive Web service selection and recommendation system in the future