Personal Smart Assistant for Digital Media and Advertisement

The expansion of the cyberspace and the enormous process in computing and software applications enabled technology to cover every aspect of our life, therefore, many of our goals are now technology driven. Consequently, the need of intelligent assistance to achieve these goals has increased. However, for this assistance to be beneficial for users, it should be targeted to them based on their needs and preferences. Intelligent software agents have been recognized as a promising approach for the development of user-centric, personalized, applications. In this thesis a generic personal smart assistant agent is proposed that provides relevant assistance to the user based on modeling his/her interests and behaviours. The main focus of this work is on developing a user behaviour model that captures the deliberative and reactive behaviours of the user in open environments. Furthermore, a prototype is built to utilize the personal assistant for personalized advertisement applications, where this assistant attempts to recommend the right advertisement to the right person at the right time

A fog based middleware for automated compliance with OECD privacy principles in Internet of Healthcare Things

Cloud-based healthcare service with the Internet of Healthcare Things (IoHT) is a model for healthcare delivery for urban areas and vulnerable population that utilizes the digital communications and the IoHT to provide flexible opportunities to transform all the health data into workable, personalized health insights, and help attain wellness outside the traditional hospital setting. This model of healthcare Web services acts like a living organism, taking advantage of the opportunities afforded by running in cloud infrastructure to connect patients and providers anywhere and anytime to improve the quality of care, with the IoHT, acting as a central nervous system for this model that measures patients' vital statistics, constantly logging their health data, and report any abnormalities to the relevant healthcare provider. However, it is crucial to preserve the privacy of patients while utilizing this model so as to maintain their satisfaction and trust in the offered services. With the increasing number of cases for privacy breaches of healthcare data, different countries and corporations have issued privacy laws and regulations to define the best practices for the protection of personal health information. The health insurance portability and accountability act and the privacy principles established by the Organization for Economic Cooperation and Development (OECD) are examples of such regulation frameworks. In this paper, we assert that utilizing the cloud-based healthcare services to generate accurate health insights are feasible, while preserving the privacy of the end-users' sensitive health information, which will be residing on a clear form only on his/her own personal gateway. To support this claim, the personal gateways at the end-users' side will act as intermediate nodes (called fog nodes) between the IoHT devices and the cloud-based healthcare services. In such solution, these fog nodes will host a holistic privacy middleware that executes a two-stage concealment process within a distributed data collection protocol that utilizes the hierarchical nature of the IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. Additionally, the proposed solution complies with one of the common privacy regulation frameworks for fair information practice in a natural and functional way-which is OECD privacy principles. We depicted how the proposed approach can be integrated into a scenario related to preserving the privacy of the users' health data that is utilized by a cloud-based healthcare recommender service in order to generate accurate referrals. Our holistic approach induces a straightforward solution with accurate results, which are beneficial to both end-users and service providers

Investigation of an intelligent personalised service recommendation system in an IMS based cellular mobile network

Success or failure of future information and communication services in general and mobile communications in particular is greatly dependent on the level of personalisations they can offer. While the provision of anytime, anywhere, anyhow services has been the focus of wireless telecommunications in recent years, personalisation however has gained more and more attention as the unique selling point of mobile devices. Smart phones should be intelligent enough to match user’s unique needs and preferences to provide a truly personalised service tailored for the individual user. In the first part of this thesis, the importance and role of personalisation in future mobile networks is studied. This is followed, by an agent based futuristic user scenario that addresses the provision of rich data services independent of location. Scenario analysis identifies the requirements and challenges to be solved for the realisation of a personalised service. An architecture based on IP Multimedia Subsystem is proposed for mobility and to provide service continuity whilst roaming between two different access standards. Another aspect of personalisation, which is user preference modelling, is investigated in the context of service selection in a multi 3rd party service provider environment. A model is proposed for the automatic acquisition of user preferences to assist in service selection decision-making. User preferences are modelled based on a two-level Bayesian Metanetwork. Personal agents incorporating the proposed model provide answers to preference related queries such as cost, QoS and service provider reputation. This allows users to have their preferences considered automatically

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment