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Online learning is rapidly expanding in both the number of programs and users. Concerns are arising on how to improve the quality of materials and delivery methods. This research intends to explore the potential use of Internet of Things (IoT) to collect data, which will be analyzed to get information useful for decision making to improve the online programmes in Higher Education Institutions (HEIs)
Biometrie systems present some important advantages over the traditional knowledge-or possess-oriented identification systems, such as a guarantee of authenticity and convenience. However, due to their widespread usage in our society and despite the difficulty in attacking them, nowadays criminals are already developing techniques to simulate physical, physiological and behavioral traits of valid...
Sensors and actuators are finding their way into our lives and our surroundings at a very fast pace. These heterogeneous sensors deployed in the environment can prove to be useful in providing insights into the behavior and trends of the environment. In this work, we capture a part of that knowledge and propose a novel concept called Urban Heartbeat using data captured by various sensors that essentially...
Cultural diversity of large crowds is one of the major concerns when participants overstep the predefined guidelines. Such behaviors eradicate crowds' safety, resulting massive casualties. Advent of tracking devices and smartphones with multiple sensing abilities can leverage to capture crowds' real-time spatio-temporal (ST) data to serve emergency service plans. In this paper, we present a Spatio-Temporal...
Computer Vision and Image Processing are emerging research paradigms. The increasing popularity of social media, micro- blogging services and ubiquitous availability of high-resolution smartphone cameras with pervasive connectivity are propelling our digital footprints and cyber activities. Such online human footprints related with an event-of-interest, if mined appropriately, can provide meaningful...
While smart city concept holds great promise of boosting living standards through effective management and utilization of scarce resources in cities, the unavailability of real-world datasets and test environments to evaluate designed models and techniques have slowed research progress. In this paper, we review existing research endeavors and develop a tool for extracting real-time smart city related...
The rapid growth of educational data mining (EDM) is an emerging field in the academic world of research and studies focusing on collection, archiving, and analysis of data related to delivery methodology, quality of materials and student learning and assessment. The information analyzed informs the learning institution on how to improve learning experiences and how to run the institutional effectively...
Ageing populations and the increase in chronic diseases all over the world demand efficient healthcare solutions for maintaining well-being of people. One strategy that has drawn significant research attention is a focus on remote health monitoring systems based on Internet of Things (IoT) technology. This concept can help decrease pressure on hospital systems and healthcare providers, reduce healthcare...
Data is becoming the world's new natural resource and big data use grows quickly. The trend of computing technology is that everything is merged into the Internet and ‘big data’ are integrated to comprise complete information for collective intelligence. With the increasing size of big data, refining big data themselves to reduce data size while keeping critical data (or useful information) is a new...
Parkinson disease (PD) cure remains one of the greatest challenges in chronic neurological disorder therapy, motivating efforts to provide actionable information to guide self-managed therapy adjustments. In this paper, we develop a data fusion approach to combine multi-dimensional data from body-worn inertial sensors to automatically identify different medication states of patients with PD. The proposed...
Since the power consumption of different electrical appliances in a household can be recorded by individual smart meters, it becomes possible to start considering in more detail the interactions of the residents with those devices throughout the day. Appliances' usages should not be considered as independent events, but rather as enablers for activities. Leveraging activity knowledge over time will...
As a novel concept, "Informed Design" is proposed in a multidisciplinary project "Livable Places" in Singapore to innovate place design from empirical to evidential by harnessing geo-referenced "Big Data" for a responsive design. As a final delivery, an Informed Design Platform (IDP) is being implemented as a design support tool interpreting multi-source big data to adaptive...
Anomaly or outlier detection is a fundamental task of data mining and widely used in various application domains. The main aim of anomaly detection is to identify all the data points with significant deviation from other normal data points. Mining the outliers become more challenging in environments where data is received at extreme pace. Such environments demand detection of outliers on-the-fly mode...
Hyperspectral technology has made significant advancements in the past two decades. Current sensors onboard airborne and space-borne platforms cover large areas of the Earth surface with unprecedented spectral resolutions. These characteristics enable a myriad of applications requiring fine identification of materials. Quite often, these applications rely on complicated methods of data analysis. In...
The inter-departmental interactions and coordination of resources are two essential components for realising a smart city platform. In this study, we investigated citizens' role in enhancing and facilitating the delivery of services by merging three key aspects of the smart city research field, namely Internet of People, Internet of Things and Web of Data. To this end, we developed a hybrid approach...
Analyzing and visualizing large datasets generated by real-time spatio-temporal activities (e.g. vehicle mobility or large crowd movement) are a very challenging task. Recursive delays both at middleware and front end applications limit the of usefulness of the real-time analysis. In this paper, we present a framework “Spatial-Crowd” that first handles spatial-temporal data acquisition and processing...
In this paper we introduce an automated mechanism for knowledge discovery from data streams. As a part of this work, we also present a new approach to the creation of classifiers ensemble based on a wide variety of models. Furthermore, we describe an innovative, highly scalable feature extraction and selection framework designed to work with the MapReduce programming model and the application of designed...
The main contribution of this paper is a study of the applicability of data smashing - a recently proposed data mining method - for vehicle classification according to the “Nordic system for intelligent classification of vehicles” standard, using measurements of road surface vibrations and magnetic field disturbances caused by passing vehicles. The main advantage of the studied classification approach...
Analog-to-information converters and Compressed Sampling (CS) sensor front-ends try to only extract the relevant, information-bearing elements of an incoming data stream. Information extraction and recognition tasks can run directly on the compressed data stream without needing full signal reconstruction. The accuracy of the extracted information or classification is strongly determined by the front-end...
This paper proposes a novel method for combining multiple fingerprint templates to improve the successful matching rate in fingerprint verification systems. Firstly, SVD is used to optimal aligning of two minutia sets. Then, based on the newly obtained overlapping area, we introduce a post-processing step to collect previously missing minutiae pairs due to nonlinear distortion. The overall advantages...
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