2,667 research outputs found
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Predicting Traffic Flow Size and Duration
Current networks suffer from poor traffic management that leads to traffic congestion,
even when some parts of the network are still unused. In traditional networks each node
decides how to forward traffic based only on local reachability knowledge in a setting
where optimizing the cost and efficiency of the network is a complex task.
Modern networking technologies like Software-Defined Networking (SDN) provide
automation and programmability to Networks. In such networks control functions can be
applied in a different manner to each specific traffic flow and a variety of traffic information
can be gathered from several different sources.
This dissertation studies the feasibility of an intelligent network that can predict traffic
characteristics, when the first packets arrive. The goal is to know the duration and size of
flow to improve scheduling, load balancing and routing capabilities.
An OpenFlow application is implemented in an SDN Data Collecting Controller (DCC),
that shows how the first few packets of a traffic flow can be gathered with scalability
concerns and in a non-intrusive way.
The use of different classifiers such as Random Forest, Naive Bayes, Support Vector
Machines, Multi-layer Perceptron and K-Neighbour for effective flow duration and size
classification is studied. The results of using each of these classifiers to predict flow size
and duration using the DCC gathered data are presented and compared
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