Browsing by Subject "Machine Learning"
Now showing items 1-13 of 13
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AUTOMATED FUNCTIONAL ASSESSMENT OF SMART HOME RESIDENTS
(2015)This dissertation proposes smart home-based intelligent techniques that perform automated assessment of a resident's well-being by monitoring their behavior inside the home. We hypothesize that the everyday behavior of ... -
DETECTING DYSKINESIA AND TREMOR IN PEOPLE WITH PARKINSON'S DISEASE OR ESSENTIAL TREMOR DURING ACTIVITIES OF DAILY LIVING USING BODY WORN ACCELEROMETERS AND MACHINE LEARNING ALGORITHMS
(2014)DETECTING DYSKINESIA AND TREMOR IN PEOPLE WITH PARKINSON'S DISEASE OR ESSENTIAL TREMOR DURING ACTIVITIES OF DAILY LIVING USING BODY WORN ACCELEROMETERS AND MACHINE LEARNING ALGORITHMSAbstractBy Nathaniel David Darnall, ... -
DYNAMIC ADAPTATION OF RECOGNITION ALGORITHMS ON WEARABLES WITH MINIMAL HUMAN SUPERVISION
(2018)Wearable sensors utilize machine learning algorithms to infer important events such as behavioral routine and health status of their end-users from time-series sensor data. A major obstacle in the large-scale utilization ... -
Identification of novel differentially methylated dna regions using active learning and imbalanced class learners
(2014)Epigenetics refers to the changes in gene expression which are caused by other mechanisms other than the DNA sequence. Environmental influences can alter epigenetic states in the germ line that can be further transmitted ... -
IMPROVING SENSOR NETWORK PREDICTIONS THROUGH THE IDENTIFICATION OF GRAPHICAL FEATURES
(2019)We propose a framework that represents sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the most useful features to be used by a classifier for prediction tasks ... -
INVESTIGATING THE HUMAN BEHAVIOR SIDE OF BUILDING ENERGY EFFICIENCY
(2013)Society is becoming increasingly aware of the impact that our lifestyle preferences have on energy usage and the environment. In this dissertation, we look more closely at the impact that human behavior has on energy ... -
Knowledge Transfer in Reinforcement Learning: How agents should benefit from prior knowledge
(2019)Reinforcement learning (RL) has had many successes in different tasks, but in practice, it often requires significant amounts of data or training time to learn high-performing policies. For complicated tasks, the learning ... -
Preparing Smart Environments for Life in the Wild: Feature-space and Multi-view Heterogeneous Transfer Learning
(2014)With the ever-increasing abundance of sensing and computing devices embedded into our environments we have the opportunity to create personalized activity recognition ecosystems. Two key challenges must first be overcome, ... -
Scaling Activity Discovery and Recognition to Large, Complex Datasets
(2011)In the past decade, activity discovery and recognition has been studied by many researchers. However there are still many challenges to be addressed before deploying such technologies in the real world. We try to address ... -
STRUCTURAL PATTERN DISCOVERY IN DYNAMIC GRAPHS
(2015)Structural pattern discovery problems, which contain graph isomorphism as a sub-problem, are computationally very hard. The runtime of complete algorithms for discovering structural patterns such as frequent subgraphs and ... -
TINGLE - TOPIC-INDEPENDENT GAMIFICATION LEARNING ENVIRONMENT
(2018)Motivating student commitment and engagement in learning, using gamification, or games embedded in non-game settings, has been sought after by game researchers, game designers, academic content developers, and teachers ... -
Towards Energy Efficient And Reliable 3D Manycore Chip Enabled By Machine Learning
(2018)As the demand for high performance and energy efficient computation has increased significantly, manycore chip architectures have emerged as a mainstream solution paradigm. A three-dimensional Network-on-Chip (3D NoC) that ... -
Wireless NoC and Voltage Frequency Island Co-Design for Energy-Efficient Manycore Platforms
(2016)Multiple Voltage Frequency Island (VFI)-based designs present a scalable power management strategy for manycore chips. However, the overall communication backbone, which relies predominantly on Networks-on-Chip (NoCs), ...