8 results match your criteria ACM Transactions on Intelligent Systems and Technology[Journal]

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Indirectly-Supervised Anomaly Detection of Clinically-Meaningful Health Events from Smart Home Data.

ACM Trans Intell Syst Technol 2021 Mar 11;12(2):1-18. Epub 2021 Feb 11.

Washington State University.

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly-Supervised Detector of Relevant Anomalies from time series data. Read More

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A Survey of Unsupervised Deep Domain Adaptation.

ACM Trans Intell Syst Technol 2020 Sep 5;11(5):1-46. Epub 2020 Jul 5.

Washington State University, USA.

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Read More

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September 2020

SNAP: A General Purpose Network Analysis and Graph Mining Library.

ACM Trans Intell Syst Technol 2016 Oct 3;8(1). Epub 2016 Oct 3.

Stanford University.

Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy to use, high-level operations for analysis and manipulation of large networks. Read More

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October 2016

Topic-Aware Physical Activity Propagation with Temporal Dynamics in a Health Social Network.

ACM Trans Intell Syst Technol 2016 Aug;8(1)

University of Oregon.

Modeling physical activity propagation, such as activity level and intensity, is a key to preventing obesity from cascading through communities, and to helping spread wellness and healthy behavior in a social network. However, there have not been enough scientific and quantitative studies to elucidate how social communication may deliver physical activity interventions. In this work, we introduce a novel model named opic-aware ommunity-level hysical Activity Propagation with emporal Dynamics (TCPT) to analyze physical activity propagation and social influence at different granularities (i. Read More

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Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via Feature-Space Remapping (FSR).

ACM Trans Intell Syst Technol 2015 Apr;6(1)

School of Electrical Engineering and Computer Science, Washington State University.

Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature- Space Remapping (FSR), which transfers knowledge between domains with different feature spaces. This is accomplished without requiring typical feature-feature, feature instance, or instance-instance co-occurrence data. Read More

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A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data.

ACM Trans Intell Syst Technol 2013 Sep;4(4)

University of Pittsburgh.

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Read More

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September 2013

Analysis of Friendship Network and its Role in Explaining Obesity.

ACM Trans Intell Syst Technol 2013 Jun;4(3)

Institute for scientific Interexchange (I.S.I.) Foundation, 11/C via Alassio, 10126 Torino, Italy.

We employ Add Health data to show that friendship networks, constructed from mutual friendship nominations, are important in building weight perception, setting weight goals and measuring social marginalization among adolescents and young adults. We study the relationship between individuals' perceived weight status, actual weight status, weight status relative to friends' weight status and weight goals. This analysis helps us understand how individual weight perceptions might be formed, what these perceptions do to the weight goals, and how does friends' relative weight affect weight perception and weight goals. Read More

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Intelligent Systems and Technology for Integrative and Predictive Medicine: An ACP Approach.

ACM Trans Intell Syst Technol 2013 Mar;4(2):32

Systematic Bioengineering Laboratory, The University of Arizona, Tucson, Arizona, USA.

One of the principal goals in medicine is to determine and implement the best treatment for patients through fastidious estimation of the effects and benefits of therapeutic procedures. The inherent complexities of physiological and pathological networks that span across orders of magnitude in time and length scales, however, represent fundamental hurdles in determining effective treatments for patients. Here we argue for a new approach, called ACP-based approach that combines methods in intelligent systems and technology for integrative and predictive medicine, or more general, precision medicine and smart health management. Read More

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