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MULTI-SOURCED INFORMATION TRUSTWORTHINESS ANALYSIS: APPLICATIONS AND THEORY
(State University of New York at Buffalo, 2018)
In the era of Big Data, data entries, even describing the same objects or events, can come from a variety of sources. There are some sources that typically provide accurate information, but due to various reasons such as ...
A NOVEL CROWD SENSING FRAMEWORK FOR URBAN COMPUTING APPLICATIONS
(State University of New York at Buffalo, 2018)
Driven by the proliferation of sensor-rich mobile devices, crowd sensing has emerged as a new paradigm of gathering information about the physical world. In crowd sensing applications, humans work as the sensor carriers ...
Integrating Network Science and Computational Topology with Applications in Neuroscience Data Analytics
(State University of New York at Buffalo, 2018)
Real world systems are complex, dynamic and exist across multiple scales. Recent revolutions in data collection and storage have provided researchers with unprecedented access to information about these systems in greater ...
Flexible Data Management on Mobile Systems
(State University of New York at Buffalo, 2019)
Mobile systems have gradually become the predominant platform for everyday computing, and their capabilities are getting powerful by the day. Apps on mobile systems employ sophisticated data management solutions by making ...
Multi-omic integrative network analysis
(State University of New York at Buffalo, 2019)
With advanced biotechnology, we have accumulated vast amounts of genomic, epigenomic, transcriptomic, and proteomic data -- collectively called multi-omic data. Integrating and analyzing these multi-omic data poses great ...
Application of Inertial Measurement Unit (IMU) in Advanced Human Health and Safety Surveillance: A Data Fusion and Machine Learning Approach
(State University of New York at Buffalo, 2019)
Accurate and reliable quantification of human physical and physiological state using wearable sensing devices is paramount in health monitoring and safety surveillance, which necessitates the measurement of relevant signals ...
GPGPU–Based Fast Counting in Machine Learning Applications
(State University of New York at Buffalo, 2019)
We propose a new method to execute counting queries using General Purpose Graphical Processing Units (GPGPUs). Counting queries are used to provide conditional probabilities in machine learning applications, but are ...
Algorithms for Relation Extraction from Biomedical Texts
(State University of New York at Buffalo, 2019)
The boost in the capacity and volume of biomedical texts, including biomedical literatures and electronic health records (EHRs), has created a tremendous opportunity for biomedical research and practice. It is widely ...
Algorithmic Approaches for Determining Spatial Patterns in Several Biomedical Applications
(State University of New York at Buffalo, 2019)
Finding the structural pattern from a set of objects is a commonly encountered prototype learning problem and has a wide range of applications in machine learning and pattern recognition. In cell biology, there is growing ...
Adaptive Visual Slam for Indoor Environments
(State University of New York at Buffalo, 2019)
A fundamental building block for autonomous robots is Simultaneous Localization and Mapping (SLAM). Beyond robots, many wearables (such as smart glasses) provide augmented reality and virtual reality applications that could ...