Now showing items 1-20 of 35

  • 3D Hand Pose Estimation Via a Lightweight Deep Learning Model 

    Suggala, Prudhvi Sai (University of Missouri -- Kansas City, 2018)
    Deep Learning with depth cameras has enabled 3D hand pose estimation from RGBD images. Commercial solutions like Leap Motion and Intel RealSense™ use stereoscopic sensors or IR illumination-based methods to capture the ...
  • Active Mobile Interface for smart health 

    Gorla, Pratima (2013)
    Computer interfaces are rapidly evolving beyond the traditional keyboard-mouse-monitor triad. The widespread availability of touch screens and voice recognition software has made it possible to execute commands in many ...
  • Context Based Multi-Image Visual Question Answering (VQA) in Deep Learning 

    Peddinti, Sudhakar Reddy (University of Missouri--Kansas City, 2017)
    Image question answering has gained huge popularity in recent years due to advancements in Deep Learning technologies and computer processing hardware which are able to achieve higher accuracies with faster processing ...
  • Context-Aware Adaptive Model for Smart Energy 

    Soni, Swati (2013)
    Building energy awareness and providing feedback on energy use is a vital component in transforming the behavior of individuals and communities towards a more efficient use of electric power. An enormous amount of energy ...
  • CR-GAN: Content-Based Recommender System with Conditional Generative Adversarial Networks 

    Vundela, Karthik Reddy (University of Missouri--Kansas City, 2018)
    Recommender systems have become increasingly popular by providing a wide range of products with a variety of styles. This trend has resulted in consumers expecting more intelligent and highly dynamic recommenders. The ...
  • CSISE: cloud-based semantic image search engine 

    Walunj, Vijay (2014-03-27)
    Due to rapid exponential growth in data, a couple of challenges we face today are how to handle big data and analyze large data sets. An IBM study showed the amount of data created in the last two years alone is 90% of the ...
  • Deep Assertion discovery using word embeddings 

    Nagabhushan, Megha (University of Missouri -- Kansas City, 2018)
    In recent years, there has been explosive growth in the amount of biomedical data (e.g., publications, notes from EHRs, clinical trial results), with the majority being unstructured data. As the volume of data is ...
  • Deep Learning for Semi-Automated Brain Claustrum Segmentation on Magnetic Resonance (MR) Images 

    Albishri, Ahmed Awad H. (University of Missouri--Kansas City, 2018)
    In recent years, Deep Learning (DL) has shown promising results with regard to conducting AI tasks such as computer vision and speech recognition. Specifically, DL demonstrated the state-of-the-art in computer vision ...
  • Distributed RDF query processing and reasoning for big data / linked data 

    Perasani, Anudeep (2014-08-27)
    The Linked Data Movement is aimed at converting unstructured and semi-structured data on the documents to semantically connected documents called the "web of data." This is based on Resource Description Framework (RDF) ...
  • DL-DI: A Deep Learning Framework for Distributed, Incremental Image Classification 

    Maddula, Manikanta (University of Missouri--Kansas City, 2017)
    Deep Learning technologies show promise for dramatic advances in fields such as image classification and speech recognition. Deep Learning (DL) is a class of Machine Learning algorithms that involves learning of multiple ...
  • DMLA: A Dynamic Model-Based Lambda Architecture for Learning and Recognition of Features in Big Data 

    Yadavalli, Ravi Kiran (University of Missouri--Kansas City, 2016)
    Real-time event modeling and recognition is one of the major research areas that is yet to reach its fullest potential. In the exploration of a system to fit in the tremendous challenges posed by data growth, several big ...
  • Dynamic Model Generation and Semantic Search for Open Source Projects using Big Data Analytics 

    Punyamurthula, Sravani (2015)
    Open source software is quite ubiquitous and caters to most common software needs developers come across. Many open source projects are considered better than their commercial equivalents as a larger pool of developers ...
  • Event driven querying of semantic sensor web services 

    Padmanabha, Shruthi (University of Missouri--Kansas City, 2012-05-15)
    In today's world, there is a tremendous increase in the usage of sensor technology in several fields including agriculture, medicine, and weather. Sensors either in-site or remotely placed are usually deployed in the form ...
  • Evidence based medical query system on large scale data 

    Bavirisetty, Venkata Pramod Gupta (2014-07-30)
    As huge amounts of data are created rapidly, the demand for the integration and analysis of such data has been growing steadily. It is especially essential to retrieve relevant and accurate evidence in healthcare and ...
  • Feature-based Analysis for Open Source using Big Data Analytics 

    Krishnan, Malathy (2015)
    The open source code base has increased enormously and hence understanding the functionality of the projects has become extremely difficult. The existing approaches of feature discovery that aim to identify functionality ...
  • A Graph Analytics Framework for Knowledge Discovery 

    Shen, Feichen (2016)
    In the current data movement, numerous efforts have been made to convert and normalize a large number of traditionally structured and unstructured data to semi-structured data (e.g., RDF, OWL). With the increasing number ...
  • H3DNET: A Deep Learning Framework for Hierarchical 3D Object Classification 

    Patel, Marmikkumar (University of Missouri--Kansas City, 2017)
    Deep learning has received a lot of attention in the fields such as speech recognition and image classification because of the ability to learn multiple levels of features from raw data. However, 3D deep learning is ...
  • iHear – Lightweight Machine Learning Engine with Context Aware Audio Recognition Model 

    Mannava, Guru Teja (University of Missouri–Kansas City, 2016)
    With the increasing popularity and affordability of smartphones, there is a high demand to add machine-learning engines to smartphones. However, Machine Learning with smartphones is typically not feasible due to the heavy ...
  • KB4DL: Building a Knowledge Base for Deep Learning 

    Punyamurtula, Ruthvic (University of Missouri -- Kansas City, 2019)
    Deep Learning (DL) has received considerable attention from the AI community. However, we suffer from the lack of ability in interpretation and annotation of the outcomes from extensive and exhausting learning efforts. ...
  • MDRED: Multi-Modal Multi-Task Distributed Recognition for Event Detection 

    Nandigam, Nageswara Rao (University of Missouri -- Kansas City, 2018)
    Understanding users’ context is essential in emerging mobile sensing applications, such as Metal Detector, Glint Finder, Facefirst. Over the last decade, Machine Learning (ML) techniques have evolved dramatically for ...