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Evidence based medical query system on large scale data
(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 biomedical research. Even though...
A semantic framework for event-driven service composition
(University of Missouri--Kansas City, 2011-09-14)
Service Oriented Architecture (SOA) has become a popular paradigm for designing
distributed systems where loosely coupled services (i.e. computational entities) can be
integrated seamlessly to provide complex composite ...
DeepSampling: Image Sampling Technique for Cost-Effective Deep Learning
(2020)
Deep learning is beneficial from big data while facing computationally expensive, with an increase in data size. Some severe data issues, such as the presence of highly skewed, sparse, and imbalanced data, would substantially influence the findings...
Context-Aware Adaptive Model for Smart Energy
(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 consumption data is been...
StoryNet: A 5W1H-based knowledge graph to connect stories
(2021)
the information from all around the globe. Even though there have been efforts to consolidate the information on a large scale like Wikipedia, Wiki Data, etc, they are devoid of any real-time happenings. With the recent advances in Natural Language Processing (NLP...
Event driven querying of semantic sensor web services
(University of Missouri--Kansas City, 2012-05-15)
of the observation data is also necessary. To address this integral issue, we present the Event Driven Querying of Semantic Sensor Web Services. The Event Condition Action (ECA) based model is intended for providing a platform for querying the cluster of sensors...
Dynamic Model Generation and Semantic Search for Open Source Projects using Big Data Analytics
(2015)
the open source and provide the user a way to search functionality. The first step of this process is to extract the metadata and dependency information from the source code using a call graph. A call graph is a directed graph that represents the execution...
SigsSpace-Text: Parallel and Distributed Signature Learning in Text Analytics
(University of Missouri--Kansas City, 2016)
Big data analytics uncover hidden patterns and useful information from big data. It is a complex and time-consuming process. Recent advancements in parallel and distributed approaches have led to the evolution of big data analytics. It also claimed...
Feature-based Analysis for Open Source using Big Data Analytics
(2015)
is to create an automated and scalable model which produces accurate results. The initial step is to extract the meta-data and perform pre-processing. The next step is to dynamically discover topics using Latent Dirichlet Allocation and to form components...
Context Based Multi-Image Visual Question Answering (VQA) in Deep Learning
(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 ...
A Novel Deep Learning-Based Framework for Context Aware Semantic Segmentation in Medical Imaging
(2023)
datasets. The method adopted literature to apply leave-one-out and k-fold strategies for unordered data distributions. The use of context information in predicting target pixels bring added precision to the vision-critical system. We also designed a fully...
AI-based Edge Computing System for Event Based Analytics
(2021)
, desirable availability, and privacy protection. However, cloud-based AI solutions are not readily deployable to the edge in IoT's data-driven world because of the difficulties of dealing with diverse sources, lack of availability, and network traffic...
CSISE: cloud-based semantic image search engine
(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 data in the world today...
SAF-DL: Semantic Analysis Framework for Deep Learning Open Source Projects
(University of Missouri--Kansas City, 2018)
There are a lot of open source projects available on the internet. Specifically, due to the
increasing interest of Deep Learning (DL), the number of DL open source projects is also
increased. This project is motivated ...
Deep Open Representative Learning for Image and Text Classification
(2020)
. Additionally, semantic information and external information are added to CR-Graph to make the decision more capable of dealing with real-world data. The automated semantic information's ability to the graph is illustrated with a case study of biomedical...
ADInsight: A Multimodal and Explainable Framework for Alzheimer's Disease Progression and Conversion Prediction
(2023)
). Beginning with an examination of models grounded in individual research modalities, such as clinical data and advanced imaging, the research underscores the potential and limitations of singular approaches. As a response to these findings, this dissertation...
Explainable AI framework through Multi-Context Multi-Dimensional Graph Neural Network
(2023)
In this research, we explored the multifaceted realm of digital communication, emphasizing social media channels such as Twitter and Reddit, complemented by conventional data-gathering techniques like focus group discussions and structured digital...
RUPEE: A Big Data Approach to Indexing and Searching Protein Structures
(2021)
a novel approach to encoding sequences of torsion angles with established techniques from information retrieval and big data. RUPEE can compare the query structure to every available structure in the searched database with fast response times...
MDRED: Multi-Modal Multi-Task Distributed Recognition for Event Detection
(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 ...