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Dynamic Activity Predictions using Graph-based Neural Networks for Time Series Forecasting
(2023)
Time series forecasting is a vital task in numerous fields, and traditional methods,
machine learning models, and neural graph networks have been employed to improve
prediction accuracy. However, these techniques need ...
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 ...
Second chance competitive autoencoders for understanding textual data
(2021)
three novel autoencoders, SCAT (Second Chance Autoencoder for Text), SSCAT (Similarity-based SCAT), and CSCAT (Coherent-based SCAT). Our autoencoders utilize competitive learning among the k winner neurons in the bottleneck layer, which become...
Feature-based Analysis for Open Source using Big Data Analytics
(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 are typically semi...
GraphEvo: Evaluating Software Evolution Using Machine Learning Based Call Graph Analytics And Network Portrait Divergence
(2022)
propose a graph-based software framework called GraphEvo based on deep learning modeling for graphs. We applied the recent network comparison advancement to software networks via information theory-based metric Network portrait divergence (NPD). NPD...
A Graph Analytics Framework for Knowledge Discovery
(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 ...
Multiple-valued logic: technology and circuit implementation
(2021)
Computing technologies are currently based on the binary logic/number system, which is dependent on the simple on and off switching mechanism of the prevailing transistors. With the exponential increase of data processing and storage needs...
A Semantic Approach for Automatic Recovery of Software Architecture
(2014)
Open source projects have been continuously growing in popularity. As a result, a number of open source projects begin to play an important role in current software development. In practice, limited assistance has been provided on searching...
Deep Open Representative Learning for Image and Text Classification
(2020)
representatives of the discovery process from data to the knowledge that can be used to perform reasoning. In this dissertation, a novel model named Class Representative Learning (CRL) is proposed, a class-based classifier designed with the following unique...
Domain Playground: Extending Deep Learning Models to Open Domain Boundaries
(2021)
the limitations of existing domain adaptation approaches. Based on our previous work, Class Representatives (CRs), the DP framework extracts features from pre-trained models and aggregates them to build a model dynamically in the form of discrete representations...
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...
Design of Multi-modality Deep Fusion Architecture for Deep Acoustic Analytics
(2021)
-feature multi-modality fusion-based audio classification to solve these problems in acoustic classification. We have proposed the multi-modality fusion architecture with Deep Acoustics (DA) and Multimodal Deep Acoustics (MDA). The contributions are (1...
DMLA: A Dynamic Model-Based Lambda Architecture for Learning and Recognition of Features in Big Data
(University of Missouri--Kansas City, 2016)
the components of the DMLA architecture. In the DMLA framework, a dynamic predictive model, learned from the training data in Spark, is loaded from the context information into a Storm topology to recognize/predict the possible events. The event-based context...
StoryNet: A 5W1H-based knowledge graph to connect stories
(2021)
to any field. We have evaluated two approaches for generating results - training-based and rule-based. For the rule-based approach, we used Stanford NLP parsers to identify patterns for the 5W + 1H terms, and for the training based approach, BERT...
Topic Sentiment Trend Detection and Prediction for Social Media
(2020)
in social media. The proposed framework was designed to cope with the following tasks: topic trend detection, sentiment analysis, and topic prediction. The VADER-based time series sentiment analysis and the KATE-based topic modeling methods were applied...
AudioCNN: Audio Event Classification With Deep Learning Based Multi-Channel Fusion Networks
(2020)
In recent years, there is growing interest in environmental sound classification with a plethora of real-world applications, especially in audio fields like speech and music. Recent research works have proven spectral images based on deep learning...
ADInsight: A Multimodal and Explainable Framework for Alzheimer's Disease Progression and Conversion Prediction
(2023)
ADInsight represents the crux of this dissertation, introducing an integrated and explainable framework centered on predicting Alzheimer's disease (AD) conversion, particularly for those at the early stage of mild cognitive ...
Software Analytics for Improving Program Comprehension
(2021)
Program comprehension is an essential part of software development and maintenance. Traditional methods of program comprehension, such as reviewing the codebase and documentation, are still challenging for understanding ...
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 ...
A Novel Deep Learning-Based Framework for Context Aware Semantic Segmentation in Medical Imaging
(2023)
automated screening system based on a unified modeling approach of diagnosis. The system can extract multiple ocular features with a novel semantic segmentation network to early detect the symptoms of retinal disease. We proposed a novel technique of dynamic...