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Found 17 papers, 5 papers with code
ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN
In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both univariate and multivariate time series data in an unsupervised setting.
Informed Machine Learning, Centrality, CNN, Relevant Document Detection, Repatriation of Indigenous Human Remains
This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information.
Unsupervised Visual Time-Series Representation Learning and Clustering
Time-series data is generated ubiquitously from Internet-of-Things (IoT) infrastructure, connected and wearable devices, remote sensing, autonomous driving research and, audio-video communications, in enormous volumes.
A Semi-automatic Data Extraction System for Heterogeneous Data Sources: A Case Study from Cotton Industry
With the recent developments in digitisation, there are increasing number of documents available online.
Investigation of Topic Modelling Methods for Understanding the Reports of the Mining Projects in Queensland
In the mining industry, many reports are generated in the project management process.
Nonnegative Matrix Factorization to understand Spatio-Temporal Traffic Pattern Variations during COVID-19: A Case Study
The outputs of the analysed spatio-temporal traffic pattern variation behaviours will be useful in the fields of traffic management in Intelligent Transportation System and management in various stages of pandemic or unavoidable scenarios in-relation to road traffic.
Deep Learning for Bias Detection: From Inception to Deployment
To create a more inclusive workplace, enterprises are actively investing in identifying and eliminating unconscious bias (e. g., gender, race, age, disability, elitism and religion) across their various functions.
Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study
Social media platforms facilitate mankind a data-driven world by enabling billions of people to share their thoughts and activities ubiquitously.
Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering
Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability.
Topic, Sentiment and Impact Analysis: COVID19 Information Seeking on Social Media
A systematic collection, analysis, and interpretation of social media data across time and space can give insights on local outbreaks, mental health, and social issues.
QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content Identification in Hindi Language
More specifically, it is a binary classification problem where a system is required to classify tweets into two classes: (a) \emph{Hate and Offensive (HOF)} and (b) \emph{Not Hate or Offensive (NOT)}.
Misogynistic Tweet Detection: Modelling CNN with Small Datasets
Online abuse directed towards women on the social media platform Twitter has attracted considerable attention in recent years.
Propensity-to-Pay: Machine Learning for Estimating Prediction Uncertainty
This paper presents a case-study, conducted on a dataset from an energy organisation, to explore the uncertainty around the creation of machine learning models that are able to predict residential customers entering financial hardship which then reduces their ability to pay energy bills.
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security.
Columnwise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization
Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information.
Efficient Nonnegative Tensor Factorization via Saturating Coordinate Descent
With the advancements in computing technology and web-based applications, data is increasingly generated in multi-dimensional form.
Parallel Streaming Signature EM-tree: A Clustering Algorithm for Web Scale Applications
We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters.
