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Found 10 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.
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.
Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh
Newspapers are trustworthy media where people get the most reliable and credible information compared with other sources.
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.
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.
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.
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)}.
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.
