no code implementations • 13 Aug 2023 • Md Abul Bashar, Richi Nayak
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.
no code implementations • 25 Mar 2023 • Md Abul Bashar, Richi Nayak, Gareth Knapman, Paul Turnbull, Cressida Fforde
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.
no code implementations • 12 Oct 2021 • Md Abul Bashar, Richi Nayak, Anjor Kothare, Vishal Sharma, Kesavan Kandadai
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.
no code implementations • 24 Jan 2021 • Fahim Shahriar, Md Abul Bashar
Newspapers are trustworthy media where people get the most reliable and credible information compared with other sources.
no code implementations • 19 Sep 2020 • Thirunavukarasu Balasubramaniam, Richi Nayak, Md Abul Bashar
Social media platforms facilitate mankind a data-driven world by enabling billions of people to share their thoughts and activities ubiquitously.
1 code implementation • 28 Aug 2020 • Md Abul Bashar, Richi Nayak, Thirunavukarasu Balasubramaniam
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.
1 code implementation • 28 Aug 2020 • Md Abul Bashar, Richi Nayak, Nicolas Suzor, Bridget Weir
Online abuse directed towards women on the social media platform Twitter has attracted considerable attention in recent years.
1 code implementation • 28 Aug 2020 • Md Abul Bashar, Richi Nayak
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)}.
1 code implementation • 27 Aug 2020 • Md Abul Bashar, Astin-Walmsley Kieren, Heath Kerina, Richi Nayak
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.
1 code implementation • 21 Aug 2020 • Md Abul Bashar, Richi Nayak
Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security.