1 code implementation • Pacific-Asia Conference on Knowledge Discovery and Data Mining 2022 • David Hason Rudd, Huan Huo, Guandong Xu
We attempt to leverage the Mel spectrogram by decomposing distinguishable acoustic features for exploitation in our proposed architecture, which includes a novel feature map generator algorithm, a CNN-based network feature extractor and a multi-layer perceptron (MLP) classifier.
1 code implementation • Pacific-Asia Conference on Knowledge Discovery and Data Mining 2023 • David Hason Rudd, Huan Huo, Guandong Xu
Emotion recognition (ER) from speech signals is a robust approach since it cannot be imitated like facial expression or text based sentiment analysis.
Ranked #1 on Speech Emotion Recognition on EMODB (using extra training data)
1 code implementation • 18 Dec 2023 • David Hason Rudd, Huan Huo, Guandong Xu
We propose the SMOGN-COREG model for semi-supervised regression, applying SMOGN to deal with unbalanced datasets and a nonparametric multi-learner co-regression (COREG) algorithm for labeling.
no code implementations • 3 Dec 2023 • David Hason Rudd, Huan Huo, Md Rafiqul Islam, Guandong Xu
Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91. 2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid fusion learning technique compared with late fusion and baseline models.
no code implementations • 23 Apr 2023 • David Hason Rudd, Huan Huo, Guandong Xu
Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period.
1 code implementation • International Conference on Digital Society and Intelligent Systems (DSInS) 2021 • David Hason Rudd, Huan Huo, Guandong Xu
Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives.
1 code implementation • Human-Centric Intelligent Systems 2022 • David Hason Rudd, Huan Huo, Guandong Xu
We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems.