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Found 7 papers, 5 papers with code
Predicting Financial Literacy via Semi-supervised Learning
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.
Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition
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.
An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance
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)
Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data
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.
Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions
Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period.
Causal Analysis of Customer Churn Using Deep Learning
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.
Improved Churn Causal Analysis Through Restrained High‑Dimensional Feature Space Efects in Financial Institutions
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.
