Publications

You can also find my articles on my Google Scholar profile.

Reproducible and generalizable speech emotion recognition via an Intelligent Fusion Network

Published in Biomedical Signal Processing and Control, 2025

We propose the Intelligent Fusion Network (IFN), a novel architecture combining dual attention, feature refinement, and multiplicative fusion to enhance speech emotion recognition (SER) performance and reproducibility. Extensive experiments across six benchmark datasets demonstrate IFN’s superior accuracy and generalizability, establishing it as a reliable and effective solution for advancing human-computer interaction.

Recommended citation: Zhang, H., Zhao, P., Tang, G., Li, Z., & Yuan, Z. (2025). Reproducible and generalizable speech emotion recognition via an Intelligent Fusion Network. Biomedical Signal Processing and Control, 109, 107996. https://www.sciencedirect.com/science/article/abs/pii/S1746809425005075

Applied statistical methods for identifying features of heart rate that are associated with nicotine vaping

Published in The American Journal of Drug and Alcohol Abuse, 2025

This paper introduces a series of statistical methods to analyze heart rate data and identify features associated with nicotine vaping.

Recommended citation: Zhao, P., Yang, J.J., & Buu, A. (2025). Applied statistical methods for identifying features of heart rate that are associated with nicotine vaping. The American Journal of Drug and Alcohol Abuse. https://doi.org/10.1080/00952990.2024.2441868 https://doi.org/10.1080/00952990.2024.2441868

Sparse Temporal Aware Capsule Network for Robust Speech Emotion Recognition

Published in Engineering Applications of Artificial Intelligence, 2025

This paper introduces a novel Sparse Temporal-Aware Capsule Network (STACN) architecture designed to enhance the accuracy and reliability of speech emotion recognition systems.

Recommended citation: Zhang, H., Huang, H., Zhao, P., & Yu, Z. (2025). Sparse Temporal Aware Capsule Network for Robust Speech Emotion Recognition. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2025.110060

CENN: Capsule-Enhanced Neural Network with Innovative Metrics for Robust Speech Emotion Recognition

Published in Knowledge-Based Systems, 2024

This paper introduces a groundbreaking Capsule-enhanced neural network (CENN) that significantly advances the state of SER through a robust and reproducible deep learning framework.

Recommended citation: Huiyun Zhang, Heming Huang, Puyang Zhao, Xiaojun Zhu, Zhenbao Yu, CENN: Capsule-Enhanced Neural Network with Innovative Metrics for Robust Speech Emotion Recognition, Knowledge-Based Systems (2024). https://www.sciencedirect.com/science/article/pii/S095070512401133X

DiGAN Breakthrough: Advancing Diabetic Data Analysis with Innovative GAN-Based Imbalance Correction Techniques

Published in Computer Methods and Programs in Biomedicine Update, 2024

Breakthrough in Diabetes Data Analysis: DiGAN represents a breakthrough approach in the field of medical diagnostics, especially in diabetes classification.

Recommended citation: P. Zhao, X. Liu, Z. Yue et al., DiGAN Breakthrough: Advancing diabetic data analysis with innovative GAN-based imbalance correction techniques, Computer Methods and Programs in Biomedicine Update (2024), doi: https://doi.org/10.1016/j.cmpbup.2024.100152. https://doi.org/10.1016/j.cmpbup.2024.100152.

An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams

Published in 2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS), 2022

A novel Attention-based Long Short-Term Memory (A-LSTM) method is proposed for the classiﬁcation problem to determine whether a transaction is involved in Ponzi schemes or other cyber scams, or is a non-scam transaction.

Recommended citation: Zhao, P., Tian, W., Xiao, L., Liu, X., & Wu, J. (2022, December). An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams. In 2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS) (pp. 21-26). IEEE. https://arxiv.org/pdf/2210.14408.pdf

Research on Prediction of Solar Power Considering the Methods of Statistical and Machine Learning – Based on the Data of Australian Solar Power Market

Published in IOP conference, 2022

The results show that the regression model with correlated errors is better than the machine learning-based LSTM algorithm, which is based on the differential MSE performance, and can accurately predict solar power generation.

Recommended citation: Zhao, P., & Tian, W. (2022, June). Research on prediction of solar power considering the methods of statistical and machine learning–based on the data of Australian solar power market. In IOP Conference Series: Earth and Environmental Science (Vol. 1046, No. 1, p. 012006). IOP Publishing. https://iopscience.iop.org/article/10.1088/1755-1315/1046/1/012006/pdf