Jerry Zeng
Master of Science in Information Networking @CMU
2021.09 〜 2023.08
🌐 . 📊
Research Assistant
Achievements
As the first author, I contributed to the paper: Zeng J, Xiao F. A fractal belief KL divergence for decision fusion[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 106027. This research was featured in the esteemed JCR Q1 journal EAAI.
IF = 8.0
As the first author, my research titled "A High Order Fractal-based Kullback-Leibler Divergence with Application in Classification" is currently under review after minor-revision resubmission in the renowned JCR Q1 journal ESWA.
IF = 8.5
Over two enriching years in research, I honed my skills in guiding peers in research methodologies, and actively participated in review of manuscripts.
MethodologyOverview
Our primary aim was to harness the power of divergence theory and fractal characteristics to significantly enhance pattern recognition and classification tasks within machine learning.
🔍 Fractal-based KL Divergence
In our initial study, I introduced a fractal-based KL divergence, blending the principles of KL divergence with the process of fractal. This approach was applied to real-world classification tasks and pattern recognition.
📊 High-Order Fractal Entropy and Divergence
As an extension of my initial work, I developed a high-order fractal entropy. This proposed entropy highlights the optimal fractal time for an information source, ensuring a more reasonable and accurate information fusion process. In its practical application, our method was rigorously tested on various complex datasets, consistently outperforming other state-of-the-art methodologies.
🌡️ Application in Health Systems
Building upon the foundational concepts of multiscale entropy (MSE), our research delved deeper into the health domain. We embarked on a journey to refine entropy and divergence measures, aiming to enhance their application in health diagnostics and insights, particularly in the context of cardiac interbeat interval time series.