Syarifah Inayati | Modeling | Research Excellence Award

Dr. Syarifah Inayati at Universitas Negeri Yogyakarta | Indonesia

Syarifah Inayati is an academic researcher at Universitas Negeri Yogyakarta with expertise in statistics, mathematical finance, and optimization, and a scholarly record that demonstrates strong engagement with advanced quantitative modeling and applied statistical analysis. Her research primarily focuses on time series modeling, particularly Markov Switching Autoregressive (MSAR) and Bayesian time-varying parameter models, which she applies to dynamic economic forecasting and financial market analysis. Several of her studies address financial risk and investment analysis, including stock market contagion between Indonesia and the United States, portfolio analysis using Gaussian mixture distributions with expectation–maximization algorithms, and risk measurement through Value at Risk methods under Bayesian mixture frameworks. Beyond financial applications, she has made notable contributions to socio-economic and public policy research, such as forecasting BPJS health insurance beneficiaries using fuzzy time series methods and modeling the Human Development Index of Central Java using three-parameter gamma regression. Her work in optimization includes nonlinear multiobjective optimization problems solved through Pareto front and weighting approaches, demonstrating methodological depth and versatility. In addition to theoretical and applied research, Syarifah Inayati is actively involved in community service and capacity building, contributing to workshops and training programs on nonparametric analysis, factor analysis, logistic regression, and statistical methods for social sciences and education. With 39 citations, an h-index of 4, and consistent citation growth since 2020, her research reflects a balanced integration of rigorous statistical methodology, interdisciplinary collaboration, and practical relevance. Overall, her scholarly contributions strengthen the application of modern statistical and econometric techniques in finance, economics, public policy, and applied mathematics, while also supporting knowledge dissemination through educational and community-oriented initiatives.

Citation Metrics (Google Scholar)

Citations
39

h-index
4

i10-index
0

🟦 Citations 🟥 h-index 🟩 i10-index

Google Scholar Scoupas Orcid

Featured Publications

Prediksi Jumlah Peserta BPJS Penerima Bantuan Iuran (PBI) APBN Menggunakan Metode Fuzzy Time Series Cheng

– Barekeng, 2021

A Markov Switching Autoregressive Model with Time-Varying Parameters

– Forecasting, 2024

3-Parameter Gamma Regression Model for Analyzing Human Development Index of Central Java Province

– Barekeng, 2022

Training and Development of Flipbook Learning Resources for Master Bimbel in Malang Regency

– Community Empowerment, 2024

Loss Distribution Approach for Company Operational Risk Analysis

– Journal of Physics: Conference Series, 2020

Wenyan Wu | Modeling | Best Researcher Award

Dr. Wenyan Wu at Guangdong University of Technology | China

Dr. Wenyan Wu is an emerging researcher whose work focuses on the intersection of artificial intelligence, multimodal learning, and intelligent systems with applications in emotion recognition, sentiment analysis, and human-computer interaction. Since creating her ORCID record in August 2022, Dr. Wu has actively contributed to advancing research in cross-modal data analysis, integrating deep learning frameworks with cognitive and affective computing techniques. Her recent publication, “Modality-Enhanced Multimodal Integrated Fusion Attention Model for Sentiment Analysis” (Applied Sciences, 2025), introduces a novel attention-based fusion approach to improve sentiment analysis accuracy by effectively capturing inter-modal dependencies across text, audio, and visual cues. In “Collaborative Analysis of Learners’ Emotional States Based on Cross-Modal Higher-Order Reasoning” (Applied Sciences, 2024), Dr. Wu explores emotion-aware learning environments, presenting innovative reasoning mechanisms for identifying and analyzing learners’ affective states to enhance adaptive education systems. Her research on “Mask-Wearing Detection in Complex Environments Based on Improved YOLOv7” (Applied Sciences, 2024) demonstrates her interdisciplinary expertise, combining computer vision and deep neural networks to address real-world safety monitoring challenges. Earlier, her foundational study, “A Novel Method for Cross-Modal Collaborative Analysis and Evaluation in the Intelligence Era” (Applied Sciences, 2022), laid the groundwork for her later research by proposing an integrated model for data collaboration across modalities in intelligent environments. Dr. Wu’s scholarly output reflects her strong analytical and technical acumen, emphasizing multimodal integration, attention mechanisms, and deep learning optimization. Her contributions not only advance theoretical understanding but also provide practical frameworks for developing emotionally intelligent and context-aware AI systems, bridging the gap between computational models and human-centered design in modern intelligent applications.

Profile: Orcid

Featured Publications

Zhang, Z., Wu, W., Yuan, T., & Feng, G. (2025). Modality-enhanced multimodal integrated fusion attention model for sentiment analysis. Applied Sciences, 15(19), 10825.

Wu, W., Zhao, J., Shen, X., & Feng, G. (2024). Collaborative analysis of learners’ emotional states based on cross-modal higher-order reasoning. Applied Sciences, 14(13), 5513.

Feng, G., Yang, Q., Tang, C., Liu, Y., Wu, X., & Wu, W. (2024). Mask-wearing detection in complex environments based on improved YOLOv7. Applied Sciences, 14(9), 3606.

Wu, W., Hu, Q., Feng, G., & He, Y. (2022). A novel method for cross-modal collaborative analysis and evaluation in the intelligence era. Applied Sciences, 13(1), 163.