Lecturer, University of Tabriz, Iran
Dr. Zari Farhadi is a dedicated lecturer and researcher at the University of Tabriz, Iran, with expertise in Data Science, Machine Learning, and Predictive Modeling. Her passion for academic excellence is evident in her work, particularly in the development of hybrid models to enhance data analysis accuracy. With a Ph.D. in Data Science, she has contributed extensively to advancing predictive models through innovative techniques like ensemble learning and deep regression. 🌟📚
Publication Profile
Google Scholar
Education
Zari Farhadi holds a Ph.D. in Data Science, specializing in machine learning, deep learning, and statistical techniques, from the University of Tabriz. Her academic foundation supports her pioneering work in hybrid machine learning models. 🎓
Experience
As a lecturer and researcher, Dr. Farhadi has contributed to various research papers, focusing on machine learning and deep learning. She teaches at both the Computerized Intelligence Systems Laboratory and the Department of Statistics at the University of Tabriz. Her research experience spans across several high-impact areas of data science, including predictive modeling and statistical learning. 🧑🏫
Awards and Honors
Though not currently affiliated with professional organizations, Dr. Farhadi’s work has been recognized in academic circles through the citation of her research in top journals, underlining her growing impact in the field of data science. 🏅
Research Focus
Dr. Farhadi’s research centers on Machine Learning, Predictive Modeling, Ensemble Learning Methods, Statistical Learning, and Hybrid Models like ADeFS, which integrate deep learning with statistical shrinkage methods. She strives to improve model performance in real-world applications, including gold price prediction and real estate valuation. 🤖📊
Conclusion
Zari Farhadi continues to innovate and drive research in the fields of machine learning and data science. Through her groundbreaking work in hybrid models, she is shaping the future of predictive analytics and advancing the boundaries of artificial intelligence in academic and industrial applications. 🌍
Publications
An Ensemble Framework to Improve the Accuracy of Prediction Using Clustered Random-Forest and Shrinkage Methods,
Appl. Sci., vol. 12, no. 20, 2022, doi: 10.3390/app122010608
Cited by: 15 articles.
Improving random forest algorithm by selecting appropriate penalized method
Commun. Stat. Simul. Comput., vol. 0, no. 0, pp. 1–16, 2022, doi: 10.1080/03610918.2022.2150779
Cited by: 10 articles.
ERDeR: The combination of statistical shrinkage methods and ensemble approaches to improve the performance of deep regression,
IEEE Access, DOI: 10.1109/ACCESS.2024.3368067
Cited by: 3 articles.
ADeFS: A deep forest regression-based model to enhance the performance based on LASSO and Elastic Net,
Mathematics and Computer Science, MDPI, 13 (1), 118, 2024.
Cited by: Pending.
Combining Regularization and Dropout Techniques for Deep Convolutional Neural Network,
IEEE Glob. Energy Conf. GEC 2022, pp. 335–339, 2022, doi: 10.1109/GEC55014.2022.9986657
Cited by: 5 articles.
Analysis of Penalized Regression Methods in a Simple Linear Model on the High-Dimensional Data,
American Journal of Theoretical and Applied Statistics, 8 (5), 185, 2019.
Cited by: 2 articles.
An Ensemble-Based Model for Sentiment Analysis of Persian Comments on Instagram Using Deep Learning Algorithms,
IEEE Access, DOI: 10.1109/ACCESS.2024.3473617
Cited by: Pending.
Hybrid Model for Visual Sentiment Classification Using Content-Based Image Retrieval and Multi-Input Convolutional Neural Network,
International Journal of Intelligent Systems (Under review).