Prof. Mehdi Khashei | Pharmaceutical Analysis | Editorial Board Member

Isfahan University of Technology | Iran

Prof. Mehdi Khashei is a distinguished scholar in Industrial and Systems Engineering whose extensive academic and research contributions have positioned him as a leading expert in intelligent modeling, hybrid forecasting systems, and data-driven decision-making. He completed his academic journey with a strong mathematical and computational foundation, beginning with a Bachelor’s degree in Applied Mathematics and Computer Science from the University of Isfahan. Building on this analytical background, he pursued advanced studies at the Isfahan University of Technology, earning both his Master’s and Ph.D. in Industrial and Systems Engineering, where he focused on forecasting, analysis of industrial systems, and the integration of artificial intelligence into complex decision environments. His Post-Doctorate research at the same institution further advanced theoretical developments in Modeling Spaces Continuity, reinforcing his expertise in the intersection of systems theory, soft computing, and intelligent analytics.Throughout his academic career, Prof. Khashei has pioneered numerous innovative methodologies in the fields of time series forecasting, intelligent classification, hybrid modeling, and decision science. His research is particularly recognized for introducing discrete learning-based intelligent algorithms, reliability-based hybrid models, and advanced statistical-intelligent approaches for forecasting, classification, and optimization across diverse domains. His work spans energy systems, medicine, chemometrics, financial time series, predictive maintenance, load forecasting, and biomedical decision-making, demonstrating a unique capacity to bridge theory with real-world applications.Prof. Khashei has authored and co-authored an extensive portfolio of high-impact publications across leading international journals. His studies present cutting-edge solutions to challenges in artificial neural networks, fuzzy hybrid models, regression systems, clinical diagnostics, optimization strategies, and intelligent data preprocessing. His contributions have significantly shaped modern approaches to forecasting through hybridization frameworks such as series-parallel models, fuzzy-intelligent seasonal systems, and reliability-based methodologies. With groundbreaking insights into energy classification, breast cancer detection, heart disease diagnosis, and optimal financial turning point detection, his work continues to influence both academic research and applied industry practices.Prof. Mehdi Khashei is widely respected for his scientific rigor, interdisciplinary expertise, and commitment to advancing intelligent systems that enhance decision-making in complex, uncertain environments. His legacy is reflected not only in his prolific scholarly contributions but also in the transformative impact of his methodologies across engineering, computational intelligence, and data-driven industries.

Profile: Google Scholar

Featured Publications

Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for time series forecasting. Expert Systems with Applications, 37(1), 479‑489.

Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664‑2675.

Khashei, M., Bijari, M., & Ardali, G. A. R. (2009). Improvement of auto‑regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing, 72(4‑6), 956‑967.

Khashei, M., Hejazi, S. R., & Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets and Systems, 159(7), 769‑786.

Hajirahimi, Z., & Khashei, M. (2019). Hybrid structures in time series modeling and forecasting: A review. Engineering Applications of Artificial Intelligence, 86, 83‑106.

Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39(4), 4344‑4357.

Khashei, M., Hamadani, A. Z., & Bijari, M. (2012). A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Systems with Applications, 39(3), 2606‑2620.

Khashei, M., Bijari, M., & Ardali, G. A. R. (2012). Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs). Computers & Industrial Engineering, 63(1), 37‑45.

Khashei, M., & Hajirahimi, Z. (2019). A comparative study of series ARIMA/MLP hybrid models for stock price forecasting. Communications in Statistics – Simulation and Computation, 48(9), 2625‑2640.