Mehdi Khashei | Pharmaceutical Analysis | Editorial Board Member

Published on 17/11/2025 by Top Pharmaceutical

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.

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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.

Dibyanshu | Pharmaceutical Analysis | Best Researcher Award

Published on 19/09/2025 by Top Pharmaceutical

Dr. Dibyanshu | Pharmaceutical Analysis | Best Researcher Award

Freiberg University of Mining and Technology | Germany

Dr. Dibyanshu is an accomplished researcher in the field of Environmental Engineering, with expertise spanning emerging contaminants, fate and transport mechanisms, colloid filtration, and groundwater remediation. He completed his doctoral studies at the prestigious Indian Institute of Technology, Patna, where his dissertation, Fate and Transport Behavior of Engineered Nanoparticles through Porous Media, contributed valuable insights into contaminant migration in subsurface environments. His academic journey includes an Integrated M-Tech in Water Engineering and Management from the Central University of Jharkhand, followed by a series of impactful research and teaching positions, including Assistant Professorship and Research Assistantship roles at leading institutions.As an Alexander von Humboldt post-doctoral fellow at Technische Universität Bergakademie Freiberg, Germany, Dr. Dibyanshu focuses on the Fate and Transport of Pharmaceuticals through Unsaturated Porous Media via Infiltration, emphasizing sustainable solutions for groundwater quality management. His earlier work on In-situ Remediation of Arsenic Using Immobilized Iron Sulphide in 3-D Tank Systems reflects his commitment to practical environmental applications alongside theoretical advancements.Dr. Dibyanshu’s research portfolio features an impressive array of peer-reviewed publications in high-impact journals. Notable works include Influence of Agricultural Practices and Environmental Conditions on Pharmaceuticals in Recharge Waters in Science of The Total Environment, Emerging Contaminants: Assessing the Release of Pharmaceuticals via Managed Aquifer Recharge in Environmental Science and Pollution Research, and Trace Compounds in the Urban Water Cycle in the Freiberg Region, Germany in Frontiers in Water. His contributions extend to modeling studies such as Modeling the Transport Behavior of Zinc Oxide Nanoparticles in Soil under Various Environmental Conditions in Water, Air, & Soil Pollution and comprehensive reviews like Fate and Transport Behavior of Engineered Nanoparticles

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Featured Publications

Dibyanshu, K., Chhaya, T., & Raychoudhury, T. (2023). A review on the fate and transport behavior of engineered nanoparticles: Possibility of becoming an emerging contaminant in the groundwater. International Journal of Environmental Science and Technology, 20(4), 4649–4672.

Dibyanshu, & Raychoudhury, T. (2019). Co-transport behavior of nano-ZnO particles in the presence of metal-nanoparticles through saturated porous media. Journal of Environmental Chemical Engineering, 7(3), 103103.

Dibyanshu, & Raychoudhury, T. (2020). Transport behaviour of different metal-based nanoparticles through natural sediment in the presence of humic acid and under the groundwater condition. Journal of Earth System Science, 129(1), 1–12.

Kumar, A., Dibyanshu, & Raychoudhury, T. (2020). Long-term fate of ZnO-FexOy mix-nanoparticles through the saturated porous media under constant head condition. Science of The Total Environment, 721, 137669.

Dibyanshu, Pradhan, I., Nayak, A., & Raychoudhury, T. (2021). Variation in porous media compositions influence the co-transport behavior of ZnO and FexOy mixed nanoparticles. Groundwater for Sustainable Development, 100710.

Chhaya, S., Dibyanshu, S., Singh, S., & Raychoudhury, T. (2022). Nanoparticles and nanocomposite materials for water treatment: Application in fixed bed column filter. In Sustainable Water Treatment: Advances and Technological Interventions (pp. 171–244).

Seetha, N., Dibyanshu, & Raychoudhury, T. (2024). Modeling the transport behavior of zinc oxide nanoparticles in soil under various environmental conditions. Water, Air, & Soil Pollution, 235(1), 55.

Dibyanshu, Kern, M., & Scheytt, T. (2024). Trace compounds in the urban water cycle in the Freiberg region, Germany. Frontiers in Water, 6, 1335766.