Prof. Vali Rasooli Sharabiani | Biological Sciences | Editorial Board Member

University of Mohaghegh Ardabili | Iran

Dr. Vali Rasooli Sharabiani is a distinguished Professor at the University of Mohaghegh Ardabili, Iran, whose research centers on precision agriculture, smart farming technologies, and non-destructive measurement methods for sustainable crop production and food processing. His scientific work integrates artificial intelligence, hyperspectral imaging, and multivariate data analysis to enhance agricultural efficiency, resource management, and environmental protection. Dr. Sharabiani’s contributions have significantly advanced the understanding of variable rate technology, remote sensing, and the application of machine learning models such as ANNs, ANFIS, and fuzzy logic in agricultural systems. His interdisciplinary approach bridges engineering, agronomy, and data science, making his research highly influential in both academic and industrial sectors. With more than 1,500 citations, an h-index of 21, and an i10-index of 37 on Google Scholar, along with high-impact publications indexed in Scopus, his scholarly achievements reflect strong global recognition. Dr. Sharabiani’s research outputs are widely referenced in the fields of agricultural mechanization, energy-efficient drying systems, and precision monitoring of crop and soil properties.

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Featured Publications

Kaveh, M., Sharabiani, V. R., Chayjan, R. A., & Taghinezhad, E. (2018). ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption of potato, garlic, and cantaloupe drying under convective hot air dryer. Information Processing in Agriculture, 5(3), 372–387.

Kaveh, M., Chayjan, R. A., Taghinezhad, E., Sharabiani, V. R., & Motevali, A. (2020). Evaluation of specific energy consumption and GHG emissions for different drying methods (Case study: Pistacia Atlantica). Journal of Cleaner Production, 259, 120963.

Jahanbakhshi, A., Kaveh, M., Taghinezhad, E., & Rasooli Sharabiani, V. (2020). Assessment of kinetics, effective moisture diffusivity, and specific energy consumption in the pistachio kernel drying process in microwave drying. Journal of Food Processing and Preservation, 44(6), e14449.

Jahedi Rad, S., Kaveh, M., Sharabiani, V. R., & Taghinezhad, E. (2018). Fuzzy logic, artificial neural network, and mathematical model for prediction of white mulberry drying kinetics. Heat and Mass Transfer, 54(11), 3361–3374.

Rasooli Sharabiani, V., Kaveh, M., Abdi, R., Szymanek, M., & Tanaś, W. (2021). Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling. Scientific Reports, 11(1), 9155.

Mr. Jing Zhang | Biomedical Signal Processing | Best Researcher Award

Mr. Jing Zhang | lecturer | Taiyuan University of Science and Technology | China

Jing Zhang is a dedicated researcher and lecturer at the School of Electronic Information Engineering, Taiyuan University of Science and Technology, China. His research primarily focuses on signal processing, emotion recognition, and video coding and transmission, with a strong interdisciplinary approach bridging neuroscience, artificial intelligence, and communication systems. His innovative work in multimodal neural signal analysis leverages EEG and fNIRS data to explore causal brain connectivity and emotional decoding. By integrating Granger causality with deep learning architectures such as convolutional and graph convolutional networks, as well as attention mechanisms, his research contributes significantly to affective computing and brain–computer interface (BCI) applications. Dr. Zhang has published several high-impact papers in reputed international journals indexed in SCI and Scopus, with over 75 citations and an h-index of 6 on Google Scholar, reflecting the growing influence and recognition of his work in the scientific community. His research outcomes demonstrate both theoretical and practical implications for advancing emotion-aware technologies, neuroadaptive systems, and hybrid video transmission models. His scholarly contributions include publications in prestigious journals such as IEEE Transactions on Circuits and Systems for Video Technology and Frontiers in Neuroscience.

Featured Publications

Zhang, J., Zhang, X., Chen, G., Huang, L., & Sun, Y. (2022). EEG emotion recognition based on cross-frequency Granger causality feature extraction and fusion in the left and right hemispheres. Frontiers in Neuroscience, 16, 974673.

Zhang, J., Wang, A., Liang, J., Wang, H., Li, S., & Zhang, X. (2018). Distortion estimation-based adaptive power allocation for hybrid digital–analog video transmission. IEEE Transactions on Circuits and Systems for Video Technology, 29(6), 1806–1818.

Zhang, J., Zhang, X., Chen, G., & Zhao, Q. (2022). Granger-causality-based multi-frequency band EEG graph feature extraction and fusion for emotion recognition. Brain Sciences, 12(12), 1649.

Chen, G., Zhang, X., Zhang, J., Li, F., & Duan, S. (2022). A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN. Frontiers in Neurorobotics, 16, 995552.

Li, P., Yang, F., Zhang, J., Guan, Y., Wang, A., & Liang, J. (2020). Synthesis-distortion-aware hybrid digital analog transmission for 3D videos. IEEE Access, 8, 85128–85139.