Raziyeh Pourdarbani | Artificial Intelligence | Best Paper Award

Published on by Computer Science Engineering

Prof. Raziyeh Pourdarbani | Artificial Intelligence | Best Paper Award

Faculty Membr | University of Mohaghegh Ardabili | Iran

Dr. Raziyeh Pourdarbani is a Professor of Biosystems Engineering at the University of Mohaghegh Ardabili and an internationally recognized researcher in precision agriculture, image processing, machine vision, artificial intelligence, and hyperspectral imaging. Her research is dedicated to developing advanced computational approaches that enhance automation, sustainability, and non-destructive assessment within agricultural production systems. She has established a strong scholarly footprint through extensive publications that explore cutting-edge deep learning architectures, including the application of 2D and 3D convolutional neural networks, majority voting ensemble strategies, hybrid neural networks, and metaheuristic optimization techniques for quality evaluation and decision-making in crop and fruit management. Her studies have significantly advanced non-destructive methodologies for detecting bruises, internal defects, and ripening stages in fruits, as well as monitoring excessive nitrogen consumption and estimating chemical and physicochemical properties in plant leaves using hyperspectral, visible, and near-infrared spectral data. In addition to agricultural sensing and classification research, she has contributed impactful work on sustainable bioenergy, including biomethane production from agricultural residues, biodiesel engine performance enhancement using nanomaterials, and advanced exergy and life-cycle analysis of hybrid geothermal–solar power systems. She has authored multiple academic books addressing renewable energy and intelligent grading technologies and has led numerous research projects involving automated fruit identification algorithms, orchard-based robotic systems, video-based fruit maturity estimation, spectral wavelength optimization, agricultural development modeling, and geothermal heating-system design. Dr. Pourdarbani actively disseminates her findings through national and international conferences and contributes to the scientific community through reviewing and collaborative roles in multidisciplinary research initiatives. Her work is widely acknowledged for its scientific value and practical relevance in improving agricultural resource efficiency, enhancing food-quality monitoring, and promoting environmentally responsible production strategies. As a leading figure in the integration of computational intelligence with agricultural engineering, she continues to shape research directions that support global progress toward smart, sustainable, and technologically empowered agriculture.

Profile : Google Scholar

Featured Publication

Alibaba, M., Pourdarbani, R., Manesh, M. H. K., Ochoa, G. V., & Forero, J. D. (2020). Thermodynamic, exergo-economic and exergo-environmental analysis of hybrid geothermal–solar power plant based on ORC cycle using emergy concept. Heliyon, 6(4).

Pourdarbani, R., Sabzi, S., Kalantari, D., Hernández-Hernández, J. L., & Arribas, J. I. (2019). A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties.

Pourdarbani, R., Sabzi, S., García-Amicis, V. M., García-Mateos, G., Hernández-Hernández, J. L., & Arribas, J. I. (2019). Automatic classification of chickpea varieties using computer vision techniques. Agronomy, 9(11), 672.

Ebrahimi, S., Pourdarbani, R., Sabzi, S., Rohban, M. H., & Arribas, J. I. (2023). From harvest to market: Non-destructive bruise detection in kiwifruit using convolutional neural networks and hyperspectral imaging. Horticulturae, 9(8), 936.

Pourdarbani, R., Sabzi, S., Rohban, M. H., Hernández-Hernández, J. L., & Arribas, J. I. (2021). One-dimensional convolutional neural networks for hyperspectral analysis of nitrogen in plant leaves. Applied Sciences, 11(24), 11853

Muhammad Asif Munir | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Mr. Muhammad Asif Munir | Machine Learning | Best Researcher Award

Assistant Professor| Swedish College of Engineering and Technology | Pakistan

Dr. Muhammad Asif Munir is an accomplished researcher and academic in the field of Electrical Engineering, currently serving as an Assistant Professor at the Swedish College of Engineering and Technology, District Rahim Yar Khan, Punjab, Pakistan, and pursuing his Ph.D. at The Islamia University of Bahawalpur. His research primarily focuses on machine learning and deep learning applications in biomedical image analysis, with a particular emphasis on addressing the challenges of small and imbalanced radiomics datasets. With six peer-reviewed publications indexed in SCI and Scopus journals, including IEEE Access and Future Internet (MDPI), and a growing citation record of 56 citations (h-index: 4, i10-index: 2), Dr. Munir has demonstrated consistent academic excellence and research innovation. His notable contribution, the GSRA-KL framework, introduces a novel sparse regularized autoencoder–based methodology that significantly enhances synthetic data generation and improves the predictive accuracy of gene mutation analysis in lung cancer radiomics. This work not only contributes to the evolution of precision oncology but also exemplifies the integration of AI-driven data synthesis with clinical applications. His ongoing research explores the incorporation of explainable artificial intelligence (XAI) into radiomics for more interpretable, transparent, and reliable predictive modeling, fostering clinically explainable AI systems in healthcare. Dr. Munir’s interdisciplinary approach bridges data science, medical imaging, and clinical decision support, aiming to make AI tools both scientifically robust and ethically transparent. A member of professional organizations such as IEEE and IAENG, he remains actively engaged in promoting research collaboration and advancing the global discourse on intelligent healthcare systems. Through his scholarly contributions, Dr. Munir is significantly impacting the development of data-efficient, interpretable, and patient-centered AI frameworks, reinforcing the global transition toward smart healthcare technologies and next-generation precision medicine. His commitment to research excellence and translational impact continues to position him as a promising figure in the convergence of engineering and medical AI research.

Featured Publication

Aslam, M. A., Munir, M. A., & Cui, D. (2020). Noise removal from medical images using hybrid filters of technique. Journal of Physics: Conference Series, 1518(1), 012061.

Aslam, M. A., Xue, C., Wang, K., Chen, Y., Zhang, A., Cai, W., Ma, L., Yang, Y., Sun, X., & Munir, M. A. (2020). SVM based classification and prediction system for gastric cancer using dominant features of saliva. Nano Biomedicine and Engineering, 12(1), 1–13.

Munir, M. A., Aslam, M. A., Shafique, M., Ahmed, R., & Mehmood, Z. (2022). Deep stacked sparse autoencoders – A breast cancer classifier. Mehran University Research Journal of Engineering and Technology, 41(1), 41–52.

Aslam, M. A., Munir, M. A., Ahmad, R., Samiullah, M., Hassan, N. M., & Mahnoor, S. (2022). Deep neural networks for prediction of cardiovascular diseases. Nano Biomedicine and Engineering, 14(1).

Munir, M. A., Shah, R. A., Ali, M., Laghari, A. A., Almadhor, A., & Gadekallu, T. R. (2024). Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis. IEEE Access.

Dr. Muhammad Asif Munir’s research advances intelligent healthcare by integrating machine learning and explainable AI to enhance diagnostic accuracy and transparency in medical imaging. His innovations in radiomics and synthetic data generation foster data-efficient, interpretable, and globally applicable solutions that strengthen precision oncology and next-generation healthcare systems.

Sirous Rafiei Asl | Computer Vision | Best Researcher Award

Published on by Computer Science Engineering

Dr. Sirous Rafiei Asl | Computer Vision | Best Researcher Award

Medical Student | Ahvaz Jundishapur University of Medical Sciences | Iran

Dr. Safa Najafi’s research focuses on the intersection of medical education and parasitology, with particular attention to Leishmaniasis and other parasitic diseases prevalent in tropical and subtropical regions. Her work emphasizes evaluating medical students’ knowledge, awareness, and performance toward parasitic infections to identify gaps that hinder effective disease prevention and control. Through descriptive and analytical studies, she explores the relationship between demographic factors, clinical exposure, and academic performance in shaping medical students’ understanding of zoonotic diseases such as Leishmania infections. The findings of her research highlight that enhanced awareness and practical performance among future healthcare professionals play a critical role in public health preparedness and vector control strategies. Safa Najafi also investigates behavioral and environmental determinants of disease transmission and advocates for integrating targeted educational programs, including mobile-based learning and seminar-based interventions, into medical curricula to strengthen clinical competencies and promote early prevention. Her studies contribute to developing evidence-based strategies to reduce leishmaniasis transmission by bridging the gap between theoretical knowledge and field application. By analyzing key epidemiological factors, her research supports the design of culturally relevant training programs that empower medical students and healthcare providers to adopt preventive practices effectively. This work aligns with broader goals in global health to mitigate the burden of parasitic diseases through informed medical practice and community education. Overall, her research advances understanding of how educational approaches can shape health behavior and influence disease outcomes, reinforcing the significance of awareness, attitudes, and practices in sustainable disease management. Safa Najafi’s scholarly contributions are reflected in her academic record, with 2 Citations, 3 Documents, and an h-index of 1. View h-index.

Profiles: Google Scholar |  Scopus | ORCID
Featured Publication

Elahi, R. K., Asl, S., & Shahian, F. (2013). Study on the effects of various doses of Tribulus terrestris extract on epididymal sperm morphology and count in rat. Iranian Journal of Reproductive Medicine, 11(3), 207–212. Citations: 46

Mahdavinia, M., Alizadeh, S., Vanani, A. R., Dehghani, M. A., Shirani, M., et al. (2019). Effects of quercetin on bisphenol A-induced mitochondrial toxicity in rat liver. Iranian Journal of Basic Medical Sciences, 22(5), 499. Citations: 18

Moradi, M., Montazeri, E. A., Rafiei Asl, S., Pormohammad, A., Farshadzadeh, Z., et al. (2025). In vitro and in vivo antibacterial and antibiofilm activity of zinc sulfate (ZnSO₄) and carvacrol (CV) alone and in combination with antibiotics against Pseudomonas aeruginosa. Antibiotics, 14(4), 367. Citations: 5

Rafiei-Asl, S., Gh., K., Jalali, S. M., Jamshidian, J., & Rezaie, A. (2021). Protective effects of bromelain against cadmium-induced pulmonary intoxication in rats: A histopathologic and cytologic study. Archives of Razi Institute, 76(5), 1427–1436. Citations: 3

Rafiei-Asl, S., Khadjeh, G., Jalali, S. M., Jamshidian, J., & Rezaie, A. (2020). Investigating the protective effects of bromelain against inflammatory marker alterations induced by cadmium pulmonary intoxication in rat. Iranian Veterinary Journal, 16(2), 75–88. Citations: 3