Prof. Dr. Raziyeh Pourdarbani | AI in Engineering | Research Excellence Award

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Prof. Dr. Raziyeh Pourdarbani | AI in Engineering | Research Excellence Award

Prof. Dr. Raziyeh Pourdarbani | University of Mohaghegh Ardabili | Iran

Prof. Dr. Raziyeh Pourdarbani is a distinguished professor in the Department of Biosystems Engineering at the University of Mohaghegh Ardabili, highly regarded for her academic and research contributions in smart and sustainable agriculture. She holds a Ph.D. in Agricultural Mechanization Engineering from the University of Tabriz and has developed deep expertise in precision agriculture, image processing, artificial intelligence, and machine vision with a focus on non-destructive quality evaluation of agricultural products. Her work advances the use of hyperspectral imaging, convolutional neural networks, metaheuristic algorithms, and Vis-NIR spectroscopy to address key challenges such as fruit bruise detection, nitrogen stress monitoring in plant leaves, and estimation of internal chemical properties in horticultural crops. She has also contributed impactful studies on sustainable energy systems related to agriculture, including biomethane production, hybrid geothermal–solar power plant optimization, and exergy-based diesel engine performance enhancement. Her research portfolio consists of 45 scientific documents with 762 citations from 639 citing documents, supported by an h-index of 17, demonstrating strong global visibility and scholarly influence. Through her innovative work integrating computational intelligence with biosystems engineering, she plays a leading role in advancing intelligent agriculture technologies that enhance productivity, reduce environmental impacts, and support long-term sustainability in the agricultural sector.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

  • Pourdarbani, R., Ghassemzadeh, H. R., Seyedarabi, H., Nahandi, F. Z., & others. (2015). Study on an automatic sorting system for Date fruits. Journal of the Saudi Society of Agricultural Sciences, 14(1), 83-90.

  • 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., Karimzadeh, R., Ilbeygi, E., & Arribas, J. I. (2020). Automatic non-destructive video estimation of maturation levels in Fuji apple (Malus pumila) fruit in orchard based on colour (Vis) and spectral (NIR) data. Biosystems Engineering, 195, 136-151.

  • Pourdarbani, R., Sabzi, S., Kalantari, D., & Arribas, J. I. (2020). Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different stages. Chemometrics and Intelligent Laboratory Systems, 206, 104147.

  • Razieh Pourdarbani, D. K. J. M. M. M., Sabzi, S., Hernández-Hernández, M., & José Luis … (2019). Comparison of different classifiers and the majority voting rule for the detection of plum fruits in garden conditions. Remote Sensing, 11(2546).

  • Salimi, M., Pourdarbani, R., & Nouri, B. A. (2020). Factors affecting the adoption of agricultural automation using Davis’s acceptance model (case study: Ardabil). Acta Technologica Agriculturae, 23(1), 30-39.

 

 

Ms. Asima Sarwar | AI in Engineering | Best Researcher Award

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Ms. Asima Sarwar | AI in Engineering | Best Researcher Award

Ms. Asima Sarwar | Ghulam Ishaq Khan Institute of Engineering Sciences and Technology | Pakistan

Ms. Asima Sarwar is a computer engineer and researcher with expertise in Artificial Intelligence, Data Engineering, and Machine Learning. She is pursuing a PhD in Computer Engineering with research focused on AI, data analytics, and distributed computing systems. Her academic background includes a master’s degree in Computer Systems Engineering, specializing in Smart Grids and the Internet of Things, and a bachelor’s degree in Electrical Engineering (Communication). She has professional experience as a computer engineer, research assistant, and lecturer, contributing to projects in secure IoT device development, cyber-secure systems, and AI-based data processing. Ms. Asima has taught various undergraduate and postgraduate courses including Big Data Analytics, Machine Learning, Generative AI, Operating Systems, and Ethical AI. Her work emphasizes technical innovation, algorithmic optimization, and the integration of intelligent systems for real-world applications. With strong analytical and problem-solving skills, she is actively involved in advancing research in AI-driven technologies, data engineering, and computer vision. Her contributions reflect a balance between academic rigor, applied research, and technological development aimed at improving system efficiency and advancing modern computing solutions.

Profile: Scopus

Featured Publications

  • Sarwar, A., Usman, M., Hussain, M., Jadoon, K. K., Manzoor, T., & Ali, S. (2025). AI-powered deep ultraviolet laser diode design for resource-efficient optimization. Journal of Computational Electronics, 24(4), 1–19.

  • Mahmood, M. A., Maab, I., Sibtain, M., Sarwar, A., Arsalan, M., & Hussain, M. (2025, March). Advancements in sentiment analysis: A methodological examination of news using multiple LLMs. In Proceedings of the 31st Annual Meeting of the Association for Natural Language Processing.

  • Sarwar, A., Khan, W. U., Marwat, S. N. K., & Ahmed, S. (2022). Enhanced anomaly detection system for IoT based on improved dynamic SBPSO. Sensors MDPI, 22(4926).

  • Sarwar, A., Hassan, S., Khan, W. U., Marwat, S. N. K., & Ahmed, S. (2022). Design of an advance intrusion detection system for IoT networks. In Proceedings of the 2nd International Conference on Artificial Intelligence (ICAI) (pp. 46–51).

  • Ijaz, A. Z., Ali, R. H., Sarwar, A., Khan, T. A., & Baig, M. M. (2022). Importance of synteny in homology inference. In Proceedings of the IEEE International Conference on Emerging Technologies (ICET).

  • Azam, T., Tahir, F. A., Sarwar, A., & Qayyum, M. A. (2023). A high gain wide band compact size dual band patch antenna for 5G application. In Proceedings of the IEEE International Conference on Emerging and Sustainable Technologies (ICEST) (pp. 1–3).

 

 

Dr. S. Thirunavukkarasu | AI in Engineering | Best Researcher Award

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Dr. S. Thirunavukkarasu | AI in Engineering | Best Researcher Award

Dr. S. Thirunavukkarasu | Indira Gandhi Centre for Atomic Research | India

Dr. S. Thirunavukkarasu research focuses on quantitative nondestructive evaluation (NDE), finite element (FE) modeling, digital signal and image processing, and the development of innovative sensors and instrumentation for advanced inspection applications. His work emphasizes multi-parametric linear and nonlinear regression, radial basis function (RBF), and multidimensional RBF neural networks for accurate flaw sizing in eddy current testing. He has contributed to FE modeling of electromagnetic NDE phenomena, including the optimization of remote field eddy current probe parameters for ferromagnetic steam generator tube inspections and modeling of magnetic flux leakage considering nonlinear magnetic permeability. His studies extend to the simulation of pulsed and sweep frequency eddy current methods to improve detection efficiency. Additionally, his research in wavelet transform–based digital signal processing enhances the interpretation of eddy current signals from complex regions such as bends and support plate intersections. He has also advanced in-house development of remote field eddy current techniques for the inspection of modified 9Cr-1Mo steel steam generator tubes. His computational expertise includes MATLAB, Python, and LabVIEW, alongside specialized software such as COMSOL, FEMM, and CIVA for modeling and simulation in electromagnetic and NDE applications.

Profile: Orcid

Featured Publications

Arun, A. D., Rajiniganth, M. P., Chandra, S., & Thirunavukkarasu, S. (2025). A numerical model of parallel disc capacitor probe used in nondestructive dielectric permittivity evaluation by algebraic topological method. International Journal of Applied Electromagnetics and Mechanics, 2025-09.

Sharatchandra Singh, W., Haneef, T. K., Thirunavukkarasu, S., & Kumar, A. (2025). In-situ measurement of tensile deformation-induced magnetic fields in high strength low alloy steels using GMR based metal magnetic memory technique. International Journal of Applied Electromagnetics and Mechanics, 2025-09-10.

Arun, A. D., Chandra, S., Thirunavukkarasu, S., Rajiniganth, M. P., Malathi, N., & Sivaramakrishna, M. (2025). A novel algebraic topological method-based approach for evaluating stored electrostatic energy and 3D Maxwellian capacitance. Journal of Electrostatics, 2025-06.

Thirunavukkarasu, S., Kumar, A., Martin, J. P., Harini, T., Reddy, S., Emil, S., & Balu, C. (2025). Automated detection of defects in eddy current inspection data using machine learning methods. International Journal of Applied Electromagnetics and Mechanics, 2025-06-03.

Balakrishnan, S., Das, C. R., Thirunavukkarasu, S., & Kumar, A. (2025). In-situ hardness evaluation of hard-faced coatings through eddy current NDE. International Journal of Applied Electromagnetics and Mechanics, 2025-05-23.

Vijayachandrika, T., Arjun, V., Thirunavukkarasu, S., & Kumar, A. (2025). Design, fabrication, and characterization of staggered array radial coil RFEC probe for small diameter ferritic steel tube. IEEE Sensors Journal, 2025-05-01.