Dr. Partha Ghosh | AI in Engineering | Best Researcher Award

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Dr. Partha Ghosh | AI in Engineering | Best Researcher Award

Dr. Partha Ghosh | Netaji Subhash Engineering College | India

Dr. Partha Ghosh is a seasoned academic and researcher with more than 22 years of professional experience in Computer Science and Information Technology, currently serving as Associate Professor in the Department of Information Technology and Head of the Department of Computer Science and Business Systems at Netaji Subhash Engineering College, Kolkata. His research expertise spans Computer Networking, Machine Learning, Cloud Computing, Intrusion Detection Systems, Optimization Algorithms, Feature Selection and Classification Techniques, with a focus on developing secure, intelligent and high-performance cloud-based computational environments. His scholarly impact is reflected through 16 SCOPUS-indexed documents, 194 citations by 173 documents and an h-index of 7. Additionally, his ORCID profile lists 20 research works, and according to Google Scholar he has 333 citations (244 since 2020), an h-index of 10 (9 since 2020) and an i10-index of 10 (9 since 2020), demonstrating consistent and growing research visibility. To date, he has authored 24 publications including indexed journal papers, international conference papers and book chapters. He has taught a wide range of core and advanced courses such as Computer Organisation, Computer Networks, Advanced Computer Networking, Microprocessors and Microcontrollers and Database Management Systems at undergraduate and postgraduate levels. His academic engagement also includes serving as Editor-in-Chief and Editorial Board Member of reputed journals and holding multiple Fellow and Life Membership roles across professional bodies, underscoring his continued commitment to research innovation, knowledge dissemination and academic leadership.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Ghosh, P., Mandal, A. K., & Kumar, R. (2015). An efficient cloud network intrusion detection system. In Information Systems Design and Intelligent Applications: Proceedings of …

Ghosh, P., Karmakar, A., Sharma, J., & Phadikar, S. (2018). CS-PSO based intrusion detection system in cloud environment. In Emerging Technologies in Data Mining and Information Security: Proceedings …

Ghosh, P., & Mitra, R. (2015). Proposed GA-BFSS and logistic regression based intrusion detection system. In Proceedings of the 2015 Third International Conference on Computer …

Ghosh, P., Sarkar, D., Sharma, J., & Phadikar, S. (2021). An intrusion detection system using modified-firefly algorithm in cloud environment. International Journal of Digital Crime and Forensics, 13(2), 77–93.

Ghosh, P., Debnath, C., Metia, D., & Dutta, R. (2015). An efficient hybrid multilevel intrusion detection system in cloud environment. IOSR Journal of Computer Engineering, 16(4), 16–26.

Ghosh, P., Shakti, S., & Phadikar, S. (2016). A cloud intrusion detection system using novel PRFCM clustering and KNN based dempster-shafer rule. International Journal of Cloud Applications and Computing, 6(4), 18–35.

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.

 

 

Dr. Zhu Jingwen | AI in Engineering | Best Researcher Award

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Dr. Zhu Jingwen | AI in Engineering | Best Researcher Award

Dr. Zhu Jingwen | Jiangsu University | China

Dr. Zhu Jingwen is a researcher in control science and engineering whose work focuses on intelligent detection systems and advanced sensing technologies for agricultural safety. His research emphasizes the development of nondestructive testing methods for grains and edible oils through the integration of microwave, millimeter-wave, and near-infrared technologies with chemometric modeling and machine learning algorithms. Dr. Zhu has designed FPGA-based microwave detection systems capable of accurately identifying contaminants such as heavy metals and aflatoxins, contributing significantly to the field of food safety monitoring. His studies have been widely published in respected international journals, including Microchemical Journal, Sensors and Actuators A: Physical, and Spectrochimica Acta Part A. Beyond research, he has demonstrated leadership in innovation and entrepreneurship, leading projects recognized with national honors such as the China Postgraduate “Rural Revitalization – Sci-Tech Empowering Agriculture+” Competition. His scientific contributions are reflected through 13 published documents, cited 46 times by 41 other documents, demonstrating a growing academic impact and an h-index of 5. His efforts also led to the establishment of Dongfang Xiangyu (Jiangsu) Technology Co., Ltd., translating research outcomes into practical industrial applications. With a strong command of programming and embedded system development, Dr. Zhu continues to explore interdisciplinary approaches that merge intelligent algorithms with hardware systems to advance the precision and reliability of agricultural quality assessment technologies.

Profile: Scopus

Featured Publications

Zhu, J., Deng, J., Zhao, X., Xu, L., & Jiang, H. (2024). Quantitative determination of cadmium content in peanut oil using microwave detection method combined with multivariate analysis. Microchemical Journal, 110946.

Zhu, J., Deng, J., Zhao, X., Xu, L., & Jiang, H. (2024). Accurate identification of cadmium pollution in peanut oil using microwave technology combined with SVM-RFE. Sensors and Actuators A: Physical, 368, 115085.

Zhu, J., Chen, Y., Deng, J., & Jiang, H. (2024). Improving the accuracy of FT-NIR determination of zearalenone content in wheat using a characteristic wavelength optimization algorithm. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 313, 124169.

Ji, Z., Zhu, J., Deng, J., Jiang, H., & Chen, Q. (2024). Quantitative determination of zearalenone in wheat by the CSA-NIR technique combined with chemometric algorithms. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 323, 124858.

Zhu, J., Deng, J., Xu, L., & Jiang, H. (2024). Enhancing the performance of natural pigment sensor arrays for the detection of Procymidone residues in Allium tuberosum using outcome-corrected decision-making method. Journal of Food Composition and Analysis, 128, 107059.