Yiran Feng | Machine Learning | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Yiran Feng
Dalian Polytechnic University

Yiran Feng
Affiliation Dalian Polytechnic University
Country China
Scopus ID 57195510624
Documents 18
Citations 96
h-index 5
Subject Area Machine Learning
Event Global CSE Awards
ORCID 0000-0003-3968-051X

The Innovative Research Award recognizes scholarly achievements that contribute to the advancement of scientific knowledge and technological innovation. This article presents an academic overview of Yiran Feng, a researcher affiliated with Dalian Polytechnic University, whose work in machine learning demonstrates engagement with contemporary computational methodologies and interdisciplinary research applications. The profile summarizes research activities, publication contributions, scholarly influence, and suitability for recognition within the framework of the Global CSE Awards.[1]

Abstract

Yiran Feng is a researcher associated with Dalian Polytechnic University whose academic activities are situated within the field of machine learning. Through published scholarly work, Feng has contributed to computational research involving data-driven methods, intelligent systems, and analytical modeling approaches. The researcher’s publication record, citation performance, and documented scholarly output indicate active participation in contemporary scientific inquiry. This profile evaluates research accomplishments, academic influence, publication contributions, and broader relevance to innovation-oriented research recognition programs. The assessment further examines the suitability of the candidate for the Innovative Research Award under the Global CSE Awards framework.[1][2]

Keywords

Machine Learning, Artificial Intelligence, Computational Intelligence, Data Analytics, Research Innovation, Scientific Publications, Scholarly Impact, Academic Recognition.

Introduction

Machine learning continues to influence scientific research by enabling automated analysis, predictive modeling, and intelligent decision-making across multiple disciplines. Researchers working in this area contribute to technological advancement through algorithm development, data interpretation, and applied computational solutions. Yiran Feng’s academic activities align with these objectives and reflect participation in modern research efforts that support innovation and knowledge generation.[2]

Research Profile

Yiran Feng is affiliated with Dalian Polytechnic University in China and has established a documented publication record indexed within recognized scholarly databases. The researcher has produced 18 indexed documents and accumulated 96 citations, resulting in an h-index of 5. These indicators reflect measurable scholarly engagement and participation in ongoing scientific discourse within the machine learning community.[1]

Research Contributions

The research contributions associated with Yiran Feng emphasize machine learning methodologies and computational analysis. Published studies demonstrate engagement with data-centric approaches designed to improve prediction accuracy, automation capabilities, and intelligent decision-support systems. These contributions support broader scientific efforts to develop scalable and adaptable computational frameworks suitable for real-world applications and interdisciplinary research environments.[3]

  • Application of machine learning techniques to analytical challenges.
  • Development of computational models for intelligent systems.
  • Contribution to data-driven scientific investigations.
  • Support for interdisciplinary innovation through algorithmic research.

Publications

The publication portfolio reflects consistent scholarly activity within machine learning and related computational domains. Indexed articles contribute to academic discussions concerning intelligent algorithms, predictive analysis, and advanced data processing methodologies. Publication metrics suggest that the research has achieved visibility within the scientific community and has generated measurable citation-based engagement.[1][4]

Research Impact

Research impact may be assessed through publication productivity, citation performance, and contribution to emerging scientific fields. The documented citation count demonstrates that the published work has received scholarly attention and has been referenced by other researchers. Such engagement indicates relevance within ongoing research discussions and supports the dissemination of knowledge within machine learning and computational science communities.[1][5]

Award Suitability

The Innovative Research Award seeks to recognize individuals whose scholarly efforts contribute to scientific advancement and innovation. Based on available publication metrics, research specialization, citation record, and demonstrated engagement with machine learning research, Yiran Feng exhibits characteristics consistent with the objectives of the award. The researcher’s documented achievements reflect meaningful participation in knowledge creation and technological development within a rapidly evolving academic field.[1][5]

Conclusion

Yiran Feng’s research profile reflects active scholarly participation in machine learning and computational research. Through documented publications, citation impact, and engagement with contemporary scientific challenges, the researcher contributes to ongoing advancements in intelligent systems and data-driven methodologies. These achievements support consideration for academic recognition initiatives focused on innovation, research excellence, and scientific contribution.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Yiran Feng, Author ID 57195510624. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57195510624
  2. Yiran Feng, Xueheng Tao, Eung-Joo Lee. (2021). Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning. Wiley Online Library.
    https://doi.org/10.1155/2021/1966848
  3. Xindan Zhang,YiRan Feng,Xu Zhang,Jinshi Lu &Xueheng Tao. (2018). Numerical simulation of solid–liquid two-phase flow field for shellfish precooking processing machine. Taylor & Francis.
    https://doi.org/10.1080/14484846.2018.1468234
  4. Weizi Lu, Maojun Zhou, Yiran Feng. (2022). Research and development of folding bathing bed for the elderly driven by civil water. International Conference on Artificial Intelligence and Advanced Manufacture.
    https://doi.org/10.1145/3495018.3501105
  5. Global CSE Awards. (n.d.). Innovative Research Award Evaluation Framework.
    https://cseawards.com/

Aizihaierjiang Yusufu | Artificial Intelligence | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Aizihaierjiang Yusufu
Xinjiang Normal University, China

Aizihaierjiang Yusufu
Affiliation Xinjiang Normal University
Country China
Scopus ID 58660267400
Documents 4
Citations 30
h-index 3
Subject Area Artificial Intelligence
Event Global CSE Awards
Google Scholar fL7v0koAAAAJ&hl

The Innovative Research Award recognizes emerging scholarly contributions that advance scientific understanding and technological innovation. Aizihaierjiang Yusufu has developed research activities in artificial intelligence and natural language processing, particularly focusing on sentiment analysis, language understanding, and computational methods for multilingual environments. The available publication record demonstrates engagement with contemporary AI methodologies and their application to challenging linguistic datasets. Research outputs and citation indicators suggest growing academic visibility within specialized areas of artificial intelligence research.[1]

Abstract

Aizihaierjiang Yusufu is a researcher associated with artificial intelligence and natural language processing, with particular emphasis on sentiment analysis and multilingual language technologies. His scholarly work investigates advanced machine learning approaches for extracting opinions, aspects, and semantic relationships from textual data. Available publications demonstrate engagement with neural architectures, attention mechanisms, and computational linguistic frameworks designed to improve analytical performance across underrepresented languages. Citation activity and documented research outputs indicate growing academic recognition within specialized AI domains. These achievements collectively support consideration for the Innovative Research Award in recognition of emerging scholarly contributions and measurable research influence.[1]

Keywords

Artificial Intelligence, Natural Language Processing, Sentiment Analysis, Machine Learning, Aspect-Based Sentiment Analysis, Neural Networks, Computational Linguistics, Deep Learning, Multilingual Computing, Text Analytics.

Introduction

Artificial intelligence continues to transform modern research through advanced data-driven methodologies. Within this landscape, natural language processing has emerged as a critical discipline for interpreting textual information and supporting intelligent decision-making systems. Researchers working in multilingual and low-resource language environments contribute significantly to expanding the inclusiveness and applicability of AI technologies across diverse linguistic communities.[2]

Research Profile

The research profile of Aizihaierjiang Yusufu centers on computational language analysis, sentiment classification, and machine learning applications. His work addresses challenges associated with extracting meaningful insights from textual datasets and improving performance through modern neural architectures. Available publication metrics indicate an active contribution to AI-focused scholarly research and interdisciplinary computational studies.[1]

Research Contributions

  • Development of sentiment analysis methodologies for multilingual textual datasets.
  • Application of biaffine attention mechanisms for enhanced aspect extraction and classification.
  • Research supporting computational processing of underrepresented languages.
  • Integration of deep learning techniques into natural language understanding frameworks.

These contributions reflect a consistent focus on improving language intelligence systems and expanding analytical capabilities within natural language processing research.[2]

Publications

  • Enhanced UrduAspectNet: Leveraging Biaffine Attention for Superior Aspect-Based Sentiment Analysis.DOI:https://doi.org/10.1016/j.jksuci.2024.102221
  • Research contributions involving natural language processing, sentiment analytics, and machine learning methodologies documented through indexed scholarly publications.

Research Impact

The documented citation count, publication activity, and h-index indicate measurable scholarly engagement. Research outcomes contribute to ongoing developments in sentiment analysis and computational linguistics. By addressing language-specific challenges and employing contemporary neural approaches, the work supports broader efforts toward inclusive and scalable artificial intelligence systems for multilingual applications.[1]

Award Suitability

Based on available scholarly indicators, Aizihaierjiang Yusufu demonstrates characteristics aligned with the objectives of the Innovative Research Award. Relevant factors include research activity in artificial intelligence, publication of peer-reviewed work, citation-based evidence of academic visibility, and contributions addressing practical challenges in language technology. These attributes support recognition as an emerging researcher contributing to advancements in natural language processing and computational intelligence.[1]

Conclusion

Aizihaierjiang Yusufu has established an emerging research presence within artificial intelligence and natural language processing. His work on sentiment analysis, machine learning, and multilingual computational methods demonstrates technical relevance and scholarly value. Considering available publication metrics, citation performance, and documented research outputs, the profile reflects a meaningful contribution to contemporary AI research and provides a credible basis for consideration within the Global CSE Awards Innovative Research Award category.

References

  1. Elsevier. (n.d.). Scopus author details: Aizihaierjiang Yusufu, Author ID 58660267400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58660267400
  2. Aziz, K., Ahmed, N., Hadi, H.J., Yusufu, A., et al. (2024). Uzbek news corpus for named entity recognition.
    https://doi.org/10.1007/s10579-024-09786-0
  3. Google Scholar. (n.d.). Scholar Profile of Aizihaierjiang Yusufu.
    https://scholar.google.com/citations?user=fL7v0koAAAAJ&hl=en

Yuan Xiaolin | Machine Learning | Editorial Board Member

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Dr. Yuan Xiaolin | Machine Learning | Editorial Board Member

Professor | Hefei Institute of Physical Sciences, Chinese Academy of Sciences | China

Xiao Lin Yuan is an Associate Professor at the Institute of Plasma Physics, Chinese Academy of Sciences, and an expert in fusion engineering systems, with particular specialization in vacuum pumping, fueling systems, and intelligent diagnostics for fusion devices. He earned a doctoral degree in Nuclear Science and Engineering, following comprehensive academic training that laid a strong foundation in plasma physics and large-scale scientific instrumentation. His professional experience includes long-term research and technical roles at a national fusion research institute, where he has contributed to the design, integration, and optimization of critical subsystems for advanced tokamak facilities, as well as participation in nationally and internationally funded collaborative projects. His research focuses on vacuum system design, leak detection technologies, molecular pump fault diagnosis, and the application of artificial intelligence methods such as support vector machines and deep learning models to enhance reliability and predictive maintenance in fusion devices. He has published extensively in leading peer-reviewed journals and international conference proceedings in the fields of fusion engineering, nuclear science, and vacuum technology, demonstrating both methodological rigor and practical impact. Through his sustained research output, project involvement, and academic leadership, he has earned professional recognition within the fusion research community and actively contributes to the advancement of intelligent control and diagnostic technologies for next-generation fusion systems.

Profile : ORCID

Featured Publications

Yuan, X.-L., Chen, Y., Hu, J.-S., et al. (2016). Development and implementation of flowing liquid lithium limiter control system for EAST. Fusion Engineering and Design, 112, 332–337.

Yuan, X.-L., Chen, Y., Hu, J.-S., et al. (2018). 10 Hz pellet injection control system integration for EAST. Fusion Engineering and Design, 126, 130–138.

Yuan, X.-L., Chen, Y., et al. (2018). Development and implementation of supersonic molecular beam injection for EAST tokamak. Fusion Engineering and Design, 134, 62–67.

Yuan, X.-L., Chen, Y., et al. (2023). A support vector machine framework for fault detection in molecular pump. Journal of Nuclear Science and Technology, 60, 72–82.

Zhou, Y., Jiang, M., Yuan, X.-L., et al. (2024). Fault prediction of molecular pump based on DE-Bi-LSTM. Fusion Science and Technology, 80, 1001–1011.

Raziyeh Pourdarbani | Artificial Intelligence | Best Paper Award

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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

Niti Kant | Computational Theory | Best Researcher Award

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Prof. Dr. Niti Kant | Computational Theory | Best Researcher Award

Professor | University of Allahabad | India

Prof. Dr. Niti Kant is a distinguished physicist currently serving in the Department of Physics, University of Allahabad, Prayagraj, India. With a Ph.D. from the Indian Institute of Technology (IIT) Delhi (2005) under the supervision of Dr. A. K. Sharma, his research focuses on laser–plasma interaction, self-focusing of lasers, harmonic generation, laser-induced electron acceleration, and terahertz (THz) radiation generation. Over the past two decades, Dr. Kant has made significant contributions to theoretical plasma physics, employing advanced analytical and numerical modeling approaches using Mathematica and Origin. He has published over 150 research papers in reputed international journals indexed by SCI, earning an H-index of 33 on Google Scholar, reflecting the global impact of his research. His academic journey includes postdoctoral research at POSTECH, South Korea, and academic leadership at Lovely Professional University, Punjab, where he served as Professor before joining the University of Allahabad. Dr. Kant has successfully led several sponsored research projects funded by CSIR, SERB, and DST, totaling over ₹50 lakhs, and has guided more than ten Ph.D. scholars in cutting-edge areas such as THz generation, nonlinear optics, and high-power laser–matter interaction. A life member of several prestigious scientific societies, including the Indian Science Congress Association, Optical Society of India, and Plasma Science Society of India, he also serves on editorial and review boards of international journals and as a peer reviewer for top publishers like Elsevier, IOP, and AIP. His work has been recognized with multiple honors, including the Merit Award (2024) by the University of Allahabad, Research Excellence Awards (2020, 2021), and the Outstanding Scientist Award (2020). With active international collaborations across the UK, Czech Republic, South Korea, and the USA, Dr. Kant’s research continues to advance the frontiers of laser–plasma physics, contributing to innovations in photonics, clean energy, and applied plasma technologies with profound implications for scientific and technological progress.

Featured Publication

Kamboj, O., Azad, T., Rajput, J., & Kant, N. (2025). The effect of density ramp on self-focusing of q-Gaussian laser beam in magnetized plasma. Journal of Optics (India). Citations: 2

Azad, T., Kant, N., & Kamboj, O. (2025). Efficient THz generation by Hermite–cosh–Gaussian lasers in plasma with slanting density modulation. Journal of Optics (India). Citations: 23

Singh, J., Kumar, S., Kant, N., & Rajput, J. (2025). Effect of frequency-chirped ionization laser on accelerated electron beam characteristics in plasma wakefield acceleration. European Physical Journal Plus. Citations: 1

Anshal, L., Kant, N., Azad, T., Rajput, J., & Kamboj, O. (2025). Propagation of Hermite–cosh–Gaussian laser beam in free-electron laser device under upward plasma density ramp. Laser Physics Letters. Citations: 1

Azad, T., Kant, N., & Kamboj, O. (2025). Enhanced third harmonic generation and SRS suppression in magnetized rippled plasma using Hermite cosh–Gaussian laser beam. Journal of Optics (India). Citations: 2

Prof. Dr. Niti Kant’s pioneering research in laser–plasma interaction, nonlinear optics, and terahertz generation has advanced the understanding of high-power laser applications, enabling innovations in photonics, clean energy, and next-generation communication technologies. His work bridges fundamental physics with practical technologies, fostering global scientific collaboration and contributing to sustainable technological progress.

Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

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Dr. Christian Peluso | Artificial Intelligence | Excellence in Ethical AI Development Award

Libero professionista | Consiglio Nazionale delle RicercheThis link is disabled | Italy

Dr. Christian Peluso is a researcher specializing in artificial intelligence with expertise in federated learning, deep learning, and cybersecurity, focusing on privacy-preserving systems for mobile and distributed environments. His research aims to develop intelligent models capable of processing complex and varied data while safeguarding user privacy and ensuring compliance with data protection regulations. Christian earned his Master’s degree in Artificial Intelligence from the University of Pisa with the highest distinction, presenting a thesis titled PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection, which introduced an innovative federated learning approach for mobile malware analysis using convolutional neural networks optimized for image-based data. He has actively collaborated with the Consiglio Nazionale delle Ricerche (CNR) and several academic and research institutions, contributing to projects that merge AI, cybersecurity, and data privacy. His publications, including “PrivNet: Advancing Mobile Security through Privacy-Preserving Federated Learning for Malware Detection” and “An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning,” reflect his deep involvement in advancing secure and decentralized AI solutions. He has also contributed to research on explainability-driven malware analysis using deep learning, aimed at improving model interpretability and aiding analysts in identifying malicious software components efficiently. Christian’s technical proficiency covers Python, machine learning frameworks, and reverse engineering methodologies, enabling him to design intelligent systems with strong analytical and practical impact. His academic achievements and professional experiences in software engineering, mobile application security, and AI-driven analysis demonstrate a consistent pursuit of excellence and innovation. His work not only strengthens theoretical understanding in federated machine learning but also delivers practical tools for protecting digital ecosystems. Through his commitment to research, collaboration, and ethical AI development, he continues to contribute meaningfully to the evolving landscape of artificial intelligence and data security. 17 Citations 3 Documents 2 h-index View h-index

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Iadarola, G., Casolare, R., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2021). A semi-automated explainability-driven approach for malware analysis through deep learning. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. Cited by: 19

Ciaramella, G., Martinelli, F., Mercaldo, F., Peluso, C., & Santone, A. (2024). An approach for privacy-preserving mobile malware detection through federated machine learning. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024). SciTePress.
Cited by: 5

Peluso, C., Ciaramella, G., Mercaldo, F., Santone, A., & Martinelli, F. (2024). A federated learning-based Android malware detector through differential privacy. In International Conference on Computer Aided Systems Theory (EUROCAST 2024) (pp. 307–319).

Sirous Rafiei Asl | Computer Vision | Best Researcher Award

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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

Ye Tao | Machine Learning | Best Researcher Award

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Dr. Ye Tao | Machine Learning | Best Researcher Award

PhD Student | China University of Petroleum, Beijing| China

Dr Ye Tao is a dedicated researcher focusing on sedimentology, unconventional oil and gas exploration, and the integration of artificial intelligence into geological studies. His work emphasizes fine characterization and sweet spot evaluation of shale gas reservoirs, tectonic evolution, sedimentary system reconstruction, and deepwater hydrocarbon accumulation models. Ye Tao has served as principal investigator and key researcher on multiple funded projects, including studies on shale reservoir heterogeneity in the Wufeng–Longmaxi Formations, tectonic evolution of the North Uscult Basin, and migration and accumulation mechanisms in the Guyana Basin. His expertise spans seismic data interpretation, fracture classification, mechanical modeling, and stress field simulation, contributing to accurate prediction of reservoir sweet spots and caprock sealing capacity. Ye Tao has actively published in peer-reviewed journals, presenting significant contributions such as deep learning-aided shale reservoir analysis, isotope-based sea-level reconstructions, and machine learning-based carbonate fossil recognition. His interdisciplinary approach bridges geology with computer vision and artificial intelligence, providing innovative methodologies for improving exploration accuracy. Ye Tao has been awarded multiple national and institutional prizes, including first prizes at China University of Petroleum’s Graduate Academic Forum and the National Doctoral Student Academic Forum, showcasing his academic excellence and leadership. His skillset includes seismic processing, petrographic thin section analysis, carbon and oxygen isotope testing, and restoration of paleoenvironments, enabling comprehensive understanding of sedimentary processes. By applying deep learning techniques to geological data, Ye Tao is contributing to next-generation exploration strategies that enhance prediction of hydrocarbon distribution and optimize resource development. His work demonstrates strong potential for advancing both theoretical sedimentology and applied petroleum exploration, making significant impact on energy resource evaluation and development strategies in complex geological settings.

Profile:  ORCID
Featured Publication

Tao, Y., Bao, Z., & Ma, F. (2025). Analyzing key controlling factors of shale reservoir heterogeneity in “thin” stratigraphic settings: A deep learning-aided case study of the Wufeng-Longmaxi Formations, Fuyan Syncline, Northern Guizhou. Applied Computing and Geosciences, 100293.

Tao, Y., Bao, Z., Yu, J., & Li, Y. (2025). The petrophysical characteristics and controlling factors of the Wufeng Formation–Longmaxi Formation shale reservoirs in the Fuyan Syncline, Northern Guizhou. Geological Journal.

Tao, Y., Gao, D., He, Y., Ngia, N. R., Wang, M., Sun, C., Huang, X., & Wu, J. (2023). Carbon and oxygen isotopes of the Lianglitage Formation in the Tazhong area, Tarim Basin: Implications for sea-level changes and palaeomarine conditions. Geological Journal, 58, 967–980.

Tao, Y., He, Y., Zhao, Z., Wu, D., & Deng, Q. (2023). Sealing of oil-gas reservoir caprock: Destruction of shale caprock by micro-fractures. Frontiers in Earth Science, 10, 1065875.

Khaista Rahman | Artificial Intelligence| Best Paper Award

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Dr. Khaista Rahman | Artificial Intelligence| Best Paper Award

Assistant Professor | Shaheed Benazir Bhutto University Sheringal | Pakistan 

Dr. Khaista Rahman is a distinguished researcher specializing in fuzzy set theory, fuzzy logic, aggregation operators, and artificial intelligence-based decision support systems, with a strong focus on solving decision-making problems under uncertainty. His work explores advanced mathematical structures like Pythagorean fuzzy numbers, interval-valued fuzzy models, and complex fuzzy systems to create robust solutions for multi-attribute group decision-making processes. Dr. Rahman has published extensively on generalized and induced aggregation operators, developing new models that enhance decision accuracy and reliability in diverse applications such as plant location selection, hospital siting during COVID-19, vaccine selection, and railway optimization problems. His research integrates t-norm and t-conorm-based approaches, Einstein hybrid operators, and logarithmic intuitionistic fuzzy techniques to handle complex decision environments. He has also supervised several M.Phil., M.Sc., and BS scholars, contributing significantly to academic mentorship and knowledge dissemination. Recognized among the top 2% scientists worldwide by Stanford University from 2022 to 2025, he has made substantial contributions to granular computing, soft computing, and intelligent systems literature. His work during the COVID-19 pandemic stands out for developing emergency response models using complex fuzzy information to predict and manage disease spread in Pakistan. As Principal Investigator of a funded project on complex intelligent decision support models, Dr. Rahman has bridged theoretical advancements with practical implementations, making his research highly impactful. With an H-index of 26 and over 1900 citations, his scholarly influence spans mathematics, operations research, and computational intelligence, providing frameworks that empower policymakers and industries to make optimal decisions in uncertain and dynamic scenarios. Dr. Khaista Rahman has achieved 776 citations across 532 documents with an impressive h-index of 16.

Profile:  Scopus | ORCID
Featured Publication

  1. Rahman, K., & Khishe, M. (2024). Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to decision-making process [Retracted]. Scientific Reports, 14(1), 15253.

  2. Rahman, K., & Khishe, M. (2024). Retraction Note: Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to decision-making process. Scientific Reports, 14(1).

  3. Rahman, K., et al. (2025). Unraveling vegetation diversity and environmental influences in the Sultan Kha Valley, Dir Upper, Pakistan: An advanced multivariate analysis approach. Polish Journal of Environmental Studies.

  4. Rahman, K. (2024). Some new types induced complex intuitionistic fuzzy Einstein geometric aggregation operators and their application to decision-making problem. Neural Computing and Applications.

Vandana Rajput | Machine Learning | Best Researcher Award

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Ms. Vandana Rajput | Machine Learning | Best Researcher Award

Research Scholar| Netaji Subhas University of Technology | India

Ms. Vandana Rajput, currently a Research Scholar at Netaji Subhas University of Technology, am pleased to nominate myself for the Best Researcher Award. I received my B.E. (2015) and M.Tech (2017) in Information Technology from MITS, Gwalior, and gained valuable industry experience as a Senior Research Analyst at TechieShubhdeep Itsolution Pvt. Ltd. in 2019. Additionally, I served as guest faculty at MNNIT Allahabad and SRCEM colleges, sharing knowledge and guiding students. I have worked as a Junior Research Fellow (JRF) on the prestigious IIT Mandi iHub research project, which helped strengthen my expertise in machine learning and research methodology. My work involves designing innovative concepts, developing methodologies, conducting experiments, and validating results to ensure accuracy and scientific rigor. I have authored one Scopus-indexed publication and continue to contribute to research through original manuscripts. My areas of research focus on machine learning and its applications in solving real-world challenges. I remain committed to advancing research excellence and innovation, collaborating with peers, and producing high-quality, impactful work. I hereby declare that the information provided is accurate to the best of my knowledge and agree to abide by all rules, terms, and conditions of the award nomination process.

Profile:  Scopus

Featured Publication

1. Rajput, V., Jain, A., & Jain, M. (2025). An Automatic Approach for Detecting Cognitive Distortion from Spontaneous Thinking. Procedia Computer Science, 260, 768-775 Citations: 2