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

Joung hwan mun | Machine learning | Best Scholar Award

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Prof. Dr. Joung hwan mun | Machine learning | Best Scholar Award

Professor | Sungkyunkwan University | South Korea

Professor Joung Hwan Mun, Ph.D., is a distinguished Professor in the Department of Biomechatronic Engineering at Sungkyunkwan University, Korea, where he also serves as Director of the Institute of Biotechnology and Bioengineering and the Center for Bio-Information & Communication Technology. He earned his B.S. and M.S. degrees in Biomechatronic Engineering from Sungkyunkwan University and a Ph.D. in Mechanical Engineering from The University of Iowa, USA. With a prolific academic career spanning over two decades, Dr. Mun has significantly contributed to advancing biomechatronics, biomedical engineering, and intelligent healthcare technologies. His primary research interests encompass embedded systems in healthcare, artificial intelligence applications in medical devices, Internet of Things (IoT) integration for medical systems, and wearable sensor technologies for human motion analysis. He has authored more than 250 peer-reviewed publications, including 151 journal articles and 105 conference papers, reflecting his extensive influence in biomechanics, gait analysis, and machine learning-driven motion prediction. His work on AI-based gait and fall detection models, center of pressure trajectory prediction, and exoskeleton design has been widely recognized for improving human mobility, rehabilitation, and clinical diagnostics. Dr. Mun holds over 30 international and national patents, including innovations in surgical navigation, wearable exoskeletons, and fall detection systems, demonstrating his commitment to translational research with direct societal benefits. His leadership in integrating AI, sensor fusion, and biomechanical modeling has fostered interdisciplinary collaborations across Korea, the United States, and Japan. A former Adjunct Associate Professor at The University of Iowa and Invited Associate Professor at Tokyo Denki University, Dr. Mun continues to advance next-generation biomedical systems that merge artificial intelligence and human biomechanics to enhance healthcare accessibility, safety, and quality worldwide.

Featured Publication

Oh, S. E., Choi, A., & Mun, J. H. (2013). Prediction of ground reaction forces during gait based on kinematics and a neural network model. Journal of Biomechanics, 46(14), 2372–2380.

Mun, J. H., & Youn, S. H. (2020). Apparatus and method for discriminating biological tissue, surgical apparatus using the apparatus (U.S. Patent No. 10,864,037).

Choi, A., Kim, T. H., Yuhai, O., Jeong, S., Kim, K., Kim, H., & Mun, J. H. (2022). Deep learning-based near-fall detection algorithm for fall risk monitoring system using a single inertial measurement unit. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 2385–2394.

Park, H. J., Sim, T., Suh, S. W., Yang, J. H., Koo, H., & Mun, J. H. (2016). Analysis of coordination between thoracic and pelvic kinematic movements during gait in adolescents with idiopathic scoliosis. European Spine Journal, 25(2), 385–393.

Choi, A., Lee, J. M., & Mun, J. H. (2013). Ground reaction forces predicted by using artificial neural network during asymmetric movements. International Journal of Precision Engineering and Manufacturing, 14(3), 475–483.

Choi, A., Joo, S. B., Oh, E., & Mun, J. H. (2014). Kinematic evaluation of movement smoothness in golf: Relationship between the normalized jerk cost of body joints and the clubhead. Biomedical Engineering Online, 13(1), 20.

Dr. Joung Hwan Mun’s pioneering research integrates artificial intelligence, biomechanics, and wearable sensing to advance intelligent healthcare systems and human–machine interaction. His innovations in gait analysis, fall detection, and exoskeleton technologies have significantly enhanced mobility, rehabilitation, and safety, driving global progress in personalized healthcare and biomedical engineering.

Bincy Baburaj Kaluvilla | Machine Learning | Best Researcher Award

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Dr. Bincy Baburaj Kaluvilla | Machine Learning | Best Researcher Award

Head of Academics | Learners University College | United Arab Emirates

Dr. Bincy B. Kaluvilla is an accomplished academic and researcher specializing in sustainable finance, investment management, and hospitality education, with a particular emphasis on integrating environmental, social, and governance (ESG) principles into financial and hospitality frameworks. She currently serves as Head of Academics and Partnerships at Learners University College, UAE, and previously worked as Assistant Professor and Undergraduate Program Manager at the Emirates Academy of Hospitality Management, where she played a central role in program leadership, faculty coordination, and industry collaboration. Holding a Ph.D. in Accounting from the University of Leicester, an M.Res in Accounting and Finance from the University of Glasgow, and professional recognition as a Fellow of the Higher Education Academy (UK) and CPA Australia, Dr. Kaluvilla combines strong academic foundations with practical insight. Her research encompasses real estate finance, green finance, ESG reporting, and digital transformation in hospitality, contributing over fifteen peer-reviewed publications and book chapters in leading journals such as Frontiers in Computer Science, Asia Pacific Journal of Tourism Research, and Library Hi Tech News, with growing citation impact across Scopus and Web of Science databases. She has authored chapters for major publishers including Springer Nature, Emerald, IGI Global, and Apple Academic Press, addressing emerging issues in sustainable investment, digital currencies, and responsible finance. Her academic influence extends globally through conference presentations at EuroCHRIE in Vienna, GHLS in Dubai, and IPoE in the UAE. Beyond research, she has led significant corporate training initiatives with the Jumeirah Group, Omran Group, and the UAE Ministry of Foreign Affairs, advancing professional development and gender empowerment within the hospitality industry. Through her research, teaching, and leadership, Dr. Kaluvilla continues to advance global understanding of sustainable finance and investment practices, fostering stronger links between academia, industry, and community development.

Featured Publication

Fahad, Z., Kaluvilla, B. B., & Mulla, T. (2024). Embracing the new era: Artificial intelligence and its multifaceted impact on the hospitality industry. Journal of Open Innovation: Technology, Market, and Complexity, 10(4), 100390.

Ghazanfar, U., Kaluvilla, B. B., & Zahidi, F. (2023). The post-COVID emergence of dark kitchens: A qualitative analysis of acceptance and the advantages and challenges. Research in Hospitality Management, 13(1), 23–30.

Kaluvilla, B. B. (2024). Cultural preservation through technology in UAE libraries. Library Hi Tech News, 41(8), 6–9.

Kalarikkal, S. A., Thamilvannan, G., & Kaluvilla, B. B. (2024). Enhancing access to missionary archives: The role of digital libraries and online repositories. Library Hi Tech News.

Kaluvilla, B. B., Mulla, T., Zahidi, F., & Wondirad, A. (2024). Driving sustainable choices through understanding consumer behaviour and underlying factors that influence the purchasing intention of refurbished furniture. SSRN Electronic Journal.

Md. Habibullah Shakib | Machine Learning | Best Researcher Award

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Mr. Md. Habibullah Shakib | Machine Learning | Best Researcher Award

Researcher| World University of Bangladesh| Bangladesh

Mr. Md. Habibullah Shakib is an emerging researcher and analyst from Bangladesh with over 3.5 years of research experience in artificial intelligence, supervised and deep learning, genetic AI, and foundation models. He holds a Bachelor of Science in Computer Science and Engineering from the World University of Bangladesh and a Diploma in Computer Technology from the National Polytechnic Institute. His research focuses on developing intelligent and secure computing systems, with significant contributions to Android malware detection, federated learning, autonomous systems, and IoT-based smart home automation. Among his key projects are the Active Federated YOLOR Model for enhancing autonomous vehicle safety, deep learning and genetic AI approaches for Android malware detection, and the integration of Conformer, Active Learning, and Federated Learning models for encrypted malware traffic detection. His ongoing work on Autonomous Generative AI for Android malware detection reflects his interest in advancing cutting-edge AI-driven cybersecurity solutions. Recognized for his scholarly engagement, he received a Certificate of Reviewing from the Information Processing and Management journal (Elsevier, 2024). He has built a growing academic presence with profiles on Google Scholar, ORCID, SSRN, GitHub, and the AD Scientific Index. Fluent in Bangla and English, he combines strong analytical and organizational skills with a commitment to innovation and teamwork. Through his dedication to ethical AI development, quantitative data analysis, and research collaboration, Md. Habibullah Shakib aims to contribute globally to the progress of intelligent systems, data-driven decision-making, and digital security for sustainable technological advancement.

Featured Publication

Shakib, M. (2023). Android malware detection approach based on genetic AI, CNN, RNN, LSTM, GRU, and active learning. SSRN. Cited by: 1

Shakib, M. H., Yeasin, M., Rahman, M. H., Rahman, K. M., Hossain, S., & Mahi, F. F. (2025). Active learning model used for Android malware detection. Machine Learning with Applications, 100680. Cited by: 8

Shakib, M. D. H. (2024). Android malware detection using transformer and encoder models. SSRN. Cited by: 5

Shakib, M. H. (2024). Comparing conformer, genetic artificial intelligence conformer, and active learning conformer approaches for encrypted Android malware traffic detection. SSRN. Cited by: 4

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

Marco Capogni | Data Science | Best Researcher Award

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Prof. Dr. Marco Capogni | Data Science | Best Researcher Award

Researcher | ENEA – National Institute for Ionizing Radiation Metrology | Italy

Prof. Dr. Marco Capogni’s research focuses on the precise measurement and standardization of radionuclides, with a strong emphasis on ionizing radiation metrology and its applications in medicine, industry, and environmental monitoring. He has developed and maintained primary national standards for radioactivity, collaborating with international institutions such as the Bureau International des Poids et Mesures (BIPM) and contributing to interlaboratory comparisons to ensure global consistency in radionuclide measurements. His work includes the implementation of absolute measurement techniques and computational codes such as GEANT, MCNP, Penelope, and Fluka for both direct and indirect activity determination. Marco has been actively involved in projects producing medical radionuclides like Mo-99 and Cu-64, utilizing neutron activation and absolute or relative measurement systems, and has contributed to the development of innovative sources of fusion neutrons for radioisotope production under the Sorgentina-RF project. His expertise spans gamma spectrometry, liquid scintillation counting, and coincidence counting methods, and he has served as a member of international working groups including the International Committee for Radionuclide Metrology (ICRM) and the European Metrology Network for Radiation Protection (EURAMET). Marco has led and coordinated numerous European research projects funded by EMRP and EMPIR, focusing on robust production chains for medical radionuclides, radiological early warning networks, and metrology for decommissioning nuclear facilities. He has also contributed to the training of students at the master’s and doctoral levels in physics, engineering, and medical physics, supervising multiple theses on radionuclide metrology and measurement techniques. His work has resulted in significant publications, patents, and participation in international conferences, reflecting his leadership in metrological science and nuclear applications. Marco Capogni’s contributions demonstrate a blend of experimental expertise, computational proficiency, and collaborative engagement with international metrology and research networks, addressing challenges in nuclear measurement, radioprotection, and medical isotope production. He has achieved 1,882citations, authored 133 documents, and holds an h-index of 21.

Profiles: Scopus | ORCID
Featured Publication

Capogni, M., … (2024). Assessment of impurity production upon 14 MeV fusion neutron irradiation of both natural and isotopically enriched 100Mo samples. European Physical Journal Plus.
Citations: 1

Capogni, M., … (2024). Measurements of the absolute gamma-ray emission intensities from the decay of 166Ho. Applied Radiation and Isotopes.
Citations: 2

Capogni, M., … (2024). Future of 99Mo reactor-independent supply. Nature Reviews Physics.
Citations: 3

Capogni, M., … (2023). Analytical study of low energy proton interactions in the SORGENTINA’s fusion ion source-Part II: beam-gas: SORGENTINA ion beam interactions. European Physical Journal Plus.
Citations: 2

Capogni, M., … (2023). The international reference system for beta-particle emitting radionuclides: Validation through the pilot study CCRI(II)-P1.Co-60. Applied Radiation and Isotopes.
Citations: 5

Capogni, M., … (2023). The importance of uncertainty analysis and traceable measurements in routine quantitative 90Y-PET molecular radiotherapy: A multicenter experience. Pharmaceuticals.
Citations: 1

Capogni, M., … (2023). Experimental campaign on ordinary and baritic concrete samples for the SORGENTINA-RF plant: The SRF-bioshield tests. European Physical Journal Plus.
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.