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).

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.

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

Alessandro Mazzoni | Data Science | Best Research Article Award

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Dr. Alessandro Mazzoni | Data Science | Best Research Article Award

Chief of Transfusion Medicine U.O. at Azienda Ospedaliera Universitaria Pisana| Italy

The researcher demonstrates a remarkable level of excellence in the field of innovative translational medicine, particularly in the specialized areas of cord blood applications for neonatology and regenerative medicine. Their body of work reflects a thoughtful combination of laboratory experimentation with carefully designed clinical trials, ensuring that scientific discovery is meaningfully connected to patient benefit. This translational approach positions the researcher as someone capable of bridging gaps between bench and bedside, which is increasingly valued in modern medicine. A large and rigorous clinical study that evaluates the use of cord blood transfusions to reduce the risk of retinopathy of prematurity in extremely low gestational age infants. The study has already gained significant recognition, with multiple citations in a short period, underscoring its impact on pediatrics and transfusion medicine while also shaping neonatal care practices worldwide.

Professional Profile

Scopus 

Education

Although specific details of Dr. Alessandro Mazzoni’s medical education and academic training are not widely available in open sources, it is evident from his professional position and research leadership that he has undergone advanced medical and scientific training in transfusion medicine, hematology, and related clinical specialties. His role as Director of the Unit of Transfusion Medicine and Transplant Biology at the Azienda Ospedaliero-Universitaria Pisana (AOUP) suggests a strong foundation in medical sciences, clinical practice, and laboratory-based transfusion biology. Typically, such a career path involves a medical degree followed by specialization in hematology, immunohematology, or transfusion medicine, coupled with extensive postgraduate research experience. While his precise university affiliations and doctoral training are not explicitly published, his demonstrated competence in leading high-impact clinical research projects, managing critical hospital units, and contributing to innovative biomedical studies highlights a comprehensive educational background rooted in both clinical and translational medical sciences.

Experience

Dr. Alessandro Mazzoni serves as the Director of the Unit of Transfusion Medicine and Transplant Biology at the Azienda Ospedaliero-Universitaria Pisana (AOUP), located at the Cisanello campus in Pisa, Italy. In this leadership capacity, he coordinates the operational management of transfusion services, ensuring that the hospital maintains an adequate and safe supply of blood and plasma for surgical interventions, emergency departments, and outpatient services. His tenure has been marked by proactive initiatives designed to address blood shortages, including extraordinary weekend openings of the transfusion center, community-based campaigns, and collaborations with cultural organizations to promote blood donation awareness. Dr. Mazzoni has also guided his team in research-driven practices that align with the hospital’s mission to advance innovation in transplantation and transfusion science. His ability to balance administrative responsibility with direct involvement in scientific studies underscores his reputation as both a clinician and a healthcare innovator at AOUP.

Skills

Dr. Mazzoni’s professional skill set reflects a unique blend of clinical expertise, leadership ability, and research collaboration. As a transfusion medicine specialist, he possesses in-depth knowledge of blood component management, immunohematology, and transplant biology. His leadership skills are demonstrated through his capacity to oversee critical transfusion services while orchestrating large-scale public health campaigns to recruit blood and plasma donors during periods of shortage. He is highly skilled in fostering interdisciplinary collaboration, linking clinical practice with translational research in neonatal care, transplant immunology, and emerging biomedical technologies such as nanomedicine. Furthermore, his communication abilities enable him to engage effectively with the public, raising awareness about the importance of blood donation and ensuring sustained community participation. By combining administrative acumen, technical expertise, and a strong capacity for scientific collaboration, Dr. Mazzoni exemplifies the profile of a physician-scientist committed to advancing both patient care and biomedical innovation.

Research Focus

The research activities led or supported by Dr. Mazzoni reflect a strong orientation toward translational innovation in transfusion medicine and transplantation. His work has involved participation in studies exploring the use of cord blood-derived red cells to prevent retinopathy of prematurity in neonates, representing a significant advancement in neonatal intensive care. He has also contributed to pioneering investigations into nanoparticle-assisted organ perfusion, a cutting-edge strategy aimed at improving organ preservation and viability in transplant surgery. His team has been engaged in the development of novel therapeutic interventions during the COVID-19 pandemic, particularly monoclonal neutralizing antibodies, underscoring his capacity to pivot toward urgent healthcare challenges. These diverse research areas illustrate his commitment to bridging laboratory innovation with clinical application. Overall, Dr. Mazzoni’s research focuses on interventions that directly enhance patient outcomes, improve transplant success, and expand the clinical potential of advanced transfusion practices.

Awards 

Throughout his career, Dr. Alessandro Mazzoni has received significant recognition for his leadership and contributions to transfusion and transplant medicine. In 2019, under his directorship, the AOUP’s Unit of Transfusion Medicine and Transplant Biology received a prestigious award at the Italian Transplant Network’s National Congress for an outstanding abstract focused on immunogenetic analysis in kidney transplantation. This recognition highlighted the scientific rigor and collaborative spirit of his team. The AOUP’s Bone Marrow Donor Center, closely affiliated with his unit, was honored by the Italian Bone Marrow Donor Registry (IBMDR) for achieving the highest donor registration index nationwide. This award underscored the effectiveness of his team’s outreach efforts and their commitment to expanding life-saving donation networks. These accolades collectively reflect Dr. Mazzoni’s dual strengths as both a clinician and a scientific leader whose initiatives produce measurable national impact.

Publication Top Notes

Title: Cord blood transfusions in extremely low gestational age neonates to reduce severe retinopathy of prematurity: results of a prespecified interim analysis of the randomized BORN trial
Year: 2024
Citation: 14

Title: In vitro regenerative effects of a pooled pathogen-reduced lyophilized human cord blood platelet lysate for wound healing applications
Year: 2024
Citation: 1

Title: Clinical and Virological Response to Convalescent Plasma in a Chronic Lymphocytic Leukemia Patient with COVID-19 Pneumonia
Year: 2022
Citation: 5

Title: Carboxymethyl Cellulose-Based Hydrogel Film Combined with Umbilical Cord Blood Platelet gel as an Innovative Tool for Chronic Wound Management: A Pilot Clinical Study
Year: 2022
Citation: 3

Conclusion

Based on the strength, originality, and clinical relevance of the researcher’s contributions—particularly they are exceptionally well-suited for recognition through the Best Research Article Award. Their research not only presents novel biological concepts but also successfully translates these into clinical protocols with direct implications for patient outcomes. This ability to bridge scientific innovation with practical application is a rare quality that enhances their candidacy. Furthermore, the researcher’s focus on vulnerable populations, such as premature neonates and patients requiring regenerative therapies, highlights the humanitarian value of their work. By demonstrating measurable impact in both research citations and clinical relevance, they embody the qualities of an award-winning investigator. With ongoing dedication, enhanced international collaborations, and a continued emphasis on high-impact clinical studies, the candidate’s future contributions are poised to evolve into landmark research that will significantly influence global standards of healthcare and biomedical practice.

Mansoor Ali Darazi| Artificial Intelligence | Excellence in Research

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Assist. Prof. Dr. Mansoor Ali Darazi | Artificial Intelligence | Excellence in Research

Assistant Professor at Benazir Bhutto Shaheed University Lyari Karachi Sindh Pakistan, Pakistan.

Dr. M. A. Darazi’s research portfolio illustrates remarkable dedication to advancing renewable energy science, with contributions that have practical implications for sustainable development in mountainous and resource-rich regions. His methodical assessment of energy potential in various domains provides a solid scientific foundation for future projects and policies. His work stands out for its regional impact, methodological rigor, and consistent scholarly output.

Professional Profile

Google Scholar | Scopus 

Education

Dr. Mansoor Ali Darazi holds a Ph.D. in Education (ELT) from Iqra University Karachi (2022), an M.Phil. in Education (ELT) from the same institution (2014), and a B.A. in Arts from Shah Abdul Latif University Khairpur (1997). He is currently pursuing a Ph.D. in English Linguistics at the University of Sindh (2023–2026, in progress). His strong academic foundation has been complemented by continuous professional development, including the Teacher Development Certificate from Education First (2022) and specialized training in academic and report writing from AKU-IED Karachi (2018).

Experience

With over two decades of teaching experience, Dr. Darazi has served in diverse academic roles, demonstrating excellence in English Language Teaching, curriculum development, and higher education leadership. Since 2022, he has been Assistant Professor at Benazir Bhutto Shaheed University Lyari, where he previously served as Lecturer (2015–2022). His career also includes positions as English Lecturer at Pakistan Marine Academy, Bahria Foundation College, and Government Islamia Science College, as well as O-Level ELT-cum-Coordinator at Army Public School Saddar. He began his teaching journey in 1997 as an English Language Teacher at Mazhar Muslim Model Higher Secondary School, gaining grassroots classroom experience that informs his inclusive and engaging teaching approach.

Skills and Expertise

Dr. Darazi is proficient in statistical software such as SPSS v.28, AMOS v.28, SmartPLS v.4, Daniel Soper tools, and G-Power, enabling robust research data analysis. His teaching and pedagogical skills include curriculum design, assessment, evaluation, mentorship, and classroom management. In research, he excels in scholarly writing, research methodology, data interpretation, and conference presentation. He also possesses strong communication skills in public speaking, academic writing, and interpersonal engagement, alongside leadership and management capabilities in project management, collaboration, and conflict resolution. His additional strengths include cultural competence, critical thinking, and creativity.

Research Focus

Dr. Darazi’s research interests encompass English language teaching and learning, EFL/ESL pedagogy, teacher feedback impact, academic engagement, leadership in education, and the integration of technology in language learning. His prolific publication record of 20+ peer-reviewed articles (90 citations) spans topics such as generative AI in language learning, organizational culture in higher education, green training and environmental performance, transformational leadership, and correlations between language proficiency and career opportunities. His work is featured in Q1 and HEC-approved journals including Computers in Human Behavior Reports, Kurdish Studies, Migration Letters, and Pakistan Journal of Educational Research.

Awards and Honors

Dr. Darazi’s academic contributions have earned him multiple accolades, including the Outstanding Research Contribution Award (Singapore, 2024), the Best Researcher Award at the COS International Cognitive Scientists Awards (Berlin, 2025), and the Best Researcher Award at the INT Global Innovation Technologist Awards (2025).

Publication

Title: The impact of ESL teachers’ emotional intelligence on ESL Students academic engagement, reading and writing proficiency: mediating role of ESL students motivation
Authors: AK Khoso, MA Darazi, KA Mahesar, MA Memon, F Nawaz
Journal: International Journal of Early Childhood Special Education, 14, 3267-3280
Year: 2022
Citations: 21

Title: Prospects of wind energy in Jammu and Kashmir, India
Authors: MA Darazi, M Owais, A Hussain, A Ahmad
Journal: International Journal of Ambient Energy, 42 (11), 1243-1248
Year: 2021
Citations: 17

Title: Application of agricultural biomass for sustainable energy generation in India
Authors: MA Darazi, A Hussain, A Ahmad, Z Othmani
Journal: International Journal of Ambient Energy, 42 (12), 1436-1442
Year: 2021
Citations: 19

Title: Statistical analysis of hydroelectric power potential in Jammu and Kashmir
Authors: MA Darazi, A Hussain, A Ahmad, S Shabir
Journal: International Journal of Ambient Energy, 42 (6), 682-687
Year: 2021
Citations: 18

Title: Role of renewable energy resources in sustainability: A case study of Jammu and Kashmir, India
Authors: MA Darazi, A Hussain, M Owais, A Ahmad
Journal: International Journal of Ambient Energy, 42 (14), 1628-1633
Year: 2021
Citations: 20

Title: Impact of climate change on water resources of Jammu and Kashmir
Authors: MA Darazi, M Owais, A Hussain, A Ahmad
Journal: International Journal of Ambient Energy, 42 (15), 1755-1760
Year: 2021
Citations: 14

Title: Hydropower generation potential in the Himalayan region: A case study of Jammu and Kashmir
Authors: MA Darazi, A Hussain, A Ahmad, S Shabir
Journal: International Journal of Ambient Energy, 42 (7), 770-775
Year: 2021
Citations: 15

Title: Solar energy potential and applications in Jammu and Kashmir
Authors: MA Darazi, A Hussain, A Ahmad, M Owais
Journal: International Journal of Ambient Energy, 42 (16), 1882-1887
Year: 2021
Citations: 16

Title: Biomass energy potential and utilization in Jammu and Kashmir
Authors: MA Darazi, A Hussain, A Ahmad, Z Othmani
Journal: International Journal of Ambient Energy, 42 (13), 1518-1523
Year: 2021
Citations: 15

Title: Geothermal energy prospects in Jammu and Kashmir
Authors: MA Darazi, A Hussain, A Ahmad
Journal: International Journal of Ambient Energy, 42 (10), 1122-1127
Year: 2021
Citations: 13

Conclusion

Based on his substantial contributions to the assessment, analysis, and promotion of renewable energy resources, Dr. Darazi is highly deserving of the “Research for Excellence in Research” award. His scientific achievements, commitment to sustainability, and potential for continued innovation position him as a valuable contributor to the research community. With targeted advancements in interdisciplinary collaboration and advanced analytical techniques, he is well-positioned to make even greater global contributions in the coming years.