Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

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Assoc. Prof. Dr. Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Assoc. Prof., Northwestern University, United States

Dr. Ulas Bagci is a distinguished researcher and tenured Associate Professor at Northwestern University, specializing in Radiology, Electrical and Computer Engineering, and Biomedical Engineering. He is also a courtesy professor at the University of Central Florida’s Center for Research in Computer Vision. As the Director of the Machine and Hybrid Intelligence Lab, Dr. Bagci focuses on the integration of artificial intelligence, deep learning, and medical imaging. His extensive research contributions include over 330 peer-reviewed articles in these domains. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health (NIH), where he played a pivotal role in advancing AI-driven medical imaging applications. Dr. Bagci actively contributes to leading scientific journals, serving as an associate editor for IEEE Transactions on Medical Imaging, Medical Physics, and Medical Image Analysis.

Publication Profile

🎓 Education

Dr. Ulas Bagci holds a Ph.D. in Computer Science from the University of Nottingham (2010), where he conducted pioneering research in medical imaging. He was a Visiting Research Fellow in Radiology at the University of Pennsylvania (2008-2009), further refining his expertise in AI applications for biomedical sciences. He earned his M.Sc. in Electrical and Computer Engineering from Koç University (2005) and his B.Sc. in Electrical and Computer Engineering from Bilkent University (2003).

💼 Experience

Dr. Bagci has built an impressive academic and research career across top institutions. Since 2021, he has been an Associate Professor at Northwestern University, where he leads research in AI-driven medical imaging. Before that, he served as an Assistant Professor in Computer Science at the University of Central Florida (2014-2020), fostering innovation in deep learning for radiology. From 2010 to 2014, he was a Staff Scientist and Lab Manager at the National Institutes of Health (NIH), playing a key role in infectious disease imaging and AI applications in radiology.

🏅 Awards and Honors

Dr. Bagci has received numerous recognitions for his outstanding contributions to artificial intelligence and medical imaging. He has secured multiple NIH grants (R01, U01, R15, R21, R03) as a Principal Investigator and is a steering committee member for the NIH Artificial Intelligence Resource (AIR). Additionally, he has been honored with best paper and reviewer awards in top-tier AI and medical imaging conferences such as MICCAI and IEEE Medical Imaging.

🔬 Research Focus

Dr. Bagci’s research revolves around artificial intelligence, deep learning, radiology, and computer vision. His work has significantly impacted medical imaging applications, including MRI, CT scans, nuclear medicine imaging, and disease diagnosis. He has contributed extensively to federated learning, probabilistic modeling, and AI-powered decision-making in healthcare. His recent studies include advancements in brain tumor segmentation, bias field correction in MRI, and AI-driven road network prediction.

🔚 Conclusion

Dr. Ulas Bagci is a leading expert in AI-powered medical imaging, consistently pushing the boundaries of deep learning, radiology, and computer vision. His impactful contributions in academia and research have earned him global recognition. With a strong presence in prestigious institutions, his pioneering work continues to shape the future of AI in healthcare. 🚀

📚 Publications

Evidential Federated Learning for Skin Lesion Image Classification (2025) – Published in a book chapter DOI: 10.1007/978-3-031-78110-0_23 📖

Paradoxical Response to Neoadjuvant Therapy in Undifferentiated Pleomorphic Sarcoma (2025) – Published in Cancers DOI: 10.3390/cancers17050830 🏥

Foundational Artificial Intelligence Models and Modern Medical Practice (2025) – Published in BJR | Artificial Intelligence DOI: 10.1093/bjrai/ubae018 🧠

A Probabilistic Hadamard U-Net for MRI Bias Field Correction (2024) – Published in arXiv arXiv:2403.05024 🖥️

AI-Powered Road Network Prediction with Fused Low-Resolution Satellite Imagery and GPS Trajectory (2024) – Published in Earth Science Informatics 🌍

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation (2024) – Presented at the IEEE/CVF Winter Conference on Applications of Computer Vision 🤖

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation  (2024) – Published in arXiv arXiv:2405.18383 🏥

 

Gege Bao | Forestry Engineering | Best Researcher Award

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Dr. Gege Bao | Forestry Engineering | Best Researcher Award

Doctor, International Centre for Bamboo and Rattan, China

Dr. Gege Bao is a distinguished researcher specializing in Forestry Engineering with a keen focus on sustainable materials. As a dedicated scientist at the International Centre for Bamboo and Rattan, she pioneers eco-friendly innovations in bamboo and wood protection. Her expertise in nanomaterials and surface engineering has led to groundbreaking advancements in enhancing material durability and environmental adaptability. Passionate about promoting green development, Dr. Bao’s research contributes significantly to sustainable architecture and ecological civilization. 🌱🔬

Publication Profile

Scopus

🎓 Education:

Dr. Bao holds a Ph.D. in Forestry Engineering, where she honed her expertise in sustainable materials and surface protection technologies. Her academic journey has been deeply rooted in exploring advanced nanomaterials, high-durability coatings, and eco-friendly solutions for bamboo and wood applications. 📚🎓

💼 Experience:

With a strong background in research, Dr. Bao has led numerous projects on functional protective coatings for bamboo surfaces. Her work focuses on surface modification, interface regulation, and high-performance materials engineering. Through her innovative research, she has developed novel coatings that enhance bamboo’s durability, fire resistance, and mold protection. She actively collaborates on projects aimed at revolutionizing eco-friendly material protection technologies. 🏗️🔍

🏆 Awards and Honors:

Dr. Bao’s contributions to sustainable materials and forestry engineering have been widely recognized. Her innovative work in bamboo surface protection has positioned her as a leading researcher in the field. She has been nominated for prestigious awards such as the Best Researcher Award, Women Research Award, and Excellence in Innovation, acknowledging her impact on green technology and sustainable development. 🏅🌍

🔬 Research Focus:

Dr. Bao’s research revolves around functional protective technologies for bamboo surfaces, surface modification, and environmentally friendly material protection. Her work on high-durability coatings and interface regulation mechanisms has opened new avenues for sustainable architecture and ecological resource management. By integrating nanomaterials and bio-based solutions, she strives to enhance the performance and longevity of bamboo and wood materials. 🌿🧪

🔚 Conclusion:

Dr. Gege Bao is a visionary researcher dedicated to transforming sustainable materials through innovative engineering solutions. Her expertise in bamboo surface protection and eco-friendly material science continues to make a profound impact on environmental sustainability. As a thought leader in her field, she remains committed to advancing green technology, paving the way for a more sustainable future. 🌎🔬

🔗 Publications:

Preparation of 3-Iodo-2-Propargyl-Butyl-Carbamate-Loaded Microcapsules for Long-Term Mold Resistance in Bamboo – Polymers (2024) 🔗 Read here

A Green and Eco-Friendly Method to Enhance Bamboo Flame Resistance via Calcium Alginate Assisted In-Situ Mineralization of Hydroxyapatite – Chemical Engineering Journal (2024) 🔗 Read here

An Inorganic Hydrophobic Layer Connected by Rivets Endows the Bamboo with High Fire Retardant, Mechanical, and Mold-Proof Properties – Industrial Crops and Products (2023) 🔗 Read here

Eco-Friendly In-Situ Mineralization of Bamboo for Flame Retardancy – Industrial Crops and Products (2023) 🔗 Read here

Xingyan Chen | Computer Networks | Best Researcher Award

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Assoc. Prof. Dr. Xingyan Chen | Computer Networks | Best Researcher Award

Associate Professor, Southwestern University of Finance and Economics, China

Dr. Chen Xingyan is an esteemed Associate Professor at the School of Computer and Artificial Intelligence, Southwestern University of Finance and Economics. He holds a Ph.D. in Engineering from Beijing University of Posts and Telecommunications 🎓. His research spans generative AI applications, large language models, distributed computing networks, multimedia communication, and reinforcement learning 🤖. With over 30 publications in top-tier journals and conferences, including IEEE INFOCOM, IEEE TMC, IEEE TMM, and IEEE TCSVT, Dr. Chen has made significant contributions to advancing AI-driven networked systems. He has led multiple national and provincial research projects, making him a distinguished figure in AI and computing research.

Publication Profile

🎓 Education

Dr. Chen Xingyan earned his Ph.D. in Engineering from Beijing University of Posts and Telecommunications, where he specialized in AI-driven multimedia communication and networked computing. His academic journey has been marked by excellence in research, leading to impactful contributions in distributed AI and reinforcement learning applications 📡.

💼 Experience

Dr. Chen is currently an Associate Professor at Southwestern University of Finance and Economics, where he actively engages in cutting-edge research and mentorship. He has been a principal investigator for several prestigious projects, including the NSFC Youth Fund and Sichuan Provincial Natural Science Fund. His consultancy work with leading tech firms like Huawei and China Electronics Technology Group further highlights his industry influence. Additionally, Dr. Chen has played a pivotal role in research projects related to 5G streaming, blockchain-based cloud computing, and immersive video transmission 🎥.

🏆 Awards and Honors

Dr. Chen has been recognized for his groundbreaking research with several prestigious grants and awards. He has received funding from the National Natural Science Foundation of China and the Sichuan Provincial Science and Technology Department. His expertise in AI and multimedia systems has earned him notable accolades in academia and industry 🏅.

🔬 Research Focus

Dr. Chen’s research is centered on generative AI, multimedia communication, federated learning, and reinforcement learning. His work on immersive video transmission, cloud-edge computing, and blockchain-enhanced computing frameworks has been widely cited and influential. He continues to innovate in the field, developing AI-driven methodologies for large-scale distributed networks and next-generation communication systems 🌐.

🔚 Conclusion

Dr. Chen Xingyan stands at the forefront of AI-driven computing and multimedia systems, making substantial contributions through innovative research and industry collaborations. His work in AI, distributed computing, and multimedia communication has not only advanced theoretical knowledge but also influenced practical applications in 5G, blockchain, and federated learning. With a strong research portfolio, prestigious awards, and impactful industry partnerships, Dr. Chen continues to shape the future of AI-powered networked systems 🚀.

🔗 Publications

Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling. IEEE Transactions on Computers. [Cited by 15] 🔗

A Novel Adaptive 360° Livestreaming with Graph Representation Learning-based FoV Prediction. IEEE Transactions on Emerging Topics in Computing. [Cited by 12] 🔗

A Federated Transmission Framework for Panoramic Livecast with Reinforced Variational Inference. IEEE Transactions on Multimedia. [Cited by 20] 🔗

A Multi-user Cost-efficient Crowd-assisted VR Content Delivery Solution in 5G-and-beyond Heterogeneous Networks. IEEE Transactions on Mobile Computing. [Cited by 18] 🔗

A Universal Transcoding and Transmission Method for Livecast with Networked Multi-Agent Reinforcement Learning. IEEE INFOCOM. [Cited by 25] 🔗

Augmented Queue-Based Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration. IEEE Transactions on Circuits and Systems for Video Technology. [Cited by 22] 🔗

Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks. IEEE Transactions on Knowledge and Data Engineering. [Cited by 30] 🔗

BC-Mobile Device Cloud: A Blockchain-Based Decentralized Truthful Framework for Mobile Device Cloud. IEEE Transactions on Industrial Informatics. [Cited by 17] 🔗

Differential Privacy Oriented Distributed Online Learning for Mobile Social Video Prefetching. IEEE Internet of Things Journal. [Cited by 19] 🔗

Optimal Information Centric Caching in 5G Device-to-Device Communications. IEEE Transactions on Circuits and Systems for Video Technology. [Cited by 23] 🔗

BC-MetaCast: A Blockchain-enhanced Intelligent Computing Framework for Metaverse Livecast. IEEE Network. [Cited by 14] 🔗

Rab Nawaz Bashir | Machine Learning | Best Researcher Award

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Dr. Rab Nawaz Bashir | Machine Learning | Best Researcher Award

Assistant Professor, COMSATS University, Pakistan

Dr. Rab Nawaz Bashir 🎓 is a distinguished computer scientist and Assistant Professor at COMSATS University Islamabad, Vehari Campus. He holds a Ph.D. in Computer Science from Islamia University Bahawalpur and has been instrumental in shaping research and education in artificial intelligence, machine learning, and the Internet of Things (IoT). With a passion for academic excellence, he has mentored numerous undergraduate, graduate, and Ph.D. students, guiding them toward impactful research and industry readiness. His dedication to scholarly contributions and innovative teaching has made him a respected figure in the field of computer science.

Publication Profile

Education 🎓

Dr. Bashir has a strong academic foundation, earning his Ph.D. in Computer Science (2021) from Islamia University Bahawalpur. Prior to that, he completed an MS in Computer Science (2015) from the same institution. His educational journey began with a Master in Computer Science (MCS) from Pir Mehr Ali Shah University of Arid Agriculture (2008). To further enhance his expertise, he is currently pursuing a Fellowship in Computer Science at Prince Sultan University, Saudi Arabia.

Experience 👨‍🏫

With over 15 years of experience, Dr. Bashir has significantly contributed to academia and research. Since 2022, he has served as an Assistant Professor at COMSATS University Islamabad, Vehari Campus, where he leads curriculum development, organizes seminars, and supervises undergraduate and Ph.D. research. Previously, he was a Lecturer at COMSATS University (2015–2022), University of Agriculture Faisalabad (2014–2015), and Institute of Southern Punjab, Multan (2010–2014). Before transitioning fully into academia, he worked in software development at the University of Agriculture Faisalabad (2008–2010), specializing in secure and scalable web applications using ASP.NET and SQL Server.

Awards and Honors 🏆

Dr. Bashir has been recognized multiple times for his outstanding contributions to research and academic excellence. He has received several Research Awards (2021, 2022, 2023, 2024) and Annual Performance Awards (2022, 2023, 2024) from his institution. Additionally, his leadership in securing National Standard for Education Accreditation Council (NSEAC) accreditation for the university in 2021 and 2024 highlights his commitment to academic quality and institutional development.

Research Focus 🔬

Dr. Bashir’s research spans various cutting-edge domains, including machine learning, IoT, and computer programming. His work emphasizes real-world applications, such as IoT-enabled smart agriculture, machine learning-based fraud detection, and deep learning for natural image processing. His interdisciplinary collaborations have resulted in high-impact research, contributing to advancements in federated learning, fog computing, and agricultural technology.

Conclusion 🌟

Dr. Rab Nawaz Bashir is a leading academic and researcher in computer science, with a strong focus on machine learning, IoT, and programming. His dedication to mentoring students, publishing impactful research, and advancing academic excellence has earned him numerous awards and recognitions. With a vision for future innovations, he continues to push the boundaries of computer science, contributing to both academia and industry through groundbreaking research and leadership.

Publications 📚

A Novel 1-Dimensional Cosine Chaotic Equation and Digital Image Encryption Technique (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3447889

Federated Learning (FL) Model of Wind Power Prediction (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3415781

IoT-Enabled Firmness Grades of Tomato in Cold Supply Chain Using Fusion of Whale Optimization Algorithm and Extreme Learning Machine (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3379327

Mushroom Species Classification in Natural Habitats Using Convolutional Neural Networks (CNN) (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3502543

Machine Learning and Fog Computing-Enabled Sensor Drift Management in Precision Agriculture (IEEE Sensors Journal, 2024) – DOI: 10.1109/JSEN.2024.3451662

Principal Component Analysis (PCA) and Feature Importance-Based Dimension Reduction for Reference Evapotranspiration (ET0) Predictions of Taif, Saudi Arabia (Computers and Electronics in Agriculture, 2024) – DOI: 10.1016/j.compag.2024.109036

Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transactions (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3310576

Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network  (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3301504

Stacked Ensemble Model for Tropical Cyclone Path Prediction (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3292907

Smart Reference Evapotranspiration Using Internet of Things and Hybrid Ensemble Machine Learning Approach (Internet of Things, 2023) – DOI: 10.1016/j.iot.2023.100962

Ivan Nastasijevic | Biotechnology | Excellence in Research

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Dr. Ivan Nastasijevic | Biotechnology | Excellence in Research

Principal Research Scientist, Institute of Meat Hygiene and Technology, Serbia

Dr. Ivan Nastasijevic is a distinguished expert in food safety, meat hygiene, and microbiology, with extensive experience spanning research, academia, and policy development. Currently serving as a Principal Research Fellow in Food Systems at the Institute of Meat Hygiene and Technology in Belgrade, Serbia, he has made significant contributions to the advancement of food safety protocols. His expertise is widely recognized, having worked with the World Health Organization (WHO), the University of Central Lancashire, and Serbia’s Federal Ministry of Agriculture. Dr. Nastasijevic’s career reflects a commitment to enhancing food safety, public health, and antimicrobial resistance mitigation, making him a key figure in global food systems research. 🌍🍖

Publication Profile

🎓 Education

Dr. Nastasijevic holds a Doctor of Veterinary Medicine (DVM) from the Faculty of Veterinary Medicine, University of Belgrade. Furthering his expertise, he completed a Master of Biochemical Sciences (MPH) at the University of Belgrade and earned a Ph.D. in Veterinary Sciences from the University of Novi Sad in collaboration with the U.S. Meat Animal Research Center (USMARC). His extensive qualifications include certifications in HACCP principles, veterinary licensing, food safety management, and quality assurance auditing, obtained from institutions such as the Royal Institute of Public Health (UK), USDA/WHO, and EU Academy. His global academic exposure underscores his interdisciplinary expertise in veterinary science and food microbiology. 📚🦠

💼 Experience

With over 25 years of experience, Dr. Nastasijevic has held key positions in research, policy advisory, and education. He previously served as Associate Director and Head of the Development and Technology Transfer Department at the Institute of Meat Hygiene and Technology. His international engagement includes working as a Technical Officer for Food Safety at WHO (Copenhagen, Denmark) and a Lecturer in Food Safety Management at the University of Central Lancashire (UK). His work with Serbia’s Ministry of Agriculture and Veterinary Services contributed significantly to the development of food safety regulations and risk assessment strategies. His diverse professional journey has positioned him as a leading voice in global food safety initiatives. 🌍🔬

🏆 Awards and Honors

Dr. Nastasijevic has been recognized for his contributions to food safety research, antimicrobial resistance tracking, and meat hygiene advancements. His research has influenced international food safety policies, and his efforts in One Health approaches to mitigate risks in food production have earned him numerous accolades. His engagement in scientific cooperation and technological innovation has led to various fellowships, including the prestigious Norman-Borlaug Fellowship (USA). 🏅🔍

🔍 Research Focus

His research primarily revolves around meat safety, biosensor technology, foodborne pathogen control, and antimicrobial resistance mitigation. Dr. Nastasijevic has played a pivotal role in developing risk assessment models and technological advancements in the meat industry. His work on computer vision systems for meat safety assurance, risk categorization of abattoirs, and modified atmosphere packaging for shelf-life enhancement has set new standards in food safety management. His One Health approach integrates microbiology, food technology, and public health policies to create sustainable food systems. 🧬🥩

🔚 Conclusion

Dr. Ivan Nastasijevic is a leading authority in food safety, meat microbiology, and antimicrobial resistance, with an extensive career dedicated to scientific research, policy-making, and technological innovation. His contributions have influenced global food safety policies, industry best practices, and One Health initiatives. With a strong publication record, international collaborations, and leadership in research, Dr. Nastasijevic continues to play a crucial role in ensuring food security and public health on a global scale. 🌍🔬

📚 Publications

Recent Advances in Biosensor Technologies for Meat Production Chain – Foods (2025) DOI: 10.3390/foods14050744

Antimicrobial Resistance in Aquaculture: Risk Mitigation within the One Health Context – Foods (2024) DOI: 10.3390/foods13152448

Technologies to Address Risk Assessment, Food Safety and Public Health in Food Production Chain – Frontiers in Microbiology (2024)

Biosensors in the Meat Chain: Farm-to-Fork Continuum – Conference Paper (2024) DOI: 10.46793/8FDSQ.ILA1IN

Tracking Antimicrobial Resistance Along the Meat Chain: One Health Context – Food Reviews International (2023) DOI: 10.1080/87559129.2023.2279590

Risk Categorisation of Abattoirs in Europe: Current State of Play – Food Control (2023) DOI: 10.1016/j.foodcont.2023.109863

Applications of Computer Vision Systems for Meat Safety Assurance in Abattoirs: A Systematic Review – Food Control (2023) DOI: 10.1016/j.foodcont.2023.109768

Radwan Al-omary | Rings theory | Excellence in Research

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Prof. Dr. Radwan Al-omary | Rings theory | Excellence in Research

Professor, Ibb university, Yemen

Dr. Radwan Mohammed Al-Omary is a distinguished mathematician and researcher specializing in algebra and prime rings. Currently affiliated with Ibb University, Yemen, he has made significant contributions to mathematical research, particularly in generalized derivations, prime rings, and algebraic identities. With an H-index of 4 in recent years and 37 overall, his work has been recognized through numerous citations. His expertise extends beyond research, as he is also an active reviewer for multiple prestigious mathematical journals.

Publication Profile

🎓 Education

Dr. Al-Omary holds a Ph.D. in Mathematics, focusing on algebra and its applications. His academic journey has been centered around exploring mathematical structures, particularly prime rings, derivations, and commutativity conditions, which have shaped his research contributions.

💼 Experience

As a faculty member at Ibb University, Dr. Al-Omary has been involved in both teaching and research. He has published extensively in reputed journals such as Axioms, Mathematics, and Communications of the Korean Mathematical Society. His work includes exploring algebraic identities via generalized derivations, prime rings, and involution theory. Additionally, he has reviewed over 30 research papers for renowned international journals, contributing significantly to the mathematical community.

🏆 Awards and Honors

Dr. Al-Omary’s contributions to mathematics have been acknowledged through his extensive citation record and participation in international research collaborations. His scholarly impact is reflected in his H-index and the citations his work has received across multiple domains of algebra and applied mathematics.

🔬 Research Focus

His research primarily focuses on algebraic identities, prime rings, generalized derivations, and commutativity conditions. His publications delve into the structural properties of algebraic systems, using derivations to analyze the fundamental behavior of mathematical rings. His studies have contributed valuable insights into mathematical logic, computational algebra, and theoretical mathematics.

🔚 Conclusion

Dr. Radwan Mohammed Al-Omary is a highly regarded mathematician known for his contributions to algebra and prime rings. His dedication to research, teaching, and peer-reviewing underscores his influence in the mathematical community. Through his extensive work in derivations and algebraic structures, he continues to shape mathematical research and inspire future generations of mathematicians.

📚 Publications 

Factor Rings with Algebraic Identities via Generalized Derivations (2025) – Axioms
🔗 DOI: 10.3390/AXIOMS14010015
📌 Cited by: 0

Exploring Commutativity via Generalized (α, β)-Derivations Involving Prime Ideals (2024) – Mathematics
🔗 DOI: 10.3390/MATH12152325
📌 Cited by: 1

GENERALIZED DERIVATIONS ON PRIME RINGS SATISFYING CERTAIN IDENTITIES (2021) – Communications of the Korean Mathematical Society
🔗 DOI: 10.4134/CKMS.C200227
📌 Cited by: 3

Commutativity of Prime Rings with Generalized (α,β)-Reverse Derivations Satisfying Certain Identities (2022) – Bulletin of the Transilvania University of Brașov Series III Mathematics and Computer Science
🔗 DOI: 10.31926/BUT.MIF.2022.2.64.2.1
📌 Not Indexed in Web of Science

On Prime Rings with Involution and Generalized Derivations (2022) – Discussiones Mathematicae – General Algebra and Applications
🔗 DOI: 10.7151/DMGAA.1404
📌 Not Indexed in Web of Science

Zakia Al-Amery | Pure algebra | Best Researcher Award

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Ms. Zakia Al-Amery | Pure algebra | Best Researcher Award

Lecture, Aden University, Yemen

Zakia Zaid Ali Amer is a dedicated mathematician and educator with a strong passion for research and teaching. She is currently pursuing her Ph.D. in Mathematics while serving as a Lecturer at the Faculty of Education, University of Aden. With a decade of experience in teaching and academic research, she has significantly contributed to mathematical education and development. Her work focuses on algebraic structures, prime ideals, and generalized derivations, with publications in reputable journals.

Publication Profile

Google Scholar

📚 Education:

Zakia holds a Master’s degree in Mathematics from the Faculty of Education, University of Aden, and a Bachelor’s degree in Mathematics from the Faculty of Education, Abyan, University of Aden. She is currently advancing her expertise through her Ph.D. studies, further deepening her understanding of mathematical theories and their applications.

👩‍🏫 Experience:

She has been a Lecturer at the Faculty of Education, University of Aden, since 2023 and has been actively engaged in research at the Center for Educational Research and Development, Aden, since 2017. Before transitioning into academia, she worked as a mathematics teacher under the Ministry of Education in Abyan from 2010 to 2017, honing her instructional skills and contributing to foundational education.

🏆 Awards and Honors:

Zakia has earned recognition for her contributions to mathematics and education. Her publications in indexed journals and participation in mathematical research projects have strengthened her reputation in the field.

🔬 Research Focus:

Her research primarily revolves around algebra, prime ideals, and generalized derivations. She explores mathematical structures and their properties, aiming to advance theoretical mathematics and its educational applications. Her recent work delves into commutativity conditions involving prime ideals and algebraic identities in factor rings.

🔎 Conclusion:

As a mathematician, educator, and researcher, Zakia Zaid Ali Amer continues to bridge the gap between theoretical mathematics and education. Her dedication to academia, research, and teaching underscores her commitment to fostering mathematical knowledge and inspiring future scholars.

📖 Publications:

Exploring Commutativity via Generalized (α, β)-Derivations Involving Prime Ideals (2024) – Mathematics 12 (15), 2325
🔗 Link to Paper – Cited by: 1

Factor Rings with Algebraic Identities via Generalized Derivations
🔗 [Link to Paper] (Add link when available) – Cited by: Unknown

Awais Khan Jumani | Image processing | Best Researcher Award

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Mr. Awais Khan Jumani | Image processing | Best Researcher Award

PhD Scholar, South China University of Technology, Guangzhou, Guangdong, China

Dr. Awais Khan is a dedicated researcher specializing in deep learning, multimedia cloud computing, and artificial intelligence 🌐. Currently pursuing a Ph.D. in Information & Communication Engineering at South China University of Technology 🎓, his research focuses on deep learning models for Quality of Experience (QoE) in cloud environments. With a strong academic and professional background, Dr. Khan has contributed significantly to the fields of machine learning, computer vision, and multimedia processing. His work integrates innovative AI techniques for real-world applications, making him a prominent figure in computational research 🤖.

Publication Profile

📚 Education

Dr. Khan is on track to complete his Ph.D. (2021-2025) at South China University of Technology 🇨🇳, where he explores deep learning techniques for emotion-based QoE in cloud computing. He previously earned his M.S. in Computer Science (2016-2018) from Shah Abdul Latif University, Pakistan 🇵🇰, focusing on Sindhi text categorization using Support Vector Machines. His academic journey began with a B.S. in Computer Science (2011-2014) from the same institution, achieving commendable academic performance 📊.

👨‍🏫 Experience

Dr. Khan served as an Assistant Professor at ILMA University (2019-2022) in Pakistan, where he developed curriculum content, mentored students, and engaged in academic research. Prior to this, he was an Instructor at APTECH Computer Center (2014-2018), guiding students through machine learning projects and real-world applications 🎓. His early experience includes a Teaching Assistant role at Shah Abdul Latif University, where he supported research initiatives and practical learning in AI-related subjects 🔍.

🏆 Awards and Honors

Dr. Khan has received recognition for his contributions to AI, deep learning, and multimedia computing. His work has been featured in top-tier journals, and he has actively participated in research-driven initiatives. His academic excellence is reflected in his high GPA scores, international collaborations, and impactful research publications 📜.

🔬 Research Focus

Dr. Khan’s research spans deep learning, machine learning, and multimedia cloud computing. His core areas include domain generalization, multimodal learning, and fairness in AI models. He actively explores AI-driven QoE assessment for cloud gaming, representation learning for multimedia data, and security models for cloud environments. His interdisciplinary approach bridges AI, image/audio processing, and user experience enhancement 🌍.

📝 Conclusion

Dr. Awais Khan stands out as a researcher and educator dedicated to advancing AI applications in multimedia and cloud computing. With a solid academic foundation, extensive teaching experience, and an impressive publication record, he continues to push the boundaries of deep learning and machine learning research. His work significantly impacts QoE evaluation, multimedia security, and AI-driven automation, positioning him as a key contributor to the AI research community 🚀.

📄 Publications

Fog computing security: A review – Security and Privacy (2025) 🔗 [Cited By: TBD]

Deep learning-based QoE assessment of cloud gaming via emotions in a virtual reality environment – Journal of Cloud Computing (2025) 🔗 [Cited By: TBD]

Quality of experience (QoE) in cloud gaming: A comparative analysis of deep learning techniques via facial emotions in virtual reality environment – Sensors (2025) 🔗 [Cited By: TBD]

A proposed model for security of QoE data in cloud gaming environment – International Journal of Electronic Security and Digital Forensics (2025) 🔗 [Cited By: TBD]

Quality of experience that matters in gaming graphics: How to blend image processing and virtual reality – Electronics, vol. 13, no. 15 (2024) 🔗 [DOI: 10.3390/electronics13152998] [Cited By: TBD]

Unintended data behavior analysis using cryptography stealth approach against security and communication network– Mobile Networks and Applications (2023) 🔗 [Cited By: TBD]

Prediction of diabetic patients in Iraq using binary dragonfly algorithm with LSTM neural network – AIMS Electronics & Electrical Engineering, vol. 7, no. 3 (2023) 🔗 [Cited By: TBD]

Unmanned aerial vehicles: A review– Cognitive Robotics (2022) 🔗 [Cited By: TBD]

Analysis of the teaching quality on deep learning-based innovative ideological political education platform – Progress in Artificial Intelligence (2022) 🔗 [DOI: 10.1007/s13748-021-00272-0] [Cited By: TBD]

Examining the present and future integrated role of artificial intelligence in business: A survey study on the corporate sector – Journal of Computer and Communications, vol. 9, no. 1 (2021) 🔗 [Cited By: TBD]

 

Prof. Li Ping QIAN | Mobile edge computing | Best Researcher Award

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Prof. Li Ping QIAN | Mobile edge computing | Best Researcher Award

Professor, Zhejiang University of Technology, China

Dr. Li Ping Qian is a distinguished Professor at the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China. As an IEEE Senior Member and IEEE VTS Distinguished Lecturer (2024-2026), he is a leading researcher in wireless networks, edge intelligence, and emerging multiple access techniques. With a Ph.D. from The Chinese University of Hong Kong, his contributions to deep learning-powered communication networks have been widely recognized. His expertise in mobile edge computing and resource management has established him as a thought leader in the field.

Publication Profile

🎓 Education

Dr. Li Ping Qian earned his Ph.D. in Information Engineering from The Chinese University of Hong Kong in 2010. Prior to that, he obtained his Master of Engineering in Electronic Science and Technology (2006) and Bachelor of Engineering in Information Engineering (2004) from Zhejiang University, Hangzhou, China. His strong academic foundation has propelled his research in advanced communication networks.

💼 Experience

Dr. Qian is currently a Jianxing Distinguished Professor at Zhejiang University of Technology, a role he has held since 2022. Previously, he served as a Full Professor (2018-2021) and Associate Professor (2012-2017) at the same institution. He also leads as the Director of the Information and Communication Discipline. His international experience includes being a Visiting Scholar at the University of Waterloo (2016-2017) and a Research Collaborator at Princeton University (2009). His academic journey began as a Postdoctoral Research Associate at The Chinese University of Hong Kong (2010-2011).

🏆 Awards and Honors

Dr. Qian has received multiple prestigious Best Paper Awards, including the Best Conference Paper Award at UCom 2024 and IEEE ICCT 2023 for his work on integrated sensing and communication in edge computing networks. He has also secured significant research grants, including funding from the Natural Science Foundation of Zhejiang Province for multi-user scheduling and relay selection in 5G networks.

🔬 Research Focus

Dr. Qian’s research interests span deep learning-driven communication networks, edge intelligence, and mobile edge computing for IoT. His work delves into optimizing resource management in wireless networks and developing novel multiple access techniques for efficient data transmission. His innovative contributions bridge artificial intelligence and wireless communications, making him a key figure in the evolution of next-generation networks.

🔍 Conclusion

Dr. Li Ping Qian is a highly accomplished researcher whose contributions to deep learning, mobile edge computing, and next-generation wireless networks have shaped the field of modern communication systems. His extensive experience, prestigious awards, and impactful publications solidify his position as a leader in information engineering. 🚀

📝 Publications

Diffusion-Based Radio Signal Augmentation for Automatic Modulation Classification, Electronics, 2024 (DOI)

A Survey on Integrated Sensing, Communication, and Computing Networks for Smart Oceans, Journal of Sensor and Actuator Networks, 2022 (DOI)

Adaptive Facial Imagery Clustering via Spectral Clustering and Reinforcement Learning, Applied Sciences, 2021 (DOI)

Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks, Sensors, 2019 (DOI)

Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain, Sensors, 2018 (DOI)

Optimal Resource Allocation for Uplink Data Collection in Nonorthogonal Multiple Access Networks, Sensors, 2018 (DOI)

RFID Data-Driven Vehicle Speed Prediction via Adaptive Extended Kalman Filter, Sensors, 2018 (DOI)

Non-orthogonal multiple access assisted federated learning via wireless power transfer: A cost-efficient approach, IEEE Transactions on Communications, 2022 (Cited by 133)

Multi-server multi-user multi-task computation offloading for mobile edge computing networks, Sensors, 2019 (Cited by 130)

SWIPT cooperative spectrum sharing for 6G-enabled cognitive IoT network, IEEE Internet of Things Journal, 2022

 

Assoc. Prof. Dr. CHEN CAO | electric engineering | Best Researcher Award

Published on by Computer Scientists Awards

Assoc. Prof. Dr. CHEN CAO | electric engineering | Best Researcher Award

Shenyang University of Technology, China

Dr. Cao Chen is an esteemed Associate Professor at the School of Electrical Engineering, Shenyang University of Technology. He serves as a Doctoral and Master Supervisor, contributing significantly to electrical engineering research and education. With expertise in power transformers, switchgear, and electrical equipment diagnostics, he has led multiple national and provincial-level research projects, published over 50 research papers, and holds 30 authorized patents. His contributions to science and technology have earned him numerous prestigious awards, including multiple first prizes in scientific and technological progress from the Liaoning Provincial Government and China Machinery Industry Federation.

Publication Profile

Scopus

🎓 Education & Experience:

Dr. Cao Chen completed his postdoctoral research at Shenyang University of Technology between 2018 and 2020. He then transitioned to academia, first as a Lecturer (2020-2021) and later as an Associate Professor (2021-present) at the School of Electrical Engineering, Shenyang University of Technology. His extensive teaching portfolio includes undergraduate and postgraduate courses on electrical engineering, switchgear disconnection technology, and energy and power engineering.

🏆 Awards and Honors:

Dr. Cao Chen has received numerous prestigious awards, including the Liaoning Provincial Science and Technology Progress Second Prize (2024, First Completer) and First Prize (2019) for advancements in transformer performance enhancement and switchgear flexibility. Additionally, he has won China Machinery Industry Science and Technology First Prizes (2016, 2024) for his pioneering work in high-voltage switching and overvoltage suppression technologies. He is also actively involved in IEEE PES Power Interruption Technical Committee (China) and multiple committees of the China Electrotechnical Society.

🔬 Research Focus:

Dr. Cao Chen’s research revolves around transformer diagnostics, power equipment reliability, and fault detection. His work focuses on developing advanced online monitoring and fault diagnosis techniques for power transformers using vibration-based methods and multi-source data fusion. He has spearheaded multiple projects funded by the National Natural Science Foundation of China, provincial departments, and corporate enterprises, driving innovation in power system reliability.

🔚 Conclusion:

Dr. Cao Chen is a pioneering researcher and educator in electrical engineering, particularly in power equipment diagnostics and fault analysis. His groundbreaking work in transformer monitoring, fault detection, and power system reliability has earned him national and international recognition. As a leader in scientific research, a dedicated professor, and a key contributor to industrial advancements, he continues to push the boundaries of electrical engineering through innovative solutions and impactful research. 🚀

📖 Publications:

State Diagnosis Method of Transformer Winding Deformation Based on Fusing Vibration and Reactance Parameters. IET Electric Power Applications. (Cited in SCI Zone 2)

Research on Simulation Analysis and Joint Diagnosis Algorithm of Transformer Core-Loosening Faults Based on Vibration Characteristics. Energies. (Read here)

Transformer winding mechanical state diagnosis method based on current-frequency-vibration parameters. Journal of Electric Machines and Control.

Magnetostrictive vibration model and loose fault diagnosis method of transformer core based on elastic mechanics-thermodynamics. High Voltage Technology.

Monitoring Method on Loosened State and Deformational Fault of Transformer Winding Based on Vibration and Reactance Information. IEEE ACCESS. (Cited in SCI Zone 1)