Yansheng Wu | Computer Science | Best Researcher Award

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Dr. Yansheng Wu | Computer Science | Best Researcher Award

Associate Professor, Nanjing University of Posts and Telecommunications, China

Dr. Yansheng Wu is a distinguished researcher and Associate Professor at the School of Computer Science, Nanjing University of Posts and Telecommunications, China. With a strong background in Pure Mathematics, his expertise lies in Finite Fields, Cryptography, and Coding Theory. He has made significant contributions to algebraic coding theory and applications of algebra in cryptographic systems. Dr. Wu has held esteemed positions as a Postdoctoral Research Fellow at Ewha Womans University and a Visiting Scholar at the Hong Kong University of Science and Technology. His scholarly work is widely recognized, with numerous publications in prestigious journals such as IEEE Transactions on Information Theory and Finite Fields and Their Applications. In addition to his research, he actively contributes as a reviewer for multiple high-impact journals, making him a key figure in the field of applied mathematics and cryptography. 📚✨

Publication Profile

🎓 Education

Dr. Yansheng Wu holds a Ph.D. in Pure Mathematics from Nanjing University of Aeronautics and Astronautics (2019), where he focused on algebra and number theory applications in coding theory. He earned his MSc in Pure Mathematics from Guangxi Teachers Education University (2016), working on matrix rings and finite group rings. His academic journey began with a BSc in Mathematics and Applied Mathematics from Anhui Normal University (2013), where he explored number partitions. His rigorous training in algebra, number theory, and their cryptographic applications has shaped his prolific research career. 🎓🔢

💼 Experience

Dr. Wu’s professional journey includes serving as an Associate Professor at Nanjing University of Posts and Telecommunications since 2020. Prior to that, he completed a postdoctoral fellowship at Ewha Womans University, South Korea, where he collaborated on advanced research in finite fields. His academic engagements also include a visiting scholar position at the Hong Kong University of Science and Technology in 2023-2024, enhancing international collaborations in cryptography and coding theory. He has participated in prestigious research forums, including the East Asian Core Doctoral Forum at The University of Tokyo. 🌍📊

🏆 Awards and Honors

Dr. Wu has secured multiple prestigious research grants, including the National Natural Science Foundation of China (2022-2024) and the Talent Introduction Fund at Nanjing University of Posts and Telecommunications (2021-2023). His research excellence is also recognized through editorial board memberships at leading journals like AIMS Mathematics and numerous reviewing roles for top-tier mathematical and cryptographic journals, including IEEE Transactions on Information Theory and Finite Fields and Their Applications. 🏅🔬

🔬 Research Focus

Dr. Wu’s research interests span Finite Fields, Coding Theory, Cryptography, and Algebraic Structures. His work explores the design and analysis of linear codes, algebraic cryptosystems, and combinatorial structures over finite fields. He has extensively studied the properties of MDS codes, Reed-Solomon codes, and quaternary codes, contributing novel constructions with optimal parameters. His interdisciplinary approach integrates number theory with applied cryptography, making his research pivotal in modern data security and error correction. 🔢🛡️

🔚 Conclusion

Dr. Yansheng Wu is a leading figure in the field of mathematics, cryptography, and coding theory. His contributions to algebraic coding, finite fields, and cryptographic structures have significantly impacted secure communications and data integrity. Through his research, editorial roles, and academic collaborations, he continues to shape the future of cryptographic mathematics, making lasting contributions to theoretical and applied aspects of the discipline. 🚀🔢

📚 Publications

Two classes of twisted generalized Reed-Solomon codes with two twists. Finite Fields and Their Applications, 104, 102595. [Cited by: TBD] 🔗 Link

When Does the Extended Code of an MDS Code Remain MDS? IEEE Transactions on Information Theory, 71(1), 263-272. [Cited by: TBD] 🔗 Link

Two classes of narrow-sense BCH codes and their duals. IEEE Transactions on Information Theory, 70(1), 131-144. [Cited by: TBD] 🔗 Link

Linear Complementary Dual Codes Constructed from Reinforcement Learning. Journal of System Science and Complexity. [Cited by: TBD] 🔗 Link

Two families of linear codes with desirable properties from some functions over finite fields. IEEE Transactions on Information Theory, 70(11), 8320-8342. [Cited by: TBD] 🔗 Link

Optimal few-weight codes and their subfield codes. Journal of Algebra and Its Applications, 23(4), 2450248. [Cited by: TBD] 🔗 Link

Two Infinite Families of Quaternary Codes. IEEE Transactions on Information Theory, 70(12), 8723-8733. [Cited by: TBD] 🔗 Link

Quaternary codes and their binary images. IEEE Transactions on Information Theory, 70(7), 4759-4768. [Cited by: TBD] 🔗 Link

 

slimane arbaoui | Artificial Intelligence | Young Scientist Award

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Mr. slimane arbaoui | Artificial intellegence | Young Scientist Award

Cube-SDC team, INSA Strasbourg, University of Strasbourg , 24 Bd de la Victoire, Strasbourg, 67000, France, insa strasbourg, France

Slimane Arbaoui is a dedicated final-year Computer Science student at École Supérieure en Informatique (ESI) in Sidi Bel Abbess, Algeria, specializing in Android application development and machine learning. 🎓 His skills span Java-based Android development, data integration, and advanced problem-solving in software, alongside a versatile understanding of multiple programming languages, including Python and Kotlin. Slimane has applied his AI knowledge to impactful projects, even authoring a research paper. 📚 Known for his innovation and strong analytical skills, Slimane is passionate about tackling real-world challenges with technology.

Publication Profile

Scopus

Education

Slimane completed his State Engineering and Master’s degrees in Computer Science at ESI SBA in 2023. 🎓 His academic journey has strengthened his technical expertise and provided a foundation in both theoretical and applied computing, with a focus on machine learning, mobile app development, and web technologies.

Experience

During his internship at INSA-Strasbourg, France 🇫🇷, Slimane applied machine learning to improve battery health prediction, developing models that track and identify factors contributing to battery degradation. At CNAS in Algeria, he gained practical insights into network database applications and web app development. 💻 As a freelancer on Upwork, Slimane developed Android applications and managed web back-end services, demonstrating his versatility in real-world projects.

Research Focus

Slimane’s research interests center on artificial intelligence and machine learning, with a special focus on NLP applications, sentiment analysis, and health data prediction. 🧠 His projects include sentiment analysis and fake news detection in Arabic language datasets, alongside health management applications that leverage data-driven insights to enhance service quality. His work in battery health prediction highlights his proficiency in machine learning model development and evaluation.

Awards and Honours

Slimane holds several certifications, including Microsoft Certified: Azure Fundamentals and the Android Basics Nanodegree. 🏅 His achievements in AI include completing courses on deep learning and machine learning through Kaggle and Coursera, which demonstrate his commitment to continuous learning and professional development.

Publication Top Notes

Dual-model approach for one-shot lithium-ion battery state of health sequence prediction

SOCXAI: Leveraging CNN and SHAP Analysis for Battery SOC Estimation and Anomaly Detection

Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries

 

 

Hsiu Hsia Lin | Machine learning | Best Researcher Award

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Prof. Hsiu Hsia Lin | Machine learning | Best Researcher Award

Research Fellow, Chang Gung Memorial Hospital, Taiwan

Dr. Hsiu-Hsia Lin is a dedicated Research Fellow at the Craniofacial Research Center, Chang Gung Memorial Hospital, Taiwan, and an Adjunct Assistant Professor at the Graduate Institute of Dental and Craniofacial Science, Chang Gung University. With a strong foundation in AI and 3D craniofacial image processing, her research contributes significantly to advancements in orthognathic surgery. Dr. Lin’s expertise in surgical navigation and CAD/CAM-assisted surgery is pivotal in improving craniofacial surgical outcomes. 🌟

Publication Profile

Education:

Dr. Lin earned her Ph.D. in Computer Science and Engineering from National Chung Hsing University, Taiwan, following a Master’s in Computer Science from Tunghai University. Her academic journey is deeply rooted in computer science, blending AI with craniofacial research. 🎓📚

Experience:

Dr. Lin has held key research positions, including Assistant Research Fellow and Postdoctoral Fellow at the Craniofacial Research Center, Chang Gung Memorial Hospital. Her postdoctoral work also extended to the Department of Computer Science and Engineering at National Chung Hsing University. Her extensive experience has helped bridge the gap between AI technology and clinical applications. 💼🔬

Research Focus:

Dr. Lin’s research revolves around Pattern Recognition, Artificial Intelligence, and 3D Craniofacial Image Processing. She specializes in computer-aided surgical simulation for orthognathic surgery, surgical navigation, and CAD/CAM-assisted procedures, aiming to optimize outcomes in facial surgery. 🧠💻

Awards and Honors:

Dr. Lin has received multiple recognitions for her contributions to craniofacial research and AI in surgery. Her work continues to shape modern surgical approaches, particularly in orthognathic surgery, enhancing patient outcomes. 🏆👏

Publication Top Notes:

Dr. Lin’s publications focus on integrating AI with medical applications, particularly in 3D craniofacial analysis and orthognathic surgery. Her studies offer novel methods for surgical planning, facial attractiveness assessment, and facial symmetry evaluation.

Quantification of facial symmetry in orthognathic surgery (Dec. 2024) in Comput Biol Med., cited by 5 articles. DOI

Average 3D virtual sk

eletofacial model for surgery planning (Feb. 2024) in Plast Reconstr Surg., cited by 3 articles. DOI

Facial attractiveness assessment using transfer learning (Jan. 2024) in Pattern Recognit., cited by 4 articles. DOI

Optimizing Orthognathic Surgery (Nov. 2023) in J. Clin. Med., cited by 6 articles. DOI

Single-Splint, 2-Jaw Orthognathic Surgery (Nov. 2023) in J Craniofac Surg., cited by 2 articles. DOI

Applications of 3D imaging in craniomaxillofacial surgery (Aug. 2023) in Biomed J., cited by 7 articles. DOI

Facial Beauty Assessment using Attention Mechanism (Mar. 2023) in Diagnostics, cited by 8 articles. DOI

 

MUNMI DUTTA | Machine Learning | Best Researcher Award

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Mrs. MUNMI DUTTA | Machine Learning | Best Researcher Award

Research Scholar, Assam Engineering College, India

🔬 Munmi Dutta is a dedicated academic and researcher with expertise in Artificial Intelligence and Machine Learning. Her research focuses on speaker identification, product categorization, and generative AI for online education systems. Currently pursuing her Ph.D. at Gauhati University, she has contributed significantly to AI-driven applications in e-commerce and speech processing.

Publication Profile

Scopus

Strengths for the Award

  1. Academic Excellence: Munmi Dutta’s academic journey, including a Ph.D. in progress and an M.Tech in Electronics and Communication Technology, demonstrates her commitment to research and knowledge advancement.
  2. Project Experience: She has completed several significant projects, such as developing a fire alarm system, a remote-controlled fan regulator, and a pitch determination system using neural networks. These projects showcase her practical and research skills in both hardware and software domains.
  3. Research in AI and Machine Learning: Dutta’s work in speaker identification using Artificial Neural Networks (ANN) and product categorization in e-commerce using machine learning reflects her proficiency in cutting-edge technologies, especially Artificial Intelligence. Her research also addresses real-world problems, adding practical relevance.
  4. Publications: She has multiple journal publications, including in the prestigious Applied Soft Computing Journal, which demonstrates her research output in emerging technologies like machine learning and neural networks. The acceptance of a book chapter on AI and IoT in online education further highlights her versatility.
  5. Collaborative Research: The variety of co-authors in her publications suggests that Dutta is capable of working in teams and contributes effectively to collaborative research, which is a valuable quality in any researcher.

Areas for Improvement

  1. Broader Research Impact: Although her work in machine learning and AI is commendable, the scope of her research could be expanded to other interdisciplinary areas to broaden the impact. This would also enhance her chances of being recognized as a top researcher in her field.
  2. PhD Completion: As she is still pursuing her PhD, completing this degree could further strengthen her candidacy for the Best Researcher Award, as a completed doctoral degree adds academic credibility.
  3. Leadership and Mentorship: While her publications and research experience are impressive, demonstrating leadership in research groups or mentorship roles would help solidify her position as a leading researcher.
  4. International Exposure: Although she has participated in conferences and published research, gaining more international exposure by attending or presenting at global conferences could help elevate her recognition and contribution to the global research community.

Education

Munmi Dutta holds an M.Tech in Electronics and Communication Technology from IST, Gauhati University, with a CGPA of 7.13. She completed her B.E. in Applied Electronics and Instrumentation Engineering from GIMT, also under Gauhati University, achieving a percentage of 67.67%. Her academic journey began at Don Bosco High School, followed by J. B. College for higher secondary education. 🎓💡

Experience

💼 Munmi Dutta has extensive experience in academic research, with a focus on AI applications in speech processing, product categorization, and e-commerce. She has presented at national and international conferences and co-authored several notable publications. Her work includes building speaker identification systems and applying neural networks for speech recognition.

Research Focus

🧠 Munmi Dutta’s research interests include speaker identification using artificial neural networks, machine learning for product categorization in e-commerce, and generative AI in education systems. She has worked on innovative projects such as pitch determination for speaker identification, remote-controlled fan regulators, and fire alarms using temperature sensors.

Awards and Honors

🏆 Munmi Dutta has earned recognition for her contributions to AI and technology, including presenting at prestigious conferences like the International Conference on Recent Developments in Science, Technology, Engineering, and Management (ICRDSTEM-2022). Her work in the fields of AI and e-commerce has garnered respect within academic circles.

Publication Top Notes

📝 “Closed-Set Text Independent Speaker Identification System Using Multiple ANN Classifiers” – Advances in Intelligent Systems and Computing, 2014. Cited by several researchers, this paper focuses on the application of ANN for speaker identification Link.

📝 “Product Categorization in Fashion and Lifestyle Commerce using Machine Learning” – Journal of Emerging Technologies and Innovation Research, 2022. This study explores the use of machine learning in e-commerce product categorization Link.

📝 “Incremental-based YoloV3 model with Hyper-parameter Optimization for Product Image Classification in E-commerce Sector” – Applied Soft Computing Journal, 2024. A detailed examination of YoloV3 model optimization for product image classification Link.

Conclusion

Munmi Dutta has demonstrated strong potential as a researcher with her contributions in AI, machine learning, and electronics. Her numerous publications, research projects, and continued pursuit of a Ph.D. make her a promising candidate for the Best Researcher Award. However, achieving more interdisciplinary impact, completing her PhD, and gaining further international exposure will significantly bolster her qualifications for this award.