Yi Zi Lu | Artificial Intelligence | Best Researcher Award

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Ms. Yi Zi Lu | Artificial Intelligence | Best Researcher Award

Student at Tianjin University of Commerce, China

Ms. Yi Zi Lu is a talented researcher and current Master’s student in Information and Communication Engineering at Tianjin University of Commerce, China. With a Bachelor’s degree in Automation, she has developed a strong academic foundation in Artificial Intelligence, focusing on Natural Language Processing (NLP). Her primary research revolves around deep learning, recommendation systems, and intelligent algorithms. Notably, her study titled “Hybrid News Recommendation Method Based on Title-Content Matching” addresses the challenge of inaccurate user interest modeling in news recommendation systems. By utilizing innovative techniques such as interactive attention networks and Siamese Neural Networks, her research enhances recommendation accuracy, showcasing her ability to tackle complex problems with creative solutions.

 

📝professional profile

ORCID

🎓Educational Details:

Ms. Yi Zi Lu holds a Bachelor’s degree in Automation from Tianjin University of Commerce, China, and is currently pursuing a Master’s degree in Information and Communication Engineering at the same institution. Her specialization in Artificial Intelligence, particularly in Natural Language Processing, showcases her strong academic foundation and focus on cutting-edge technologies.

👨‍🏫Innovation and Extension Activities:

Ms. Lu’s research is innovative, combining neural collaborative filtering and factorization machines to tackle the challenges of title-content mismatching. This approach highlights her ability to think creatively and develop solutions with practical applications. Her work has the potential to influence the development of more accurate and user-centric recommendation systems, indicating her commitment to advancing technology in her field.

Recognition and Professional Impact:

Although Ms. Lu is still a student, her contributions to the field of Artificial Intelligence are noteworthy. Her research has been published in reputable journals, and her involvement in cutting-edge projects positions her as a promising researcher. With a focus on deep learning and AI, her work is aligned with current technological trends, making her a valuable asset to the research community.

Research :

Ms. Lu has a solid research portfolio with a focus on deep learning, recommendation systems, and intelligent algorithms. Her notable research, titled “Hybrid News Recommendation Method Based on Title-Content Matching,” addresses critical issues in news recommendation systems. The study enhances user interest modeling by utilizing an interactive attention network and Siamese Neural Network, resulting in improved recommendation performance. This research has been validated through experiments and published in peer-reviewed journals, demonstrating her ability to contribute to the academic community.

Conclusion:

Ms. Yi Zi Lu’s academic background, research contributions, and innovative approach make her a strong candidate for the Best Researcher Award. Her work in Artificial Intelligence and Natural Language Processing, particularly in developing advanced recommendation systems, demonstrates her potential to make significant contributions to the field. While she is still early in her career, her achievements thus far suggest a promising future, making her deserving of recognition through this award.

📚Publications to Noted

The publication titled “A Hybrid News Recommendation Approach Based on Title–Content Matching” was published in the journal Mathematics in July 2024. The article’s DOI is 10.3390/math12132125. The authors of this publication are:

Shuhao Jiang

Yizi Lu

Haoran Song

Zihong Lu

Yong Zhang​

 

Subhayu Ghosh | Artificial Intelligence | Best Researcher Award

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Mr. Subhayu Ghosh | Artificial Intelligence | Best Researcher Award

 

PhD Scholar at National Institute of Technology Durgapur, India

Subhayu Ghosh is a full-time Ph.D. research scholar in the Department of Computer Science and Engineering at the National Institute of Technology Durgapur, India. His research interests encompass audio and speech processing, biomedical image processing, multi-modal artificial intelligence, generative artificial intelligence, and machine learning and deep learning. With a strong academic background and several notable achievements, Ghosh demonstrates significant potential and dedication to his field.

 

📝professional profile

ORCID

Scopus Profile

Google Scholar

🎓Educational Details:

Subhayu Ghosh’s academic journey is impressive, beginning with his high school education at Burdwan Municipal High School, where he achieved outstanding results in both secondary (94%) and higher secondary (91.6%) examinations. He earned his B.Tech in Computer Science and Engineering from the National Institute of Technology Durgapur with a CGPA of 7.97. His undergraduate project focused on regional speaker identification using convolutional neural networks, supervised by Dr. Nanda Dulal Jana. Ghosh is currently pursuing his Ph.D. at the same institution, with a thesis titled “Generative Artificial Intelligence Based Audio-Visual Speech Synthesis: Some Advanced Approaches,” under the supervision of Dr. Nanda Dulal Jana. He is expected to submit his thesis in November 2025.

👨‍🏫Professional Experience:

Ghosh’s professional experience includes teaching assistance roles for both B.Tech and M.Tech courses at the National Institute of Technology Durgapur. He has assisted in labs focusing on modeling and simulation in Python, object-oriented programming in C++, and networking and data communication in Python. Additionally, Ghosh has a robust technical skill set, including expertise in machine learning, deep learning, optimization techniques, data structures, socket programming, and proficiency in various programming languages such as C++, Python, and C.

Research :

Ghosh has made notable contributions to his field through various research endeavors. He has authored three journal papers, two conference papers, and five book chapters, amassing a cumulative impact factor of 8.3 and nine Google Scholar citations. His work in machine learning, deep learning, and artificial intelligence has been well-received, evidenced by his participation as a Technical Program Committee reviewer for the IEEE 42nd International Joint Conference on Neural Networks (IJCNN-2024) and his selection as a travel grant recipient from the IEEE Computational Intelligence Society for the World Congress on Computational Intelligence (WCCI-2024).

Conclusion:

Subhayu Ghosh’s comprehensive academic background, significant research contributions, professional experience, and active engagement in both academic and extracurricular activities make him a highly suitable candidate for the Research for Best Researcher Award. His dedication to advancing the field of computer science and engineering, coupled with his demonstrated expertise and achievements, underscores his potential as an outstanding researcher. Ghosh’s work in generative artificial intelligence, audio and speech processing, and other cutting-edge areas of research positions him as a promising leader and innovator in his field.

📚Publications to Noted

Title: Rectified Adam Optimizer-Based CNN Model for Speaker Identification

Authors: A. Mazumder, S. Ghosh, S. Roy, S. Dhar, N.D. Jana

Publication: Advances in Intelligent Computing and Communication: Proceedings of ICAC

Year: 2022

Citations: 6

Title: Region Normalized Capsule Network Based Generative Adversarial Network for Non-Parallel Voice Conversion

Authors: M.T. Akhter, P. Banerjee, S. Dhar, S. Ghosh, N.D. Jana

Publication: International Conference on Speech and Computer

Pages: 233-244

Year: 2023

Citations: 3

Title: CCLCap-AE-AVSS: Cycle Consistency Loss Based Capsule Autoencoders for Audio–Visual Speech Synthesis

Authors: S. Ghosh, N.D. Jana, T. Si, S. Mallik, M.A. Shah

Publication: Journal of Intelligent Systems

Volume: 33 (1)

Article Number: 20230171

Year: 2024

Title: Audio-Visual Speech Synthesis Using Vision Transformer–Enhanced Autoencoders with Ensemble of Loss Functions

Authors: S. Ghosh, S. Sarkar, S. Ghosh, F. Zalkow, N.D. Jana

Publication: Applied Intelligence

Volume: 54 (6)

Pages: 4507-4524

Year: 2024

Title: Melanoma Skin Cancer Detection Using Ensemble of Machine Learning Models Considering Deep Feature Embeddings

Authors: S. Ghosh, S. Dhar, R. Yoddha, S. Kumar, A.K. Thakur, N.D. Jana

Publication: Procedia Computer Science

Volume: 235

Pages: 3007-3015

Year: 2024