Qiang Li | Artificial Intelligence | Best Researcher Award

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Prof. Qiang Li | Artificial Intelligence | Best Researcher Award ย ย ๐Ÿ†

Professor at Anhui Agricultural University, China

Prof. Qiang Li is a distinguished mathematician specializing in applied and computational mathematics. He holds a Doctor of Science from Southeast University and has over a decade of academic experience, starting with his Bachelorโ€™s degree at Changzhi University. Currently, he serves in the Department of Applied Mathematics at Anhui Agricultural University. Prof. Liโ€™s research focuses on stochastic systems, neural networks, and state estimation, resulting in multiple high-impact publications in renowned journals like Applied Mathematics and Computation and Neural Networks. His work contributes to advancements in Markovian switching systems, semi-Markovian models, and capital systems with stochastic effects, demonstrating his expertise and innovation.

Profile

Scopus

ORCID

Education ๐ŸŽ“:

Prof. Qiang Li has an impressive educational background in mathematics, showcasing a strong foundation and expertise in the field. He earned his Doctor of Science degree from the School of Mathematics at Southeast University in 2021, focusing on applied and computational mathematics. Prior to this, he completed his Master of Science at the School of Mathematics and Information Science, North Minzu University, in 2017, where he honed his skills in mathematical modeling and analysis. He began his academic journey with a Bachelor of Science degree from the Faculty of Mathematics at Changzhi University in 2014, establishing a robust base in mathematical theories and applications.

Work Experience ๐Ÿ’ผ:

Prof. Qiang Li has accumulated extensive experience in the field of applied mathematics and computational systems. Since 2021, he has been a faculty member in the Department of Applied Mathematics at the School of Science, Anhui Agricultural University, where he engages in teaching, research, and academic mentorship. His professional journey encompasses expertise in stochastic systems, Markovian switching models, and neural network analysis, with a particular focus on state estimation, synchronization, and numerical methods. His collaborative efforts have led to significant advancements in mathematical modeling and computation, as evidenced by his impactful publications in esteemed international journals.

Skills ๐Ÿ”

Prof. Qiang Li possesses exceptional skills in advanced mathematical modeling, stochastic processes, and numerical analysis, enabling him to address complex challenges in dynamic systems. He is proficient in analyzing Markovian and semi-Markovian switching systems, with expertise in state estimation, synchronization, and stability. Prof. Li demonstrates strong capabilities in computational tools and techniques, including matrix measures and numerical methods for fractional Brownian motion and Poisson jumps. Additionally, he has a keen understanding of neural networks, specifically complex-valued neural networks (CVNNs), and their applications in dynamic systems. His analytical and problem-solving skills are complemented by a deep commitment to innovative research.

Awards and Honors ๐Ÿ†

Prof. Qiang Li has earned recognition for his significant contributions to applied mathematics and computational research. His achievements are underscored by several prestigious awards and honors, reflecting his academic excellence and impact in the field. These accolades highlight his groundbreaking work in Markovian and semi-Markovian switching systems, stability analysis, and numerical methods. His research outputs, published in high-impact journals such as Applied Mathematics and Computation and Neural Networks, have further solidified his reputation as a leading scholar, earning him respect and acknowledgment within the global academic community.

Research Interests:

Prof. Qiang Li’s research interests lie at the intersection of applied mathematics and computational science, focusing on stochastic systems, Markovian switching complex-valued neural networks (CVNNs), and semi-Markovian processes. His work delves into state estimation, synchronization, and stability analysis of dynamic systems, often under complex conditions such as missing measurements, quantization effects, and mode-dependent delays. He is also deeply engaged in exploring dissipative methods, fractional Brownian motion, and numerical methods for systems with Poisson jumps. Prof. Li’s research aims to develop innovative mathematical frameworks and computational tools for solving real-world problems in dynamic and stochastic systems.

๐Ÿ“š Publicationsย 

Asynchronous Nonfragile Guaranteed Performance Control for Singular Switched Positive Systems: An Event-Triggered Mechanism

  • Authors: J. Wang, Q. Li, S. Li, L. Zhang
  • Journal: International Journal of Robust and Nonlinear Control
  • Volume: 34, Issue 17, Pages 11451-11468
  • Publication Year: 2024
  • Cited by: 0

Improved Execution Efficiency of FPE Scheme Algorithm Based on Structural Optimization

  • Authors: X.-W. Yang, L. Wang, M.-L. Xing, Q. Li
  • Journal: Electronics (Switzerland)
  • Volume: 13, Issue 20, Article 4007
  • Publication Year: 2024
  • Cited by: 0

l1 Filtering for Uncertain Discrete-Time Singular Switched Positive Systems with Time Delay and Output Quantization

  • Authors: J. Wang, A. Gao, Q. Li, B. Xie
  • Journal: Journal of the Franklin Institute
  • Volume: 361, Issue 13, Article 107028
  • Publication Year: 2024
  • Cited by: 0

Exponential Stability of Impulsive Stochastic Neutral Neural Networks with Lรฉvy Noise Under Non-Lipschitz Conditions

  • Authors: S. Ma, J. Li, R. Liu, Q. Li
  • Journal: Neural Processing Letters
  • Volume: 56, Issue 4, Pages 208
  • Publication Year: 2024
  • Cited by: 0

Mathematical Analysis of Stability and Hopf Bifurcation in a Delayed HIV Infection Model with Saturated Immune Response

  • Authors: Z. Hu, J. Yang, Q. Li, S. Liang, D. Fan
  • Journal: Mathematical Methods in the Applied Sciences
  • Volume: 47, Issue 12, Pages 9834-9857
  • Publication Year: 2024
  • Cited by: 1

Dissipative Synchronization of Semi-Markovian Jumping Delayed Neural Networks Under Random Deception Attacks: An Event-Triggered Impulsive Control Strategy

  • Authors: H. Wei, K. Zhang, M. Zhang, Q. Li, J. Wang
  • Journal: Journal of the Franklin Institute
  • Volume: 361, Issue 8, Article 106835
  • Publication Year: 2024
  • Cited by: 8

Conclusionย 

Prof. Qiang Liโ€™s academic credentials, professional expertise, and groundbreaking research establish him as an outstanding candidate for the Best Researcher Award. His innovative contributions to Markovian systems and nonlinear mathematics position him as a leader in his field, deserving of recognition for his impact and dedication to advancing mathematical sciences.

 

Mr. NianWang | Machine Learning | Best Researcher Award

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Mr. NianWang | Machine Learning | Best Researcher Award ๐Ÿ†

Xiโ€™an Research Institute of High-tech, Xi ‘an, Shaanxi, China๐ŸŽ“

Nian Wang is a dedicated PhD candidate at the Xiโ€™an Research Institute of High-tech in Xiโ€™an, China. Specializing in machine learning and deep learning applications, Nian has established himself as a promising researcher, with extensive experience as a journal reviewer for prominent IEEE publications.

 

Professional Profileย 

  • google scholar

Education ๐ŸŽ“:

Nian Wang is currently pursuing a PhD at the Xiโ€™an Research Institute of High-tech, specializing in advanced machine learning techniques.

Work Experience ๐Ÿ’ผ:

As a PhD candidate, Nian is actively engaged in cutting-edge research and has gained valuable experience through serving as a reviewer for prominent journals such as IEEE Transactions on Image Processing and Pattern Recognition.

 

Skills ๐Ÿ”:

Nian possesses expertise in deep learning, data clustering, image enhancement, and object recognition. His skills in developing innovative solutions for complex image processing problems have been demonstrated through his research contributions.

Awards and Honors ๐Ÿ†:

In 2022, Nian received the Excellent Doctoral Dissertation award from the China Ordnance Industry Society, recognizing his significant academic contributions.

Memberships ๐Ÿค:

Currently, Nian holds no formal memberships in professional organizations, focusing primarily on his research and academic contributions.

Teaching Experience ๐Ÿ‘ฉโ€๐Ÿซ:

While there are no formal teaching roles mentioned, his involvement in research and peer review indicates a strong understanding of academic concepts, which can translate into potential future teaching opportunities.

Research Focus ๐Ÿ”ฌ:

Nianโ€™s research is centered on machine learning applications, particularly in data clustering, image dehazing, and UAV object tracking. His innovative work includes developing the Capsule Attention Network (CAN) for hyperspectral image classification, showcasing improved performance and reduced computational burden compared to existing methods.

Conclusionย 

In conclusion, Nian Wang is a highly suitable candidate for the Best Researcher Award due to his innovative contributions, recognized expertise, and commitment to advancing research in machine learning. His achievements speak volumes about his potential for future advancements in the field. By addressing areas for improvement, Nian can enhance his profile further, positioning himself as a leader in research and innovation. Awarding him this honor would not only recognize his past accomplishments but also encourage his continued contributions to the scientific community.

๐Ÿ“š Publilcationย 

 

JiaLi Zhu | Artificial Intelligence | Best Researcher Award

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Ms JiaLi Zhu | Artificial Intelligence | Best Researcher Award ๐Ÿ†

Research Fellow at University of Naples Federico II , Italy๐ŸŽ“

Jiali Zhu is a Senior Algorithm Engineer at Alipay, Ant Group with expertise in machine learning and deep learning. She holds a Masterโ€™s degree in Computer Technology from Southeast University, completed in June 2023 . Since July 2023, Jiali has worked as a Machine Learning Algorithm Engineer at Ant Groupโ€™s Alipay, focusing on cutting-edge algorithm development .

Professional Profile

Education๐ŸŽ“

Jiali Zhu earned a Master’s degree in Computer Technology from Southeast University in June 2023. Her academic journey reflects a strong foundation in advanced computing and algorithm design.

๐Ÿ’ผWork Experience

In July 2023, she embarked on her professional career as a Machine Learning Algorithm Engineer at Ant Group’s Alipay. She now holds the title of Senior Algorithm Engineer, where she works on innovative projects in machine learning and AI applications.

ย ๐Ÿ› ๏ธSkills

Jiali is skilled in machine learning, deep learning, medical imaging technologies, and multimodal language models. Her expertise spans advanced algorithm design, attention mechanisms, and quantitative susceptibility mapping.

๐Ÿ†Awards and Honors

She is a nominee for the “Best Researcher Award,” acknowledging her significant contributions in the field of machine learning and medical imaging.

ย Research Focus ๐Ÿ”ฌ

Jialiโ€™s research focuses on integrating deep learning with medical imaging. She has contributed to projects like MobileFlow, a multimodal LLM for mobile GUI agents, and DE-Net, a detail-enhanced MR reconstruction network.

 

๐Ÿ“–Publications :ย 

  • DE-Net: Detail-enhanced MR reconstruction network via global-local dependent attention
    ๐Ÿ“… Year: 2024
    ๐Ÿ“– Journal: Biomedical Signal Processing and Control
    ๐Ÿง  Authors: J. Zhu, D. Hu, W. Mao, J. Zhu, R. Hu, Y. Chen
  • MobileFlow: A Multimodal LLM For Mobile GUI Agent
    ๐Ÿ“… Year: 2024
    ๐Ÿ“– Journal: arXiv preprint (arXiv:2407.04346)
    ๐Ÿง  Authors: S. Nong, J. Zhu, R. Wu, J. Jin, S. Shan, X. Huang, W. Xu
  • Edge prior guided dictionary learning for quantitative susceptibility mapping reconstruction
    ๐Ÿ“… Year: 2022
    ๐Ÿ“– Journal: Quantitative Imaging in Medicine and Surgery
    ๐Ÿง  Authors: J. Du, Y. Ji, J. Zhu, X. Mai, J. Zou, Y. Chen, N. Gu

 

Pritom Jyoti Goutom | Artificial Intelligence | Best Researcher Award

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Mr Pritom Jyoti Goutom | Artificial Intelligence | Best Researcher Award

 

Research Scholar and Project Associate I at Dibrugarh University ,India

Profile:

Scopusย 

Education:

Pritom Jyoti Goutom is affiliated with the Centre for Computer Science and Applications at Dibrugarh University in Assam, India ๐Ÿ‡ฎ๐Ÿ‡ณ. His research focuses on natural language processing (NLP), particularly related to the Assamese language ๐Ÿ—ฃ๏ธ.

Some of his notable contributions include:

  1. Text Summarization ๐Ÿ“„: He has co-authored papers on text summarization techniques using deep learning, such as “An Abstractive Text Summarization Using Deep Learning in Assamese” and “Text Summarization in Assamese Language Using Sequence to Sequence RNNs”ใ€5โ€‹ (ORCID)โ€‹โ€‹ (Dibrugarh University)โ€‹2. Collaboration with Dr. Nomi Baruah ๐Ÿค: He often works with Dr. Baruah and others on projects aimed at improving NLP for low-resource languages.

For more details about his educational background and academic contributions, you can check his profile on academic platforms like ORCIDใ€5โ€‹ (ORCID)

Professional Experience:

๐ŸŽ“ Research Scholar at Dibrugarh University
Specializing in Natural Language Processing (NLP) and AI-generated text in Assamese.
๐Ÿ” Research Focus: Text summarization, part-of-speech tagging, and fake news detection.

๐Ÿ‘จโ€๐Ÿ’ป Project Associate I, Dept. of Computer Science and Engineering
Contributing to cutting-edge projects and innovations in AI and NLP.

Research ย Focusย  ย :

  • Text Summarization: Developing algorithms for concise representation of Assamese text.
  • Machine Translation: Enhancing language conversion models for Assamese.
  • Sentiment Analysis: Analyzing opinions and emotions expressed in Assamese text.
  • Named Entity Recognition (NER): Identifying and categorizing entities in Assamese text.
  • Fake News Detection: Implementing models to identify misinformation in Assamese news sources.
  • Language Modeling: Building computational models to understand and generate Assamese text.

 

Contributions :

  • Co-authored research on abstractive text summarization using deep learning approaches, focusing on the nuances of the Assamese language.
  • Investigated LSTM and BiLSTM algorithms for fake news detection, enhancing accuracy and reliability in Assamese news sources.
  • Developed attention-based transformer models for text summarization in Assamese, improving content extraction and generation.
  • Worked on automatic spelling error identification using deep learning algorithms tailored for Assamese language nuances.

Citations:

Total Citations: ๐Ÿ“ˆ 13

Total Documents:ย  ๐Ÿ“‚3

h-index: ๐ŸŒŸ 1

Publication Top Notes:

  • Goutom, P. J., Baruah, N., & Sonowal, P. (2023). An abstractive text summarization using deep learning in Assamese. International Journal of Information Technology, 15(5), 2365-2372. (6 citations)

 

  • Phukan, R., Goutom, P. J., & Baruah, N. (2024). Assamese Fake News Detection: A Comprehensive Exploration of LSTM and Bi-LSTM Techniques. Procedia Computer Science, 235, 2167-2177.

 

  • Goutom, P. J., Baruah, N., & Sonowal, P. (2024). Attention-based Transformer for Assamese Abstractive Text Summarization. Procedia Computer Science, 235, 1097-1104.

 

  • Phukan, R., Neog, M., Goutom, P. J., & Baruah, N. (2024). Automated Spelling Error Detection in Assamese Texts using Deep Learning Approaches. Procedia Computer Science, 235, 1684-1694.

 

  • Goutom, P. J., & Baruah, N. (2023). Text summarization in Assamese language using sequence to sequence RNNs. Indian Journal of Science and Technology, 16(SP2), 22-29.