Anuradha Choudhury | Computer Science and Artificial Intelligence | Women Researcher Award

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Ms. Anuradha Choudhury | Computer Science and Artificial Intelligence | Women Researcher Award

Student at OUTR, india

Anuradha Rani Choudhury is a dedicated and aspiring researcher currently pursuing her Master of Technology at OUTR, Bhubaneswar, with an impressive CGPA of 9.20. She holds a Bachelor’s degree in Technology from ITER, Bhubaneswar, and has demonstrated strong technical acumen through impactful projects in machine learning, including sentiment analysis and disease prediction models, both achieving high accuracy. With certifications in machine learning, web development, and Android development, she exhibits a strong foundation in diverse technological domains. Anuradha has actively participated in seminars and workshops, showcasing her commitment to continuous learning. Her proficiency in programming languages such as Python, Java, and C, along with web technologies, complements her research skills. Additionally, her involvement in extracurricular activities like sports, NCC, and student leadership reflects a well-rounded personality. While she is at an early stage in her research journey, Anuradha shows promising potential for future contributions to the field of technology and applied sciences.

Professional Profile 

🎓 Education of Anuradha Rani Choudhury

Anuradha Rani Choudhury has built a strong academic foundation in the field of technology and engineering. She is currently pursuing her Master of Technology (M.Tech) at Odisha University of Technology and Research (OUTR), Bhubaneswar, maintaining an excellent CGPA of 9.20 (2023–2025). She previously earned her Bachelor of Technology (B.Tech) from ITER, Bhubaneswar, graduating with a CGPA of 7.73 in 2022. Her earlier academic journey includes completing 12th grade (Science) from Stewart Science College, Cuttack under the CHSE Board, scoring 57.5%, and her 10th grade from D.A.V. Public School, Berhampur under the CBSE Board, where she achieved a commendable 83.6%. Her academic progression highlights her consistent dedication and growth in technical education.

💼 Professional Experience of Anuradha Rani Choudhury

Anuradha Rani Choudhury has gained valuable hands-on experience through academic projects and certified training programs in the fields of machine learning, data science, and software development. She successfully led a Sentiment Analysis project in 2024, where she implemented advanced models such as Naïve Bayes, Logistic Regression, SVM, and Neural Networks, achieving an outstanding . In 2023, she developed a Disease Prediction Model using patient physiological data, which delivered an impressive . Additionally, Anuradha has completed certification-based training in Machine Learning (Project Mantra), Web Development (Verzeo), and Android Development (Pixaflip Technologies). Her participation in workshops and seminars, along with her technical proficiency in languages like Python, Java, and SQL, further reflects her readiness for research and real-world problem-solving in the tech industry.

🔬 Research Interest of Anuradha Rani Choudhury

Anuradha Rani Choudhury’s research interests lie primarily in the dynamic fields of Machine Learning, Artificial Intelligence, and Data Science, with a strong inclination toward applying these technologies in healthcare and human-centered applications. She is passionate about exploring predictive modeling, sentiment analysis, and disease diagnosis systems that leverage advanced algorithms to generate actionable insights from complex data. Her academic projects reflect her enthusiasm for building intelligent systems capable of improving decision-making and automation. Anuradha is also interested in the integration of deep learning and neural networks to enhance model accuracy and efficiency. As she progresses in her academic and professional journey, she aims to contribute to impactful, real-world solutions through innovative and ethically grounded research in intelligent technologies.

🏅 Awards and Honors of Anuradha Rani Choudhury

Anuradha Rani Choudhury has been recognized for her diverse talents and active participation in both academic and extracurricular arenas. She earned the 2nd prize in poem writing at the Ganjam Kalaparishad in 2017, showcasing her creative expression and literary flair. In the same year, she also secured the 2nd prize in athletics at D.A.V. Public School, reflecting her athletic spirit and commitment to physical excellence. Additionally, she holds the prestigious NCC “A” Certificate (2013–2015), highlighting her discipline, leadership, and dedication to national service. These honors demonstrate her well-rounded personality, balancing academic rigor with artistic and physical achievements.

Conclusion

Anuradha Rani Choudhury is a promising emerging researcher with a solid academic background, project experience in machine learning, and proactive engagement in technical skill development. However, to be highly competitive for the Best Researcher Award, particularly at national or international levels, she would benefit from Publishing research papers. Involving in long-term, original research with measurable impact. Demonstrating broader research influence and leadership.

📚 Publications Top Noted

  1. Title: Real-time Face Mask Detection in Nuclear Power Plants: A Deep Learning Framework Using Hybrid CNN-MobileNetV2 Architecture
    Authors: S.R. Panda, A.R. Choudhury, A.K. Mishra, S. Mohanty, S. Mishra
    Year: 2025
    Citation: 0
    Published In: Intelligent Computing Techniques and Applications, pp. 87–91

  2. Title: A Comparative Analysis of Deep Learning Techniques for Face Mask Detection System in Nuclear Power Plant
    Authors: S.R. Panda, A.R. Choudhury, A.K. Mishra
    Year: 2025
    Citation: 0
    Published In: 2025 10th International Conference on Signal Processing and Communication

  3. Title: Enhancing Lung Cancer Detection by Leveraging Machine Learning Algorithms
    Authors: A.R. Choudhury, S.R. Panda, A.K. Mishra, J. Routray
    Year: 2025
    Citation: 0
    Published In: 2025 10th International Conference on Signal Processing and Communication

  4. Title: Deep Learning Based Automated Lung Cancer Detection from CT Scan Leveraging Transfer Learning
    Authors: A.R. Choudhury, J. Rautray, P. Mishra, M. Kandpal, S.S. Dalai
    Year: 2025
    Citation: 0
    Published In: Procedia Computer Science, Volume 258, Pages 2748–2759

  5. Title: Face Mask Detection System for Safety Assurance in Nuclear Power Facilities from Harmful and Hazardous Substance Using Convolutional Neural Network and Image Processing
    Authors: S.R. Panda, A.R. Choudhury, A.K. Mishra
    Year: 2025
    Citation: 0
    Published In: International Journal of Computer Applications 975, 8887

 

 

 

 

 

 

Jiantao Shi | Robotics and Automation | Best Researcher Award

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Jiantao Shi | Robotics and Automation | Best Researcher Award

Mrs Jiayun Nie, chongqing Jiaotong University, China

Jiayun Nie is a distinguished professor at Nanjing Tech University, China, specializing in cooperative control of multi-robot systems, fault diagnosis, and fault-tolerant control of distributed systems. 📡 With a Ph.D. in Control Science and Engineering from Tsinghua University, she has made pioneering contributions to multi-agent systems, UAV adaptive control, and reinforcement learning-based fault diagnosis. ✈️🔍 Her research has led to high-impact publications on fault estimation, bipartite consensus, and deep learning models for system diagnostics. 🤖 She has served as a research fellow at the Nanjing Research Institute of Electronic Technology and has received recognition as an Outstanding Reviewer for the Journal of the Franklin Institute (2017). 📚 Her latest work explores AI-driven fault-tolerant frameworks for autonomous systems and aerospace applications. 🚀 With a stellar academic record and transformative research, she is a deserving recipient of the Best Researcher Award. 🏅

Publication Profile

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Education

Jiayun Nie’s academic journey is marked by excellence in control science and automation engineering. She earned her Ph.D. in Control Science and Engineering from Tsinghua University (2011-2016), focusing on fault-tolerant systems and adaptive control strategies for multi-robot cooperation. 🤖🔍 Her doctoral research introduced novel iterative learning algorithms for fault estimation and compensation, improving system reliability. Before this, she completed her B.E. in Electrical Engineering and Automation at Beijing Institute of Technology (2007-2011), where she laid the groundwork in robotic control, embedded systems, and automation engineering. 🎛️⚡ During her studies, she was actively involved in research projects on UAV dynamics and cooperative control theory, leading to early publications and innovative designs for fault-resilient robotics. 🚀 Her strong educational foundation, combined with rigorous research, has positioned her as a global leader in fault diagnosis and control engineering. 📚

Experience

Jiayun Nie has an extensive academic and research career, currently serving as a Professor at Nanjing Tech University (2021-present), where she leads groundbreaking work on distributed control and autonomous systems. 🤖🔍 Prior to this, she was a Research Fellow (2019-2021) and Associate Research Fellow (2016-2018) at the Nanjing Research Institute of Electronic Technology, contributing to fault detection models for phased array radar transceivers and advanced control strategies for UAVs. ✈️📡 Her expertise in adaptive control and AI-driven fault detection has been instrumental in developing next-generation intelligent robotic networks. 🚀 Throughout her career, she has collaborated with leading research institutions, advancing the state-of-the-art in reinforcement learning-based fault diagnosis, consensus control, and multi-agent fault-tolerant frameworks. 🏅 Her work continues to influence aerospace, robotics, and autonomous vehicular technologies, making her an authority in the field. 📚

Awards and Honors

Jiayun Nie’s outstanding contributions to robotics and fault-tolerant control have earned her several prestigious accolades. 🎖️ She was recognized as an Outstanding Reviewer for the Journal of the Franklin Institute (2017) 📚, reflecting her expertise in control systems and automation engineering. 🚀 Her innovative research on fault diagnosis in distributed robotic systems has been cited extensively, leading to multiple best paper awards at international IEEE and IFAC conferences. 🏅 She has received multiple grants and funding awards for her pioneering work in multi-agent cooperative control and AI-driven adaptive learning control. 🤖 As a highly regarded professor and researcher, her contributions continue to impact autonomous systems, aviation safety, and smart robotics. ✈️ With her extensive publications and transformative research, she is a deserving recipient of the Best Researcher Award, recognized for her excellence in innovation and scientific advancement. 🏆📡

Research Focus

Jiayun Nie’s research revolves around cooperative control, fault diagnosis, and learning-based fault-tolerant strategies in autonomous systems. 🤖 She has made significant breakthroughs in multi-robot cooperation, bipartite consensus, and AI-driven adaptive fault detection. 📊 Her work in fault-tolerant control enhances resilience in UAVs and aerospace systems, ensuring robustness against unknown disturbances and failures. ✈️ She has developed deep learning and reinforcement learning models for self-healing robotic networks, transforming distributed control frameworks. 🏅 Her studies in event-based control, collision avoidance, and system stability have contributed to advancements in autonomous vehicle technology. 🚀 By integrating data-driven methods with real-time fault estimation, her research provides solutions for smart transportation, defense, and aerospace industries. 📡 With numerous high-impact publications, Jiayun Nie’s pioneering work defines the future of adaptive robotics and autonomous systems. 🎓

Publications Top Notes

  1. “A parallel weighted ADTC-Transformer framework with FUnet fusion and KAN for improved lithium-ion battery SOH prediction” 🔋 (2025)
  2. “Bipartite Fault-Tolerant Consensus Control for Multi-Agent Systems with a Leader of Unknown Input Under a Signed Digraph” 🤖 (2025)
  3. “Iterative learning based fault estimation for stochastic systems with variable pass lengths and data dropouts” 📊 (2025)
  4. “A Two-Stage Fault Diagnosis Method With Rough and Fine Classifiers for Phased Array Radar Transceivers” 📡 (2024)
  5. “An intuitively-derived decoupling and calibration model to the multi-axis force sensor using polynomials basis” 📊 (2024)
  6. “Event-Based Adaptive Fault Tolerant Control and Collision Avoidance of Wheel Mobile Robots With Communication Limits” 🤖 (2024)
  7. “Reinforcement Learning-Based Fault Tolerant Control Design for Aero-Engines With Multiple Types of Faults” ✈️ (2024)

Kaimin Wei | Computer Science and Artificial Intelligence | Best Researcher Award

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Kaimin Wei | Computer Science and Artificial Intelligence | Best Researcher Award

Prof Kaimin Wei, Jinan University, China

Kaimin Wei is a full professor at the College of Information Science and Technology, Jinan University, China 🇨🇳. He earned his Ph.D. in Computer Science from Beihang University (2015), Master’s in Computer Application Technology from Zhengzhou University (2010), and Bachelor’s in Computer Science from Yuncheng University (2007) 🎓. A distinguished Young Pearl River Scholar 🌟, his research focuses on mobile computing, edge intelligence, and AI security 📱🤖🔒. Prof. Wei has published extensively in top journals and conferences, contributing to advancements in algorithm optimization and security techniques 📊📚.

Publication Profile

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Academic Background 

Prof. Kaimin Wei 🌟 holds a Ph.D. in Computer Science from Beihang University (2015) 🎓, a Master’s degree from Zhengzhou University (2010) 📚, and a Bachelor’s from Yuncheng University (2007) 🎯. His academic journey showcases dedication and excellence in the field of computer science 💻. Recognized for his outstanding achievements, he was honored as a Young Pearl River Scholar 🌊, reflecting his leadership potential and scholarly impact. Prof. Wei’s contributions to academia continue to inspire, highlighting his commitment to research, innovation, and education 🚀

Research Interests 

Prof. Kaimin Wei is a distinguished expert in Mobile Computing 📱, specializing in Edge Intelligence 🌐 and Artificial Intelligence Security 🔐. His research focuses on enhancing the efficiency and security of mobile technologies, leveraging cutting-edge edge intelligence to optimize data processing and real-time decision-making. Prof. Wei’s work in AI security ensures robust protection against emerging cyber threats, contributing to the development of safer, smarter digital ecosystems. His innovative approach bridges the gap between mobile computing and advanced AI applications, driving technological progress and shaping the future of secure, intelligent systems. 🚀🤖

Research Focus

Prof. Kaimin Wei’s research focuses on privacy-preserving technologies, federated learning, mobile crowdsensing, and cybersecurity 🔐📡. His work explores UAV crowdsensing with energy efficiency, gradient inversion attacks in federated learning without prior knowledge, and group task recommendations using neural collaborative approaches 🤖✨. He also investigates secure device pairing through acceleration-based methods with visual tracking and develops robust defense mechanisms against adversarial attacks using feature purification networks 🛡️📊. Prof. Wei’s interdisciplinary research blends Internet of Things (IoT), machine learning, and security protocols to enhance data privacy and system resilience 🌍🔍.

Publication Top Notes

PD-DRL: Towards privacy-preserving and energy-sustainable UAV crowdsensing

GI-SMN: Gradient Inversion Attack Against Federated Learning Without Prior Knowledge

Group Task Recommendation in Mobile Crowdsensing: An Attention-Based Neural Collaborative Approach

Attacks on Acceleration-Based Secure Device Pairing With Automatic Visual Tracking

FePN: A robust feature purification network to defend against adversarial examples

A Survey of Sparse Mobile Crowdsensing: Developments and Opportunities

Automatic Tagging by Leveraging Visual and Annotated Features in Social Media