Parijata Majumdar | Metaheuristics | Best Researcher Award

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Dr. Parijata Majumdar | Metaheuristics | Best Researcher Award

Assistant Professor at Indian Institute of Information Technology Agartala, India📖

Dr. Parijata Majumdar is an accomplished academic and researcher with expertise in Artificial Intelligence, Machine Learning, IoT, Precision Agriculture, and Blockchain Technology. She holds a Ph.D. in Computer Science and Engineering from NIT Agartala (2023), where she developed AI approaches for precision agriculture. Currently, she serves as an Assistant Professor at the Indian Institute of Information Technology, Agartala, and an Associate Professor at Techno College of Engineering Agartala. Her work includes a postdoctoral collaboration on metaheuristic algorithms with Prof. Diego Alberto Oliva Navarro, University of Guadalajara, Mexico. Dr. Majumdar has received numerous accolades, including the EARG Award 2024 for Excellence in Research and Development, and has published significant contributions in journals indexed by Scopus and ISI Thomson Reuters.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

Dr. Majumdar completed her Ph.D. in Computer Science and Engineering from NIT Agartala in 2023, focusing on AI approaches for precision agriculture applications. She earned her M.Tech in Computer Science and Engineering with a Gold Medal from Tripura University in 2018 and her B.E. in Computer Science and Engineering from TIT Narsingarh in 2016. Additionally, she holds a Diploma in Computer Science and Technology from Women’s Polytechnic, Hapania (2013), and achieved distinction in her Madhyamik examination under the Tripura Board of Secondary Education in 2010.

Professional ExperienceđŸŒ±

Dr. Majumdar is currently an Assistant Professor at the Indian Institute of Information Technology, Agartala (since August 2024) and has been serving as an Associate Professor in Computer Science and Engineering at Techno College of Engineering Agartala since November 2023. She joined TCEA as an Assistant Professor in February 2018. Over the years, she has taken on various responsibilities, including Placement Coordinator, Coding Club Coordinator, Departmental Event Coordinator, and NAAC Student Support Team member. She has also been actively involved in coordinating admission processes, mentoring students, and organizing departmental activities.

Research Interests🔬

Dr. Majumdar’s research interests lie at the intersection of advanced technologies and real-world applications. Her areas of expertise include Machine Learning, Optimization Techniques, IoT, Green IoT, Precision Agriculture, Image Processing, Pattern Recognition, and Blockchain Technology. Her work focuses on leveraging these technologies to develop sustainable and efficient solutions for industrial and agricultural applications.

Author Metrics

Dr. Majumdar has made significant contributions to academic literature, with publications in journals indexed by Scopus and ISI Thomson Reuters. She is the author of the book Data Mining Techniques for Extractive Audio Speech Summarization (ISBN: 978-93-6048-210-7) and has collaborated internationally on research projects. Her research profiles include SCOPUS ID 57203280468 and Web of Science Researcher ID JGM-2672-2023, reflecting her impact in the academic and research community.

Publications Top Notes 📄

1. IoT for Promoting Agriculture 4.0: A Review from the Perspective of Weather Monitoring, Yield Prediction, Security of WSN Protocols, and Hardware Cost Analysis

  • Authors: P. Majumdar, S. Mitra, D. Bhattacharya
  • Journal: Journal of Biosystems Engineering
  • Volume/Issue: 46(4)
  • Pages: 440-461
  • Year: 2021
  • Citations: 30

2. Application of Green IoT in Agriculture 4.0 and Beyond: Requirements, Challenges, and Research Trends in the Era of 5G, LPWANs, and Internet of UAV Things

  • Authors: P. Majumdar, D. Bhattacharya, S. Mitra, B. Bhushan
  • Journal: Wireless Personal Communications
  • Volume/Issue: 131(3)
  • Pages: 1767-1816
  • Year: 2023
  • Citations: 26

3. Demand Prediction of Rice Growth Stage-Wise Irrigation Water Requirement and Fertilizer Using Bayesian Genetic Algorithm and Random Forest for Yield Enhancement

  • Authors: P. Majumdar, D. Bhattacharya, S. Mitra, R. Solgi, D. Oliva, B. Bhusan
  • Journal: Paddy and Water Environment
  • Volume/Issue: 21(2)
  • Pages: 275-293
  • Year: 2023
  • Citations: 15

4. Honey Badger Algorithm Using Lens Opposition-Based Learning and Local Search Algorithm

  • Authors: P. Majumdar, S. Mitra, D. Bhattacharya
  • Journal: Evolving Systems
  • Volume/Issue: 15(2)
  • Pages: 335-360
  • Year: 2024
  • Citations: 12

5. IoT and Machine Learning-Based Approaches for Real-Time Environment Parameters Monitoring in Agriculture: An Empirical Review

  • Authors: P. Majumdar, S. Mitra
  • Book Chapter: Agricultural Informatics: Automation Using the IoT and Machine Learning
  • Pages: 89-115
  • Year: 2021
  • Citations: 11

Conclusion

Dr. Parijata Majumdar stands out as a highly suitable candidate for the Best Researcher Award. Her expertise in cutting-edge technologies, significant contributions to impactful research areas like Agriculture 4.0, and her leadership in academic roles make her a strong contender. While there are opportunities to further enhance her research’s breadth and outreach, her current achievements, collaborations, and recognition are commendable. Awarding her would not only honor her individual excellence but also inspire further advancements in sustainable technology and precision agriculture.

Qinglai Wei | Self-Learning Systems | Best Researcher Award

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Prof. Dr. Qinglai Wei | Self-Learning Systems | Best Researcher Award 

Associate Director, at Institute of Automation, Chinese Academy of Sciences, China.

Professor Qinglai Wei is a distinguished researcher and educator specializing in control systems, computational intelligence, and learning-based optimization. Serving as the Associate Director at The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, he has made significant contributions to adaptive dynamic programming, nonlinear control, and reinforcement learning. With an illustrious academic journey from Northeastern University and rich professional experience, Prof. Wei has authored numerous influential papers, books, and book chapters. His awards include multiple IEEE honors and recognition as a Clarivate Highly Cited Researcher. He is a prominent figure in advancing intelligent control systems and their applications in complex scenarios.

Professional Profile

Scopus

Google Scholar

Education 🎓

  • Ph.D. in Control Theory and Control Engineering (2009): Northeastern University, China. Advised by Prof. Huaguang Zhang, his research focused on intelligent control systems.
  • M.S. in Control Theory and Control Engineering (2005): Northeastern University, China, under Prof. Xianwen Gao’s mentorship.
  • B.S. in Automation (2002): Northeastern University, China, advised by Baodong Xu.
    These academic milestones laid the foundation for his expertise in adaptive dynamic programming and intelligent systems.

Professional Experience đŸ’Œ

  • Associate Director (2018–Present): The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences.
  • Professor (2016–Present): The State Key Laboratory and the School of Artificial Intelligence, University of Chinese Academy of Sciences.
  • Visiting Scholar roles at University of Rhode Island (2018) and University of Texas at Arlington (2014) reflect his international collaboration and academic outreach.
    Earlier roles include Associate and Assistant Professor positions at The State Key Laboratory, showcasing steady growth in his academic career.

Research Interests 🔬

Prof. Wei’s research spans:

  • Computational Intelligence & Intelligent Control
  • Learning Control & Reinforcement Learning
  • Optimal & Nonlinear Control
  • Adaptive Dynamic Programming
    Applications include process control, smart grids, and multi-agent systems. His innovative methods continue to drive advancements in control theory and intelligent systems.

Awards 🏆

Prof. Wei’s excellence is marked by accolades like:

  • Best Paper Awards (2023 & 2022): International CSIS-IAC and China Automation Congress.
  • IEEE Outstanding Paper Awards (2018): Recognition for impactful contributions to the IEEE journals.
  • Highly Cited Researcher (2018 & 2019): By Clarivate Analytics for his influential publications.
    Other honors include National Natural Science Foundation Awards and Young Researcher Awards, emphasizing his leadership in the field.

Top Noted Publications 📚

  • “Learning and Controlling Multiscale Dynamics in Spiking Neural Networks” (2024, IEEE Transactions on Cybernetics): This study employs Recursive Least Square (RLS) modifications to manage multiscale dynamics in spiking neural networks. It advances neural control methods for adaptive tasks in dynamic environments【8】.
  • “Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems” (2024, IEEE/CAA Journal of Automatica Sinica): This paper focuses on event-triggered mechanisms to ensure robust consensus in multiagent systems under parallel optimal control.
  • “Primal-Dual Adaptive Dynamic Programming for Nonlinear Systems” (2024, Automatica): A framework using primal-dual adaptive dynamic programming tackles the stabilization and optimization of nonlinear systems.
  • “Class-Incremental Learning with Balanced Embedding Discrimination” (2024, Neural Networks): This work enhances class-incremental learning by introducing techniques to balance embeddings and improve discrimination among new and existing classes.

Conclusion

Qinglai Wei is exceptionally suited for the Research for Best Researcher Award. His prolific contributions to control theory, computational intelligence, and reinforcement learning, combined with his global recognition and leadership, exemplify his stature as a world-class researcher. With a proven track record of innovative research, impactful publications, and numerous accolades, he stands out as a strong candidate for this prestigious honor. Continued expansion into interdisciplinary collaborations and mentorship initiatives will further solidify his legacy as a pioneering researcher.

 

Fahimeh Dabaghi Zarandi | Community Detection | Women Researcher Award

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Assist. Prof. Dr. Fahimeh Dabaghi Zarandi | Community Detection | Women Researcher Award

Assistant Professor, at Vali-e-Asr University of Rafsanjan, Iran📖

Dr. Fahimeh Dabaghi-Zarandi is an accomplished researcher and academic in software engineering, specializing in data mining, green communication, and IoT. With a Ph.D. from the Iran University of Science and Technology, she brings a rich academic background and a passion for leveraging technology to address complex problems. As an Assistant Professor at Vali-e-Asr University, she continues to inspire students and contribute to the field through innovative research and collaboration.

Profile

Scopus Profile

Google Scholar Profile

Education Background🎓

Dr. Fahimeh Dabaghi-Zarandi holds a Ph.D. in Software Engineering from the Iran University of Science and Technology, Tehran, Iran, which she completed in September 2018. She earned her Master’s degree in Software Engineering from the prestigious Sharif University of Technology, Tehran, Iran, in August 2010, and her Bachelor’s degree in the same field from Ferdowsi University of Mashhad, Iran, in August 2008. Her academic journey reflects a consistent focus on software engineering, laying a strong foundation for her expertise in data mining, graph processing, and Internet of Things applications.

Professional ExperienceđŸŒ±

Dr. Fahimeh Dabaghi-Zarandi is an Assistant Professor at the Department of Engineering, Vali-e-Asr University of Rafsanjan, where she contributes to the advancement of computer engineering through teaching and research. She has actively participated in several national conferences on topics such as data mining and computational geometry, including the 16th CSI Computer Conference in Tehran (2011) and the Winter School on Computational Geometry at Amirkabir University (2009). Her involvement in these events reflects her commitment to staying at the forefront of developments in computer science and engineering.

Research Interests🔬

Dr. Dabaghi-Zarandi’s research focuses on:

  • Green Communication: Enhancing energy efficiency in communication systems.
  • Community Detection: Identifying clusters and patterns in large networks.
  • Data Mining: Extracting meaningful insights from large datasets.
  • Graph Processing: Algorithms and applications for analyzing graph structures.
  • Internet of Things (IoT): Developing intelligent solutions for interconnected systems.

Author Metrics 

Dr. Dabaghi-Zarandi’s publications have made significant contributions to her fields of interest, with her work cited by researchers worldwide. Her expertise in graph processing and community detection has been recognized in peer-reviewed journals and conferences, where she has shared her findings on the applications of data mining and IoT in sustainable technology

Publications Top Notes 📄

1. A survey on green routing protocols using sleep-scheduling in wired networks

  • Authors: F. Dabaghi, Z. Movahedi, R. Langar
  • Journal: Journal of Network and Computer Applications
  • Volume: 77
  • Pages: 106-122
  • Year: 2017
  • Citations: 47
  • Abstract: This paper provides a detailed survey of green routing protocols in wired networks, focusing on energy-saving methods achieved through sleep-scheduling mechanisms. The study reviews various techniques and evaluates their effectiveness, contributing valuable insights to the field of green networking.

2. Community detection in complex networks based on an improved random algorithm using local and global network information

  • Authors: F. Dabaghi-Zarandi, P. KamaliPour
  • Journal: Journal of Network and Computer Applications
  • Volume: 206
  • Article: 103492
  • Year: 2022
  • Citations: 11
  • Abstract: This work presents an enhanced random algorithm for community detection in complex networks. By integrating both local and global network information, the proposed method achieves higher accuracy and robustness compared to traditional approaches.

3. An energy‐efficient algorithm based on sleep‐scheduling in IP backbone networks

  • Authors: F. Dabaghi-Zarandi, Z. Movahedi
  • Journal: International Journal of Communication Systems
  • Volume: 30, Issue 13
  • Article: e3276
  • Year: 2017
  • Citations: 11
  • Abstract: This paper introduces an energy-efficient algorithm for IP backbone networks leveraging sleep-scheduling techniques. The algorithm optimizes energy consumption while maintaining network performance.

4. A dynamic traffic-aware energy-efficient algorithm based on sleep-scheduling for autonomous systems

  • Authors: F. Dabaghi-Zarandi, Z. Movahedi
  • Journal: Computing
  • Volume: 100, Issue 6
  • Pages: 645-665
  • Year: 2018
  • Citations: 7
  • Abstract: The study proposes a dynamic traffic-aware algorithm that enhances energy efficiency in autonomous systems by incorporating adaptive sleep-scheduling.

5. Local traffic-aware green algorithm based on sleep-scheduling in autonomous networks

  • Authors: F. Dabaghi-Zarandi
  • Journal: Simulation Modelling Practice and Theory
  • Volume: 114
  • Article: 102418
  • Year: 2022
  • Citations: 2
  • Abstract: This paper introduces a localized green algorithm tailored for autonomous networks. By integrating sleep-scheduling and traffic awareness, the proposed approach reduces energy consumption without compromising network performance.

Conclusion

Dr. Fahimeh Dabaghi-Zarandi is a highly deserving nominee for the Women Researcher Award due to her pioneering research in green communication, community detection, and energy-efficient algorithms. Her contributions address global challenges such as energy conservation and sustainable technology, making her work both impactful and timely.

With a clear trajectory of excellence and continuous innovation, Dr. Dabaghi-Zarandi exemplifies the qualities of a distinguished researcher. Addressing the identified areas for improvement would further amplify her achievements, but her existing body of work strongly supports her candidacy for this award.