Parijata Majumdar | Metaheuristics | Best Researcher Award

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.

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🎓 EDUCATION & EXPERIENCE

Dr. Parijata Majumdar is a distinguished academic and researcher in the field of Computer Science and Engineering. Her educational journey reflects a consistent record of excellence, beginning with her Madhyamik from the Tripura Board of Secondary Education in 2010, followed by a Diploma in Computer Science in Technology in 2013. She earned her B.E. in Computer Science and Engineering in 2016 and then an M.Tech in the same discipline from Tripura University in 2018, graduating as a Gold Medalist. She completed her Ph.D. in October 2023 from NIT Agartala, with a thesis focused on AI approaches for precision agriculture. Professionally, she has served as an Assistant Professor at Techno College of Engineering Agartala (TCEA) since 2018, was promoted to Associate Professor in 2023, and began serving as an Assistant Professor at IIIT Agartala in August 2024. Her responsibilities span admissions, placements, social media management, academic sections, and research guidance, showcasing a well-rounded experience in academic leadership and development.

✅ SUITABILITY SUMMARY

Dr. Majumdar’s background positions her as a valuable contributor in domains intersecting education, research, and real-world technological application. With a specialization in AI, IoT, and agriculture, she has worked on projects involving automated irrigation systems, disease detection in crops, and data-driven agriculture analytics. She is equally experienced in teaching and mentoring, has led departmental initiatives, and played an active role in academic coordination and student outreach. Her presence in both academia and applied research environments, combined with a strong publication and patent record, marks her as exceptionally suited for roles in R&D, higher education leadership, and interdisciplinary innovation projects.

🌍 PROFESSIONAL DEVELOPMENT

Dr. Parijata Majumdar is deeply committed to professional growth through lifelong learning. She has participated in numerous FDPs, workshops, and MOOCs offered by leading institutions like NPTEL (IIT Madras), AICTE-ATAL Academy, GIAN, TEQIP, and several national engineering colleges. Some of the key topics she has engaged with include Computational Intelligence, IoT Ecosystem Security, Contrast Enhancement, Python Programming, and Research Pedagogy in AI/ML. Her participation also extends to workshops planned in VIT-AP, Budge Budge Institute of Technology, and IIIT Dharwad. This extensive professional development activity emphasizes her proactive approach to staying updated with evolving technological trends and teaching methodologies.

🔬RESEARCH FOCUS

Dr. Majumdar’s core research revolves around Machine Learning, Optimization Techniques, Internet of Things (IoT), Green IoT (GIoT), Precision Agriculture, Image Processing, Pattern Recognition, and Blockchain Technology. Her doctoral work explored AI-based frameworks for smart agricultural systems. Her notable collaborations include projects with Prof. Diego Alberto Oliva Navarro (University of Guadalajara, Mexico) and Prof. Debashis De (MAKAUT, West Bengal). She has filed and published two Indian patents—one on automated crop irrigation systems and another on a CNN-based model for early crop disease detection. She has served as a reviewer and editorial board member for numerous reputed journals including Elsevier, IEEE, Hindawi, and Springer, actively contributing to the global research community.

🏆 HONORS & AWARDS

Dr. Parijata Majumdar has received several prestigious recognitions that highlight her academic and professional excellence. She was awarded the EARG Awards 2024 for Excellence in Research and Development. A Gold Medalist in her postgraduate studies, she is also a published patent holder in the domain of AI-powered agriculture. Her thought leadership is widely acknowledged through her role as a Distinguished Speaker at global conferences in Amsterdam, Barcelona, and Mauritius. Additionally, she has served as a session chair, keynote speaker, expert reviewer, and editorial board member across a range of national and international platforms. These accolades affirm her impact as a trailblazer in technology-driven education and research.

 📚 TOP NOTES PUBLICATIONS

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

Kuo-Ching Ying | Metaheuristics | Best Researcher Award

Prof Dr. Kuo-Ching Ying | Metaheuristics | Best Researcher Award

National Taipei University of Technology, Taiwan

Author Profiles

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Scopus

👨‍🏫 Current Position and Leadership Roles

Prof. Dr. Kuo-Ching Ying is currently a Distinguished Professor in the Department of Industrial Engineering and Management at National Taipei University of Technology, Taiwan. In addition to his role as a faculty member, he also serves as the Associate Dean of the College of Management and Vice Director of the EMBA Program at the university. His leadership positions reflect his deep commitment to advancing education and research in the fields of industrial engineering and management.

🎓 Early Academic Pursuits and Education

Prof. Ying’s academic journey began with a Master’s degree in Industrial Management from the National Taiwan University of Science and Technology in 1992. His academic pursuits culminated in a Ph.D. in Industrial Management from the same institution in 2003. His educational background provided the foundation for his current expertise in operations scheduling, combinatorial optimization, and various aspects of industrial management.

💼 Professional Endeavors and Editorial Contributions

Prof. Ying has significantly contributed to the academic community as an editor for several prestigious journals. He has been serving as an editor for Advances in Industrial Engineering and Management since December 2012 and has held editorial roles for other journals like IEEE Access (since 2018), Applied Sciences (since 2021), and American Journal of Operations Management and Information Systems (since 2020). His editorial work spans across more than 10 journals, underscoring his significant involvement in shaping the direction of research in industrial management and optimization fields.

🔬 Contributions and Research Focus

Prof. Ying’s primary research interests revolve around operations scheduling, combinatorial optimization, and industrial management. He has authored more than 130 academic papers in top-tier international journals such as IEEE Access, Computers and Operations Research, Applied Soft Computing, and European Journal of Operational Research. His research delves into innovative approaches to solving complex problems in industrial settings, particularly in operations management, manufacturing, and logistics.

🌍 Impact and Influence in Academia

Through his extensive research contributions and editorial roles, Prof. Ying has become a recognized figure in the fields of industrial engineering and management. His work has been cited numerous times in academic research, demonstrating his influence in advancing knowledge in operations scheduling and combinatorial optimization. His efforts have led to advancements in how industries manage production, operations, and logistics.

📚 Academic Citations and Recognition

Prof. Ying’s scholarly output has earned him wide recognition within the academic community. His work has been cited extensively across numerous journals, reflecting the importance and impact of his research. The high citation rate of his publications is a testament to the relevance and applicability of his work in both academic and industrial contexts. He is widely regarded as a thought leader in industrial management research.

💡 Technical Skills and Expertise

Prof. Ying possesses strong technical skills in areas such as operations research, optimization algorithms, and industrial management. He is particularly skilled in developing models and techniques for solving complex optimization problems related to manufacturing, logistics, and scheduling. His expertise in artificial intelligence and computational techniques further enhances his ability to contribute to cutting-edge research in these fields.

👩‍🏫 Teaching Experience and Mentorship

Prof. Ying has been a mentor to numerous students in the Department of Industrial Engineering and Management at National Taipei University of Technology. As an educator, he has significantly shaped the careers of many professionals in industrial management. His teaching philosophy emphasizes the application of theory to real-world problems, preparing students to tackle challenges in operations management and optimization. His experience in teaching and mentoring plays a key role in nurturing the next generation of scholars and industry leaders.

🌱 Legacy and Future Contributions

Prof. Ying’s legacy is already well established through his substantial academic achievements, his leadership in educational programs, and his continued contributions to the field. Looking ahead, he is likely to continue shaping the future of industrial engineering research, particularly in optimizing industrial processes and the integration of new technologies. His future contributions will likely focus on further advancing operations research, combinatorial optimization, and artificial intelligence applications in industrial contexts.

📖 Top Noted Publications 

Reinforcement Learning-based Alpha-list Iterated Greedy for Production Scheduling
  • Authors: Kuo-Ching Ying, Pourya Pourhejazy, Shih-Han Cheng
    Journal: Intelligent Systems with Applications
    Year: 2024
Minimizing makespan in two-stage assembly additive manufacturing: A reinforcement learning iterated greedy algorithm
  • Authors: Kuo-Ching Ying, Shih-Wei Lin
    Journal: Applied Soft Computing
    Year: 2023
Meta-Lamarckian-based iterated greedy for optimizing distributed two-stage assembly flowshops with mixed setups
  • Authors: Pourya Pourhejazy, Chen-Yang Cheng, Kuo-Ching Ying, Nguyen Hoai Nam
    Journal: Annals of Operations Research
    Year: 2023
Supply chain-oriented permutation flowshop scheduling considering flexible assembly and setup times
  • Authors: Kuo-Ching Ying, Pourya Pourhejazy, Chen-Yang Cheng, Ren-Siou Syu
    Journal: International Journal of Production Research
    Year: 2023
Adjusted Iterated Greedy for the optimization of additive manufacturing scheduling problems
  • Authors: Kuo-Ching Ying, Fruggiero, F., Pourhejazy, P., Lee, B.-Y.
    Journal: Expert Systems with Applications
    Year: 2022