Projects
Projects
| S. No | Name of the project | Funding Agency | PI / Co PI | National/ International | Duration | Starting month | Ending month | Amount | Summary |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Developing Millet Value-Chain for Sustainable Argroecology of Punjab: A Study of Farmers, Consumers and Other Stakeholders | ICSSR, New Delhi | Dr Shefali Bedi Dr Amitoj Singh |
National | 6 Months | Sep, 2023 | Mar, 2024 | 8.6 Lacs | The study will survey the farmers, consumers and other stakeholders to identify the challenges and opportunities for millet production and consumption in Punjab. |
| 2 | Impact of smart school policy in improving access, equity and quality in govt. schools of Punjab | SCERT, Mohali | Dr Karan Sukhija Dr Shefali Bedi |
State | 6 Months | May 2023 | Oct 2023 | 6.32 Lacs | This project is meant to study the impact of smart school policy in improving access, equity and quality of school education as per the smart school policy of government of Punjab, which was implemented in year 2019. |
| 3 | Impact of community involvement in improving enrolment and school attendance | SCERT, Mohali | Dr. Sukhpal Kaur Dr. Amitoj Singh Dr. Pinky Sra |
State | 6 Months | 9 March 2023 | September 2023 | 6.29 Lacs | Under this study, the impact of community involvement will be traced in improving enrolment and school attendance. The purpose of the study is to recognize the importance of community involvement in improving enrolment and school attendance in the government schools of Punjab. |
| 4 | Optimizing Smart City Management through Multi-Objective Optimization Strategies with Diverse Membership Functions in Artificial Intelligence Framework | Princess Nourah Bint Abdulrahman University, Saudi Arabia | Dr. Gaurav Dhiman | International | 1 Year | Jan, 2024 | Dec, 2024 | 85 Lacs | This research project endeavors to revolutionize urban development by integrating cutting-edge artificial intelligence (AI) technologies into smart city management. The primary objectives include the optimization of resource allocation, enhancement of infrastructure performance, and the integration of diverse membership functions to address the inherent complexities of urban systems. Employing a multidimensional methodology, the project utilizes machine learning models to analyze smart city data, applying multi-objective optimization algorithms to achieve a delicate balance between conflicting objectives such as energy efficiency and transportation optimization. The incorporation of diverse membership functions, including fuzzy logic, enriches the system's adaptability, enabling it to navigate uncertainties associated with real-world urban dynamics. Preliminary results demonstrate the framework's efficacy in improving energy efficiency, reducing traffic congestion, and enhancing waste management practices. However, challenges such as data privacy and system scalability necessitate ongoing consideration. The research's significance lies in its potential to reshape smart city paradigms, offering a comprehensive and adaptable solution to the intricate challenges faced by contemporary urban environments. Further refinements and validations will pave the way for real-world implementation, contributing to the evolution of smarter, and more sustainable cities. |