Dr. Tinka Singh

Assistant Professor

Alliance College of Engineering and Design

Dr. Tinka Singh has accomplished her B. Tech degree from Dibrugarh University and her M. Tech degree from NIT Arunachal Pradesh, both in the field of Computer Science and Engineering. Her academic journey encompassed a diverse range of courses including machine learning, database systems, algorithm analysis, software engineering, networking, cognitive radio networks, and mobile computing. These courses have significantly contributed to her knowledge acquisition in the realm of Computer Science and Engineering.

In her pursuit of higher education, Dr. Tinka Singh undertook doctoral research at the Interdisciplinary Centre of IIT Guwahati. Her research delved into the domain of solid waste management, employing machine learning modeling to address the specific context of Guwahati city. Her investigation primarily focused on the socio-economic and demographic aspects of this issue. The core objective of her research was to comprehensively understand the intricacies of waste management planning within the framework of smart cities.

Throughout her research, Dr. Tinka Singh examined various facets such as characterizing solid waste, identifying challenges in waste generation, implementing predictive and forecasting models using time series analysis, exploring waste-to-energy possibilities like biogas generation, investigating recycling concepts including compost production, and employing data-driven machine learning models using Python to model emissions. Moreover, her research also encompassed policy formulation for effective waste management.

  • Banerjee, S., Singh, T., & Singh, K. R. (2022). Mitigation of Spectrum Sensing Data Falsification Attack (SSDF) in Cognitive Radio Network. Journal of The Institution of Engineers (India): Series B, 103(4), 1249-1257.
  • Singh, T., & Uppaluri, R. V. S. (2022). Machine learning tool-based prediction and forecasting of municipal solid waste generation rate: a case study in Guwahati, Assam, India. International Journal of Environmental Science and Technology, 1-24.
  • Singh, T., & Uppaluri, R. V. (2023). Application of ANN and traditional ML algorithms in modelling compost production under different climatic conditions. Neural Computing and Applications, 1-20.
  • Singh, T., & Uppaluri, R. V. S. (2023). Optimizing biogas production: A novel hybrid approach using anaerobic digestion calculator and machine learning techniques on Indian biogas plant. Clean Technologies and Environmental Policy.
  • Singh, T., & Uppaluri, R. V. S. (2023). Prediction and forecasting municipal solid waste generation of Northeastern cities by retrofitting ML models of Guwahati, India in: R. Bhattacharjee, D. R. Neog, K. R. Mopuri, S. K. Vipparthi (Eds.) Artificial Intelligence and Data Science based R&D interventions: Proceedings of NERC 2022 Springer Nature Singapore, Singapore, 2023 (Book Chapter, In Press)
  • Singh, T., & Uppaluri, R. V. S. (2023). Biogas generation prediction from meteorological parameters and organic waste using machine learning approaches. Journal of Super Computing. (Accepted)
  • Singh, T., Naik, A., & Uppaluri, R. V. S. (2023). Characterization of municipal solid waste and seasonal classification for various socio-demographic groups in Guwahati city. Material Cycle and Waste Management. (Under Review)
  • Singh, T., & Uppaluri, R. V. S. (2023). ML-Based Prediction and Forecasting of GHG Emissions and Particulate Matters from MSW Landfill and Incineration in Guwahati city (Communicated)
  • Singh, T., & Uppaluri, R. V. S. (2023). Enhancing the Efficacy of Solid Waste Generation Prediction through Supervised ML and Time Series Modelling: A Case Study in Kamrup, Guwahati (Communicated)
  • Singh, T., Prasad, A., & Uppaluri, R. V. S. (2023). Machine learning for crop yield prediction and forecasting: A meteorological data-driven approach for Kamrup subdivision (Under Review)
  • Singh, T., & Uppaluri, R. V. S. (2023). Development of a GIS-based decision model and technoeconomic calculator for compost facilities: A case study for Guwahati city (Communicated)


  • Achieved meritorious scholarship (2013) from Student’s Welfare Department, Dibrugarh University.
  • Meritorious scholarship from NHPC scholarship (2015).

Other collaborative work

  • Chetia, H., Kabiraj, D., Bharali, B., Ojha, S., Barkataki, M.P., Saikia, D., Singh, T., Mosahari, P.V., Sharma, P. and Bora, U., (2019). Exploring the benefits of endophytic fungi via omics. Advances in endophytic fungal research: present status and future challenges, pp.51-81.
  • Barkataki, M.P. and Singh, T., (2019). Plant-nanoparticle interactions: Mechanisms, effects, and approaches. In Comprehensive analytical chemistry (Vol. 87, pp. 55-83). Elsevier.