Author

I am Manisha Sirsat, a senior researcher in Artificial Intelligence with a doctorate in machine learning, along with a bachelor’s and master’s degree in computer science. With over 10+ years of experience in the field, I possess good knowledge and expertise in various areas of artificial intelligence, including machine learning classification and regression methods, deep learning, computer vision, and database management systems. Moreover, my skillset has been demonstrated through a successful track record of problem-solving in the field of agriculture, specifically in soil science, precision agriculture, and plant pathology, as well as in field of medical science, particularly in the area of neurorehabilitation. 

Profile Link: Google scholar

Research Paper

  • Cernadas, E., Fernández-Delgado, M., Sirsat, M., Fulladosa, E., & Muñoz, I. (2024). MarblingPredictor: A software to analyze the quality of dry-cured ham slices. Meat Science, 109713. https://doi.org/10.1016/j.meatsci.2024.109713
  • Isla-Cernadas, D., Fernández-Delgado, M., Cernadas, E., Sirsat, M. S., Maarouf, H., & Barro, S. (2024). Closed-Form Gaussian Spread Estimation for Small and Large Support Vector Classification. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2024.3377370

  • Sirsat, M. S., Oblessuc, P. R., Ramiro, R. S (2022). Genomic Prediction of Wheat Grain Yield Using Machine Learning. Agriculture, 12, 1406. https://doi.org/10.3390/agriculture12091406
  • Almeida, Y., Sirsat, M. S., Bermúdez i Badia, S. and Fermé, E. (2020). AI-Rehab: A Framework for AI Driven Neurorehabilitation Training – The Profiling Challenge. In 13th International Joint Conference on Biomedical Engineering Systems and Technologies (Vol. 5, pp. 845–853). https://doi.org/10.5220/0009369108450853
  • M. S. Sirsat, João Mendes-Moreira, Carlos Ferreira and Mario Cunha (2019). Machine Learning Predictive Model of Grapevine Yield based on Agroclimatic Patterns. Engineering in Agriculture, Environment and Food. DOI: https://doi.org/10.1016/j.eaef.2019.07.003
  • M. Fernández-Delgado, M. S. Sirsat, E. Cernadas, S. Alawadi, S. Barro and M. Febrero-Bande (2019). An extensive experimental survey of regression methods. Neural Networks, Vol. 111, pp 11-34. DOI:https://doi.org/10.1016/j.neunet.2018.12.010
  • M. S. Sirsat, E. Cernadas, M. Fernández-Delgado and S. Barro (2018). Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods. Computers and Electronics in Agriculture, Vol. 154, pp 120–133. DOI: https://doi.org/10.1016/j.compag.2018.08.003
  • M. S. Sirsat, E. Cernadas, M. Fernández-Delgado and R. Khan (2017). Classification of agricultural soil parameters in India. Computers and Electronics in Agriculture, Vol. 135, pp 269–279. DOI: https://doi.org/10.1016/j.compag.2017.01.019
  • Sirsat, S. and Sahane, M. (2016). A Validation of The Delone And Mclean Model On The Educational Information System of the Maharashtra (India). International Journal of Education and Learning Systems, ISSN: 2367-8933, Vol. 1, pp. 9-18
  • Sahane, M., Sirsat, S., and Khan, R. (2015). Analysis of Research Data using MapReduce Word Count Algorithm. International Journal of Advanced Research in Computer and Communication Engineering, ISSN (Online) 2278-1021, ISSN (Print) 2319-5940, Vol. 4 (5), pp. 184-187. DOI: 10.17148/IJARCCE.2015.4542