sdaranda@gitam.edu
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Dr. Smita Sandeep Darandale presently working as an Associate Professor in Computer Science & Engineering department, GITAM University Bangalore Campus. She has a distinguished record of accomplishments in the fields of Data Mining, and Machine Learning. She has published over 20 technical papers in renowned refereed journals, conference proceedings and two patents. Dr. Smita is guiding three Ph.D. scholars in the areas of Machine learning, Quantum Computing, and Deep Learning.
Research Publications
- Effective infant cry signal analysis and reasoning using IARO based leaky Bi-LSTM model Mala B.M.;Darandale S.S. Computer Speech and Language, Volume 86, Year 2024
- Risk Assessment and Management using Machine Learning Approaches Darandale S.;Mehta R. Proceedings - International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022, Volume , Year 2022, Pages 663-667
- Clustering-Based Feature Selection Framework for Microarray Data Chormunge S.;Jena S. International Journal of Performability Engineering, Volume 13, Year 2017, Pages 383-389
- Efficient Feature Subset Selection Algorithm for High Dimensional Data Chormunge S.;Jena S. International Journal of Electrical and Computer Engineering (IJECE), Volume 6, Year 2016, Pages 1880-1888
- Performance efficiency and effectiveness of clustering methods for Microarray datasets Chormunge S.;Jena S. Smart Innovation, Systems and Technologies, Volume 44, Year 2016, Pages 557-567
Ongoing Research Projects
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Integration of 5G networks to enhance real-time health monitoring, teleconsultations, and mobile medical units (MMUs) that bring healthcare facilities to remote areas. Research is focusing on how improved connectivity can bridge the healthcare gap in villages
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In autism prediction, we can leverage advanced technologies like machine learning, deep learning, and data analytics to identify early indicators of autism spectrum disorder (ASD). By analyzing data from various sources, including behavioral patterns, speech development, eye movements, genetic factors, and neuroimaging, predictive models can be built to flag potential cases of autism at an early stage.
Expertise
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Data Mining
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Machine Learning
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Deep Learning
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Quantum Computing
