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Dr Sangeetha S

Dr Sangeetha S View All

Assistant Professor

School of Technology

Dr. Sangeetha S received her Ph.D. in Computer Science and Engineering from Vellore Institute of Technology, India, in 2025. Her doctoral research focused on the segmentation and classification of MRI brain tumors using machine learning and metaheuristic approaches. She earned her M.E. in Computer Science and Engineering with first class and distinction from C. Abdul Hakeem College of Engineering and Technology, securing the position of class topper, and completed her B.E. in Computer Science and Engineering from Anna University of Technology, Tiruchirappalli, with first class and distinction. Previously, she worked as an Assistant Professor on contract at Vellore Institute of Technology and gained industry experience as a Process Associate at Accenture. She has published three SCI/Scopus-indexed journal papers, holds four patents, and has contributed to three international conferences and three Scopus-indexed book chapters. Her research interests include computer vision, medical image processing, machine learning, deep learning, artificial intelligence, block chain technology, and optimization techniques.

    • Graduation In :

      B.E Computer Science & Engineering

    • Graduation From :

      Anna University, Trichy.

    • Graduation Year :

      2011

    • Post Graduation In :

      M.E Computer Science & Engineering

    • Post Graduation From :

      C. Abdul Hakeem College of Engineering and Technology, Melvisharam.

    • Post Graduation Year :

      2016

    • Doctorate In :

      Computer Science & Engineering

    • Doctorate From :

      Vellore Institute of Technology, Vellore.

    • Doctorate Year :

      2024

    • 4.5 years (Teaching and Industry)

  • Book Chapters

    • 1. Swathi Jamjala Narayanan, Boominathan Perumal, Sangeetha Saman, and Aditya Pratap Singh. ”Deep learning for person re-identification in surveillance videos.” In Deep Learning: Algorithms and Applications, pp. 263-297. Springer, Cham, 2020. Published.
    • 2. BoominathanPerumal,SwathiJamjalaNarayanan,SangeethaSaman. “ExplainableFuzzyDecisionTreeforMedical DataClassification”. InMedicalDataAnalysisandProcessingusingExplainableArtificialIntelligence,pp. CRCPress, pp. 39-57, 2023.
    • 3. Boominathan Perumal, Swathi Jamjala Narayanan, Sangeetha Saman. “Explainable Deep Learning Architectures for ProductRecommendations.” InExplainable,Interpretable,andTransparentAISystems. CRCPress,Firstedition, 2024.

    Publications in Journals

    • 1. Sangeetha Saman, Swathi Jamjala Narayanan. “Optimal feature subset selection for MRI brain tumor classification using improved ant-lion optimization”. Evolutionary Intelligence. (2024), Springer. Published [Impact factor: 2.3, Scopus].
    • 2. Sangeetha Saman, and Swathi Jamjala Narayanan. ”Active contour model driven by optimized energy functionals for MRbrain tumorsegmentation with intensity inhomogeneity correction.” Multimedia Tools and Applications 80, no. 14 (2021): 21925-21954, Springer. Published [Impact factor- 3.0, Sci-Scopus].
    • 3. Sangeetha Saman, and Swathi Jamjala Narayanan. ”Active contour model driven by optimized energy functionals for MRbrain tumorsegmentation with intensity inhomogeneity correction.” Multimedia Tools and Applications 80, no. 14 (2021): 21925-21954, Springer. Published [Impact factor- 3.0, Sci-Scopus].
    • 4. Sangeetha S., Johnsana,J.A., Rajesh, A., KishoreVerma, S.(2016). Valueandpatternanonymizationoftimeseries data for privacy preserving data mining. Journal of Chemical and Pharmaceutical Sciences, 9(4), 2221-2228.

    Publications in Conferences

    • 1. SwathiJamjalaNarayanan,ChinmayAshtikar, AdithyaSreemandiramAnil, SasankChunduri and SangeethaSaman. “Automated Brain Tumor Segmentation Using GAN Augmentation and Optimized U-Net”. In Frontiers of ICT in Healthcare: Proceedings of EAIT 2022, pp. 635-646. Singapore: Springer Nature Singapore, 2023.
    • 2. Narayanan, Swathi Jamjala, Boominathan Perumal, Aakar Mutha, Sangeetha Saman, Rajen B Bhatt. “Human Ac tivity Recognition on Accelerometer Data using Machine Learning Algorithms”. In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC), pp. 48-53. IEEE, 2022.