Dr.S.Chandrakala

Professor
Department of Computer Science and Engineering

Email

Educational Qualifications

  • 2011 Ph. D -Indian Institute of Technology Madras
  • 2002 M.Tech – SASTRA University, Thanjavur.
  • 1992 B.E(ECE) – Thiagarajar College of Engineering, Madurai.

Area of Research

  • Machine Learning
  • Deep Learning
  • Speech and Sound Technology
  • Computer Vision

Bio

Dr.S.Chandrakala is a Professor with the Department of Computer Science and Engineering, Shiv Nadar University, Chennai. She earned herPh.D degree from the department of Computer Science and Engineering, Indian Institute of Technology Madras in the year 2011.She has over 28 years of experience in academics and industry.Prior to the association of SNU, she has served as a Professor in SASTRA University, Thanjavur. As a Principal Investigator, she has carried out a DST-CSRI funded project for Rs.36 lakhs for the period from 2019 to 2022. As a research supervisor, she has guided 4 research scholars and their Ph.Ds awarded.She has presented several research papers in International Conferences & published articles and book chapters in reputed Journals.

Work Experience

Experience: 28 years (Teaching, Research and Industry) 

  • Professor with the Department of Computer Science and Engineering, Shiv Nadar University, Chennai
  • Previously served as a Professor in SASTRA University, Thanjavur

Projects

  1. As Principal Investigator: Representation Learning based Speech Assistive Tool for people with Neurological Disorders – funded by DST Cognitive Science Research Initiative (CSRI) for Rs.36 lakhs, 3 years, 2 JRFs- project completed.
  2. FIST (Fund for Improvement of S & T infrastructure) – Department of CSE, Velammal Engineering College, funded by DST for Rs.30 lakhs in 2013.

Publications

  1. Chandrakala S, Akila and Mahathi, Spectro Temporal Fusion with CLSTM-Autoencoder based approach for Anomalous Sound Detection, 10.1007/s11063-024-11485-4, in Neural Processing Letters, 56 : 39, 2024.
  2. Chandrakala S and Vishnikaveni, “Denoising convolutional autoencoder based approach for disordered speech recognition, International Journal of Artificial Intelligence Tools, Vol. 33, No. 1  2350058, 2024 
  3. Chandrakala S, Deepak K and Revathy G, “Anomaly detection in surveillance videos: A thematic taxonomy of Deep Models, Review and Performance Analysis”, Artificial Intelligence Review, 56,  3319–3368, 2023, IF 12.0
  4. Chandrakala S, “Anomalous human activity detection in videos using Bag-of-Adapted-Models based Embedding”,  Pattern Analysis and Applications, Springer, 26:1101–1112, 2023. IF 3.9
  5. Chandrakala S, LKP Vignesh, “V2AnomalyVec: Deep Discriminative Embeddings for Detecting Anomalous Activities in Surveillance Videos”, IEEE Transactions on computational Social Systems, 9(5), pg.1307-1316, 2022, IF 5.0
  6. Chandrakala S, Sreenithi B, Revathy G, A Bag-Of-Audio-Visual Words Based Approach For Environmental Sound Event and Acoustic Scene Recognition Tasks, Annals of Forest Research, 65(1), pg.4225-4241, 2022, IF 1.97
  7. Chandrakala S, Shalmiya P, Srinivas V and Deepak K,”Object centric and Memory Guided Network based normality modeling for Video Anomaly Detection”, Springer Signal, Image and Video Processing, 16, pg. 2001–2007,  2022. IF 2.3 
  8. Malini S and Chandrakala S, “Intelligibility assessment of impaired speech using Regularized
  9. self-representation based compact supervectors”, Computer, Speech and Language, Vol.74, 101355, 2022, IF 3.252
  10. Chandrakala S, Deepak K, LKP Vignesh, Bag-of-Event-Models based embeddings for detecting anomalies in surveillance videos, Expert systems with applications, Vol 190, 116168, 2022, IF 8.665
  11. Chandrakala S, Malini S and Jayalakshmi S.L., “Bag of Models based embedding for Assessment of Neurological disorders using Speech Intelligibility”, IEEE Transactions on Emerging Topics in Computing, Vol 9, Issue 3, pg. 1265-1275, JULY-SEP 2021 (IF 7.691)
  12. Chandrakala S, Malini S, Vishnikaveni S, “Histogram of States based Assistive System for Speech Impairment due to Neurological Disorders”, IEEE Transactions on Neural systems and rehabilitation Engineering, Vol. 29, pg. 2425-2434, 2021, IF 4.9
  13. Vishnikaveni S and Chandrakala S,”Investigation of DNN-HMM&Lattice Free Maximum Mutual Information Approach for Impaired Speech Recognition” IEEE ACCESS, vol 9, 168840-49, Nov 2021. IF 3.476
  14. Chandrakala S, Deepak K, Srinivas, “Residual spatiotemporal autoencoder with skip connected and memory guided network for detecting video anomalies”, Neural Information Processing Letters, Vol 53, pg. 4677–4692 (2021) IF 2.908.
  15. Chandrakala S, Deepak K, Krishnamohan C, ” Residual spatiotemporal autoencoder for unsupervised video anomaly detection, Springer Signal, Image and Video Processing, 15, pp. 215-222, 2021, IF 2.157
  16. Deepak K, SrivatsanG, Roshan S, ChandrakalaS*., “Deep Multi-View Representation Learning for Video Anomaly Detection using Spatio-Temporal Autoencoders”, Springer Circuits, Systems and Signal processing, 40:1333-1349, 2021 (IF 2.225).
  17. Chandrakala S, Venkataraman M, Shreyas N and Jayalakshmi S.L, Multiview representation for sound event recognition,  Springer Signal, Image and Video Processing, 15(1), pp.1211-1219, Jan 2021, IF 2.157
  18. Chandrakala S, Jayalakshmi S.L, Generative Model-Driven Representation Learning in a Hybrid Framework for Environmental Audio Scene and Sound Event Recognition,  IEEE Transactions on Multimedia, Vol 22, No.1, pp 3-14, Jan 2020 (IF 6.5)
  19. Chandrakala S, Jayalakshmi S.L,    Environmental  Audio  Scene and  Sound Event Recognition for Autonomous Surveillance: A Survey and Comparative Studies, ACM Computing Surveys, 2020;  52: 1-34. (IF 10.282)
  20. Deepak K, Sikkandar MY, Siddharth S and Chandrakala S, ”A similarity based representation for identifying anomalous healthcare activities”, Journal of Medical Imaging and Health Informatics, Vol. 10 (4), 787-794, 2020, (IF 0.55)
  21. Deepak K, Vignesh L K P, Chandrakala S* Autocorrelation of gradients based violence detection in surveillance videos, ICT Express, 6(3), pp.155-159, October 2020, IF 4.317

Book Chapters

  • S Chandrakala, G Revathy , Success Stories for IoT-Enabled 6G for Prediction and Monitoring of Infectious Diseases with Artificial Intelligence in 6G-Enabled IoT and AI for Smart Healthcare, pg.199-214, CRC Press, eBook ISBN 9781003321668, 2023
  • Chandrakala S, Machine Learning based Assistive Speech Technology for People with Neurological Disorders, Springer Nature – Recent Advances in Intelligent Assistive Technologies: Paradigms and Applications, 143-163, part of Intelligent Systems Reference Library (ISRL), vol 170, 2020. ISBN 978-3-030-30817-9
  • N. Shreyas, M. Venkatraman, S. Malini and S. Chandrakala Trends of Sound Event Recognition in Audio Surveillance: A Recent Review and Study The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems ISBN 978-0-12-816385-6 , Elsevier Academic Press, 2020
  • S. Roshan, G. Srivathsan, K. Deepak and S. Chandrakala, Violence Detection in Automated Video Surveillance: Recent Trends and Comparative Studies, The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems ISBN 978-0-12-816385-6 , Elsevier Academic Press, pp.157-171, 2020
  • K. Deepak, , L. K. P. Vignesh, G. Srivathsan, S. Roshan, S. Chandrakala, Statistical Features-Based Violence Detection in Surveillance Videos, Cognitive Informatics and Soft Computing ISBN 978-981-15-1451-7, pp. 197-203, Springer Jan 2020

Awards

  • IEEE Publication award by IEEE Madras section in 2020 and 2021.
  • Evaluator of AQIS Research proposals by AICTE, Govt. of India.
  • Honorary Rosalind Member of London Journals Press, United Kingdom,ID #DT22993, 2021
  • Got full sponsorship from Boston University, Massachusettes, USA and presented paper on “Support vector clustering for recognition of vowel data” International Conference on Cognitive and Neural systems, Boston, USA, May 2007.