S. Chandrakala

Orcid: 0000-0003-4723-1984

Affiliations:
  • SASTRA University, Intelligent Systems Group, Thanjavur, Tamil Nadu, India


According to our database1, S. Chandrakala authored at least 28 papers between 2007 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Spectro Temporal Fusion with CLSTM-Autoencoder based approach for Anomalous Sound Detection.
Neural Process. Lett., February, 2024

Denoising Convolutional Autoencoder Based Approach for Disordered Speech Recognition.
Int. J. Artif. Intell. Tools, February, 2024

2023
Anomalous human activity detection in videos using Bag-of-Adapted-Models-based representation.
Pattern Anal. Appl., August, 2023

Anomaly detection in surveillance videos: a thematic taxonomy of deep models, review and performance analysis.
Artif. Intell. Rev., April, 2023

2022
V2AnomalyVec: Deep Discriminative Embeddings for Detecting Anomalous Activities in Surveillance Videos.
IEEE Trans. Comput. Soc. Syst., 2022

Object-centric and memory-guided network-based normality modeling for video anomaly detection.
Signal Image Video Process., 2022

Bag-of-Event-Models based embeddings for detecting anomalies in surveillance videos.
Expert Syst. Appl., 2022

Intelligibility assessment of impaired speech using Regularized self-representation based compact supervectors.
Comput. Speech Lang., 2022

2021
Bag of Models Based Embeddings for Assessment of Neurological Disorders Using Speech Intelligibility.
IEEE Trans. Emerg. Top. Comput., 2021

Residual spatiotemporal autoencoder for unsupervised video anomaly detection.
Signal Image Video Process., 2021

Multi-view representation for sound event recognition.
Signal Image Video Process., 2021

Residual Spatiotemporal Autoencoder with Skip Connected and Memory Guided Network for Detecting Video Anomalies.
Neural Process. Lett., 2021

Deep Multi-view Representation Learning for Video Anomaly Detection Using Spatiotemporal Autoencoders.
Circuits Syst. Signal Process., 2021

Investigation of DNN-HMM and Lattice Free Maximum Mutual Information Approaches for Impaired Speech Recognition.
IEEE Access, 2021

Analysis of Global Word Representations for Depression Detection.
Proceedings of the International Semantic Intelligence Conference 2021 (ISIC 2021), 2021

2020
Generative Model Driven Representation Learning in a Hybrid Framework for Environmental Audio Scene and Sound Event Recognition.
IEEE Trans. Multim., 2020

A Similarity Based Representation for Identifying Healthcare Anomalous Activities.
J. Medical Imaging Health Informatics, 2020

Autocorrelation of gradients based violence detection in surveillance videos.
ICT Express, 2020

2019
Environmental Audio Scene and Sound Event Recognition for Autonomous Surveillance: A Survey and Comparative Studies.
ACM Comput. Surv., 2019

2017
Erratum to: MRL-SCSO: Multi-agent Reinforcement Learning-Based Self-Configuration and Self-Optimization Protocol for Unattended Wireless Sensor Networks.
Wirel. Pers. Commun., 2017

MRL-SCSO: Multi-agent Reinforcement Learning-Based Self-Configuration and Self-Optimization Protocol for Unattended Wireless Sensor Networks.
Wirel. Pers. Commun., 2017

2016
Survey on state scheduling-based topology control in unattended wireless sensor networks.
Comput. Electr. Eng., 2016

2010
Classification of varying length multivariate time series using Gaussian mixture models and support vector machines.
Int. J. Data Min. Model. Manag., 2010

2009
Classification of Multi-variate Varying Length Time Series Using Descriptive Statistical Features.
Proceedings of the Pattern Recognition and Machine Intelligence, 2009

Combination of generative models and SVM based classifier for speech emotion recognition.
Proceedings of the International Joint Conference on Neural Networks, 2009

Model Based Clustering of Audio Clips Using Gaussian Mixture Models.
Proceedings of the Seventh International Conference on Advances in Pattern Recognition, 2009

2008
A density based method for multivariate time series clustering in kernel feature space.
Proceedings of the International Joint Conference on Neural Networks, 2008

2007
Local Density Estimation based Clustering.
Proceedings of the International Joint Conference on Neural Networks, 2007


  Loading...