Year : 2024
Disease prediction mechanisms on large-scale big data with explainable deep learning models for multi-label classification problems in healthcare
Cite this Research Publication : Ganeshkumar, M., V. Sowmya, E. A. Gopalakrishnan, and K. P. Soman. "Disease prediction mechanisms on large-scale big data with explainable deep learning models for multi-label classification problems in healthcare." Healthcare Big Data Analytics: Computational Optimization and Cohesive Approaches 10 (2024): 207
Year : 2024
Transformer-based convolutional neural network approach for remote sensing natural scene classification
Cite this Research Publication : Sivasubramanian, Arrun, V. R. Prashanth, Theivaprakasham Hari, V. Sowmya, E. A. Gopalakrishnan, and Vinayakumar Ravi. "Transformer-based convolutional neural network approach for remote sensing natural scene classification." Remote Sensing Applications: Society and Environment 33 (2024): 101126
Year : 2024
Robust language independent voice data driven Parkinson’s disease detection
Cite this Research Publication : Veetil, Iswarya Kannoth, V. Sowmya, Juan Rafael Orozco-Arroyave, and E. A. Gopalakrishnan. "Robust language independent voice data driven Parkinson’s disease detection." Engineering Applications of Artificial Intelligence 129 (2024): 107494
Year : 2024
An analysis of data leakage and generalizability in MRI based classification of Parkinson’s Disease using Explainable 2D Convolutional Neural Networks
Cite this Research Publication : Veetil, Iswarya Kannoth, Divi Eswar Chowdary, Paleti Nikhil Chowdary, V. Sowmya, and E. A. Gopalakrishnan. "An analysis of data leakage and generalizability in MRI based classification of Parkinson's Disease using Explainable 2D Convolutional Neural Networks." Digital Signal Processing (2024): 104407
Year : 2023
Parkinson’s Disease Assessment from Speech Data Using Recurrence Plot
Cite this Research Publication : Mohamed Ali, Arsya, G. Jyothish Lal, V. Sowmya, and E. A. Gopalakrishnan. "Parkinson’s Disease Assessment from Speech Data Using Recurrence Plot." In International Conference on Computing, Intelligence and Data Analytics, pp. 132-142. Cham: Springer Nature Switzerland, 2023
Year : 2022
A Recurrence Network Approach for Characterization and Detection of Dynamical Transitions During Human Speech Production
Cite this Research Publication : Lal G. J., Gopalakrishnan E. A., and Govind D. “A recurrence network approach for characterization and detection of dynamical transitions during human speech production”, Circuits, Systems and Signal Processing, 2022.
Year : 2022
Emergency rate-driven control for rotor angle instability in power systems
Cite this Research Publication : Suchithra, K. S., Gopalakrishnan, E. A., Surovyatkina, E. and Kurths, J. “Emergency rate driven control for rotor angle instability in power systems”, Chaos (2022).
Year : 2022
Identification of intracranial haemorrhage (ICH) using ResNet with data augmentation using CycleGAN and ICH segmentation using SegAN
Cite this Research Publication :
Ganesh Kumar, M., Vinayakumar Ravi, V. Sowmya, and Chinmay Chakraborty. "Identification of intracranial haemorrhage (ICH) using ResNet with data augmentation using CycleGAN and ICH segmentation using SegAN." Multimedia Tools and Applications (2022): 1-17. (IF: 2.577 CiteScore: 5.3 Q1: 81 percentile).
Publisher : Multimedia Tools and Applications
Year : 2022
Odonata identification using Customized Convolutional Neural Networks
Cite this Research Publication :
Theivaprakasham, Hari, S. Darshana, Vinayakumar Ravi, V. Sowmya, E. A. Gopalakrishnan, and K. P. Soman. "Odonata identification using Customized Convolutional Neural Networks." Expert Systems with Applications (2022): 117688. (IF:8.665 CiteScore: 12.2 Q1: 97 percentile).
Publisher : Expert Systems with Applications
Year : 2021
Exploring Fake News Identification Using Word and Sentence Embeddings
Cite this Research Publication :
Priyanka VT, Sanjanasri J.P, Vijay Krishna Menon and Soman KP “Exploring Fake News Identification Using Word and Sentence Embeddings” accepted in the Journal of Intelligent and Fuzzy Systems, IOS Press, Netherlands (ISSN print 1064-1246, ISSN online 1875-8967). - SCIE Indexed
Publisher : IOS Press
Year : 2021
Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram
Cite this Research Publication : G. M., Vinayakumar Ravi, Sowmya V., Dr. E. A. Gopalakrishnan, and Dr. Soman K. P., “Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram”, IEEE Transactions on Engineering Management (IF: 6.146 Citescore: 4.3 Q1: 76 percentile), pp. 1-13, 2021.
Publisher : IEEE Transactions on Engineering Management
Year : 2020
Rate induced transitions and advanced takeoff in power systems
Cite this Research Publication : Suchithra, K. S., Gopalakrishnan, E. A., Surovyatkina, E. and Kurths, J. “Rate induced transitions and advanced takeoff in power systems”, Chaos (2020), 30(06), 061103.
Year : 2020
Explainable Artificial Intelligence for Heart Rate Variability in ECG Signal
Cite this Research Publication : Sowmya V., Sanjana, K., Gopalakrishnan, E. A., and Dr. Soman K. P., “Explainable Artificial Intelligence for Heart Rate Variability in ECG Signal”, Healthcare Technology Letters, vol. 7, no. 6, pp. 146 (IF:1.157, CiteScore: 3.1, Q2- 64 percentile), 2020.
Publisher : Healthcare Technology Letters
Year : 2020
Rate-induced transitions and advanced takeoff in power systems
Cite this Research Publication :
K. S. Suchithra, Dr. E. A. Gopalakrishnan, Surovyatkina, E., and Kurths, J., “Rate-induced transitions and advanced takeoff in power systems”, Chaos: An Interdisciplinary Journal of Nonlinear Science, American Institute of Physics Publishing, Volume 30, Issue 6, p.061103 (2020), DOI: https://doi.org/10.1063/5.0002456
Publisher : American Institute of Physics Publishing
Year : 2020
Glottal Activity Detection from the Speech Signal Using Multifractal Analysis
Cite this Research Publication :
Jyothish Lal G., Dr. E. A. Gopalakrishnan, and Dr. Govind D., “Glottal Activity Detection from the Speech Signal Using Multifractal Analysis”, Circuits, Systems, and Signal Processing, vol. 39, no. 4, pp. 2118 - 2150, 2020, DOI: https://doi.org/10.1007/s00034-019-01253-4
Publisher : Circuits, Systems, and Signal Processing
Year : 2019
Compressed Air Demand Forecasting in Manufacturing Plants using Deep Learning and Variational Mode Decomposition
Cite this Research Publication : Kalimuthu, C. K., Gopalakrishnan, E. A. and Soman, K. P. “Compressed Air Demand Forecasting in Manufacturing Plants using Deep Learning and Variational Mode Decomposition”, International Conference on Intelligent Computing and Communication Technologies. January 9 – 11, 2019, Hyderabad, India.
Year : 2019
Compressed Air Demand Forecasting in Manufacturing Plants using Deep Learning and Variational Mode Decomposition
Cite this Research Publication : Kalimuthu, C. K., Gopalakrishnan, E. A. and Soman, K. P. “Compressed Air Demand Forecasting in Manufacturing Plants using Deep Learning and Variational Mode Decomposition”. International Conference on Intelligent Computing and Communication Technologies. January 9 – 11, 2019, Hyderabad, India.
Year : 2019
Interplay Between Random Fluctuations and Rate Dependent Phenomena at Slow Passage to Limit-cycle Oscillations in a Bistable Thermoacoustic System
Cite this Research Publication :
Vishnu, R. U., Gopalakrishnan, E. A., Syam Kumar, K. S., Sujith, R. I., Surovyatkina, E. and Kurths, J. “Interplay between random fluctuations and rate dependent phenomena at slow passage to limit-cycle oscillations in a bistable thermoacoustic system”, Chaos 29 (3), 031102 (2019); https://doi.org/10.1063/1.5088943
Publisher : Chaos
Year : 2018
NSE Stock Market Prediction Using Deep-Learning Models
Cite this Research Publication :
H. M, Dr. E. A. Gopalakrishnan, Vijay Krishna Menon, and Dr. Soman K. P., “NSE Stock Market Prediction Using Deep-Learning Models”, in Procedia Computer Science, 2018, vol. 132, pp. 1351 - 1362.
Publisher : Procedia Computer Science
Year : 2018
Epoch Estimation from Emotional Speech Signals Using Variational Mode Decomposition
Cite this Research Publication :
Jyothish Lal G., Dr. E. A. Gopalakrishnan, and Dr. Govind D., “Epoch Estimation from Emotional Speech Signals Using Variational Mode Decomposition”, Circuits, Systems, and Signal Processing, vol. 37, pp. 3245–3274, 2018, DOI: https://doi.org/10.1007/s00034-018-0804-x
Publisher : Circuits, Systems, and Signal Processing
Year : 2018
Accurate Estimation of Glottal Closure Instants and Glottal Opening Instants from Electroglottographic Signal Using Variational Mode Decomposition
Cite this Research Publication : Jyotish Lal, G., Gopalakrishnan, E.A. and Govind, D. “Accurate estimation of glottal closure instants and glottal opening instants from electroglottographic signal using variational mode decomposition”, Circuits, Systems & Signal Processing, (2018), 37(2), 810-830.
Publisher : Circuits, Systems, and Signal Processing
Year : 2017
Recurrence networks to study dynamical transitions in a turbulent combustor
Cite this Research Publication :
V. Godavarthi, Unni, V. R., Dr. E. A. Gopalakrishnan, and Sujith, R. I., “Recurrence networks to study dynamical transitions in a turbulent combustor”, Chaos, vol. 27, Number 6, 063113. (2017), DOI: 10.1063/1.4985275
Publisher : American Institute of Physics Inc
Year : 2017
Experimental investigation on preconditioned rate induced tipping in a thermoacoustic system
Cite this Research Publication :
Tony, J., Subarna, S., Syam Kumar, K. S., Sudha, Gopalakrishnan, E. A. and Sujith, R. I. “Experimental investigation on preconditioned rate induced tipping in a thermoacoustic system”. Scientific Reports (Nature), (2017), 7(1), 5414.
Publisher : Scientific Reports, Nature Publishing Group,
Year : 2016
Hybrid CFD-low order modeling of thermoacoustic limit cycles
Cite this Research Publication : Jaensch, S., Merk, M., Gopalakrishnan, E. A., Bomberg, S., Emmert, T., Sujith, R. I. and Polifke, W., "Hybrid CFD-low order modeling of thermoacoustic limit cycles," Proceedings of Combustion Institute 36 (2016).
Year : 2016
Stochastic Bifurcations In A Prototypical Thermoacoustic System
Cite this Research Publication :
Dr. E. A. Gopalakrishnan, Tony, J., Sreelekha, E., and Sujith, R. I., “Stochastic Bifurcations In A Prototypical Thermoacoustic System”, Phys. Rev. E, vol. 94, no. 2, 2016.
Publisher : Phys. Rev. E
Year : 2016
Early warning signals for critical transitions in a thermoacoustic system
Cite this Research Publication :
Dr. E. A. Gopalakrishnan, Yogita, S., Tony, J., Dutta, P., and Sujith, R. I., “Early warning signals for critical transitions in a thermoacoustic system”, Scientific Reports volume 6, Article number: 35310 (2016)
Publisher : Scientific Reports Nature
Year : 2015
Effect of external noise on the hysteresis characteristics of a thermoacoustic system
Cite this Research Publication :
Dr. E. A. Gopalakrishnan and Sujith, R. I., “Effect of external noise on the hysteresis characteristics of a thermoacoustic system”, Journal of Fluid Mechanics, vol. 776, pp. 334-353, 2015.
Publisher : Journal of Fluid Mechanics
Year : 2015
Detecting Deterministic Nature Of Pressure Measurements From A Turbulent Combustor
Cite this Research Publication :
J. Tony, Dr. E. A. Gopalakrishnan, Sreelekha, E., and Sujith, R. I., “Detecting Deterministic Nature Of Pressure Measurements From A Turbulent Combustor”, Physical Review E, vol. 92, no. 6, 2015.
Publisher : Physical Review E
Year : 2014
Influence of System Parameters on the Hysteresis Characteristics of a Horizontal Rijke Tube
Cite this Research Publication :
Dr. E. A. Gopalakrishnan and Sujith, R. I., “Influence of System Parameters on the Hysteresis Characteristics of a Horizontal Rijke Tube”, International Journal of Spray and Combustion Dynamics , vol. 6, pp. 293-316, 2014.
Publisher : International Journal of Spray and Combustion Dynamics