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Dr. Gopalakrishnan E. A.

Principal, School of Computing, Bengaluru | Principal, School of Artificial Intelligence, Bengaluru | Associate Professor, School of Artificial Intelligence, Bengaluru

Qualification: B. Tech., M.Tech., Ph.D
ea_gopalakrishnan@blr.amrita.edu
Orcid ID
Google Scholar Profile
Scopus Author ID
Research Interest: Artificial Intelligence, Combustion Instabilities, Complex Systems, Data Driven Modelling and Analysis, Nonlinear Dynamics and Stochastic Systems

Bio

Dr. Gopalakrishnan E. A. obtained his Ph. D. from Indian Institute of Technology Madras in the year 2016. Dr. Gopalakrishnan completed his B-Tech in Mechanical Engineering with Honors & Third Rank from University of Calicut. Dr. Gopalakrishnan obtained his M-Tech in Engineering Design from Amrita School of Engineering, Amrita Vishwa Vidyapeetham with University First Rank.

Dr. Gopalakrishnan was awarded with SFB TRR40 Summer Fellowship to attend the summer school held at Institute of Aerodynamics and Fluid Mechanics of the Technische Universität München, Germany during July 20-Aug. 17, 2015. Dr. Gopalakrishnan was invited by the Institute for Advanced Study, Technische Universität München, Germany during June 2013 as a Visiting Scientist.

Dr. Gopalakrishnan has published articles in prestigious journals such as Nature Scientific Reports, Chaos, Physical Review E, Proceedings of Combustion Institute, IEEE Transactions, Expert Systems and Journal of Fluid Mechanics. Dr. Gopalakrishnan is a reviewer of journals such as PLOS ONE, Chaos, Physical Review E, Journal of Fluid Mechanics, Expert Systems etc.

One of the papers published by Dr. Gopalakrishnan titled “Early warning signals for critical transitions in a thermoacoustic system” was cited by the office of the UK Prime Minister in one of the policy documents pertinent to novel strategies for governance by the then Chief Adviser of UK Prime Minister, Mr. Dominic Cummings.

Dr. Gopalakrishnan’s research interests are Complex Systems, Data Driven Modelling & Analysis, Artificial Intelligence, Early Warning Signals for Catastrophic Transitions and Time Series Analysis. He is currently involved in developing robust early warning indicators for rate dependent transitions in various engineering/ natural systems by employing tools from complex system theory and AI.

Dr. Gopalakrishnan worked as an Assistant Professor in the Department of Mechanical Engineering, Amrita School of Engineering from 2007 to 2011. He worked as an Associate Professor in Center for Computational Engineering and Networking from 2016 to 2023. Currently, he is the principal of Amrita School of Computing Bengaluru, School of Artificial Intelligence Bengaluru and he heads the thematic research center “Amrita Center of Artificial Intelligence and Data Analytics”.

Publications

Journal Article

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

Conference Paper

Year : 2023

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

Cite this Research Publication : Akshay, S., E. A. Gopalakrishnan, V. Sowmya, J. Venkatramani, Dheeraj Tripathi, Jay Shankar Prasad, and Sirshendu Mondal. "Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems." IEEE Access (2023)

Year : 2021

Parkinson’s Disease Classification from Magnetic Resonance Images (MRI) using Deep Transfer Learned Convolutional Neural Networks

Cite this Research Publication : Veetil, I. K., Gopalakrishnan, E. A., Sowmya, V. and Soman, K. P. “Parkinson’s disease classification from Magnetic Resonance Images using deep transfer learned Convolutional Neural Networks”, Proceedings of 18th Indian Council International Conference, INDICON (2021).

Publisher : INDICON

Year : 2021

Early Warning Indicators for Financial Crisis During Covid-19

Cite this Research Publication : Modi, A., Jyothish Lal, G., Gopalakrishnan, E. A., Sowmya, V., Soman, K. P. and Vinayakumar, R. “Early warning indicators of financial crisis during Covid-19”, Communications in Computer and Information Science (2022), 1528, 229-243.

Publisher : Springer

Year : 2021

Performance Improvement of Deep Residual Skip Convolution Neural Network for Atrial Fibrillation Classification

Cite this Research Publication : Sanjana, K., Sowmya, V., Gopalakrishnan, E. A. and Soman, K. P. “Performance improvement of deep residual skip convolution neural network for atrial fibrillation classification”. (2021). Advances in Intelligent Systems & Computing, 1176, 755-763.

Publisher : Springer Singapore

Year : 2021

Noise Reduction of ECG using Chebyshev filter and Classification using Machine Learning Algorithms

Cite this Research Publication : Prakash, M. B, Sowmya, V., Gopalakrishnan, E. A. and Soman, K. P. “Noise reduction of ECG using Chebyshev filter and classification using machine learning algorithms”, Proceedings of International Conference on Computing, Communication and Intelligent Systems. (2021).

Publisher : IEEE

Year : 2020

Analysis of Adversarial based Augmentation for Diabetic Retinopathy Disease Grading

Cite this Research Publication : R. Balasubramanian, Vishvanathan, S., Gopalakrishnan, E. A., Menon, V., V, S. V., and Dr. Soman K. P., “Analysis of Adversarial based Augmentation for Diabetic Retinopathy Disease Grading”, in 2020 11th International Conference on Computing, Communication and Networking Technologies, 2020.

Year : 2020

An Approach to Detect and Classify Defects in Cantilever Beams Using Dynamic Mode Decomposition and Machine Learning

Cite this Research Publication : K. Nagarajan, Ananthu, J., Menon, V. Krishna, Dr. Soman K. P., Gopalakrishnan, E. A., and Dr. Ajith Ramesh, “An Approach to Detect and Classify Defects in Cantilever Beams Using Dynamic Mode Decomposition and Machine Learning”, in Smart Innovation, Systems and Technologies, Singapore, 2020.

Publisher : Springer Singapore

Year : 2019

Performance Improvement of Deep Learning Architectures for Phonocardiogram Signal Classification using Fast Fourier Transform

Cite this Research Publication : Gopika, P., Sowmya, V., Gopalakrishnan, E. A. and Soman, K. P. “Performance Improvement of Deep Learning Architectures for Phonocardiogram Signal Classification using Fast Fourier Transform”. International Conference on Advances in Computing and Communication. November 6 – 7, 2019, Hyderabad, India.

Publisher : IEEE

Year : 2019

Investigating the Effectiveness of DMD and its Variants for Complex Data Analysis

Cite this Research Publication : Akshay, S., Gopalakrishnan, E. A. and Soman, K. P. “Investigating the Effectiveness of DMD and its variants for Complex Data Analysis”. International Conference on Intelligent Computing and Control Systems. May 15 – 17, 2019, Madurai, India.

Publisher : IEEE

Year : 2019

Part-of-Speech Tagger for Biomedical Domain Using Deep Neural Network Architecture

Cite this Research Publication : Gopalakrishnan, A., Soman, K.P., Premjith, B. Part-of-Speech Tagger for Biomedical Domain Using Deep Neural Network Architecture, (2019) 2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, art. no. 8944559, DOI: 10.1109/ICCCNT45670.2019.8944559

Publisher : 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

Year : 2019

A Data-Driven Model Approach for DayWise Stock Prediction

Cite this Research Publication : Unnithan, N. A., Gopalakrishnan, E. A., Vijay, K. M. and Soman, K. P. (2019), “A data-driven model approach for day wise stock prediction” in “Emerging research in electronics, computer science and technology”, 149 – 158, Lecture Notes in Electrical Engineering, Springer, 2019

Publisher : Springer Singapore

Year : 2019

A complex network approach for plant growth analysis using images

Cite this Research Publication : Sajith, V. V., Gopalakrishnan, E. A., Sowmya, V. and Soman, K. P. “A Complex Network Approach for Plant Growth Analysis using Images”. International Conference on Communication and Signal Processing. April 4 – 6, 2019, Tamil Nadu, India.

Publisher : Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019

Year : 2018

A Spark™ Based Client for Synchrophasor Data Stream Processing

Cite this Research Publication : Vijay, K. M., Sajith, V. V., Soman, K. P., Gopalakrishnan, E. A., Sasi, K. K., Almas, M. S. and Nordstrom, L. “A SparkTM based client for synchrophasor data stream processing”. International Conference and Utility Exhibition on Green Energy for Sustainable Development. October 24 – 26, 2018, Phuket, Thailand.

Publisher : 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE)

Year : 2017

Stock price prediction using dynamic mode decomposition

Cite this Research Publication : Deepthi, P. K., Gopalakrishnan, E. A., Vijay, K. M. and Soman, K. P. “Stock price prediction using dynamic mode decomposition.” International Conference on Advances in Computing, Communications and Informatics. September 13-16, 2017, Manipal, India.

Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

Year : 2017

A first order phase transition model for Rijke oscillations

Cite this Research Publication : Dr. E. A. Gopalakrishnan, Kumar, A., Verma, M. K., and Sujith, R. I., “A first order phase transition model for Rijke oscillations”, in Accepted for presentation in 24th International Congress on Sound & Vibration, London, 2017.

Publisher : Accepted for presentation in 24th International Congress on Sound & Vibration, London, 2017.

Year : 2017

Stock Price Prediction using LSTM, RNN and CNN-sliding Window Model

Cite this Research Publication : Sreelekshmy, S., Vinayakumar, R., Gopalakrishnan, E. A., Vijay, K. M. and Soman, K. P. “Stock price prediction using LSTM, RNN and CNN-sliding window model”. International Conference on Advances in Computing, Communications and Informatics, September 13-16, 2017, Manipal, India.

Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

Year : 2017

Classification of states of bi-stable oscillator using deep learning

Cite this Research Publication : Rohit, M., Gopalakrishnan, E. A. and Soman, K. P. “Classification of states of bi-stable oscillator using deep learning.” International Conference on Advances in Computing, Communications and Informatics, September 13-16, 2017, Manipal, India.

Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI),

Year : 2017

Single Sensor Techniques for Sleep Apnea Diagnosis Using Deep Learning

Cite this Research Publication : Rahul, K. P., Vinaykumar. R., Eknath, R., Gopalakrishnan, E. A. and Soman, K. P. “Single sensor techniques for sleep apnea diagnosis using deep learning”. International Conference on Healthcare Informatics, August 23-27, 2017, UT, USA.

Publisher : IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, USA .

Year : 2017

Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning

Cite this Research Publication : Rahul, K. P., Vinaykumar. R., Eknath, R., Gopalakrishnan, E. A. and Soman, K. P. “Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning”, BHI-2017 International Conference on Biomedical and Health Informatics, February 16-19, 2017, Florida, USA.

Publisher : IEEE International Conference on Biomedical and Health Informatics, Orlando, Florida.

Year : 2007

Interference effects on flow induced oscillations of rectangular cylinders

Cite this Research Publication : Sabareesh, G. R., Gopalakrishnan, E. A., Ajithkumar, R. and Gowda, B. H. L. “Interference effects on flow induced oscillations of rectangular cylinders”, 12th International Conference on Wind Engineering and Industrial Aerodynamics, July 1-6, 2007, Cairns, Australia.

Publisher : 12th International Conference on Wind Engineering and Industrial Aerodynamics

Conference Proceedings

Year : 2019

Performance Improvement of Residual Skip Convolutional Neural Network for Myocardial Disease Classification

Cite this Research Publication : Gopika, P., Sowmya, V., Gopalakrishnan, E. A. and Soman, K. P. “Performance Improvement of Residual Skip Convolutional Neural Network for Myocardial Disease Classification”, International Conference on Intelligent Computing and Communication Technologies. January 9 – 11, 2019, Hyderabad, India.

Year : 2018

Rate Dependent Transitions in Power Systems

Cite this Research Publication : Suchithra, K. S. and Gopalakrishnan, E. A. “Rate Dependent Transitions in Power Systems”. International Conference and Utility Exhibition on Green Energy for Sustainable Development. October 24 – 26, 2018, Phuket, Thailand.

Publisher : Proceedings of the Conference on the Industrial and Commercial Use of Energy, ICUE

Year : 2016

Hybrid CFD/ low-order modeling of nonlinear thermoacoustic oscillations

Cite this Research Publication : S. Jaensch, Merk, M., Dr. E. A. Gopalakrishnan, Bomberga, S., Emmert, T., Sujith, R. I., and Polifke, W., “Hybrid CFD/ low-order modeling of nonlinear thermoacoustic oscillations”, 36th Combustion Symposium. 2016.

Publisher : 36th Combustion Symposium

Year : 2016

Early warning measures for tipping points in a thermoacoustic system

Cite this Research Publication : Dr. E. A. Gopalakrishnan, Sharma, Y., John, T., Dutta, P. Sharathy, and Sujith, R. I., “Early warning measures for tipping points in a thermoacoustic system”, Conference on Nonlinear Systems & Dynamics, December 16-18, IISER Kolkata. 2016.

Publisher : Conference on Nonlinear Systems & Dynamics,

Year : 2015

Hurst exponent and translation error as discriminating measures to identify a chaotic nature of an experimental time series

Cite this Research Publication : J. Tony, Dr. E. A. Gopalakrishnan, Sreelekha, E., and Sujith, R., “Hurst exponent and translation error as discriminating measures to identify a chaotic nature of an experimental time series”, Bifurcations and Instabilities in Fluid Dynamics, July 15-17, 2015, Paris, France. 2015.

Publisher : Bifurcations and Instabilities in Fluid Dynamics, July 15-17, 2015, Paris, France

Year : 2015

Hurst exponent and translation error as discriminating measures to identify the chaotic nature of an experimental time series

Cite this Research Publication : Dr. E. A. Gopalakrishnan, Tony, J., Sreelekha, E., and Sujith, R. I., “Hurst exponent and translation error as discriminating measures to identify the chaotic nature of an experimental time series”, Conference on Nonlinear Systems and Dynamics, Mar. 13-15, 2015, Mohali, India. 2015.

Publisher : Conference on Nonlinear Systems and Dynamics, Mar. 13-15, 2015, Mohali, India

Year : 2014

Noise induced transition in a horizontal Rijke tube

Cite this Research Publication : Dr. E. A. Gopalakrishnan and Sujith, R. I., “Noise induced transition in a horizontal Rijke tube”, 10th European Fluid Mechanics Conference, Sep. 14-18, 2014, Copenhagen, Denmark. 2014.

Publisher : 10th European Fluid Mechanics Conference, Sep. 14-18, 2014, Copenhagen, Denmark

Year : 2014

Influence of external noise on the nature of transition of a thermoacoustic system

Cite this Research Publication : Dr. E. A. Gopalakrishnan and Sujith, R. I., “Influence of external noise on the nature of transition of a thermoacoustic system”, Dynamic Days Asia Pacific-08, July 21-24, 2014, Chennai, India. Chennai, India, 2014.

Publisher : Dynamic Days Asia Pacific-08, July 21-24, 2014, Chennai, India

Year : 2013

Influence of system parameters and external noise on hysteresis characteristics of a horizontal Rijke tube.

Cite this Research Publication : Dr. E. A. Gopalakrishnan and Sujith, R. I., “Influence of system parameters and external noise on hysteresis characteristics of a horizontal Rijke tube.”, n3l - Int’l Summer School and Workshop on Non-Normal and Nonlinear Effects in Aero- and Thermoacoustics, June 18-21, 2013, Munich, Germany. Munich, Germany, 2013.



Publisher : n3l - Int’l Summer School and Workshop on Non-Normal and Nonlinear Effects in Aero- and Thermoacoustics, June 18-21, 2013, Munich, Germany.

Book Chapter

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

Classification of Class-Imbalanced Diabetic Retinopathy Images Using the Synthetic Data Creation by Generative Models

Cite this Research Publication : Kumar, Krishanth, V. Sowmya, E. A. Gopalakrishnan, and K. P. Soman. "Classification of Class-Imbalanced Diabetic Retinopathy Images Using the Synthetic Data Creation by Generative Models." In Intelligent Sustainable Systems, pp. 15-24. Springer, Singapore, 2022.

Publisher : Springer

Year : 2022

Deep Learning-Based Approach for Parkinson’s Disease Detection Using Region of Interest

Cite this Research Publication : Madan, Yamini, Iswarya Kannoth Veetil, V. Sowmya, E. A. Gopalakrishnan, and K. P. Soman. "Deep Learning-Based Approach for Parkinson’s Disease Detection Using Region of Interest." In Intelligent Sustainable Systems, pp. 1-13. Springer, Singapore, 2022.

Publisher : Springer

Year : 2021

Multi-task data driven modelling based on transfer learned features in deep learning for biomedical application

Cite this Research Publication : H. N., B., R., V, S., V., K. Menon, E.A., G., V.V., S. Variyar, and Dr. Soman K. P., “Multi-task data driven modelling based on transfer learned features in deep learning for biomedical application”, in Lecture Notes in Networks and Systems, vol. 171, 2021.

Publisher : Lecture Notes in Networks and Systems

Year : 2020

Single-layer Convolution Neural Network for Cardiac Disease Classification using Electrocardiogram Signals

Cite this Research Publication : P. Gopika, Krishnendu, C. S., M. Chandana, H., Ananthakrishnan, S., Sowmya V., Gopalakrishnan, E. A., and Soman, K. P., “Single-layer Convolution Neural Network for Cardiac Disease Classification using Electrocardiogram Signals”, in Deep Learning for Data Analytics, H. Das, Pradhan, C., and Dey, N., Eds. Academic Press, 2020, pp. 21-35, Academic Press.

Publisher : Deep Learning for Data Analytics, Academic Press

Year : 2020

Transferable Approach for Cardiac Disease Classification using Deep Learning

Cite this Research Publication : P. Gopika, Sowmya V., Gopalakrishnan, E. A., and Dr. Soman K. P., “Transferable Approach for Cardiac Disease Classification using Deep Learning”, in Deep Learning Techniques for Biomedical and Health Informatics, B. Agarwal, Balas, V. Emilia, Jain, L. C., Poonia, R. Chandra, and Manisha,, Eds. Academic Press, 2020, pp. 285-303, Academic Press.

Publisher : Deep Learning Techniques for Biomedical and Health Informatics, Academic Press

Year : 2019

A Deep Learning Approach for Patch-based Disease Diagnosis from Microscopic Images

Cite this Research Publication : A. Simon, Vinayakumar, R., Sowmya V., Soman, K. Padannayil, and Gopalakrishnan, E. Anathanara, “A Deep Learning Approach for Patch-based Disease Diagnosis from Microscopic Images”, in Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis, N. Dey, Ed. Academic Press, 2019, pp. 109-127, Academic Press.

Publisher : Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis, Academic Press

Qualification
  • Ph. D. in Aerospace Engineering (July 2011-July 2016)
    Indian Institute of Technology Madras, Chennai, India
    Thesis Title: Bistability & Noise Induced Transition in a Horizontal Rijke Tube.
    Grade Points: 9.25/10.00
    Supervisor: Prof. R. I. Sujith
  • M. Tech in Engineering Design (Aug. 2005-July 2007)
    Amrita School of Engineering, Amrita University, Coimbatore, India
    Project: Effect of Slenderness Ratio on the Flow Induced Vibration of Rectangular Cylinders.
    Grade Points: 10.00/10.00
    Supervisor: Prof. R. Ajith Kumar
  • B. Tech in Mechanical Engineering (Oct. 1999-Sep. 2003)
    N. S. S. College of Engineering Palakkad, University of Calicut, Kerala, India
    Grade Points: 77.13%
Funded Research Projects
  1. “Development and implementation of a robust pre-processing technique for detection improvement in passive SONAR” – NPOL, DRDO – Completed.
  2. “A complex system approach to develop early warning signals for the detection of Parkinson’s disease” – CSIR Labs – Ongoing
  3. “Use of Artificial Intelligence to predict dynamic transitions in stall-induced aero-elastic problems” – Core Research Grant, DST.
Research Scholars
  1. Jyothish Lal G (2017-2020) – Graduated
  2. Suchithra K S (2017 -) – Submitted the Thesis
  3. Iswarya K V (2018 -) – Ongoing
  4. Akshay S (2020 -) – Ongoing
  5. Deepa Raj ( 2021 -) – Ongoing – Co-Guide
Admissions Apply Now