Applications of AI, ML and DL

The application of artificial intelligence, machine learning and deep learning is one of the current topics in many fields. Researchers have given a lot of input to enhance the precision of machine learning and deep learning algorithms and a lot of work is carried out rapidly to enhance the intelligence of machines. Learning, a natural process in human behavior that also becomes a vital part of machines as well. AI, ML and DL had been analyzed and implemented in various applications and had shown remarkable results thus this field needs wider exploration which can be helpful for further real-world applications. Our faculty have catered applications of the ML and DL techniques to solve important human, institutional, and societal challenges. Our research spans a wide range of topics, that include: estimation, prediction, classification and learning of distributions and processes, real time optimal energy management, energy storage like batteries etc., signal processing, fault diagnosis, neuroscience and biomedical signal processing, wireless communications, image and video processing and analysis surgical robotics and robot manipulation.

Associated Faculty Members

Significant Publications

  1. Sudarsan Sahoo et. al., “A Device and A Method for Detecting Severe Acute Respiratory Syndrome (SARS) Coronavirus Based on Filtered Cough Sound Signals”, South African Patent No. 2022/01879, (Patent granted).
  2. Sudarsan Sahoo, Ruhul Amin Laskar and Sudipta Chakraborty, “Fault Identification and Classification in Motorcycle Engine Using Acoustic Emission Signal and Machine Learning Techniques”, Journal of Physics, vol. 1950(1), pp. 012029, 2021.
  3. Sudarsan Sahoo, Priyanuj Borthakur, Niharika Baruah and Bhaskar Pratim Chutia, “IoT and Machine Learning Based Health Monitoring and Heart Attack Prediction System”, Journal of Physics, vol. 1950(1), pp.012056, 2021.
  4. P.S. Pravin, Jaswin Zhi Ming Tan, Ken Shaun Yap, Zhe Wu, “Hyperparameter optimization strategies for machine learning-based stochastic energy efficient scheduling in cyber-physical production systems”, Digital Chemical Engineering, vol. 4, 2022, 100047.
  5. P.S. Pravin, Zhiyao Luo, Lanyu Li, Xiaonan Wang, “Learning-based Scheduling of Industrial Hybrid Renewable Energy Systems”, Computers & Chemical Engineering, vol. 159, 2022, 107665.
  6. Manu Suvarna, Apoorva Katragadda, Ziying Sun, Yun Bin Choh, Qianyu Chen, P.S. Pravin, Xiaonan Wang, “A machine learning framework to quantify and assess the impact of COVID-19 on the power sector: An Indian context”, Advances in Applied Energy, vol. 5, 2021, 100078.
  7. B. Mali, S. H. Laskar, “Incipient fault detection of sensors used in wastewater treatment plants based on deep dropout neural network”, Springer Nature Applied Sciences, vol. 2, 2121.
  8. S. H. Choudhury, A. Kumar, Shahedul Haque Laskar, “Adaptive Management of Multimodal Biometrics—A Deep Learning and Metaheuristic Approach, Applied Soft Computing”, vol. 106, 2021, 107344, ISSN 1568- 4946.
  9. Rupesh Mahamune, S. H. Laskar, “Classification of the four‐class motor imagery signals using continuous wavelet transform filter bank‐based two‐dimensional images”, International Journal of Imaging Systems and Technology. 2021.
  10. Debroy, Pragnaleena, and Lalu Seban, “A Fish Biomass Prediction Model for Aquaponics System Using Machine Learning Algorithms”, Machine Learning and Autonomous Systems. Springer, Singapore, pp. 383-397, 2022.
  11. P. Goswami, A. Mukherjee, R. Hazra, L. Yang, U. Ghosh, Q. Yinan and H. Wang, “AI-based energy efficient routing protocol for intelligent transportation system2022”, IEEE Transactions on Intelligent Transportation Systems, vol. 23(2), pp. 1670-1679, 2022.
  12. A. Sekhar, S. Biswas, R. Hazra, A. K. Sunaniya, A. Mukherjee and L. Yang, “Brain tumor classification using fine-tuned GoogLeNet features and machine learning algorithms: IoMT enabled CAD system”, IEEE Journal of Biomedical and Health Informatics, vol. 26(3), pp. 983-991, 2022.
  13. P. Khuntia, R. Hazra and Peter H.J. Chong, “An efficient actor-critic reinforcement learning for device-to-device communication underlaying sectored cellular network”, International Journal of Communication Systems, Wiley, vol. 33, e4315, 2020.
  14. P.Khuntia and R. Hazra, “An Efficient Reinforcement Learning for Device-to-Device Communication Underlaying Cellular Network”, IEIE Transactions on Smart Processing and Computing, vol. 9(1), 2020.
  15. P. Khuntia and R. Hazra, “An efficient Deep reinforcement learning with extended Kalman filter for device-to-device communication underlaying cellular network,” Transactions on Emerging Telecommunications Technologies, Wiley, vol. 30(9), e3671, 2019.