Barasha Mali
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Barasha Mali has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
Chandrasekhar Yadav
Ministry of Electronics and Information Technology
1 shared publicationS. H. Laskar
National Institute of Technology Silchar
1 shared publicationSantosh Kumar
Ministry of Electronics and Information Technology
1 shared publicationShahedul Haque Laskar
National Institute of Technology Silchar, Assam, India
1 shared publication- Shahedul Haque Laskar
National Institute of Technology Silchar
1 shared publication
Latest Publications
- 21 December 2020
- Published by Springer Nature
- p. 83-91
Supervised Learning-Based Classifiers in Healthcare Decision-Making
- 21 December 2020
- Published by Springer Nature
- p. 83-91
Supervised Learning-Based Classifiers in Healthcare Decision-Making
- 1 December 2020
- Published by Springer Nature in SN Applied Sciences
- Vol. 2 (12) , 1-10
Incipient fault detection of sensors used in wastewater treatment plants based on deep dropout neural network
- 1 December 2020
- Published by Springer Nature in SN Applied Sciences
- Vol. 2 (12) , 1-10
Incipient fault detection of sensors used in wastewater treatment plants based on deep dropout neural network
- 11 September 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Deep learning based automatic maintenance of soft sensors used in wastewater treatment plants
Soft sensors in wastewater treatment plants are used to estimate the difficult to measure variables, predict the parameters of concern as per the instructions of the pollution control board or monitor the plant and effluent quality. However the soft sensor deterioration is a limitation of such soft sensors which occur due to various reasons like sudden change in process characteristic or involvement of faulty data in designing and updating the soft sensor. The paper overcome this limitation by using a deep learning based method for automatic maintenance. The proposed technique identify and rectify the faulty data involved in designing and updating of soft sensor based on a ten layered deep neural network. The comparative results show that the proposed method reduces the soft sensor deterioration of a total nitrogen soft sensor used in wastewater treatment plants.
- 11 September 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Deep learning based automatic maintenance of soft sensors used in wastewater treatment plants
Soft sensors in wastewater treatment plants are used to estimate the difficult to measure variables, predict the parameters of concern as per the instructions of the pollution control board or monitor the plant and effluent quality. However the soft sensor deterioration is a limitation of such soft sensors which occur due to various reasons like sudden change in process characteristic or involvement of faulty data in designing and updating the soft sensor. The paper overcome this limitation by using a deep learning based method for automatic maintenance. The proposed technique identify and rectify the faulty data involved in designing and updating of soft sensor based on a ten layered deep neural network. The comparative results show that the proposed method reduces the soft sensor deterioration of a total nitrogen soft sensor used in wastewater treatment plants.
- 20 August 2019
- Published by Springer Nature in Lecture Notes in Electrical Engineering
- p. 123-131
PLS-Based Multivariate Statistical Approach for Soft Sensor Development in WWTP
- 20 August 2019
- Published by Springer Nature in Lecture Notes in Electrical Engineering
- p. 123-131
PLS-Based Multivariate Statistical Approach for Soft Sensor Development in WWTP