Search

Your search keyword '"Sahu, Bandita"' showing total 34 results
Start Over Author "Sahu, Bandita" Search Limiters Peer Reviewed Publication Year Range Last 50 years
34 results on '"Sahu, Bandita"'

3. Multi-robot Cooperation and Path Planning Using Modified Cuckoo Search

Author

Sahu, Bandita, Kumar Das, Pradipta, Ranjan Kabat, Manas, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Raghvendra, editor, Pattnaik, Prasant Kumar, editor, and R. S. Tavares, João Manuel, editor

Published
2023
Full Text
View/download PDF

6. Heuristic Based SCA for Twin Robot Cooperation and Path Planning

Author

Sahu, Bandita, Das, Pradipta Kumar, Kabat, Manas Ranjan, Kumar, Raghvendra, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Pattnaik, Prasant Kumar, editor, Sain, Mangal, editor, and Al-Absi, Ahmed A., editor

Published
2022
Full Text
View/download PDF

11. An Empirical Study of Green Supply Chain Management by Using an Optimisation Tool: An Eastern India Perspective

Author

Sahu, Bandita, Dhal, Prasant Ranjan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Udgata, Siba K., editor, Sethi, Srinivas, editor, and Srirama, Satish N., editor

Published
2021
Full Text
View/download PDF

12. Cuckoo Search Applied Path Planning of Twin Robot in Multi-Robot Environment

Author

Sahu, Bandita, Das, Pradipta Kumar, Kabat, Manas Ranjan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Raghvendra, editor, Mishra, Brojo Kishore, editor, and Pattnaik, Prasant Kumar, editor

Published
2021
Full Text
View/download PDF

25. Efficient detection of Parkinson's disease using deep learning techniques over medical data.

Author

Sahu, Lipsita, Sharma, Rohit, Sahu, Ipsita, Das, Manoja, Sahu, Bandita, and Kumar, Raghvendra

Subjects
DEEP learning, PARKINSON'S disease, CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, BRAIN stimulation, SPEECH perception, DEGENERATION (Pathology), REFLECTIVE learning
Abstract

Parkinson's disease is a degenerative disease that leads to brain disorder and nonfunctioning of different body parts. Deep learning tools like artificial neural network (ANN), convolution neural network (CNN), regression Analysis (RA), and so on, has been considered to a great extent in recent days. Several data sets based on the motor and nonmotor symptoms are applied to different classifier for correct identification of Parkinson's patient from healthy people. In this paper, hybridization of two deep learning tools such as, RA and ANN are done for effective diagnosis of the disease by probability estimation. The communal merits of individual approaches of the existing approaches are realized in this context for accurate probability estimation. Data preprocessing and probability estimation of preprocessed data is done in RA. The second existing approach is used to identify the PD patient by comparing with a predefined threshold value of a neuron. The estimation is performed on the data set of speech recognition, iron content, and pulse rate among a group of people. The proposed approach is compared with the existing approaches like, SVM and k‐NN classifier. The computed result reveals the superiority of the proposed algorithm with 93.46% accuracy. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

33. Energy Efficient SNR Based Clustering in Underwater Sensor Network with Data Encryption

Author

Khilar Pabitra Mohan and Sahu Bandita

Subjects
business.industry, Computer science, 010401 analytical chemistry, Real-time computing, 020206 networking & telecommunications, 02 engineering and technology, Encryption, 01 natural sciences, 0104 chemical sciences, Signal-to-noise ratio (imaging), Computer Science::Networking and Internet Architecture, 0202 electrical engineering, electronic engineering, information engineering, Underwater, Cluster analysis, business, Protocol (object-oriented programming), Wireless sensor network, Energy (signal processing), Computer network, Efficient energy use
Abstract

In Under Water Sensor Network (UWSN), design of clustering protocol is challenging due to the energy constrained sensor nodes. Therefore, energy saving is considered to be an important issue. In this paper, a new clustering protocol is proposed which is named as energy efficient SNR based clustering in UWSN with Data Encryption (EESCDE). Using this, one percent improvement in the residual energy as compared to the algorithm ESRPSDC is achieved.

Published
2015

Catalog

Books, media, physical & digital resources
See catalog results