Geetanjali Sharma

Orcid: 0000-0002-2103-853X

According to our database1, Geetanjali Sharma authored at least 13 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2024
FH-SSTNet: Forehead Creases based User Verification using Spatio-Spatial Temporal Network.
CoRR, 2024

2023
A Smart Healthcare Framework for Accurate Detection of Schizophrenia Using Multichannel EEG.
IEEE Trans. Instrum. Meas., 2023

DepCap: A Smart Healthcare Framework for EEG Based Depression Detection Using Time-Frequency Response and Deep Neural Network.
IEEE Access, 2023

Learning channel attention for decoding of visual imagined text from multi-band EEG using metric learning.
Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, 2023

Sclera Segmentation and Joint Recognition Benchmarking Competition: SSRBC 2023.
Proceedings of the IEEE International Joint Conference on Biometrics, 2023

2022
Erratum: High-Performance 32-Bit Parallel Hybrid Adder Design Using RNS and Hybrid PTL/CMOS Logic.
J. Circuits Syst. Comput., December, 2022

SzHNN: A Novel and Scalable Deep Convolution Hybrid Neural Network Framework for Schizophrenia Detection Using Multichannel EEG.
IEEE Trans. Instrum. Meas., 2022

DepML: An Efficient Machine Learning-Based MDD Detection System in IoMT Framework.
SN Comput. Sci., 2022

High-Performance 32-Bit Parallel Hybrid Adder Design Using RNS and Hybrid PTL/CMOS Logic.
J. Circuits Syst. Comput., 2022

2021
Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare.
CoRR, 2021

DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression.
Biomed. Signal Process. Control., 2021

An Automated MDD Detection System based on Machine Learning Methods in Smart Connected Healthcare.
Proceedings of the IEEE International Symposium on Smart Electronic Systems, 2021

2014
A novel high performance low power CMOS NOR gate using Voltage Scaling and MTCMOS technique.
Proceedings of the 2014 International Conference on Advances in Computing, 2014


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