Ministry of Education
A Srujana
VIDWAN ID: 652163

Dr A Srujana

Female Doctor of Philosophy
Principal | Department of Electrical and Electronics Engineering
Vidya Jyothi Institute of Technology
Telangana
Expertise: Electrical and Electronic Engineering
ORCID 0009-0003-0581-5620 Scopus 57202951363 ResearcherID Nof-2667-2025 Google Scholar 6VEkwC8AAAAJ
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17 Publications
0 Projects
23 Scopus Citations
82 CrossRef
6 Years 10 Months Total Experience
Publications
17 Total
Articles
12
Proceedings
5
Activity

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Scopus Scopus
23 Citations
2 h-index
CrossRef CrossRef
82 Citations
3 h-index
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Personal Details

Doctor of Philosophy
2011
N/A
Principal
Nov 2019 – Present
Vidya Jyothi Institute of Technology | Department of Electrical and Electronics Engineering
Engineering and Technology
Electrical and Electronic Engineering

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Scholarly Work

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Scholarly Publications

Solving constrained economic electrical energy generation and CO2 emission dispatch using hybrid algorithm

Open Access
Journal Article
Environmental Technology & Innovation. Year: 2021. Volume: 24 , Pages: 101999 .
Authors: Jebaseelan, S.D.S.; Selvan, N.B.M.; Kumar, C.; Kalaimurugan, A.; Srujana, A.; Ravi, C.N.
DOI 10.1016/j.eti.2021.101999 Crossref Crossref: 4

Power quality enhancement in micro grids by employing MPC-EKF

Journal Article
Year: 2018. Volume: 7 , Issue: 3 , Pages: 996-999 .
Authors: Narender Reddy, N.; Chandrasheker, O.; Srujana, A.
DOI 10.14419/ijet.v7i3.11182

Stabilization of the alternative current in wind based power plant using multi order harmonic remover based on synthesized multilayer power converter

Journal Article
International Journal of Innovative Technology and Exploring Engineering. Year: 2019. Volume: 8 , Issue: 9 , Pages: 1935-1944 .
Authors: Bodha, V.R.; Kuthuri, N.R.; Srujana, A.
DOI 10.35940/ijitee.i8463.078919 Crossref Crossref: 3

Predictive back-to-back SCHVC for renewable wind power system for scrutinizing quality and reliability

Journal Article
Energy Sources Part A: Recovery Utilization and Environmental Effects. Year: 2019. Volume: 41 , Issue: 24 , Pages: 3058-3075 .
Authors: Bodha, V.R.; Srujana, A.; Kuthuri, N.R.
DOI 10.1080/15567036.2019.1586013 Crossref Crossref: 3

Amplifying power quality and diminishing harmonic distortion in MG VIA adaptive MPC-based robust EKF through IPSO-SHE

Journal Article
International Journal of Power Electronics. Year: 2021. Volume: 13 , Issue: 2 , Pages: 208 .
Authors: Narender Reddy, N.; Chandrasheker, O.; Srujana, A.
DOI 10.1504/IJPELEC.2021.112987

Retraction note: Real-time congestion control using cascaded LSTM deep neural networks for deregulated power markets (Scientific Reports, (2025), 15, 1, (30581), 10.1038/s41598-025-14640-6)

Open Access
Journal Article
Scientific Reports. Year: 2025. Volume: 15 , Issue: 1 , Pages: 37726 .
Authors: Mohan, G.M.; Kumar, T.A.; Srujana, A.; Fouad, Y.; Mikhaylov, A.; Kitmo; Reddy, C.R.
DOI 10.1038/s41598-025-25358-w Crossref Crossref: 1

Real-time congestion control using cascaded LSTM deep neural networks for deregulated power markets

Open Access
Journal Article
Scientific Reports. Year: 2025. Volume: 15 , Issue: 1 , Pages: 30581 .
Authors: Mohan, G.M.; Kumar, T.A.; Srujana, A.; Fouad, Y.; Mikhaylov, A.; Baranyai, N.; Kitmo; Reddy, C.R.
DOI 10.1038/s41598-025-14640-6 Crossref Crossref: 2

Real-Time Intrusion Detection in Heterogeneous IoT Using Federated Deep Grid Neural Net.​AbstractThe rapid expansion of heterogeneous Internet of Things (IoT) systems has significantly increased the risk of cyber threats, necessitating robust real-time intrusion detection mechanisms. Traditional centralized models often fall short due to concerns about data privacy and limited scalability across diverse IoT environments. Existing intrusion detection approaches struggle with issues such as high communication overhead, inability to model complex relationships among devices, and poor adaptability to dynamic network topologies. To overcome these limitations, this study proposes a Federated Deep Grid Convolutional Net (Fed-GCN) with Attention Aggregation, a privacy-preserving framework that enables collaborative Learning across distributed IoT nodes. The Fed-GCN models local

Other
Year: 2025.
Authors: Babu, V.; Konda, S.; Srujana, A.; Kumar, K.N.
DOI 10.2139/ssrn.5602098
Showing 1 to 8 of 17 publications