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Research Article
Personality Prediction and Resume Screening using Machine Learning
Dr. A.S. Shanthi1
P. Leander Felix2
K.A. Raghul3
M. Utchimakali4
S. Yogaraj5
12345Department of Computer Science and Engineering, Tamilnadu College of Engineering, Tamilnadu, India.
Published Online: May-June 2023
Pages: 421-423
Cite this article
No DOIReferences
[1]. M.F. Arani, A.A. Jahromi, D. Kundur and M. Kassouf, “Modelling and simulation of the aurora attack on micro grid point of common coupling,” in
2019 7th workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES). IEEE, 2019, pp. 1-6.
[2]. R. Tan, V. Badrinath Krishna, D.K. Yau and Z. Kalbarczyk, “Impact of integrity attacks on real-time pricing in smart grids,” 2013, pp. 439-450.
[3]. Y. Dafalla, B. Liu, A.A Hahn, H.Wu, R. Ahmadi and A.G. Bardas, “Prosumer nanogrids: A cybersecurity assessment,” IEEE Access, vol. 8, pp. 131 150
– 131 164, 2020, event IEEE Access.
[4]. B. Liu and H. Wu, “Optimal d-facts placement in moving target defense against false data injection attacks,” IEEE Transactions on Smart Grid, pp. 1-1,
2020, event: IEEE Transactions on smart grid.
[5]. Z. Zhang, R. Deng, D.K Yau, P. Cheng and J. Chen, “Analysis of moving target defense against false data injection attacks on power grid,” IEEE
Transactions on Information Forensics and Security, vol. 15, pp.2320 – 2335, 2019.
[6]. S. Lakshminarayana and D.K. Yau, “Cost-benefit analysis of moving target defense in power grids,” IEEE Transactions on Power Systems, 2020.
[7]. J. Tian, R. Tan, X. Guan and T. Liu, “Enhanced hidden moving target defense in smart grids,” IEEE Transactions on Smart Grid, vol. 10, no. 2, pp.
2208 – 2223, 3 2019 44.
[8]. X. Niu, J. Li , J. Sun and K. Tomsovic “Dynamic detection of false data injection attack in smart grid using deep learning,” in 2019 IEEE Power Energy
Society Innovative Smart Grid Technologies Conference ( ISGT), 2019, pp. 1-6.
[9]. Y. Li, Y. Wang and S. Hu, “Online generative adversary network based measurement recovery in false data injection attacks: A cyber-physical
approach,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 2031-2043, 2020.
[10]. G. Ding, Q. Wu, Y.D. Yao, J. Wang and Y.Chen, “Kernel-based learning for statistical signal processing in cognitive radio networks: Theoretical
foundations, example applications, and future directions,” IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 126-136, 2019.
2019 7th workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES). IEEE, 2019, pp. 1-6.
[2]. R. Tan, V. Badrinath Krishna, D.K. Yau and Z. Kalbarczyk, “Impact of integrity attacks on real-time pricing in smart grids,” 2013, pp. 439-450.
[3]. Y. Dafalla, B. Liu, A.A Hahn, H.Wu, R. Ahmadi and A.G. Bardas, “Prosumer nanogrids: A cybersecurity assessment,” IEEE Access, vol. 8, pp. 131 150
– 131 164, 2020, event IEEE Access.
[4]. B. Liu and H. Wu, “Optimal d-facts placement in moving target defense against false data injection attacks,” IEEE Transactions on Smart Grid, pp. 1-1,
2020, event: IEEE Transactions on smart grid.
[5]. Z. Zhang, R. Deng, D.K Yau, P. Cheng and J. Chen, “Analysis of moving target defense against false data injection attacks on power grid,” IEEE
Transactions on Information Forensics and Security, vol. 15, pp.2320 – 2335, 2019.
[6]. S. Lakshminarayana and D.K. Yau, “Cost-benefit analysis of moving target defense in power grids,” IEEE Transactions on Power Systems, 2020.
[7]. J. Tian, R. Tan, X. Guan and T. Liu, “Enhanced hidden moving target defense in smart grids,” IEEE Transactions on Smart Grid, vol. 10, no. 2, pp.
2208 – 2223, 3 2019 44.
[8]. X. Niu, J. Li , J. Sun and K. Tomsovic “Dynamic detection of false data injection attack in smart grid using deep learning,” in 2019 IEEE Power Energy
Society Innovative Smart Grid Technologies Conference ( ISGT), 2019, pp. 1-6.
[9]. Y. Li, Y. Wang and S. Hu, “Online generative adversary network based measurement recovery in false data injection attacks: A cyber-physical
approach,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 2031-2043, 2020.
[10]. G. Ding, Q. Wu, Y.D. Yao, J. Wang and Y.Chen, “Kernel-based learning for statistical signal processing in cognitive radio networks: Theoretical
foundations, example applications, and future directions,” IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 126-136, 2019.
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