Koinonia

Ge, Huangyi; Chau, Sze Yiu; Gonsalves, Victor E; Li, Huian; Wang, Tianhao; Zou, Xukai; Li, Ninghui

doi:10.1145/3359789.3359804

Cited by 6 publications

(3 citation statements)

References 45 publications

(61 reference statements)

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“…Therefore, evaluating its effectiveness necessitates considering the significance or precedence of individual classifiers under particular conditions. This aspect has received thorough examination within the fault classification domain [52,53]. The voting method is given by the following:…”

Section: Voting Methodsmentioning

confidence: 99%

Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods

Alaerjan,

Jabeur,

Ben Chikha

et al. 2024

Symmetry

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It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid’s stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method.

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Section: Voting Methodsmentioning

confidence: 99%

Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods

Alaerjan,

Jabeur,

Ben Chikha

et al. 2024

Symmetry

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“…Thus, this topic covers secret sharing and secure multi-party communication schemes. Protection of the shares and their transmissions are also relevant here, as are commitment schemes that allow one to commit to a chosen value while keeping it hidden from others [37][38][39][40]. Module 8: Secure multi-party computation and homomorphic encryption.…”

Section: Modules Mapped Directly From E-votingmentioning

confidence: 99%

“…), Information Security Education for Cyber Resilience (pp. [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Springer International Publishing.…”

Section: Introductionmentioning

confidence: 99%

Electronic Voting Technology Inspired Interactive Teaching and Learning Pedagogy and Curriculum Development for Cybersecurity Education

Hosler

Zou

Bishop

2021

Information Security Education for Cyber Resilience

Self Cite

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Cybersecurity is becoming increasingly important to individuals and society alike. However, due to its theoretical and practical complexity, keeping students interested in the foundations of cybersecurity is a challenge. One way to excite such interest is to tie it to current events, for example elections. Elections are important to both individuals and society, and typically dominate much of the news before and during the election. We are developing a curriculum based on elections and, in particular, an electronic voting protocol. Basing the curriculum on an electronic voting framework allows one to teach critical cybersecurity concepts such as authentication, privacy, secrecy, access control, encryption, and the role of non-technical factors such as policies and laws in cybersecurity, which must include societal and human factors. Student-centered interactions and projects allow them to apply the concepts, thereby reinforcing their learning.

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Electronic Voting

Merino

2023

Trends in Data Protection and Encryption Technologies

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Remote electronic voting, where eligible voters can cast votes from anywhere in the world on their device, promises to increase voter turnout, improve accessibility, and reduce costs. However, building a secure online voting system is difficult, involving four essential requirements: integrity, privacy, coercion-resistance, and availability. Moreover, successfully deploying a secure online voting system would require close collaboration among the public and private sectors and academia. The military and intelligence agencies could play an important role in supporting the e-voting operator against cyberattacks meant to deteriorate public trust in the voting outcome.

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Koinonia

Cited by 6 publications

References 45 publications

Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods

Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods

Electronic Voting Technology Inspired Interactive Teaching and Learning Pedagogy and Curriculum Development for Cybersecurity Education

Electronic Voting

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