Optimization of Electric Automation Control Model Based on Artificial Intelligence Algorithm

Ma, Run; Kareem, Shahab Wahhab; Kalra, Ashima; Doewes, Rumi Iqbal; Kumar, Pankaj; Miah, Shahajan

doi:10.1155/2022/7762493

Cited by 11 publications

(15 citation statements)

References 19 publications

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“…In the formula, s、a、c、f、e are the weights of the five dragonfly group behaviors, respectively; w represents the inertia weight; and t is the current number of iterations, as shown in formula (7).…”

Section: Da-cnn Neural Network Evaluation Methodsmentioning

confidence: 99%

“…At present, with the emergence and wide application of various new technologies such as the Internet, cloud computing, big data, and arti cial intelligence [1][2][3][4][5][6][7][8][9], the environment faced by ideological and political management is becoming more and more complex, which puts forward new requirements for ideological and political management and gives birth to new changes in ideological and political education and management in the new era [10][11][12][13][14][15]. erefore, in the era of big data, the reform and innovation of ideological and political management are imminent [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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DA-CNN-Driven Innovative Ideological Politics Education Management System

Yingkun

Liang

Chu³

2022

Mobile Information Systems

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With the rapid development of the Internet, the network has become an important part of the study, work, and life of all teachers and students in colleges and universities. It also brings great challenges to the traditional way of ideological and political education in colleges and universities. Colleges and universities have launched various beneficial attempts in the ideological and political education into the network. In this context, in order to improve the evaluation accuracy of ideological and political classroom teaching quality in colleges and universities, this paper puts forward an evaluation method of ideological and political classroom teaching quality in colleges and universities based on DA-CNN. On the basis of constructing the evaluation index system of ideological and political classroom teaching quality in colleges and universities by analytic hierarchy process, the scores of 25 evaluation indexes affecting the ideological and political teaching quality in colleges and universities are taken as the input of DA-CNN, and the comprehensive score of ideological and political teaching quality in colleges and universities is taken as the output of DA-CNN. At the same time, an innovative ideological and political education management system based on convolutional neural network is designed and developed, and the functional modules and database of the system are planned.

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Section: Da-cnn Neural Network Evaluation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DA-CNN-Driven Innovative Ideological Politics Education Management System

Yingkun

Liang

Chu³

2022

Mobile Information Systems

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“…Account and Service Hijacking: This threat arises from various tactics such as phishing, fraud, exploiting software vulnerabilities, and credential reuse. Attackers can illicitly acqu unauthorized access to services [55][56][57].…”

Section: Artificial Intelligence General Science (Jaigs)168mentioning

confidence: 99%

“…The severity of the repercussions depends on the level of access, as individuals with higher privileges can access sensitive data and services. Malicious insiders pose significant risks, including theft of confidential data, reputational damage, financial losses, and productivity disruptions [55][56][57].…”

Section: Artificial Intelligence General Science (Jaigs)168mentioning

confidence: 99%

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Ensuring Compliance Integrity in AI ML Cloud Environments: The Role of Data Guardianship

Rana

2024

JAIGS

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Artificial intelligence (AI) has become ubiquitous across various industries, including security, healthcare, finance, and national defense. However, alongside its transformative potential, there has been a concerning rise in malicious exploitation of AI capabilities. Simultaneously, the rapid advancement of cloud computing technology has led to the emergence of cloud-based AI systems. Unfortunately, vulnerabilities inherent in cloud infrastructure also pose security risks to AI services. We recognize the critical role of maintaining the integrity of training data, as any compromise therein directly impacts the effectiveness of AI systems. In response to this challenge, we emphasize the paramount importance of preserving data integrity within AI systems. To address this need, we propose a data integrity architecture guided by the National Institute of Standards and Technology (NIST) cybersecurity framework. Leveraging blockchain technology and smart contracts presents a suitable solution for addressing integrity challenges, given their features of shared and decentralized ledgers. Smart contracts enable automated policy enforcement, facilitate continuous monitoring of data integrity, and help mitigate the risk of data tampering.

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