The Review of Cellular Automata Algorithms for Placement and Routing Problems

Zapletina, Mariya A.; Zhukov, D. V.

doi:10.1109/elconrus51938.2021.9396339

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Let C be the set of cells in a grid, and the neighborhood function, denoted by n, can be defined as [25,27]…”

Section: The Proposed Methodsmentioning

confidence: 99%

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[Retracted] Implementing the Hybrid Neuro‐Fuzzy System to Model Specific Learning Disability in Special University Education Programs

Wending¹

2022

Journal of Mathematics

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Learning difficulty is a significant issue that comprehensively needs to be taken care of by university instructors with a focus on altering their ways of educating students since various degrees of learning difficulty could be observed among students. Hence, proper approaches should be adapted and utilized that provide chances for each student, securing his/her educational right. By doing so, it guarantees that each student has a right of learning with a different pace of conducting it. For example, the same educational and teaching materials cannot be adapted to each student. Hence, the training of students with learning difficulty should follow a model that is characterized as “from bottom to top,” namely, “normal to learning with difficulties.” The characterization of this approach is very similar to that of the treatment of human beings: biological malfunctions by medicine and mechanical malfunctions by psycho-sociology. This paper proposes a diagnostic model to determine the cognitive ability of students who deal with learning disabilities. The computerized model is proposed based on a combination of fuzzy logic and machine learning utilizing association rules and cellular automata. The advantage of the proposed method is that it helps find the needs of these types of students, so the widely implemented practices that shape his/her learning behavior are avoided. Therefore, practical recognition of each student with a different pace of learning should be realized.

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“…Let C be the set of cells in a grid, and the neighborhood function, denoted by n, can be defined as [25,27]…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…is function will return True if the pair of cells that constitute the input in C are neighbors. For example, in a two-dimensional grid, a particular neighborhood function n(x, y) will return True if x and y are neighbors within the neighborhood of c o where the relationship is defined by [24][25][26][27]…”

Section: The Proposed Methodsmentioning

confidence: 99%

[Retracted] Implementing the Hybrid Neuro‐Fuzzy System to Model Specific Learning Disability in Special University Education Programs

Wending¹

2022

Journal of Mathematics

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“…In this sense, each probabilistic set is generated by randomizing the degree of participation of each element of its definition field separately. For this purpose, a probabilistic space is introduced and a random variable is assigned to each element with values between the space of the measure of similarity of the factors under consideration [ 7 , 22 , 23 ].…”

Section: Scenariosmentioning

confidence: 99%

A Machine Vision Anomaly Detection System to Industry 4.0 Based on Variational Fuzzy Autoencoder

Jiang

2022

Computational Intelligence and Neuroscience

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From a technological point of view, Industry 4.0 evolves and operates in a smart environment in which the real and virtual worlds come together through smart cyber-physical systems. These devices that control each other autonomously activate innovative functions that enhance the production process. However, the industrial environment in which the most modern digital automation and information technologies are integrated is an ideal target for large-scale targeted cyberattacks. Implementing an integrated and effective security strategy in the Industrial 4.0 ecosystem presupposes a vertical inspection process at regular intervals to address any new threats and vulnerabilities throughout the production line. This view should be accompanied by the deep conviction of all stakeholders that all systems of modern industrial infrastructure are a potential target of cyberattacks and that the slightest rearrangement of mechatronic systems can lead to generalized losses. Accordingly, given that there is no panacea in designing a security strategy that fully ensures the infrastructure in question, advanced high-level solutions should be adopted, effectively implementing security perimeters without direct dependence on human resources. One of the most important methods of active cybersecurity in Industry 4.0 is the detection of anomalies, i.e., the identification of objects, observations, events, or behaviors that do not conform to the expected pattern of a process. The theme of this work is the identification of defects in the production line resulting from cyberattacks with advanced machine vision methods. An original variational fuzzy autoencoder (VFA) methodology is proposed. Using fuzzy entropy and Euclidean fuzzy similarity measurement maximizes the possibility of using nonlinear transformation through deterministic functions, thus creating an entirely realistic vision system. The final finding is that the proposed system can evaluate and categorize anomalies in a highly complex environment with significant accuracy.

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The Review of Cellular Automata Algorithms for Placement and Routing Problems

Cited by 2 publications

References 9 publications

[Retracted] Implementing the Hybrid Neuro‐Fuzzy System to Model Specific Learning Disability in Special University Education Programs

[Retracted] Implementing the Hybrid Neuro‐Fuzzy System to Model Specific Learning Disability in Special University Education Programs

A Machine Vision Anomaly Detection System to Industry 4.0 Based on Variational Fuzzy Autoencoder

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