Multi-layer Adaptive Fuzzy Inference System for Predicting Student Performance in Online Higher Education

Ulloa-Cazarez, Rosa Leonor; García-Díaz, Noel; Soriano‐Equigua, Leonel

doi:10.1109/tla.2021.9423852

Cited by 12 publications

(4 citation statements)

References 37 publications

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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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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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“…Ulloa-Cazarez et al [61] use different types of ANN and compared them all to obtain the better one to predict student performance and give new strategies for making decisions. Ibragimov et al [62] use ANN to predict the post-treatment of Stereotactic Body Radiation Therapy in the liver and identify critical-to-spare liver regions using 3D images, obtaining better results than SVM and Random Forest.…”

Section: Data Mining Applicationsmentioning

confidence: 99%

What Is (Not) Big Data Based on Its 7Vs Challenges: A Survey

García

Álvarez-Fernández²

2022

BDCC

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Big Data has changed how enterprises and people manage knowledge and make decisions. However, when talking about Big Data, so many times there are different definitions about what it is and what it is used for, as there are many interpretations and disagreements. For these reasons, we have reviewed the literature to compile and provide a possible solution to the existing discrepancies between the terms Data Analysis, Data Mining, Knowledge Discovery in Databases, and Big Data. In addition, we have gathered the patterns used in Data Mining, the different phases of Knowledge Discovery in Databases, and some definitions of Big Data according to some important companies and organisations. Moreover, Big Data has challenges that sometimes are the same as its own characteristics. These characteristics are known as the Vs. Nonetheless, depending on the author, these Vs can be more or less, from 3 to 5, or even 7. Furthermore, the 4Vs or 5Vs are not the same every time. Therefore, in this survey, we reviewed the literature to explain how many Vs have been detected and explained according to different existing problems. In addition, we detected 7Vs, three of which had subtypes.

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“…A complementary model, called a neuro-fuzzy system, was proposed by combining the advantages of fuzzy models and neural networks to address the abovementioned issues [1][2][3][4][5]. Studies are actively being conducted on neuro-fuzzy inference systems [6][7][8][9][10][11][12][13][14][15]. Similar to FIS, neuro-fuzzy inference systems are expressed by fuzzy rules.…”

Section: Introductionmentioning

confidence: 99%

A Design and Its Application of Multi-Granular Fuzzy Model with Hierarchical Tree Structures

Yeom,

Kwak

2023

Applied Sciences

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This paper is concerned with the design of a context-based fuzzy C-means (CFCM)-based multi-granular fuzzy model (MGFM) with hierarchical tree structures. For this purpose, we propose three types of hierarchical tree structures (incremental, aggregated, and cascaded types) in the design of MGFM. In general, the conventional fuzzy inference system (FIS) has problems, such as time consumption and an exponential increase in the number of if–then rules when processing large-scale multivariate data. Meanwhile, the existing granular fuzzy model (GFM) reduces the number of rules that increase exponentially. However, the GFM not only has overlapping rules as the cluster centers become closer but also has problems that are difficult to interpret due to many input variables. To solve these problems, the CFCM-based MGFM can be designed as a smaller tree of interconnected GFMs. Here, the inputs of the high-level GFMs are taken from the output to the low-level GFMs. The hierarchical tree structure is more computationally efficient and easier to understand than a single GFM. Furthermore, since the output of the CFCM-based MGFM is a triangular fuzzy number, it is evaluated based on a performance measurement method suitable for the GFM. The prediction performance is analyzed from the automobile fuel consumption and Boston housing database to present the validity of the proposed approach. The experimental results demonstrate that the proposed CFCM-based MGFM based on the hierarchical tree structure creates a small number of meaningful rules and solves prediction-related problems by making them explainable.

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Multi-layer Adaptive Fuzzy Inference System for Predicting Student Performance in Online Higher Education

Cited by 12 publications

References 37 publications

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

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

What Is (Not) Big Data Based on Its 7Vs Challenges: A Survey

A Design and Its Application of Multi-Granular Fuzzy Model with Hierarchical Tree Structures

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