A survey of fuzzy cognitive maps forecasting methods

Al-Gunaid, Mohammed A.; Shcherbakov, Maxim; Zadiran, Konstantin S.; Melikov, Aleksej Vladimirovich

doi:10.1109/iisa.2017.8316443

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…The proposed implementation of technology optimization in cybersecurity defenses is based on the use of FCMs, which are a method of modeling complex systems capable of describing the causal relationships between critical factors' concepts that determine the behavior, symbolic description, and representation of system dynamics of cybersecurity used by the case study's sports organization [ 14 ].…”

Section: Fcm Methodologymentioning

confidence: 99%

“…The simulation activation function used calculates the value Α j of a C j concept at the end of an iteration as the sum of its causal concepts' contributions at the start of the iteration [ 6 , 14 , 18 ]:

where A j ( t ) is the value of the concept C j at the end of the iteration, A i ( t − 1) is the value of the notation C j at the beginning of the iteration, w ij is the weight of the relation between C i and C j , and f is a threshold function, which is used to normalize the values in each step. The process assumes that the weight table includes an autocorrelation by placing a unit value in the main diagonal of the table (the new value of the concept necessarily equals the previous value plus (or minus) the contribution of the other concepts associated with it).…”

Section: Fcm Methodologymentioning

confidence: 99%

“…The FCM's architecture is primarily reliant on the experience and expertise of a few experts, who, as experts, have enough knowledge to model a system and offer the initial values of the weights for the concepts' interconnections. In our situation, these weights are determined through a learning process [ 14 ]. The algorithm is iterated until a termination requirement, such as the maximum number of iterations or convergence to the target error based on a fitness metric is fulfilled.…”

Section: The Use Case Of the Volleyball Industrymentioning

confidence: 99%

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Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Xiao

Chen

2022

Computational Intelligence and Neuroscience

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Individual and team performance can be improved by utilizing “smart” devices and applications that are connected through networks. In sports, the Internet of Things (IoT) refers to all of the “smart” devices and applications linked through networks to reduce injuries to the bare minimum, develop advanced training techniques, and apply analytical advanced sports improvement methodologies to improve sports performance in general. The Internet of Things (IoT) in sports is closely related to the objective of both security and privacy in sports, which has become a topic of crucial concern for the sports business in recent years, as evidenced by the adoption of IoT in sports years. For this reason, security flaws can have catastrophic consequences, including the disclosure of personal data, the manipulation of statistical findings, the harming of organizations’ reputations, and enormous financial losses for the sporting organization. One or more of the consequences, as previously mentioned, is related to sports organizations and the athletes who are members of those organizations, and they have a direct impact on the corresponding set of sports-related, medical-related, and paramedical enterprises, specifically those that provide specialized sports equipment and associated services. A critical need to detect and quantify threats has long been recognized to better support decision-making when adopting or constructing a safe and reliable sports Internet-of-Things infrastructure, which is becoming increasingly common. Using advanced machine learning algorithms, this research provides a methodology for technology optimization in cybersecurity defenses that is then used in a unique case study utilizing volleyball players to demonstrate its effectiveness. In conjunction with a Monte Carlo optimization technique, an upgraded variant of fuzzy cognitive maps (FCM) is presented in greater detail. This model is utilized for a specific scenario of risk identification of volleyball industry, assessment, and optimization for IoT sports networks.

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Section: Fcm Methodologymentioning

confidence: 99%

Section: Fcm Methodologymentioning

confidence: 99%

Section: The Use Case Of the Volleyball Industrymentioning

confidence: 99%

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Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Xiao

Chen

2022

Computational Intelligence and Neuroscience

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“…The Fuzzy Cognitive Map (FCM) method has been used extensively in the field of decision-making. A significant number of research works have been conducted related to the use of Fuzzy Cognitive Maps (FCM) to identify the level of mutual influence of factors and determine the scenarios of behavior of the system [30,31].…”

Section: Related Workmentioning

confidence: 99%

Forecasting potential yields under uncertainty using fuzzy cognitive maps

et al. 2021

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Background The aim of the study is identification of factors influencing the reduction of the potential maximum yield of winter wheat in weather conditions of dry farming in European part of Russia, Volgograd region. The novelty of the work is forecasting potential yields under uncertainty that allows to assess the risks and potential threats that can influence and maximize the potential yield. To solve this problem, the tool for formalization, analysis and modeling of semi-structured systems and processes Fuzzy Cognitive Maps (FCM) is used. Results Based on disparate and heterogeneous information about the multitude of external influences on crop formation during plant photosynthesis, a model for analyzing the level of influencing factors on the target factor is constructed and an effective control impact scenario is developed. This model is used to identify the factors, where each one of them iteratively passes from the initial value to the stable one according to the chosen formula, based on which, the influence of the factors on each other are determined. Conclusions The conclusions obtained as a result of the work confirm the concept of precision farming: the quantity and quality of innovation in agriculture depends on the ability to apply it effectively in the field. Developed method of predicting potential yield levels can be used not only to model future agricultural performance, but also to estimate harvested yields.

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“…This research study endeavors to address this need by proposing an innovative and enhanced approach to ensuring secure learning environments through the development and implementation of a hybrid filtering system. The crux of this system lies in combining the power of deep learning techniques, particularly CNNs, with an IFL filter for explicit content detection [6][7][8]. Moreover, the integration of GPT-3, or "Generative Pre-trained Transformer 3," an advanced and state-of-the-art language model, adds contextual warnings to the system.…”

Section: Introductionmentioning

confidence: 99%

A Hypersensitive Intelligent Filter for Detecting Explicit Content in Learning Environments

Yu,

Yin

2024

JWE

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In today’s digital age, educational institutions aim to ensure safe learning environments in the light of pervasive explicit and inappropriate content. This study proposes an innovative approach to enhance safety by integrating convolutional neural networks (CNNs) for visual analysis with an intuitionistic fuzzy logic (IFL) filter for explicit content identification. Additionally, it utilizes GPT-3 to generate contextual warnings for users. A large-scale dataset comprising explicit and educational materials is used to evaluate the system. The results show that this hypersensitive filter has high accuracy performance, particularly in handling ambiguous or borderline content. The proposed approach provides an advanced solution to tackle the challenges of detecting explicit content and promotes safer learning environments by showcasing the potential of combining generative AI techniques across various domains.

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A survey of fuzzy cognitive maps forecasting methods

Cited by 8 publications

References 6 publications

Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Forecasting potential yields under uncertainty using fuzzy cognitive maps

A Hypersensitive Intelligent Filter for Detecting Explicit Content in Learning Environments

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