1,200 results on '"FUZZY MODEL"'
Search Results
2. A risk warning method for steady-state power quality based on VMD-LSTM and fuzzy model
- Author
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Shen, Yu, Hu, Wei, Dong, Mingqi, Yang, Fan, Yang, Zhichun, and Chen, Hechong
- Published
- 2024
- Full Text
- View/download PDF
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3. HFLFO: Hybrid fuzzy levy flight optimization for improving QoS in wireless sensor network
- Author
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Hemavathi, S. and Latha, B.
- Published
- 2023
- Full Text
- View/download PDF
4. Next-Generation Building Condition Assessment: BIM and Neural Network Integration.
- Author
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Amrouni Hosseini, Mani, Ravanshadnia, Mehdi, Rahimzadegan, Majid, and Ramezani, Saeed
- Subjects
- *
ARTIFICIAL neural networks , *CONSTRUCTION management , *CONSTRUCTION defects (Buildings) , *OFFICE buildings , *BUILDING information modeling - Abstract
The inspection of building components plays a crucial role in reducing maintenance costs throughout the building's life cycle. Many buildings suffer from defects in the building elements, such as surface problems, cracks, and moisture problems. Building Information Modeling (BIM) has become a widely used digital tool for the construction industry, allowing for the virtual visualization, design, and simulation of structures. At the same time, progress in neural networks, a branch of artificial intelligence, presents encouraging potential for classification and evaluation. This article explores the fusion of BIM and neural networks to improve and simplify the building inspection procedure. Neural networks are implemented with BIM to conduct real-time analysis and predictions as part of an inspection framework. A data set consisting of 500 wall samples with defects from different buildings was collected through fieldwork. These data were then classified into five categories, ranging from D1 (No damage) to D5 (Collapse), based on expert opinions. A neural network was used to create a model that can show the severity of degradation of building elements. The BIM platform is utilized in the implementation of the proposed model to facilitate the exchange of information and enhance documentation during inspection. The visualization provided by BIM allows the facility management team and building owners to assess building elements through causality analysis using distinct color codes. A case study of an office building is featured in the paper to showcase the application of the proposed model for asset management. The color-coded system used in this study denotes low-performance conditions as red, medium performance conditions as yellow, and high-performance conditions as green. This article outlines the procedures for categorizing and ranking defects in building elements, specifically focusing on walls. Practical Applications: This study explores innovative methods for assessing the condition of buildings, aiming to improve accuracy, efficiency, and predictive capabilities. Traditional building inspections are often subjective, time-consuming, and labor-intensive, making it challenging to generate consistent and comprehensive evaluations. Our research addresses these limitations by integrating BIM and neural networks. BIM provides detailed digital models of buildings, which include comprehensive information about each component. This technology enables precise and consistent assessments, reducing the subjectivity inherent in manual inspections. Neural networks, a type of artificial intelligence, can analyze complex data patterns and detect potential issues early, allowing for proactive maintenance. By combining these two technologies, our approach offers a more efficient and objective method for building condition assessment. This integration not only saves time and resources but also enhances the ability to predict and prevent future issues, ultimately extending the lifespan of building components and improving overall building performance. These advancements are particularly relevant for property managers, maintenance teams, and construction professionals seeking to optimize building maintenance and management practices. The findings of this study can significantly benefit the construction and facilities management industries by providing a more reliable and advanced method for maintaining building health and safety, thereby ensuring sustainable and cost-effective building management. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
5. OPTIMIZATION OF FUZZY INVENTORY MANAGEMENT IN INDUSTRIAL PROCESSES USING DEEP LEARNING ALGORITHMS: A HYBRID APPROACH FOR ENHANCING DEMAND FORECASTING AND SUPPLY CHAIN EFFICIENCY
- Author
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K. Kalaiarasi and S. Swathi
- Subjects
inventory ,optimization ,fuzzy model ,triangular fuzzy number ,development ,industrial processes ,deep learning ,Mechanics of engineering. Applied mechanics ,TA349-359 - Abstract
In today’s dynamic business landscape, effective inventory management is essential for minimizing costs and maximizing profitability. Traditional models like EOQ and JIT often fall short in handling demand and supply uncertainties due to their reliance on precise data. This paper introduces a novel approach that combines fuzzy logic and deep learning to address these limitations. Fuzzy logic offers a robust framework for decisionmaking under uncertainty, while deep learning improves predictive accuracy by identifying complex patterns in historical data. By transforming data into fuzzy sets and applying neural networks for demand forecasting, the proposed model optimizes inventory levels to reduce costs and prevent stockouts. A mathematical model and algorithmic implementation demonstrate the approach’s effectiveness and a numerical example highlights improvements in inventory control, including reduced holding costs. This study underscores the potential of integrating AI techniques for adaptive, data-driven inventory management with broad applications across various industrial processes. more...
- Published
- 2024
- Full Text
- View/download PDF
6. Monitoring and preservation of stone cultural heritage using a fuzzy model for predicting salt crystallisation damage
- Author
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Marta Cappai, Marta Casti, and Giorgio Pia
- Subjects
Degradation kinetics ,Fuzzy model ,Multivariable approach ,Salt crystallisation ,Stone materials ,Medicine ,Science - Abstract
Abstract In this study, a fuzzy model is presented for predicting the possibility of degradation due to salt crystallisation cycles. The formalization of the proposed model has been based on the multivariable approach which considers environmental data (such as temperature, solar radiation, wind speed, rain quantity, relative humidity), characteristic inflection points of specific salts and stone features derived from laboratory characterizations (including mechanical properties, porosity, and mineralogical composition). Modeling results have been compared with experimental data elaborations acquired by monitoring a semi-confined archaeological site situated in the city of Cagliari (Munatius Irenaus cubicle), revealing substantial alignment in the degradation kinetics trends. Moreover, the achieved outcomes show the remarkable capability to identify salt crystallisation phenomenon type (efflorescence or subflorescence). more...
- Published
- 2024
- Full Text
- View/download PDF
7. Fractional Caputo Operator and Takagi–Sugeno Fuzzy Modeling to Diabetes Analysis.
- Author
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Mustapha, Ez-zaiym, Abdellatif, El Ouissari, Karim, El Moutaouakil, and Ahmed, Aberqi
- Subjects
- *
ARTIFICIAL intelligence , *DYNAMICAL systems , *DIABETES - Abstract
Diabetes is becoming more and more dangerous, and the effects continue to grow due to the population's ignorance of the seriousness of this phenomenon. The studies that have been carried out have not been able to follow the phenomenon more precisely, which has led to the use of the fractional derivative tool, which has a very great capability to study real problems and phenomena but is somewhat limited on nonlinear models. In this work, we will develop a new fractional derivative model of a diabetic population, the Takagi–Sugeno fractional fuzzy model, which will enable us to study the phenomenon with these nonlinear terms in order to obtain greater precision in the results. We will study the existence and uniqueness of the solution using the Lipschizian theorem and then turn to the new fuzzy model, which leads us to four dynamical systems. The interpretation results show the quality of fuzzy membership in tracking the malleable phenomena of nonlinear terms existing in the system. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
8. Solving the Problem of Fuzzy Partition-Distribution with Determination of the Location of Subset Centers.
- Author
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Bulat, Anatoly, Kiseleva, Elena, Yakovlev, Sergiy, Prytomanova, Olga, and Lebediev, Danylo
- Subjects
MATHEMATICAL programming ,ARTIFICIAL intelligence ,MATHEMATICAL models ,INDUSTRIAL costs ,FUZZY algorithms ,MEMBERSHIP functions (Fuzzy logic) - Abstract
A large number of real-world problems from various fields of human activity can be reduced to optimal partitioning-allocation problems with the purpose of minimizing the partitioning quality criterion. A typical representative of such problem is an infinite-dimensional transportation problem and more generalized problems—infinite-dimensional problems of production centers placement along with the partitioning of the area of continuously distributed consumers with the purpose of minimizing transportation and production costs. The relevant problems are characterized by some kind of uncertainty level of a not-probabilistic nature. A method is proposed to solve an optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. The method is based on the synthesis of the methods of fuzzy theory and optimal partitioning-allocation theory, which is a new science field in infinite-dimensional mathematical programming with Boolean variables. A theorem was proved that determines the form of the optimal solution of the corresponding optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. An algorithm for solving fuzzy partitioning-allocation problems is proposed, which is based on the proved theorem and on a variant of Shor's r-algorithm—a non-differential optimization method. The application of the proposed method is demonstrated on model tasks, where the coefficient of mistrust is integrated to interpret the obtained result—the minimum value of the membership function, which allows each point of the set partition to be assigned to a specific fuzzy subset. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
9. Monitoring and preservation of stone cultural heritage using a fuzzy model for predicting salt crystallisation damage.
- Author
-
Cappai, Marta, Casti, Marta, and Pia, Giorgio
- Abstract
In this study, a fuzzy model is presented for predicting the possibility of degradation due to salt crystallisation cycles. The formalization of the proposed model has been based on the multivariable approach which considers environmental data (such as temperature, solar radiation, wind speed, rain quantity, relative humidity), characteristic inflection points of specific salts and stone features derived from laboratory characterizations (including mechanical properties, porosity, and mineralogical composition). Modeling results have been compared with experimental data elaborations acquired by monitoring a semi-confined archaeological site situated in the city of Cagliari (Munatius Irenaus cubicle), revealing substantial alignment in the degradation kinetics trends. Moreover, the achieved outcomes show the remarkable capability to identify salt crystallisation phenomenon type (efflorescence or subflorescence). [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
10. Three-Dimensional Fuzzy Modeling for Nonlinear Distributed Parameter Systems Using Simultaneous Perturbation Stochastic Approximation.
- Author
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Zhang, Xianxia, Wang, Tangchen, Cheng, Chong, and Wang, Shaopu
- Subjects
DISTRIBUTED parameter systems ,STOCHASTIC approximation ,MACHINE learning ,THREE-dimensional modeling ,FUZZY numbers - Abstract
Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPSs). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPSs. The Affinity Propagation clustering approach is utilized to determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
11. New hybrid model for nonlinear systems via Takagi-Sugeno fuzzy approach.
- Author
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Mabrouk, Anouar Ben, Alanazi, Abdulaziz, Bassfar, Zaid, and Alanazi, Dalal
- Subjects
FUZZY control systems ,NONLINEAR systems ,LYAPUNOV stability ,STABILITY theory ,RESEARCH personnel - Abstract
Mathematical models, especially complex nonlinear systems, are always difficult to analyze and synthesize, and researchers need effective and suitable control methods to address these issues. In the present work, we proposed a hybrid method that combines the well-known Takagi-Sugeno fuzzy model with wavelet decomposition to investigate nonlinear systems characterized by the presence of mixed nonlinearities. Here, one nonlinearity is super-linear and convex, and other is sub-linear, concave, and singular at zero, which leads to difficulties in the analysis, as is known in PDE theory. Linear and polynomial fuzzy models were combined with wavelets to ensure an improvement in both methods for investigating such problems. The results showed a high performance compared with existing methods via error estimates and Lyapunov theory of stability. The model was applied to a prototype nonlinear Schrodinger dynamical system. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
12. A fuzzy model for studying kinetic decay phenomena in Genna Maria Nuraghe: Material properties, environmental data, accelerated ageing, and model calculations
- Author
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Marta Cappai, Ulrico Sanna, and Giorgio Pia
- Subjects
Accelerated ageing ,Archaeological site ,Environmental data ,Fuzzy model ,Decay kinetic ,Monitoring ,Materials of engineering and construction. Mechanics of materials ,TA401-492 - Abstract
In this study, a fuzzy model has been proposed for the control of the degradation process in Cultural Heritage. Specifically, the focus has been on the Nuragic building, Genna Maria, in Villanovaforru, Sardinia. Environmental data and material properties were considered to formalize inferences between different variables of the model. The results highlight the possibility of significant decay processes, such as salt crystallization and freeze-thaw cycles, estimated for all months of the year. A comparison between model elaborations and experimental data (material characterization, environmental data, accelerated ageing) demonstrates the reliability of the proposed procedure. more...
- Published
- 2024
- Full Text
- View/download PDF
13. Assessment of the industrial policy to the military-industrial complex effectiveness based on neural networks based on fuzzy logic
- Author
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E.N. Starikov, N.V. Klein, and V.I. Vorobyov
- Subjects
industrial policy ,military-industrial complex ,efficiency ,evaluation indicators ,methodological approach ,fuzzy logic ,neural network ,fuzzy model ,Home economics ,TX1-1110 ,Economics as a science ,HB71-74 - Abstract
The relevance of the topic under consideration on the development of universal applied methods for assessing the effectiveness and efficiency of industrial policy in the military-industrial complex (hereinafter - MIC) is due to its high practical significance in the context of the challenges of technological development associated with digitalization and informatization. It is also due to the peculiarities of the current stage of Russia’s economic development in the conditions of financial and technological sanctions from Western states. The purpose of the study is to develop the main provisions of a methodological approach to assessing the effectiveness of industrial policy in the defense industry based on the use of economic-mathematical modeling apparatus, which involves the construction of neural networks based on fuzzy logic. In the course of the research the authors have solved the following problems: mathematically formalized the object of analysis; developed an algorithm for determining the effectiveness of industrial policy in the MIC using neural networks; formalized the model for assessing the effectiveness of such industrial policy based on fuzzy sets; proposed a system of indicators for assessing industrial policy in the MIC; determined the sequence of actions in the construction the fuzzy model for assessing the effectiveness of industrial policy in the MIC by means of Fuzzy Logic on the software platform MatLab. more...
- Published
- 2024
- Full Text
- View/download PDF
14. New hybrid model for nonlinear systems via Takagi-Sugeno fuzzy approach
- Author
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Anouar Ben Mabrouk, Abdulaziz Alanazi, Zaid Bassfar, and Dalal Alanazi
- Subjects
control ,dynamical systems ,nonlinear systems ,fuzzy model ,wavelets ,lyapunov stability ,Mathematics ,QA1-939 - Abstract
Mathematical models, especially complex nonlinear systems, are always difficult to analyze and synthesize, and researchers need effective and suitable control methods to address these issues. In the present work, we proposed a hybrid method that combines the well-known Takagi-Sugeno fuzzy model with wavelet decomposition to investigate nonlinear systems characterized by the presence of mixed nonlinearities. Here, one nonlinearity is super-linear and convex, and other is sub-linear, concave, and singular at zero, which leads to difficulties in the analysis, as is known in PDE theory. Linear and polynomial fuzzy models were combined with wavelets to ensure an improvement in both methods for investigating such problems. The results showed a high performance compared with existing methods via error estimates and Lyapunov theory of stability. The model was applied to a prototype nonlinear Schrödinger dynamical system. more...
- Published
- 2024
- Full Text
- View/download PDF
15. Evaluation of Drinking Quality of Groundwater Using Fuzzy Logic and Deterministic Method
- Author
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Sedigheh Shakour, Manouchehr Chitsazan, and Yahya Mirzaee
- Subjects
deterministic method ,fuzzy model ,groundwater ,schoeller ,Environmental sciences ,GE1-350 ,Water supply for domestic and industrial purposes ,TD201-500 - Abstract
Groundwater is the main source of drinking water in the northern Dezful-Andimeshk plain, Iran which has become saline in some cases. To check the drinking quality of groundwater, in October 2019, 16 samples were taken from different parts of the plain. The samples were analyzed for the concentration of major cations, anions, and nitrates.Two methods were used to evaluate water quality: the Schoeller deterministic method and the Fuzzy logic. The Schoeller method categorized water quality from good to bad and determined that 56.81, 20.83, 18.77, and 3.57% of the area had good, acceptable, inappropriate, and bad water quality, respectively. On the other hand, the Fuzzy method showed that 21.60% of the area had desirable groundwater with a confidence level of 70-81, 75.23% had acceptable water quality with a confidence level of 32-70, and 3.69% had non-acceptable quality with a confidence level of 20-22%. The Fuzzy method was found to be better in evaluating water quality as it provided a more comprehensive, accurate, and efficient assessment by covering uncertainties better. more...
- Published
- 2024
- Full Text
- View/download PDF
16. Finite-Time Fault-Tolerant Control of Nonlinear Spacecrafts with Randomized Actuator Fault: Fuzzy Model Approach.
- Author
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Xue, Wenlong, Jin, Zhenghong, and Tian, Yufeng
- Subjects
- *
FAULT-tolerant control systems , *MARKOV processes , *ACTUATORS , *NONLINEAR systems , *SPACE vehicles - Abstract
The primary objective of this paper is to address the challenge of designing finite-time fault-tolerant control mechanisms for nonlinear flexible spacecraft systems, which are particularly vulnerable to randomized actuator faults. Diverging from traditional methodologies, our research harnesses the capabilities of the Takagi–Sugeno (T–S) fuzzy framework. A unique feature of our model is the representation of actuator failures as stochastic signals following a Markov process, thereby offering a robust solution for addressing timeliness concerns. In this paper, we introduce a generalized reciprocally convex inequality that includes adjustable parameters, broadening the scope of previous results by accommodating them as special cases. Through the amalgamation of this enhanced inequality and flexible independent parameters, we propose an innovative controller design strategy. This approach establishes a stability standard that guarantees mean-square H ∞ performance. In order to validate the efficacy of the suggested strategy, we present a numerical illustration involving a nonlinear spacecraft system, showcasing the practical advantages and feasibility of our proposed technique. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
17. Artificial Intelligence-Based System for Retinal Disease Diagnosis.
- Author
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Orlova, Ekaterina V.
- Subjects
- *
DECISION support systems , *ARTIFICIAL intelligence , *RETINAL diseases , *EYE diseases , *DIAGNOSIS - Abstract
The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis of results and diagnostics. We propose an approach to designing an artificial intelligent diagnosis system (AI diagnosis system) which includes an electrophysiological complex to collect objective information and an intelligent decision support system to justify the diagnosis. The task of diagnosing retinal diseases based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers—one classifier is based on a fuzzy model and a fuzzy rule base (RB-classifier) and one uses the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of algorithms in a multi-class classification on unbalanced data is assessed based on two indicators—MAUC (multi-class area under curve) and MMCC (multi-class Matthews correlation coefficient). Combining two algorithms in a decision support system provides more accurate and reliable pathology identification. The accuracy of diagnostics using the proposed AI diagnosis system is 5–8% higher than the accuracy of a system using only diagnostics based on electrophysical indicators. The AI diagnosis system differs from other systems of this class in that it is based on the processing of objective electrophysiological data and socio-demographic data about patients, as well as subjective information from the anamnesis, which ensures increased efficiency of medical decision-making. The system is tested using actual data about retinal diseases from the Russian Institute of Eye Diseases and its high efficiency is proven. Simulation experiments conducted in various scenario conditions with different combinations of factors ensured the identification of the main determinants (markers) for each diagnosis of retinal pathology. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
18. SCFS-securing flying ad hoc network using cluster-based trusted fuzzy scheme.
- Author
-
Gupta, Shikha and Sharma, Neetu
- Subjects
DENIAL of service attacks ,TRUST ,SOFTWARE failures ,AD hoc computer networks ,DYNAMICAL systems ,COMPUTER network security - Abstract
Flying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
19. SCFS-securing flying ad hoc network using cluster-based trusted fuzzy scheme
- Author
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Shikha Gupta and Neetu Sharma
- Subjects
Cluster ,Observers ,Fuzzy model ,Malicious nodes ,FANET ,Omnetpp ,Electronic computers. Computer science ,QA75.5-76.95 ,Information technology ,T58.5-58.64 - Abstract
Abstract Flying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art. more...
- Published
- 2024
- Full Text
- View/download PDF
20. A Fuzzy-Based Analysis of Air Particle Pollution Data: An Index IMC for Magnetic Biomonitoring.
- Author
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Chaparro, Mauro A. E., Chaparro, Marcos A. E., and Molinari, Daniela A.
- Subjects
- *
PARTICULATE matter , *AIR pollution , *AIR analysis , *AIR pollutants , *REMANENCE , *BIOLOGICAL monitoring - Abstract
Airborne magnetic particles may be harmful because of their composition, morphology, and association with potentially toxic elements that may be observed through relationships between magnetic parameters and pollution indices, such as the Tomlinson pollution load index (PLI). We present a fuzzy-based analysis of magnetic biomonitoring data from four Latin American cities, which allows us to construct a magnetic index of contamination (IMC). This IMC uses four magnetic parameters, i.e., magnetic susceptibility χ, saturation isothermal remanent magnetization SIRM, coercivity of remanence Hcr, and SIRM/χ, and proposes summarizing the information to assess an area based exclusively on magnetic parameters more easily. The fuzzy inference system membership functions are built from the standardization of the data to become independent of the values. The proposed IMC is calculated using the baseline values for each case study, similar to the PLI. The highest IMC values were obtained in sites close to industrial areas, and in contrast, the lowest ones were observed in residential areas far from avenues or highways. The linear regression model between modeled IMC and PLI data yielded robust correlations of R2 > 0.85. The IMC is proposed as a complementary tool for air particle pollution and is a cost-effective magnetic approach for monitoring areas. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
21. A risk warning method for steady-state power quality based on VMD-LSTM and fuzzy model
- Author
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Yu Shen, Wei Hu, Mingqi Dong, Fan Yang, Zhichun Yang, and Hechong Chen
- Subjects
Power quality ,Risk warning ,Variational mode decomposition ,Long short-term memory ,Fuzzy model ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
The risk warning for steady-state power quality in the power grid is essential for its prevention and management. However, current risk warning methods fall short in predicting the power quality trend while accounting for potential risks. Consequently, this study introduces a novel steady-state power quality risk warning method utilizing VMD-LSTM and a fuzzy model. Firstly, a power quality index prediction method based on variational mode decomposition (VMD) and long short-term memory (LSTM) is proposed. This approach significantly enhances prediction accuracy. Secondly, a power quality risk warning method incorporating kernel density estimation (KDE) and a fuzzy model is proposed, which systematically addresses the uncertainty associated with power quality risks. To validate the effectiveness and practicality of the proposed method, experiments are conducted using field monitoring data from a residential load in southern China. The results affirm the reliability and applicability of the proposed method. The simulation results show that the median error of prediction of power quality indexes by the proposed method is 5.03 % during the evaluated time period, and the prediction accuracy is mostly maintained above 90 %. more...
- Published
- 2024
- Full Text
- View/download PDF
22. On the Local Coordination of Fuzzy Valuations
- Author
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G. E. Yakhyaeva
- Subjects
fuzzy model ,theory of fuzzy models ,fuzzy measure ,coordinated valuation ,locally coordinated valuation ,Mathematics ,QA1-939 - Abstract
The paper is devoted to the model-theoretic formalization of the semantic model of the object domain. The article discusses the concept of a fuzzy model, which is a model where the truth function exhibits properties of a fuzzy measure. We demonstrate that a fuzzy model is a generalization of the concept of fuzzification of a precedent (semantic) model to include a countable number of precedents. In practice, it is common to have partial expert knowledge about the set of events in the object domain, making it difficult to immediately describe the fuzzy model. Additionally, since expert valuations are subjective, they may be incorrect and inconsistent with any fuzzy model. In the article, we introduce the concepts of coordinated and locally coordinated valuation of a set of sentences, and provide proofs for interval theorems and an analogue of the compactness theorem. more...
- Published
- 2023
- Full Text
- View/download PDF
23. Solving the Problem of Fuzzy Partition-Distribution with Determination of the Location of Subset Centers
- Author
-
Anatoly Bulat, Elena Kiseleva, Sergiy Yakovlev, Olga Prytomanova, and Danylo Lebediev
- Subjects
operation research ,location-allocation problem ,optimal partitioning ,fuzzy model ,infinite-dimensional mathematical programming ,non-differentiable optimization ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
A large number of real-world problems from various fields of human activity can be reduced to optimal partitioning-allocation problems with the purpose of minimizing the partitioning quality criterion. A typical representative of such problem is an infinite-dimensional transportation problem and more generalized problems—infinite-dimensional problems of production centers placement along with the partitioning of the area of continuously distributed consumers with the purpose of minimizing transportation and production costs. The relevant problems are characterized by some kind of uncertainty level of a not-probabilistic nature. A method is proposed to solve an optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. The method is based on the synthesis of the methods of fuzzy theory and optimal partitioning-allocation theory, which is a new science field in infinite-dimensional mathematical programming with Boolean variables. A theorem was proved that determines the form of the optimal solution of the corresponding optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. An algorithm for solving fuzzy partitioning-allocation problems is proposed, which is based on the proved theorem and on a variant of Shor’s r-algorithm—a non-differential optimization method. The application of the proposed method is demonstrated on model tasks, where the coefficient of mistrust is integrated to interpret the obtained result—the minimum value of the membership function, which allows each point of the set partition to be assigned to a specific fuzzy subset. more...
- Published
- 2024
- Full Text
- View/download PDF
24. Fractional Caputo Operator and Takagi–Sugeno Fuzzy Modeling to Diabetes Analysis
- Author
-
Ez-zaiym Mustapha, El Ouissari Abdellatif, El Moutaouakil Karim, and Aberqi Ahmed
- Subjects
fractional derivative ,Takagi–Sugeno fractional ,diabetes ,artificial intelligence ,fuzzy model ,Mathematics ,QA1-939 - Abstract
Diabetes is becoming more and more dangerous, and the effects continue to grow due to the population’s ignorance of the seriousness of this phenomenon. The studies that have been carried out have not been able to follow the phenomenon more precisely, which has led to the use of the fractional derivative tool, which has a very great capability to study real problems and phenomena but is somewhat limited on nonlinear models. In this work, we will develop a new fractional derivative model of a diabetic population, the Takagi–Sugeno fractional fuzzy model, which will enable us to study the phenomenon with these nonlinear terms in order to obtain greater precision in the results. We will study the existence and uniqueness of the solution using the Lipschizian theorem and then turn to the new fuzzy model, which leads us to four dynamical systems. The interpretation results show the quality of fuzzy membership in tracking the malleable phenomena of nonlinear terms existing in the system. more...
- Published
- 2024
- Full Text
- View/download PDF
25. Three-Dimensional Fuzzy Modeling for Nonlinear Distributed Parameter Systems Using Simultaneous Perturbation Stochastic Approximation
- Author
-
Xianxia Zhang, Tangchen Wang, Chong Cheng, and Shaopu Wang
- Subjects
distributed parameter system ,fuzzy model ,simultaneous perturbation stochastic approximation ,fuzzy modeling ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPSs). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPSs. The Affinity Propagation clustering approach is utilized to determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy. more...
- Published
- 2024
- Full Text
- View/download PDF
26. A Target Threat Assessment Method for Application in Air Defense Command and Control Systems
- Author
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Xuan Truong Nguyen, Kim Phuong Phung, Quang Hieu Dang, Xung Ha Vo, and Hoa Tien Vu
- Subjects
fuzzy logic ,fuzzy model ,threat value ,air defense ,command and control system ,Electronics ,TK7800-8360 - Abstract
Introduction. This paper presents a solution for threat assessment of air targets using the fuzzy logic inference method. The approach is based on the Sugeno fuzzy model, which has multiple inputs representing target trajectory parameters and a single output representing the target threat value. A set of IF–THEN fuzzy inference rules, utilizing the AND operator, is developed to assess the input information.Aim. To develop and test an algorithm model to calculate the threat value of an air target for use in real-time automated command and control systems.Materials and methods. An algorithm model was developed using a fuzzy model to calculate the threat value of a target. The model is presented in the form of a flowchart supported by a detailed stepwise implementation process. The accuracy of the proposed algorithm was evaluated using the available toolkit in MATLAB. Additionally, a BATE software testbed was developed to assess the applicability of the algorithm model in a real-time automated command and control system.Results. The efficiency of the proposed fuzzy model was evaluated by its simulation and testing using MATLAB tools on a set of 10 target trajectories with different parameters. Additionally, the BATE software was utilized to test the model under various air defense scenarios. The proposed fuzzy model was found to be capable of efficiently computing the threat value of each target with respect to the protected object.Conclusion. The proposed fuzzy model can be applied when developing tactical supporting software modules for real-time air defense command and control systems. more...
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- 2023
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27. Evaluation of Fuzzy Clustering and Artificial Neural Network Methods in Spatial Zoning of Annual Precipitation in Iran
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A. Shahbaee Kotenaee and H. Asakereh
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self-organizing map neural network ,fuzzy model ,zoning ,precipitation ,iran ,Agriculture ,Agriculture (General) ,S1-972 - Abstract
Precipitation is one of the main elements of the Earth's hydro-climatic cycle and its variability depends on the complex and non-linear relationships between the climate system and environmental factors. Understanding these relationships and doing environmental planning based on them is difficult. Therefore, classifying data and dividing information into homogeneous and small categories can be helpful in this regard. In the present study, an attempt was made to prepare precipitation, altitude, slope, slope direction, and station density data for 3423 synoptic, climatological, and gauge stations in Iran in the 1961-2015 years’ period. These data were entered into fuzzy (FCM), self-organizing map neural network (SOM-ANN) models and precipitation-spatial zoning. The outputs of the two models were compared in terms of accuracy and efficiency. The results obtained from the output of the models have divided the rainfall conditions of Iran into four zones concerning environmental factors. Evaluations also showed that both models had high accuracy in classifying precipitation parameters; However, the fuzzy model has a relative advantage over the neural network model in the accuracy of results. more...
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- 2023
28. Artificial Intelligence-Based System for Retinal Disease Diagnosis
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Ekaterina V. Orlova
- Subjects
artificial intelligence ,decision support system ,fuzzy model ,rule-based algorithms ,stochastic gradient boosting algorithm ,retinal disease diagnosis ,Industrial engineering. Management engineering ,T55.4-60.8 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis of results and diagnostics. We propose an approach to designing an artificial intelligent diagnosis system (AI diagnosis system) which includes an electrophysiological complex to collect objective information and an intelligent decision support system to justify the diagnosis. The task of diagnosing retinal diseases based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers—one classifier is based on a fuzzy model and a fuzzy rule base (RB-classifier) and one uses the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of algorithms in a multi-class classification on unbalanced data is assessed based on two indicators—MAUC (multi-class area under curve) and MMCC (multi-class Matthews correlation coefficient). Combining two algorithms in a decision support system provides more accurate and reliable pathology identification. The accuracy of diagnostics using the proposed AI diagnosis system is 5–8% higher than the accuracy of a system using only diagnostics based on electrophysical indicators. The AI diagnosis system differs from other systems of this class in that it is based on the processing of objective electrophysiological data and socio-demographic data about patients, as well as subjective information from the anamnesis, which ensures increased efficiency of medical decision-making. The system is tested using actual data about retinal diseases from the Russian Institute of Eye Diseases and its high efficiency is proven. Simulation experiments conducted in various scenario conditions with different combinations of factors ensured the identification of the main determinants (markers) for each diagnosis of retinal pathology. more...
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- 2024
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29. Finite-Time Fault-Tolerant Control of Nonlinear Spacecrafts with Randomized Actuator Fault: Fuzzy Model Approach
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Wenlong Xue, Zhenghong Jin, and Yufeng Tian
- Subjects
nonlinear spacecraft ,fuzzy model ,fault-tolerant control ,randomized actuator fault ,Mathematics ,QA1-939 - Abstract
The primary objective of this paper is to address the challenge of designing finite-time fault-tolerant control mechanisms for nonlinear flexible spacecraft systems, which are particularly vulnerable to randomized actuator faults. Diverging from traditional methodologies, our research harnesses the capabilities of the Takagi–Sugeno (T–S) fuzzy framework. A unique feature of our model is the representation of actuator failures as stochastic signals following a Markov process, thereby offering a robust solution for addressing timeliness concerns. In this paper, we introduce a generalized reciprocally convex inequality that includes adjustable parameters, broadening the scope of previous results by accommodating them as special cases. Through the amalgamation of this enhanced inequality and flexible independent parameters, we propose an innovative controller design strategy. This approach establishes a stability standard that guarantees mean-square H∞ performance. In order to validate the efficacy of the suggested strategy, we present a numerical illustration involving a nonlinear spacecraft system, showcasing the practical advantages and feasibility of our proposed technique. more...
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- 2024
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30. Fuzzy Inference System (FIS) Model for the Seismic Parameters of Code-Based Earthquake Response Spectra.
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Mangir, Atakan
- Subjects
FUZZY logic ,FUZZY systems ,EARTHQUAKES ,EARTHQUAKE resistant design ,FUZZY sets ,EARTHQUAKE zones ,EARTHQUAKE hazard analysis ,FUZZY neural networks - Abstract
The response spectra defined in seismic design codes include crisp classifications of seismic parameters, which directly affect the spectra's shape and greatly alter seismic design loads. The optimum design phase seismic forces have an important role in the efficiency of the construction costs and structural safety. Various parameters are used to calculate the seismic design forces, especially presented in the codes with earthquake design spectra. This study presents a rule-based fuzzy inference model with fuzzy sets to determine these parameters using fuzzy inference system (FIS) modelling, which is the most appropriate approach among the different alternatives because both the input and output variables have numerical and linguistic uncertainties in the earthquake problem. Using the seismic zone factor of the region and shear wave velocity of the soil profile as inputs, the model generates the seismic coefficients and peak ground acceleration values of the response spectra specified in the Uniform Building Code (UBC, 1997). The response spectra in this code can be easily generated with these seismic coefficients after their fuzzification. Response spectra of twenty-five different sample cases with and without the FIS model are generated, which provide comparisons for the model superiority assessment. Significant differences are observed between the crisp logic and the FIS model-generated spectra. It is suggested that the FIS model can be modified and applied to various parameters to generate response spectra in different seismic design codes. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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31. A fuzzy risk assessment model used for assessing the introduction of African swine fever into Australia from overseas
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Hongkun Liu, YongLin Ren, Huanhuan Chu, Hu Shan, and Kok Wai Wong
- Subjects
Risk assessment ,Fuzzy model ,African swine fever (ASF) ,ASF introduction ,Australia ,Agriculture - Abstract
African swine fever (ASF) is a contagious and lethal hemorrhagic disease with a high case fatality rate. Since 2007, ASF has been spreading into many countries, especially in Europe and Asia. Given that there is no effective vaccine and treatment to deal with ASF, prevention is an important way for a country to avoid the effects of the virus. Australia is currently ASF-free but the disease has been reported in many neighboring countries, such as Indonesia, Timor-Leste, and Papua New Guinea. Therefore, it is necessary for Australia to maintain hyper-vigilance to prevent the ASF introduction. In this paper, we propose the use of fuzzy concepts to establish a fuzzy risk assessment model to predict the ASF introduction risk in Australia. From the analysis, the international passengers (IP) and international import trade (IIT) are concluded as the two main ASF introduction factors based on transmission features and past research. From the established fuzzy risk assessment model based on the analysis of the 2019 and 2020 data, the risks of ASF introduction into Australia are considered to be low. The model further deduced that the Asian region was the major source of potential risks. Finally, in order to validate the effectiveness of the established fuzzy risk assessment model, the qualitative data from the Department for Environment, Food & Rural Affairs of the United Kingdom was used. From the validation results, it has shown that the results were consistent when the same data is adopted, and thus proved that the functionality of the established fuzzy risk assessment model for assessing the risk in Australia. more...
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- 2023
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32. Fuzzy Model for Adjusting Stakeholder Engagement Strategies of a Company That Has Joined a Regional Strategic Alliance
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Alexander A. Gresko, Konstantin S. Solodukhin, and Natalia V. Rubtsova
- Subjects
strategic alliance ,strategies for interaction with stakeholders ,fuzzy model ,experience industry of a territory ,region ,mamdani algorithm ,Regional economics. Space in economics ,HT388 - Abstract
Introduction. The relevance of the study lies in the need to support managerial decision-making in the field of interaction between the participants in regional strategic alliances. The purpose of the article is to develop a method for adjusting strategies for interaction with stakeholders for an organization that has joined a regional strategic alliance. Materials and Methods. The proposed method is based on a fuzzy model of choosing strategies for the interaction of an organization with stakeholders before and after the entry into the alliance. The quantitative values of the relationship characteristics within the model framework are estimated for each resource component participating in the resource exchange of stakeholders with the organization, using a base of fuzzy production rules and a fuzzy inference algorithm. Results. It presents the main difference between the model and other well-known models for evaluating the characteristics of relations of an organization with stakeholders. The method is based on the assumption of significant changes in the characteristics of relations with stakeholders due to the entry of an organization into a strategic alliance. The paper assumes that these changes, in turn, lead to significant changes in the weighting coefficients of the feasibility of using various types of strategies for the interaction of an organization with stakeholders. The developed tools are tested on the example of the experience industry of territory – a regional strategic alliance between the Shtykovskie Prudy art park and the Tokyo restaurant chain (Primorye Territory). The paper depicts the change of relations with the stakeholders of the art park due to its joining the alliance. Besides, the paper shows how the expediency of using different types of strategies for the organization’s interaction with stakeholders changes. The paper focuses on identifying further directions of the development. Discussion and Conclusion. The results of the work can be useful to specialists in the field of management of regional strategic alliances, in particular, employees of the tourism administration, the business community, scientific and pedagogical personnel in the relevant field, and can be used in the training of specialists of higher and secondary professional education in strategic management. more...
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- 2022
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33. Prioritizing Heritage Building Maintenance: A Fuzzy Model Approach for Arc De Berà, Spain.
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Ababneh, Alaa
- Subjects
- *
BUILDING maintenance , *STRUCTURAL stability , *HISTORIC sites , *CONSERVATION & restoration , *ENVIRONMENTAL risk , *PRESERVATION of architecture - Abstract
The "Art-Risk 3.0" research project has developed a new tool to address the challenges faced in maintaining and preserving cultural heritage. This tool aims to evaluate the functional operational age and life cycle of heritage buildings in the Arc de Berà area in Tarragona, Catalonia, Spain, with the goal of promoting preventive conservation through a multidisciplinary approach. The tool considers various factors that contribute to the condition of heritage buildings, including structural stability, material decay, environmental risks, and usage patterns. By employing a fuzzy model, it provides an assessment of the building's condition in historical sites. One of the key features of the Art-Risk tool is its ability to prioritize intervention among different case studies within a specific urban context. It generates three output results: vulnerability, risk, and functionality index. The vulnerability value indicates the level of vulnerability of a building, with lower values suggesting better structural stability and resilience against potential hazards. The risk value signifies the level of risk associated with a building, with lower values indicating a reduced likelihood of damage or deterioration. The functionality index reflects the operational condition and suitability of a building for its intended use, with higher values indicating better functionality and operational performance, by considering these diverse valuations, stakeholders and conservation plan managers can effectively establish priorities for intervention. Buildings with higher vulnerability, risk, or lower functionality index scores are given higher priority for intervention. This approach ensures that limited resources are allocated to the buildings that require immediate attention, maximizing the impact of conservation efforts. Art-Risk3.0 model incorporates 19 input variables, with five variables automatically assigned based on the building's geographic location. Users are required to provide valid geographical coordinates and the remaining 14 associated values to obtain an accurate assessment of the building's condition. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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34. Developed Models Based on Transfer Learning for Improving Fake News Predictions.
- Author
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Wotaifi, Tahseen A. and Dhannoon, Ban N.
- Abstract
In conjunction with the global concern regarding the spread of fake news on social media, there is a large flow of research to address this phenomenon. The wide growth in social media and online forums has made it easy for legitimate news to merge with comprehensive misleading news, negatively affecting people's perceptions and misleading them. As such, this study aims to use deep learning, pre-trained models, and machine learning to predict Arabic and English fake news based on three public and available datasets: the Fake-or-Real dataset, the AraNews dataset, and the Sentimental LIAR dataset. Based on GloVe (Global Vectors) and FastText pre-trained models, A hybrid network has been proposed to improve the prediction of fake news. In this proposed network, CNN (Convolution Neural Network) was used to identify the most important features. In contrast, BiGRU (Bidirectional Gated Recurrent Unit) was used to measure the long-term dependency of sequences. Finally, multi-layer perceptron (MLP) is applied to classify the article news as fake or real. On the other hand, an Improved Random Forest Model is built based on the embedding values extracted from BERT (Bidirectional Encoder Representations from Transformers) pre-trained model and the relevant speaker-based features. These relevant features are identified by a fuzzy model based on feature selection methods. Accuracy was used as a measure of the quality of our proposed models, whereby the prediction accuracy reached 0.9935, 0.9473, and 0.7481 for the Fake-or-Real dataset, AraNews dataset, and Sentimental LAIR dataset respectively. The proposed models showed a significant improvement in the accuracy of predicting Arabic and English fake news compared to previous studies that used the same datasets. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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35. Risk Evaluation of Cost Overruns (COs) in Public Sector Construction Projects: A Fuzzy Synthetic Evaluation.
- Author
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Chadee, Aaron Anil, Martin, Hector Hugh, Gallage, Sihara, Banerjee, Kailas Sekhar, Roopan, Ryan, Rathnayake, Upaka, and Ray, Indrajit
- Subjects
COST overruns ,CONSTRUCTION projects ,PUBLIC sector ,PROJECT finance ,RISK assessment - Abstract
In the Small Island Developing States (SIDS), public sector infrastructure projects (PSIPs) fail to both meet targeted performance metrics and deliver on the intended benefits to society. In terms of the cost performance metric, cost overruns (COs) beyond the initial contract value are more of a norm than a unique occurrence. Therefore, to ensure economic sustainability for SIDS, and value for money on PSIPs, there is a need to investigate and evaluate the risk impacts on COs. The purpose of this research was to identify and evaluate the perceived cost overrun risk factors that are within the primary project stakeholders' sphere of control, and to reduce the ongoing ambiguities that exist in the prioritization of these risks. This was achieved by extracting critical risk factors from selected comparative studies in developing countries to formulate a closed-ended questionnaire to be administered to construction professionals in Trinidad and Tobago. Thereafter, the process of fuzzy synthetic evaluation (FSE) was used to develop a risk model based on three tiers of risks: 11 critical risk factors, 3 critical risk groupings (CRGs) and an overall risk level (ORL). The results showed that the two highest-ranked critical risks were project funding problems and variations by client. The leading critical risk grouping was client-related risk (5.370), followed by professional-related risk (4.815) and physical risk (4.870). The ORL was 5.068. Based on the FSE's linguistic scaling, the CRGs and the ORL are perceived to be high risks in PSIPs. This research adds to the CO body of knowledge in primarily three ways. Firstly, the study extends the comparative assessment previously undertaken in scholarship into the context of SIDS to build on the generalizability of this context-specific phenomenon. Secondly, the FSE evaluation undertaken provides a practical tool to be promoted for use in SIDS' construction industry among practitioners to focus and prioritize the critical risks in the planning phases and improve on contemporary risk practices in the execution phases of projects. Finally, this quantitative model approach is recommended to supplement the traditional qualitative risk management practices adopted in SIDS, thus contributing towards the overall improved economic sustainability and viability of PSIPs. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
- Full Text
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36. A Fuzzy-Based Analysis of Air Particle Pollution Data: An Index IMC for Magnetic Biomonitoring
- Author
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Mauro A. E. Chaparro, Marcos A. E. Chaparro, and Daniela A. Molinari
- Subjects
fuzzy model ,fuzzy number ,magnetic biomonitoring ,airborne magnetic particle ,statistics ,PLI index ,Meteorology. Climatology ,QC851-999 - Abstract
Airborne magnetic particles may be harmful because of their composition, morphology, and association with potentially toxic elements that may be observed through relationships between magnetic parameters and pollution indices, such as the Tomlinson pollution load index (PLI). We present a fuzzy-based analysis of magnetic biomonitoring data from four Latin American cities, which allows us to construct a magnetic index of contamination (IMC). This IMC uses four magnetic parameters, i.e., magnetic susceptibility χ, saturation isothermal remanent magnetization SIRM, coercivity of remanence Hcr, and SIRM/χ, and proposes summarizing the information to assess an area based exclusively on magnetic parameters more easily. The fuzzy inference system membership functions are built from the standardization of the data to become independent of the values. The proposed IMC is calculated using the baseline values for each case study, similar to the PLI. The highest IMC values were obtained in sites close to industrial areas, and in contrast, the lowest ones were observed in residential areas far from avenues or highways. The linear regression model between modeled IMC and PLI data yielded robust correlations of R2 > 0.85. The IMC is proposed as a complementary tool for air particle pollution and is a cost-effective magnetic approach for monitoring areas. more...
- Published
- 2024
- Full Text
- View/download PDF
37. Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame
- Author
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Taufik Hidayat, Kalamullah Ramli, R. Deiny Mardian, and Rahutomo Mahardiko
- Subjects
Virtual Machine Server ,Resource Balancing ,Fuzzy Model ,5G Quality ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Technology (General) ,T1-995 - Abstract
The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly. more...
- Published
- 2023
- Full Text
- View/download PDF
38. Fuzzy model for assessing the impact of pricing factors on the cost of primary housing
- Author
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A. A. Kopnin and D. V. Prokoshev
- Subjects
fuzzy model ,fuzzy control technologies ,fuzzytech ,data mining ,primary housing cost ,pricing ,real estate market ,Home economics ,TX1-1110 ,Economics as a science ,HB71-74 - Abstract
The article presents the results of a study on the creation of a fuzzy model for assessing the impact of pricing factors on the cost of primary real estate. The aim of the study is to develop a fuzzy logic-based tool for analyzing and evaluating the complex relationships between various factors that affect value. The article covers important aspects of creating a fuzzy model, including the definition of pricing factors, linguistic variables, and membership functions. The process of forming a rule base is considered, which determines the logical connections between factors and their influence on the price of real estate. The essence of the concept of primary housing and factors influencing pricing based on data on the situation in the real estate market are considered. The results of the study can be a useful tool for developing pricing strategies and decision making in the real estate market. The model developed by the authors can be improved considering the characteristics of a particular market and available data. more...
- Published
- 2023
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39. ارزیابی روشهای خوشهبندی فازی و شبکه عصبی مصنوعی در پهنهبندی فضایی بارش سالانۀ ایران.
- Author
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علی شاهبایی کوتن and حسین عساکره
- Subjects
- *
SELF-organizing maps , *ALTITUDES , *ZONING , *EARTH (Planet) ,ENVIRONMENTAL protection planning - Abstract
Precipitation is one of the main elements of the Earth's hydro-climatic cycle and its variability depends on the complex and non-linear relationships between the climate system and environmental factors. Understanding these relationships and doing environmental planning based on them is difficult. Therefore, classifying data and dividing information into homogeneous and small categories can be helpful in this regard. In the present study, an attempt was made to prepare precipitation, altitude, slope, slope direction, and station density data for 3423 synoptic, climatological, and gauge stations in Iran in the 1961-2015 years’ period. These data were entered into fuzzy (FCM), self-organizing map neural network (SOM-ANN) models and precipitation-spatial zoning. The outputs of the two models were compared in terms of accuracy and efficiency. The results obtained from the output of the models have divided the rainfall conditions of Iran into four zones concerning environmental factors. Evaluations also showed that both models had high accuracy in classifying precipitation parameters; However, the fuzzy model has a relative advantage over the neural network model in the accuracy of results. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
40. Combining Fuzzy, Multicriteria and Mapping Techniques to Assess Soil Fertility for Agricultural Development: A Case Study of Firozabad District, Uttar Pradesh, India.
- Author
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Saraswat, Anuj, Ram, Shri, AbdelRahman, Mohamed A. E., Raza, Md Basit, Golui, Debasis, HC, Hombegowda, Lawate, Pramod, Sharma, Sonal, Dash, Amit Kumar, Scopa, Antonio, and Rahman, Mohammad Mahmudur
- Subjects
SOIL fertility ,AGRICULTURAL development ,GEOGRAPHIC information systems ,ANALYTIC hierarchy process ,CHEMICAL properties ,SOIL sampling - Abstract
Soil fertility (SF) assessment is an important strategy for identifying agriculturally productive lands, particularly in areas that are vulnerable to climate change. This research focuses on detecting SF zones in Firozabad district, Uttar Pradesh, India, for agricultural purposes, so that they can be prioritized for future management using the fuzzy technique in the Arc GIS model-builder. The model computing technique was also deployed to determine the different fertility zones, considering 17 soil parameters. The derived fuzzy technique outperformed the traditional method of dividing the sampling sites into clusters to correlate soil fertility classes with the studied soil samples. The prioritization of the soil factors and a spatial analysis of the fertility areas were carried out using the Analytic Hierarchy Process (AHP) and GIS tools, respectively. The AHP analysis outcome indicated that hydraulic properties had the highest weighted value, followed by physical and chemical properties, regarding their influence on SF. The spatial distribution map of physico-chemical properties also clearly depicts the standard classification. A fuzzy priority map was implemented based on all the classes parameters to identify the five fertility classes of the soil, namely very high (0.05%); high (16.59%); medium (60.94%); low (22.34%); and very low (0.07% of total area). This study will be of significant value to planners and policymakers in the future planning and development of activities and schemes that aim to solve similar problems across the country. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
- Full Text
- View/download PDF
41. DE-RF 与模糊模型在热轧带钢板坯 弯曲控制中的应用.
- Author
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魏志鹏, 崔桂梅, 皮理想, and 李天豪
- Subjects
HOT rolling ,DIFFERENTIAL evolution ,MULTISENSOR data fusion ,REGRESSION analysis ,PROBLEM solving ,RANDOM forest algorithms - Abstract
Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...
- Published
- 2023
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42. Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan.
- Author
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Tavarov, Saidjon Shiralievich, Matrenin, Pavel, Safaraliev, Murodbek, Senyuk, Mihail, Beryozkina, Svetlana, and Zicmane, Inga
- Abstract
Seasonal fluctuations in electricity consumption, and uneven loading of supply lines reduce not only the energy efficiency of networks, but also contribute to a decrease in the service life of elements of power supply systems. To solve the problem of forecasting power consumption, it is proposed to use the theory of fuzzy sets to assess the effective development of the energy system of the Republic of Tajikistan. According to the statistical data of power consumption for the previous period, a fuzzy logic model with membership functions is proposed, which makes it possible to evaluate consumer satisfaction using the criteria unsatisfactory, satisfactory, conditionally satisfactory, and satisfactory, as well as the efficiency of the consumption mode of compliance using the criteria high, medium, and low, allowing the evaluation of the efficiency plan for the development of the energy system of the Republic of Tajikistan. To obtain and set more accurate data on electricity consumption, calculations were made for the winter period of the year. Based on the proposed calculation model of fuzzy logic, a quantitative component of electricity consumption, the corresponding satisfaction of the consumer, and the efficiency of the regime for nine cities of the Republic of Tajikistan were proposed in the form of diagrams of seasonal electricity consumption. The obtained seasonal power consumption makes it possible to improve the accuracy of estimating power consumption, thereby equalizing the balance of consumption and generation. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
- Full Text
- View/download PDF
43. Assessment of Ecotourism capabilities using FUZZY ANP method (A case study of Margavar Rural District of Urmia County)
- Author
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asadollah hejazi, Mohammad Hossein Rezaei Moghaddam, and zahra ghasemizadgonbad
- Subjects
ecotourism ,sustainable development ,margavar rural district ,anp ,fuzzy model ,Geography (General) ,G1-922 - Abstract
Introduction Ecotourism is one of the common types of tourism activities that has attracted a lot of attention in recent years. The ecotourism concept is based on the ideals of environmental protection and sustainable development and refers to a responsible journey to nature with an emphasis on ensuring the improvement of local community life and environmental protection (Seifi and Janbaz, 2017: 479). Ecotourism has a deep connection with sustainable development, which stems from the interactions between tourists and the environment. Proper planning and management in order to develop ecotourism are essential to preserve and maintain the environmental richness of the region as well as the economic improvement of local people (Bunruamkaew and Murayama, 2011: 269). Iran is one of the countries that have a lot of potential for ecotourism development, however, studies show that Iran's natural tourism assets are vast array of scattered, unstabilized resources, and in some cases are on the verge of extinction. The main object of the current paper is to assess the ecotourism potentials and capabilities of the Margavar rural district of Urmia County in northwest of Iran. For this purpose, Analytic Network Process (ANP) and Fuzzy method have been used and ecotourism capabilities zoning map of the study area has been produced. Data and Method The current research is a Multicriteria-based study and the Analytic network process (ANP) method and Geographical Information System (GIS) has been used to analyse the data. Spatial criteria were clustered based on reviewing the background into five main groups including climate, human, topography, geology, and tourism and criteria have divided into these 5 groups. At first, the criteria map was prepared in the ArcGIS environment, then all the maps were reclassified with the Reclassify function. In the next step, the criteria maps are standardized with a Fuzzy linear function and the ANP model was run in Super Decisions software, and pairwise comparisons and related super matrices were calculated for the criteria and the relative weight of each criterion was obtained and the resulting weights were applied to maps. Finally, the weighted maps were combined each other using the 0.9 fuzzy gamma operator, and the Fuzzy ecotourism capability zoning map was produced in value 0 to 1. Results and Discussion After designing the network structure, matrices and related super-matrices were calculated and the relative weights of all criteria were determined. Results showed that temperature, geological structure, slope, rain, and tourism facilities have the most importance and weight in relation to the ecotourism ability of the study area, respectively. According to the ecotourism capabilities zoning map, the study area was divided into four class: completely suitable, relatively suitable, relatively unsuitable, and completely unsuitable. The resulting map analysis shows that areas located in the central and western parts of the region, which have a low slope percentage and also include rich pastures and natural tourist attractions, are in the completely suitable group. Furthermore, some parts of the study area have mountain slopes and difficult topographic conditions that are very difficult to access grouped in completely unsuitable lands for ecotourism activities. The final results of the study show that 14.50% of the study area is in the completely suitable class, 26.32% in the relatively suitable class, 27% in the relatively unsuitable class and 32.15% are in the completely unsuitable class Conclusion Research evaluations show that In general, Margavar rural district, both in terms of potential for the future development of ecotourism and in terms of Its current situation has a lot of potential in this regard in terms of receiving a large number of tourists That comprehensive planning and formulation of effective solutions in this field can be considered an effective way and an important step to achieve sustainable regional development. Application of multi-criteria analysis techniques such as ANP model and Fuzzy model in this research indicates the great flexibility of these methods, which makes it possible to determine different scenarios and combine different criteria with each other. On the other hand, the use of GIS has provided a good platform for feasibility studies, assessment, and identification of the natural environment. Therefore, it can be said that due to the high potential of the Margavar rural district, there is a need to review measures and pay more attention to tourism development plans and studies in this region. more...
- Published
- 2022
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44. A FUZZY BASED DEEP LEARNING MODEL TO IDENTIFY THE PATTERN RECOGNITION FOR LICENSED PLATES IN SMART VEHICLE MANAGEMENT SYSTEM
- Author
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B Chellapraba, D Manohari, M S Kavitha, and K Periyakaruppan
- Subjects
vehicle management ,fuzzy model ,deep learning ,Computer engineering. Computer hardware ,TK7885-7895 - Abstract
In general, vehicle management is based on the proper maintenance and safety of a vehicle. Based on this the quality of the vehicle is calculated. Most of the older vehicles are currently of poor quality and are producing high levels of pollution. Thus, it is necessary to find information about those vehicles. The number plate is helpful to find the information about the vehicle. In this paper, the number blood detection method is proposed. It is based on the fuzzy model and developed in the way of deep learning. Its main purpose is to provide accurate vehicle details from a given set of data. It has also been upgraded to provide its safety measures to its owner based on the vehicle data. Thus, this proposed model prevents major accidents. These functions can also be very helpful in recovering vehicles based on data from stolen vehicles. more...
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- 2022
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45. Spatiotemporal kernel-local-embedding modeling approach for nonlinear distributed parameter systems.
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Xu, Bowen and Lu, Xinjiang
- Subjects
- *
DISTRIBUTED parameter systems , *ENERGY transfer , *FUZZY algorithms , *FUNCTION spaces , *LITHIUM-ion batteries - Abstract
In actual distributed parameter systems (DPSs), each spatial point has nonlinear energy transfer with its local neighbor points. Also, this energy transfer is affected by the past states. However, these properties are often ignored by most of modeling methods, which causes these methods ineffective in modeling of DPSs. Aiming for this problem, a spatiotemporal kernel-local-embedding (STKLLE) Modeling approach is proposed here to reconstruct the nonlinear spatiotemporal dynamics of DPSs. First, in order to present the complex dynamics on space, a STKLLE strategy is developed to extract space basis functions (SBFs). On the one hand, this STKLLE method represents the energy transfer relation with its neighboring points and maintains this local relation in model. On the other hand, it considers the influence of the adjacent past states to the current state. Then, using T–S fuzzy algorithm, a temporal model is designed to represent the temporal dynamics of DPSs in each sampling period. Integrating these SBFs and the temporal fuzzy model, a spatiotemporal model is constructed to well present and predict the nonlinear spatiotemporal dynamics in DPSs. Through the actual experiment on Lithium-ion batteries and heating oven, the effectiveness of this proposed model is detailly verified, and quantitative comparisons with several data-driven modeling algorithms are further carried out to demonstrate the model efficiency. • A spatiotemporal KLLE method is designed to extract nonlinear relations of the local neighbor points and adjacent states. • A T-S fuzzy temporal model is developed to construct the temporal dynamics of DPSs. • A spatiotemporal model is developed to reconstruct the spatiotemporal dynamics of DPSs. • This proposed method is verified by theoretical analysis and experimental studies. [ABSTRACT FROM AUTHOR] more...
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- 2022
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46. Multi-objective models for biomass supply chain planning with economic and carbon footprint consideration
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Duy Nguyen Duc, Pasakorn Meejaroen, and Narameth Nananukul
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Biomass supply chain management ,Epsilon-constraint method ,Multi-objective model ,Fuzzy model ,Stochastic model ,Carbon footprint ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
This paper focuses on designing multi-objective biomass supply chain planning models that aim to simultaneously minimize the total cost and the carbon footprint from the transportation. Stochastic and fuzzy models were developed for making strategic (optimal plant locations) and tactical decisions (material flows, truck types, etc.), while capturing the uncertainty of the demand. An epsilon-constraint method was applied to generate optimal solutions from these models. Managerial insights are provided based on a practical case study at a biomass plant in the Lower Northern region of Thailand; nine biomass plant candidates, nine rice husks suppliers and eight trucks were considered in the case study. A sensitivity analysis has been conducted to compare the results from the two models. The stochastic model can take into account all the scenarios of demand, while the fuzzy model can handle only a certain level of demand defined by a centroid value. The stochastic model needs to take into account a large number of variables and constraints, and therefore, requires more runtime to define an optimal solution, as compared to the fuzzy model. The trade-off between the operating cost and carbon emissions from both the models are provided with managerial insights. more...
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- 2021
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47. Fuzzy Modelling on the Evolution of COVID-19 Epidemic under the Effects of Intervention Measures
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Glaucia Maria Bressan and Elenice Weber Stiegelmeier
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COVID-19 ,Fuzzy model ,population dynamics ,measure interventions. ,Biotechnology ,TP248.13-248.65 - Abstract
Abstract This paper proposes to analyze how the intervention measures such as lockdown, partial lockdown and no-lockdown help to impede the spread of the severe outbreak of COVID-19 in Brazil. A p-fuzzy model, considering as input variables, the infected population and the intervention measures and as output variable the level of infestation, is proposed. The numerical results show that intervention measures play a crucial role in determining the success of COVID-19 eradication programs, while the population is being vaccinated in stages. Therefore, the model proposed assists government decision making in order to minimize the spread of the pandemic. more...
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- 2022
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48. Free running ship model tests of interaction between a moored ship and a passing ship.
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Raszeja, Magdalena, Hejmlich, Andrzej, Nowicki, Jacek, and Jaworski, Tomasz
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- *
SHIP models , *RESEARCH vessels , *MOORING of ships , *SHIPS , *SHIP handling - Abstract
For many reasons, ship model interaction tests are performed in experimental towing tanks. This paper presents research on the hydrodynamic forces acting on a ship tied up at the solid berth, which is produced by other ships passing by using free-running ship models with much larger dimensions than those used in towing tanks. A passing ship model was controlled by a human operator - an experienced master. This enabled a study of the influence of the interaction impact on the course of the maneuver. The research was carried out at the Ship Handling Research and Training Centre in Iława. The ship model was moored alongside and equipped with multi-directional force sensors linking the ship model with a solid berth. Forces were measured as a function of the passing ship speed, side distance between both ships, ship sizes, and depth-to-draft ratio (H/T). Forces were measured in two planes: the longitudinal (surge) and the transversal (sway). A numerical database was processed and ordered according to the variables. The fuzzy model was created within a "Matlab" computing environment using a Sugeno-type self-learning neuron network model. The proposed Sugeno model was evaluated with other methods presented by Flory (2002), Seelig (2001), and PASS-MOOR by Wang (1975). The ultimate goal of this study was to simplify the method of predictive calculations for adjusting speed and distance when passing by the moored ship, which ensures compliance with safe port mooring requirements. [ABSTRACT FROM AUTHOR] more...
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- 2022
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49. Fuzzy model for estimating the number of unemployed women on parental leave.
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Matrosova, Elena, Tikhomirova, Anna, and Gerasimova, Natalya
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PARENTAL leave ,NUMERICAL calculations ,UNEMPLOYMENT statistics ,SOFT computing ,UNEMPLOYED people ,CHILD development - Abstract
The article is devoted to the problem of underutilization of the country's labor potential in the face of women with young children. The relevance of research in the field of support for families with young children and the development of additional measures to support them is further explained. The authors propose to use a mathematical model based on soft computing for predictive estimation of the number of women with children from 0 to 3 years old, who are ready to go to work, taking into account the age of the child. The calculation of numerical indicators is based on the prediction of the Federal Statistics Service of the Russian Federation for the estimated population of the Russian Federation in the context of ages up to 2035. [ABSTRACT FROM AUTHOR] more...
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- 2022
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50. Document Clustering Using Graph Based Fuzzy Association Rule Generation.
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Perumal, P.
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DOCUMENT clustering ,FUZZY logic ,FEATURE extraction ,ASSOCIATION rule mining ,REDUNDANCY in engineering - Abstract
With the wider growth of web-based documents, the necessity of automatic document clustering and text summarization is increased. Here, document summarization that is extracting the essential task with appropriate information, removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task. In this research, a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation (gFAR). Initially, the graph model is used to map the relationship among the data (multi-source) followed by the establishment of document clustering with the generation of association rule using the fuzzy concept. This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy. This framework is provided in an interpretable way for document clustering. It iteratively reduces the error rate during relationship mapping among the data (clusters) with the assistance of weighted document content. Also, this model represents the significance of data features with class discrimination. It is also helpful in measuring the significance of the features during the data clustering process. The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns (RRP), ROUGE score, and Discrimination Information Measure (DMI) respectively. Here, DailyMail and DUC 2004 dataset is used to extract the empirical results. The proposed gFAR model gives better trade-off while compared with various prevailing approaches. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
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