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Статті в журналах з теми "INTELLIGENT OWA"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Reformat, Marek Z., Ronald R. Yager, Zhan Li, and Naif Alajlan. "Human-inspired Identification of High-level Concepts using OWA and Linguistic Quantifiers." International Journal of Computers Communications & Control 6, no. 3 (September 10, 2011): 473. http://dx.doi.org/10.15837/ijccc.2011.3.2132.

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Intelligent agent based system can be used to identify high-level concepts matching sets of keywords provided by users. A new human-inspired approach to concept identification in documents is introduced here. The proposed method takes keywords and builds concept structures based on them. These concept structures are represented as hierarchies of concepts (HofC). The ontology is used to enrich HofCs with terms and other concepts (sub-concepts) based on concept definitions, as well as with related concepts. Additionally, the approach uses levels of importance of terms defining the concepts. The levels of importance of terms are continuously updated based on a flow of documents using an Adaptive Assignment of Term Importance (AATI) schema. The levels of activation of concepts identified in a document that match these in the HofC are estimated using ordered weighted averaging (OWA) operators with linguistic quantifiers. A simple case study presented in the paper is designed to illustrate the approach.

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2

Liang, Shiyuan, Hui Cao, and Henglong Shen. "Intelligent Evaluation Method of Marine Engine Simulator Based on Combination Weighting." Journal of Physics: Conference Series 2173, no. 1 (January 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2173/1/012003.

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Abstract In order to solve the problems of objectivity and inaccuracy in the current evaluation of marine engine simulator, an evaluation model is established based on C-OWA (combination ordered weighted averaging)-the Coefficient of Variation method-minimum discriminant information principle combination weighting method and Vague set. Taking standby of marine generator project as an experiment example, the evaluation index system is established, and the evaluation model is applied to evaluate each evaluation team, and the final operation score of each team is obtained. The results show that (1) the index system is comprehensive and targeted for the operation evaluation of generator standby project; (2) the combination weighting method can improve the rationality of index weighting; (3) Vague set fuzzy decision-making can improve the accuracy of fuzzy evaluation of simulator operation; (4) the model shows rationality and reliability in evaluation results and can provide a reasonable and feasible method for intelligent evaluation of marine simulator.

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3

P V R N S S V Sai Leela, Bankapalli Jyothi, Pullagura Indira priyadarsini,. "Towards Intelligent Machine Learning Models for Intrusion Detection System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 643–55. http://dx.doi.org/10.17762/turcomat.v12i5.1062.

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The Internet has become an important resource for mankind. Explicitly information security is an interminable domain to the present world. Hence a more potent Intrusion Detection System (IDS) should be built. Machine Learning techniques are used in developing proficient models for IDS. Imbalanced Learning is a crucial task for many classification processes. Resampling training data towards a more balanced distribution is an effective way to combat this issue. There are most prevalent techniques like under sampling and oversampling.In this paper, the issues of imbalanced data distribution and high dimensionality are addressed using a novel oversampling technique and an innovative feature selection method respectively. Our work suggests a novel hybrid algorithm, HOK-SMOTE which considers an ordered weighted averaging (OWA) approach for choosing the best features from the KDD cup 99 data set and K-Means SMOTE for imbalanced learning. Here an ensemble model is compared against the hybrid algorithm. This ensemble integrates Support Vector Machine (SVM), K Nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB) and Decision Tree (DT). Then weighted average voting is applied for prediction of outputs. In this work, much Experimentationwas conducted on various oversampling techniques and traditional classifiers. The results indicate that the proposed work is the most accurate one among other ML techniques. The precision, recall, F-measure, and ROC curve show notable outcomes. Hence K-Means SMOTE in parallel with ensemble learning has given satisfactory results and a precise solution to the imbalanced learning in IDS. It is ascertained whether ensemble modeling or oversampling techniques are dominating for Intrusion data set.

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4

Xue, Xingsi, Jianhua Guo, Miao Ye, and Jianhui Lv. "Similarity Feature Construction for Matching Ontologies through Adaptively Aggregating Artificial Neural Networks." Mathematics 11, no. 2 (January 16, 2023): 485. http://dx.doi.org/10.3390/math11020485.

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Ontology is the kernel technique of Semantic Web (SW), which enables the interaction and cooperation among different intelligent applications. However, with the rapid development of ontologies, their heterogeneity issue becomes more and more serious, which hampers communications among those intelligent systems built upon them. Finding the heterogeneous entities between two ontologies, i.e., ontology matching, is an effective method of solving ontology heterogeneity problems. When matching two ontologies, it is critical to construct the entity pair’s similarity feature by comprehensively taking into consideration various similarity features, so that the identical entities can be distinguished. Due to the ability of learning complex calculating model, recently, Artificial Neural Network (ANN) is a popular method of constructing similarity features for matching ontologies. The existing ANNs construct the similarity feature in a single perspective, which could not ensure its effectiveness under diverse heterogeneous contexts. To construct an accurate similarity feature for each entity pair, in this work, we propose an adaptive aggregating method of combining different ANNs. In particular, we first propose a context-based ANN and syntax-based ANN to respectively construct two similarity feature matrices, which are then adaptively integrated to obtain a final similarity feature matrix through the Ordered Weighted Averaging (OWA) and Analytic hierarchy process (AHP). Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track are used to verify the effectiveness of our method. The experimental results show that our approach’s results are better than single ANN-based ontology matching techniques and state-of-the-art ontology matching techniques.

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5

Nabeeh, Nada A., Mohamed Abdel-Basset, Abduallah Gamal, and Victor Chang. "Evaluation of Production of Digital Twins Based on Blockchain Technology." Electronics 11, no. 8 (April 17, 2022): 1268. http://dx.doi.org/10.3390/electronics11081268.

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A blockchain, as a form of distributed ledger technology, represents the unanimity of replication, synchronization, and sharing of data among various geographical sites. Blockchains have demonstrated impressive and effective applications throughout many aspects of the business. Blockchain technology can lead to the advent of the construction of Digital Twins (DTs). DTs involve the real representation of physical devices digitally as a virtual representation of both elements and dynamics prior to the building and deployment of actual devices. DT products can be built using blockchain-based technology in order to achieve sustainability. The technology of DT is one of the emerging novel technologies of Industry 4.0, along with artificial intelligence (AI) and the Internet of Things (IoT). Therefore, the present study adopts intelligent decision-making techniques to conduct a biased analysis of the drivers, barriers, and risks involved in applying blockchain technologies to the sustainable production of DTs. The proposed model illustrates the use of neutrosophic theory to handle the uncertain conditions of real-life situations and the indeterminate cases evolved in decision-makers’ judgments and perspectives. In addition, the model applies the analysis of Multi-criteria Decision Making (MCDM) methods through the use of ordered weighted averaging (OWA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to achieve optimal rankings for DT production providers based on consistent weighted decision-maker’s judgments in order to maintain and to assure sustainability. An empirical study is applied to the uncertain environment to aid decision-makers in achieving ideal decisions for DT providers with respect to various DT challenges, promoting sustainability and determining the best service providers. The Monte Carlo simulation method is used to illustrate, predict, and forecast the importance of the weights of decision-makers’ judgments as well as the direct impact on the sustainability of DT production.

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6

Aló, Richard, and Vladik Kreinovich. "Selected Papers from InTech'04." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (May 20, 2006): 243–44. http://dx.doi.org/10.20965/jaciii.2006.p0243.

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The main objective of the annual International Conference on Intelligent Technologies (InTech) is to bring together researchers and practitioners who implement intelligent and fuzzy technologies in real-world environment. The Fifth International Conference on Intelligent Technologies InTech'04 was held in Houston, Texas, on December 2-4, 2004. Topics of InTech'04 included mathematical foundations of intelligent technologies, traditional Artificial Intelligent techniques, uncertainty processing and methods of soft computing, learning/adaptive systems/data mining, and applications of intelligent technologies. This special issue contains versions of 15 selected papers originally presented at InTech'04. These papers cover most of the topics of the conference. Several papers describe new applications of the existing intelligent techniques. R. Aló{o} et al. show how traditional <I>statistical</I> hypotheses testing techniques – originally designed for processing measurement results – need to be modified when applied to simulated data – e.g., when we compare the quality of two algorithms. Y. Frayman et al. use <I>mathematical morphology</I> and <I>genetic algorithms</I> in the design of a machine vision system for detecting surface defects in aluminum die casting. Y. Murai et al. propose a new faster <I>entropy</I>-based placement algorithm for VLSI circuit design and similar applications. A. P. Salvatore et al. show how <I>expert system</I>-type techniques can help in scheduling botox treatment for voice disorders. H. Tsuji et al. propose a new method, based on <I>partial differential equations</I>, for automatically identifying and extracting objects from a video. N. Ward uses <I>Ordered Weighted Average</I> (OWA) techniques to design a model that predicts admission of computer science students into different graduate schools. An important aspect of intelligence is ability to <I>learn</I>. In A. Mahaweerawat et al., neural-based machine learning is <I>used</I> to identify and predict software faults. J. Han et al. show that we can drastically <I>improve</I> the quality of machine learning if, in addition to discovering traditional (positive) rules, we also search for negative rules. A serious problem with many neural-based machine learning algorithms is that often, the results of their learning are un-intelligible rules and numbers. M. I. Khan et al. show, on the example of robotic arm applications, that if we allow neurons with different input-output dependencies – including linear neurons – then we can <I>extract</I> meaningful <I>knowledge</I> from the resulting network. Several papers analyze the Equivalent Transformation (ET) model, that allows the user to <I>automatically generate code from specifications</I>. A general description of this model is given by K. Akama et al. P. Chippimolchai et al. describe how, within this model, we can transform a user's query into an equivalent more efficient one. H. Koike et al. apply this approach to <I>natural language processing</I>. Y. Shigeta et al. show how the existing <I>constraint</I> techniques can be translated into equivalent transformation rules and thus, combined with other specifications. I. Takarajima et al. extend the ET approach to situations like <I>parallel computations</I>, where the order in which different computations are performed on different processors depends on other processes and is, thus, non-deterministic. Finally, a paper by J. Chandra – based on his invited talk at InTech'04 – describes a <I>general framework</I> for robust and resilient critical infrastructure systems, with potential applications to transportation systems, power grids, communication networks, water resources, health delivery systems, and financial networks. We want to thank all the authors for their outstanding work, the participants of InTech'04 for their helpful suggestions, the anonymous reviewers for their thorough analysis and constructive help, and – last but not the least – to Professor Kaoru Hirota for his kind suggestion to host this issue and to the entire staff of the journal for their tireless work.

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7

Jetlund, Onstein, and Huang. "Adapted Rules for UML Modelling of Geospatial Information for Model-Driven Implementation as OWL Ontologies." ISPRS International Journal of Geo-Information 8, no. 9 (August 22, 2019): 365. http://dx.doi.org/10.3390/ijgi8090365.

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This study aims to improve the implementation of models of geospatial information in Web Ontology Language (OWL). Large amounts of geospatial information are maintained in Geographic Information Systems (GIS) based on models according to the Unified Modeling Language (UML) and standards from ISO/TC 211 and the Open Geospatial Consortium (OGC). Sharing models and geospatial information in the Semantic Web will increase the usability and value of models and information, as well as enable linking with spatial and non-spatial information from other domains. Methods for conversion from UML to OWL for basic concepts used in models of geospatial information have been studied and evaluated. Primary conversion challenges have been identified with specific attention to whether adapted rules for UML modelling could contribute to improved conversions. Results indicated that restrictions related to abstract classes, unions, compositions and code lists in UML are challenging in the Open World Assumption (OWA) on which OWL is based. Two conversion challenges are addressed by adding more semantics to UML models: global properties and reuse of external concepts. The proposed solution is formalized in a UML profile supported by rules and recommendations and demonstrated with a UML model based on the Intelligent Transport Systems (ITS) standard ISO 14825 Geographic Data Files (GDF). The scope of the resulting ontology will determine to what degree the restrictions shall be maintained in OWL, and different conversion methods are needed for different scopes.

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8

Yager, Ronald R. "Prioritized OWA aggregation." Fuzzy Optimization and Decision Making 8, no. 3 (June 11, 2009): 245–62. http://dx.doi.org/10.1007/s10700-009-9063-4.

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9

Emrouznejad, Ali. "MP-OWA: The most preferred OWA operator." Knowledge-Based Systems 21, no. 8 (December 2008): 847–51. http://dx.doi.org/10.1016/j.knosys.2008.03.057.

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10

Amarante, Massimiliano. "Mm-OWA: A Generalization of OWA Operators." IEEE Transactions on Fuzzy Systems 26, no. 4 (August 2018): 2099–106. http://dx.doi.org/10.1109/tfuzz.2017.2762637.

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11

De Miguel, Laura, Humberto Bustince, Edurne Barrenechea, Miguel Pagola, and Javier Fernandez. "Unbalanced interval-valued OWA operators." Progress in Artificial Intelligence 5, no. 3 (February 25, 2016): 207–14. http://dx.doi.org/10.1007/s13748-016-0086-0.

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12

D'Urso, Pierpaolo, and Jalal Chachi. "OWA fuzzy regression." International Journal of Approximate Reasoning 142 (March 2022): 430–50. http://dx.doi.org/10.1016/j.ijar.2021.12.009.

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13

Fan, Taihe, and Dan A. Ralescu. "On the Comparisons of OWA Operators and Ordinal OWA Operators." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 01 (February 1997): 1–12. http://dx.doi.org/10.1142/s0218488597000026.

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In this paper, comparisons of OWA operators and ordinal OWA operators are carried out by using lattice theoretic methods. It is proved first that in both cases, the set of all aggregation operators forms a lattice, then the concept of positive valuation is used to measure the "orness" of aggregation operators and the structures of all such possible "orness" measures are determined.

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14

Carlsson, Christer, Robert Fullér, and Péter Majlender. "A note on constrained OWA aggregation." Fuzzy Sets and Systems 139, no. 3 (November 2003): 543–46. http://dx.doi.org/10.1016/s0165-0114(03)00185-4.

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15

Majlender, Péter. "OWA operators with maximal Rényi entropy." Fuzzy Sets and Systems 155, no. 3 (November 2005): 340–60. http://dx.doi.org/10.1016/j.fss.2005.04.006.

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16

Srivastava, Vikas, and Amit K. Singh. "Beta-Bézier OWA operator." International Journal of Approximate Reasoning 152 (January 2023): 33–45. http://dx.doi.org/10.1016/j.ijar.2022.10.010.

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17

Cena, Anna, and Marek Gagolewski. "Genie+OWA: Robustifying hierarchical clustering with OWA-based linkages." Information Sciences 520 (May 2020): 324–36. http://dx.doi.org/10.1016/j.ins.2020.02.025.

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18

MITCHELL, H. B. "AN INTUITIONISTIC OWA OPERATOR." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 06 (December 2004): 843–60. http://dx.doi.org/10.1142/s0218488504003247.

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The OWA (Ordered Weighted Average) operator is a powerful non-linear operator for aggregating a set of inputs ai,i∈{1,2,…,M}. In the original OWA operator the inputs are crisp variables ai. This restriction was subsequently removed by Mitchell and Schaefer who by application of the extension principle defined a fuzzy OWA operator which aggregates a set of ordinary fuzzy sets Ai. We continue this process and define an intuitionistic OWA operator which aggregates a set of intuitionistic fuzzy sets Ãi. We describe a simple application of the new intuitionistic OWA operator in multiple-expert multiple-criteria decision-making.

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19

Xu, Z. S., and Q. L. Da. "The uncertain OWA operator." International Journal of Intelligent Systems 17, no. 6 (April 25, 2002): 569–75. http://dx.doi.org/10.1002/int.10038.

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20

Schaefer, P. A., and H. B. Mitchell. "A generalized OWA operator." International Journal of Intelligent Systems 14, no. 2 (February 1999): 123–43. http://dx.doi.org/10.1002/(sici)1098-111x(199902)14:2<123::aid-int1>3.0.co;2-e.

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21

Torra, Vicen�. "The weighted OWA operator." International Journal of Intelligent Systems 12, no. 2 (February 1997): 153–66. http://dx.doi.org/10.1002/(sici)1098-111x(199702)12:2<153::aid-int3>3.0.co;2-p.

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22

TROIANO, LUIGI, and RONALD R. YAGER. "RECURSIVE AND ITERATIVE OWA OPERATORS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 06 (December 2005): 579–99. http://dx.doi.org/10.1142/s0218488505003680.

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An important issue when using the OWA aggregation operators is the determination of weights. One approach is to link the weights to a desired attitudinal character for the aggregation. The ME-OWA operators provide a pioneering example of this approach. Here we first present an alternative approach to generating OWA weights with a desired attitudinal character. We accomplish this by using a family of recursive OWA operators (R-OWA). We then generalize this with a class that allows of OWA aggregation by iteration (It-OWA). Both families are built with the constraint of keeping constant the attitudinal character at any recursion or any iteration step. This is particularly useful in aggregations that sequentially add arguments to the aggregation.

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23

Xu, Binbin, Chang Chen, Jinrui Tang, and Ruoli Tang. "A novel coevolving differential evolution and its application in intelligent device-to-device communication systems." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 1607–21. http://dx.doi.org/10.3233/jifs-211008.

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Due to the increasingly demand of wireless broadband applications in modern society, the device-to-device (D2D) communication technique plays an important role for improving communication spectrum efficiency and quality of service (QoS). This study focuses on the optimal allocation of link resource in D2D communication systems using intelligent approaches, in order to obtain optimal energy efficiency of D2D-pair users (DP) and also ensure communication QoS. To be specific, the optimal resource allocation (ORA) model for ensuring the cooperation between DP and cellular users (CU) is established, and a novel coding strategy of ORA model is also proposed. Then, for efficiently optimizing the ORA model, a novel swarm-intelligence-based algorithm called the dynamic topology coevolving differential evolution (DTC-DE) is developed, and the efficiency of DTC-DE is also tested by a comprehensive set of benchmark functions. Finally, the DTC-DE algorithm is employed for optimizing the proposed ORA model, and some state-of-the-art algorithms are also employed for comparison. Result of case study shows that the DTC-DE outperforms its competitors significantly, and the optimal resource allocation can be obtained by DTC-DE with robust performance.

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24

Yager, Ronald R., and Naif Alajlan. "Probabilistically Weighted OWA Aggregation." IEEE Transactions on Fuzzy Systems 22, no. 1 (February 2014): 46–56. http://dx.doi.org/10.1109/tfuzz.2013.2245899.

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25

Mesiar, Radko, Ladislav Sipeky, Pankaj Gupta, and Jin LeSheng. "Aggregation of OWA Operators." IEEE Transactions on Fuzzy Systems 26, no. 1 (February 2018): 284–91. http://dx.doi.org/10.1109/tfuzz.2017.2654482.

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26

Beliakov, Gleb. "Learning Weights in the Generalized OWA Operators." Fuzzy Optimization and Decision Making 4, no. 2 (April 2005): 119–30. http://dx.doi.org/10.1007/s10700-004-5868-3.

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27

García-Lapresta, José Luis, and Miguel Martínez-Panero. "Linguistic-based voting through centered OWA operators." Fuzzy Optimization and Decision Making 8, no. 4 (September 18, 2009): 381–93. http://dx.doi.org/10.1007/s10700-009-9067-0.

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28

Yager, Ronald R., and Naif Alajlan. "On characterizing features of OWA aggregation operators." Fuzzy Optimization and Decision Making 13, no. 1 (July 18, 2013): 1–32. http://dx.doi.org/10.1007/s10700-013-9167-8.

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29

Zeng, Wenyi, Deqing Li, and Yundong Gu. "Monotonic argument-dependent OWA operators." International Journal of Intelligent Systems 33, no. 8 (June 22, 2018): 1639–59. http://dx.doi.org/10.1002/int.21955.

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30

Saltos Atiencia, Ramiro, and Richard Weber. "Rough-Fuzzy Support Vector Clustering with OWA Operators." Inteligencia Artificial 25, no. 69 (March 21, 2022): 42–56. http://dx.doi.org/10.4114/intartif.vol25iss69pp42-56.

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Rough-Fuzzy Support Vector Clustering (RFSVC) is a novel soft computing derivative of the classical Support Vector Clustering (SVC) algorithm, which has been used already in many real-world applications. RFSVC’s strengths are its ability to handle arbitrary cluster shapes, identify the number of clusters, and e?ectively detect outliers by the means of membership degrees. However, its current version uses only the closest support vector of each cluster to calculate outliers’ membership degrees, neglecting important information that remaining support vectors can contribute. We present a novel approach based on the ordered weighted average (OWA) operator that aggregates information from all cluster representatives when computing ?nal membership degrees and at the same time allows a better interpretation of the cluster structures found. Particularly, we propose the induced OWA using weights determined by the employed kernel function. The computational experiments show that our approach outperforms the current version of RFSVC as well as alternative techniques ?xing the weights of the OWA operator while maintaining the level of interpretability of membership degrees for detecting outliers.

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31

Hun Hong, Dug. "On proving the extended minimax disparity OWA problem." Fuzzy Sets and Systems 168, no. 1 (April 2011): 35–46. http://dx.doi.org/10.1016/j.fss.2010.08.008.

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32

Flores-Sosa, Martha, Ezequiel Avilés-Ochoa, and José M. Merigó. "Induced OWA operators in linear regression." Journal of Intelligent & Fuzzy Systems 38, no. 5 (May 29, 2020): 5509–20. http://dx.doi.org/10.3233/jifs-179642.

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33

SANG, XIUZHI, and XINWANG LIU. "PARAMETERIZED 2-TUPLE LINGUISTIC MOST PREFERRED OWA OPERATORS AND THEIR APPLICATION IN DECISION MAKING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, no. 06 (December 2013): 799–819. http://dx.doi.org/10.1142/s0218488513500384.

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The most preferred OWA (MP-OWA) operator is a new method to aggregate preference information with crisp numbers, whose weights are related with the frequency of the most preferred assessment to each criteria. However, people are usually not able to estimate their preference degrees with crisp number, since they have a vague knowledge about the preference assessment. In this paper, we propose a 2-tuple linguistic MP-OWA (LMP-OWA) operator. It is useful because it can be used to make decision with linguistic preference relations, and the weighting vector is not only connected to the maximum frequency of the assessment to the criteria, but also to the assessment values. Meanwhile, we introduce the parameterized 2-tuple LMP-OWA operator and the parameterized 2-tuple LMP-OWA operator with power function, which provide multiple aggregation results for decision makers to select. The paper ends up with an example of decision making with linguistic preference relations in movie recommender system.

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34

García-Zamora, Diego, Álvaro Labella, Rosa M. Rodríguez, and Luis Martínez. "Symmetric weights for OWA operators prioritizing intermediate values. The EVR-OWA operator." Information Sciences 584 (January 2022): 583–602. http://dx.doi.org/10.1016/j.ins.2021.10.077.

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35

De Miguel, Laura, Daniel Paternain, Inmaculada Lizasoain, Gustavo Ochoa, and Humberto Bustince. "Some Characterizations of Lattice OWA Operators." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 1 (December 2017): 5–17. http://dx.doi.org/10.1142/s0218488517400013.

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Анотація:

Ordered Weighted Averaging (OWA) operators are a family of aggregation functions for data fusion. If the data are real numbers, then OWA operators can be characterized either as a special kind of discrete Choquet integral or simply as an arithmetic mean of the given values previously ordered. This paper analyzes the possible generalizations of these characterizations when OWA operators are defined on a complete lattice. In addition, the set of all n-ary OWA operators is studied as a sublattice of the lattice of all the n-ary aggregation functions defined on a distributive lattice.

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36

Lenz, Oliver Urs, Daniel Peralta, and Chris Cornelis. "Scalable Approximate FRNN-OWA Classification." IEEE Transactions on Fuzzy Systems 28, no. 5 (May 2020): 929–38. http://dx.doi.org/10.1109/tfuzz.2019.2949769.

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37

Yager, R. R. "Norms Induced from OWA Operators." IEEE Transactions on Fuzzy Systems 18, no. 1 (February 2010): 57–66. http://dx.doi.org/10.1109/tfuzz.2009.2035812.

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38

Yager, R. R., and G. Beliakov. "OWA Operators in Regression Problems." IEEE Transactions on Fuzzy Systems 18, no. 1 (February 2010): 106–13. http://dx.doi.org/10.1109/tfuzz.2009.2036908.

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39

Ovchinnikov, Sergei. "Invariance properties of ordinal OWA operators." International Journal of Intelligent Systems 14, no. 4 (April 1999): 413–18. http://dx.doi.org/10.1002/(sici)1098-111x(199904)14:4<413::aid-int4>3.0.co;2-q.

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40

Yager, Ronald R. "Quantifier guided aggregation using OWA operators." International Journal of Intelligent Systems 11, no. 1 (December 7, 1998): 49–73. http://dx.doi.org/10.1002/(sici)1098-111x(199601)11:1<49::aid-int3>3.0.co;2-z.

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41

Mitchell, H. B., and D. D. Estrakh. "An OWA operator with fuzzy ranks." International Journal of Intelligent Systems 13, no. 1 (January 1998): 69–81. http://dx.doi.org/10.1002/(sici)1098-111x(199801)13:1<69::aid-int6>3.0.co;2-v.

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42

Yager, Ronald R. "New modes of OWA information fusion." International Journal of Intelligent Systems 13, no. 7 (July 1998): 661–81. http://dx.doi.org/10.1002/(sici)1098-111x(199807)13:7<661::aid-int5>3.0.co;2-i.

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43

Liaw, Cheng-Shih, Yung-Chia Chang, Kuei-Hu Chang, and Thing-Yuan Chang. "ME-OWA based DEMATEL reliability apportionment method." Expert Systems with Applications 38, no. 8 (August 2011): 9713–23. http://dx.doi.org/10.1016/j.eswa.2011.02.029.

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44

Krasikov, Sergey, Aaron Tranter, Andrey Bogdanov, and Yuri Kivshar. "Intelligent metaphotonics empowered by machine learning." Opto-Electronic Advances 5, no. 3 (2022): 210147. http://dx.doi.org/10.29026/oea.2022.210147.

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45

VASYLKIVSKYI, Mykola, Ganna VARGATYUK, and Olha BOLDYREVA. "INTELLIGENT OPTIMIZATION OF MULTIPLE ACCESS INFOCOMMUNICATION NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 315, no. 6 (December 29, 2022): 32–39. http://dx.doi.org/10.31891/2307-5732-2022-315-6(2)-32-39.

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The methods of multiple access with multiplexing of resources are studied and the advantages and disadvantages of orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are considered. A comparative analysis of data transmission schemes in the radio network was also performed, taking into account resource planning, in particular: transmission with service information and transmission without service information. The structure of the NOMA uplink receiver based on OFDM signals is proposed. The peculiarities of providing supermassive connection within limited radio resources on the basis of grantless access using NOMA have been studied. At the same time, methods of solving the problems inherent in the current application of GF-transmission and NOMA in the implementation of supermassive connection to the access network based on 6G technology were considered. Prospects for the introduction of an artificial intelligence transmitter based on a multiple access transmission scheme with low cost, low PAPR, low delay, high reliability and wide connectivity are determined. And features of artificial intelligence receiver design using artificial intelligence / machine learning techniques that can play a role in facilitating MUD design for NOMA are also considered.

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46

Liu, Xinwang. "On the properties of equidifferent OWA operator." International Journal of Approximate Reasoning 43, no. 1 (September 2006): 90–107. http://dx.doi.org/10.1016/j.ijar.2005.11.003.

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47

Tian, Yunxin, Xiaofeng Hu, Jun Jiang, Xiaoqian Tang, Zhiquan Tian, Zhaowei Zhang, and Peiwu Li. "Smartphone-Based Quantitative Detection of Ochratoxin A in Wheat via a Lateral Flow Assay." Foods 12, no. 3 (January 17, 2023): 431. http://dx.doi.org/10.3390/foods12030431.

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Ochratoxin A (OTA) poses a severe health risk to livestock along the food chain. Moreover, according to the International Agency for Research on Cancer, it is also categorized as being possibly carcinogenic to humans. The lack of intelligent point-of-care test (POCT) methods restricts its early detection and prevention. This work establishes a smartphone-enabled point-of-care test for OTA detection via a fluorescent lateral flow assay within 6 min. By using a smartphone and portable reader, the assay allows for the recording and sharing of the detection results in a cloud database. This intelligent POCT provided (iPOCT) a linearity range of 0.1–3.0 ng/mL and a limit of detection (LOD) of 0.02 ng/mL (0.32 µg/kg in wheat). By spiking OTA in blank wheat samples, the recoveries were 89.1–120.4%, with a relative standard deviation (RSD) between 3.9–9.1%. The repeatability and reproducibility were 94.2–101.7% and 94.6–103.4%, respectively. This work provides a promising intelligent POCT method for food safety.

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48

Mitchell, H. B., and D. D. Estrakh. "A Modified OWA Operator and its Use in Lossless DPCM Image Compression." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 04 (August 1997): 429–36. http://dx.doi.org/10.1142/s0218488597000324.

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The ordered weighted averaging (OWA) operator of Yager was introduced to provide a method for aggregating several inputs which lies between the Max and Min operators. The fundamental aspect of the OWA operator is a reordering step in which the input arguments are re-arranged according to their actual relative value. In this paper we describe a modified OWA operator in which the input arguments are not re-arranged according to their actual relative values but rather according to their estimated relative values. We describe an unusual application of this operator to lossless image compression.

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49

Yager, Ronald R. "Time Series Smoothing and OWA Aggregation." IEEE Transactions on Fuzzy Systems 16, no. 4 (August 2008): 994–1007. http://dx.doi.org/10.1109/tfuzz.2008.917299.

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50

Troiano, Luigi, and Irene Díaz. "An Analytical Solution to Dujmovic’s Iterative OWA." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, Suppl. 2 (December 2016): 165–79. http://dx.doi.org/10.1142/s0218488516400158.

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Iterative OWA (ItOWA) as proposed by Dujmovic, is a two-stage procedure for computing the weighting vector by a double nested iteration: (i) weights at step h are computed as limit to infinity of a matrix power, (ii) the result is used to start the computation at step h + 1, until the OWA operator arity n is reached. Thereafter Dujmovic suggested a computational solution based on the conjecture that the limit exists, and numerical simulations have being supported the hypothesis that the conjecture is correct. In this paper, we prove that the limit actually exists and we provide an analytical solution to the procedure, so the weighting vector can be computed directly instead of an iterative time-consuming procedure. This theoretical result enables a faster computation of the weighting vector and characterization in terms of weights values, attitudinal character and entropy.

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