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Journal articles on the topic 'Multi-Group models'

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Author: Grafiati

Published: 8 June 2024

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1

Bichara, Derdei, and Abderrahman Iggidr. "Multi-patch and multi-group epidemic models: a new framework." Journal of Mathematical Biology 77, no. 1 (November 17, 2017): 107–34. http://dx.doi.org/10.1007/s00285-017-1191-9.

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2

Ahmad, Qazi Shoeb, Mohammad Faisal Khan, and Naeem Ahmad. "Multi-Criteria Group Decision-Making Models in a Multi-Choice Environment." Axioms 11, no. 11 (November 21, 2022): 659. http://dx.doi.org/10.3390/axioms11110659.

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The best–worst method (BWM) has recently demonstrated its applicability in addressing various decision-making problems in a practical setting. The traditional BWM method is based on deterministic information gathered from experts as pairwise comparisons of several criteria. The advantage of BWM is that it uses fewer calculations and analyses while maintaining good, acceptable consistency ratio values. A multi-choice best–worst method (MCBWM), which considers several options for pairwise comparison of preferences between the criteria, has recently been developed. The experts are given the option to select values from several comparison scales. The MCBWM technique has been shown to be better. Presenting the options for which an optimal solution has been found simplifies the calculation and establishes the ideal weight values. This study proposes two different mathematical programming models for solving multi-criteria decision-making problems having multiple decision-makers. The two methods are proposed considering the multi-choice uncertainty assumption in pairwise criteria comparisons. Additionally, it considers the best–worst method as the base model. The multi-choice uncertainty is applied to determine the best choice out of multiple choices. It gives a real-life scenario to the decision-making problems. Although there are many other forms of uncertainty, such as fuzzy, intuitionistic fuzzy, neutrosophic, probabilistic, etc., it focuses on choices instead of ambiguity in terms of the probabilistic or fuzzy nature of parameters. The parameter considered as multi-choice is the pairwise comparison. These parameters are handled by applying the Lagrange interpolating polynomial method. The proposed models are novel in terms of their mathematical structure and group decision-making approach. The models are formulated and further validated by solving numerical examples. It provides a framework for solving mcdm problems where the weightage to the decision-makers is also incorporated. The CR values for all the models of example 1 and 2, and the case study has been found acceptable.

3

农, 俊叶. "Stationary Distribution Analysis for Stochastic Multi-Group Models." Advances in Applied Mathematics 10, no. 01 (2021): 1–15. http://dx.doi.org/10.12677/aam.2021.101001.

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4

农, 俊叶. "Stationary Distribution Analysis for Stochastic Multi-Group Models." Advances in Applied Mathematics 10, no. 01 (2021): 1–15. http://dx.doi.org/10.12677/aam.2021.101001.

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5

Al-Shabi, Mohammad, Chaouki Ghenai, Maamar Bettayeb, Fahad Faraz Ahmad, and Mamdouh El Haj Assad. "Estimating PV models using multi-group salp swarm algorithm." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 398. http://dx.doi.org/10.11591/ijai.v10.i2.pp398-406.

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<span id="docs-internal-guid-ea798321-7fff-3e0c-24d7-776c9b1165b3"><span>In this paper, a multi-group salp swarm algorithm (MGSSA) is presented for estimating the photovoltaic (PV) solar cell models. The SSA is a metaheuristic technique that mimics the social behavior of the salp. The salps work in a group that follow a certain leader. The leader approaches the food source and the rest follows it, hence resulting in slow convergence of SSA toward the solution. For several groups, the searching mechanism is going to be improved. In this work, a recently developed algorithm based on several salp groups is implemented to estimate the single-, double-, triple-, Quadruple-, and Quintuple-diode models of a PV solar cell. Six versions of MGSSA algorithms are developed with different chain numbers; one, two, four, six, eight and half number of the salps. The results are compared to the regular particle swarm optimization (PSO) and some of its newly developed forms. The results show that MGSSA has a faster convergence rate, and shorter settling time than SSA. Similar to the inspired actual salp chain, the leader is the most important member in the chain; the rest has less significant effect on the algorithm. Therefore, it is highly recommended to increase the number of leaders and reduce the chain length. Increasing the number of leaders (number of groups) can reduce the root mean squared error (RMSE) and maximum absolute error (MAE) by 50% of its value.</span></span>

6

Cwojdziński, Leszek, Jerzy Lewitowicz, and Andrzej Żyluk. "Models of Air Missions / Modele Misji Lotniczych." Journal of KONBiN 22, no. 1 (June 1, 2012): 35–44. http://dx.doi.org/10.2478/jok-2013-0020.

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Abstract Combat missions of both a single multi-role aircraft and a group of aircraft. Compatibility and readiness - properties determining accomplishment of an air operation. Scenarios of air operations. Models of air-to-air and air-to-surface operations (AAO and ASO, respectively). Computer modelling of air missions.

7

Guo, Ying, Xiaohua Ding, and Yingjian Li. "On input-to-state stability for stochastic multi-group models with multi-dispersal." Applicable Analysis 96, no. 16 (October 21, 2016): 2800–2817. http://dx.doi.org/10.1080/00036811.2016.1246659.

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8

Duan, Rong, Qing Bang Han, and Zuo Wang. "Multi-Attribute Group Decision Making Models under Intuitionistic Fuzzy Environment." Applied Mechanics and Materials 263-266 (December 2012): 3225–29. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.3225.

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In order to solve the problem of multi-attribute group-decision making with the elements of evaluation matrix are intuitionistic fuzzy sets, this paper offers corresponding TOPSIS models based on the information entropy weights and examples to be verified. The examples show the feasibility and effectiveness of the proposed models.

9

Painter, Jay, and Ethan A. Merritt. "TLSMDweb server for the generation of multi-group TLS models." Journal of Applied Crystallography 39, no. 1 (January 12, 2006): 109–11. http://dx.doi.org/10.1107/s0021889805038987.

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TheTLSMDweb server extracts information about dynamic properties of a protein based on information derived from a single-crystal structure. It does so by analyzing the spatial distribution of individual atomic thermal parameters present in an input structural model. The server partitions the protein structure into multiple, contiguous chain segments, each segment corresponding to one group in a multi-group description of the protein's overall dynamic motion. For each polypeptide chain of the input protein, the analysis generates the optimal partition into two segments, three segments, … up to 20 segments. Each such partition is optimal in the sense that it is the best approximation of the overall spatial distribution of input thermal parameters in terms ofNchain segments, each acting as a rigid group undergoing TLS (translation/libration/screw) motion. This multi-group TLS model may be used as a starting point for further crystallographic refinement, or as the basis for analyzing inter-domain and other large-scale motions implied by the crystal structure.

10

Fan, Dejun, Pengmiao Hao, Dongyan Sun, and Junjie Wei. "Global stability of multi-group SEIRS epidemic models with vaccination." International Journal of Biomathematics 11, no. 01 (January 2018): 1850006. http://dx.doi.org/10.1142/s1793524518500067.

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In this paper, a susceptible–exposed–infective–recovered–susceptible (SEIRS) epidemic model with vaccination has been formulated. We studied the global stability of the corresponding single-group model, multi-group model with strongly connected network and multi-group model without strongly connected network by means of analyzing their basic reproduction numbers and the application of Lyapunov functionals. Finally, we provide some numerical simulations to illustrate our analysis results.

11

Ding, Deqiong, and Xiaohua Ding. "Global stability of multi-group vaccination epidemic models with delays." Nonlinear Analysis: Real World Applications 12, no. 4 (August 2011): 1991–97. http://dx.doi.org/10.1016/j.nonrwa.2010.12.015.

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12

Li, Michael Y., Zhisheng Shuai, and Chuncheng Wang. "Global stability of multi-group epidemic models with distributed delays." Journal of Mathematical Analysis and Applications 361, no. 1 (January 2010): 38–47. http://dx.doi.org/10.1016/j.jmaa.2009.09.017.

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13

Lindstrøm, Jonas Christoffer, and Fredrik A. Dahl. "Model Selection with Lasso in Multi-group Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 27, no. 1 (August 14, 2019): 33–42. http://dx.doi.org/10.1080/10705511.2019.1638262.

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14

Liu, Yan, Anran Liu, and Wenxue Li. "Synchronized stationary distribution of stochastic multi-group models with dispersal." Neural Computing and Applications 32, no. 9 (December 7, 2018): 5001–13. http://dx.doi.org/10.1007/s00521-018-3918-y.

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15

Xu, Yao, Teng Lin, and Jiqiang Feng. "Exponential stabilization for fractional intermittent controlled multi-group models with dispersal." Neurocomputing 445 (July 2021): 220–30. http://dx.doi.org/10.1016/j.neucom.2021.02.063.

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16

Zhang, Chunmei, Wenxue Li, and Ke Wang. "Graph-theoretic approach to stability of multi-group models with dispersal." Discrete & Continuous Dynamical Systems - B 20, no. 1 (2015): 259–80. http://dx.doi.org/10.3934/dcdsb.2015.20.259.

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17

Li, Gen, Xiaokang Liu, and Kun Chen. "Integrative multi‐view regression: Bridging group‐sparse and low‐rank models." Biometrics 75, no. 2 (March 29, 2019): 593–602. http://dx.doi.org/10.1111/biom.13006.

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18

Krzanowski, W. J. "Antedependence models in the analysis of multi-group high-dimensional data." Journal of Applied Statistics 26, no. 1 (January 1999): 59–67. http://dx.doi.org/10.1080/02664769922656.

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19

Liu, Qun, Daqing Jiang, Ningzhong Shi, Tasawar Hayat, and Ahmed Alsaedi. "Asymptotic behavior of stochastic multi-group epidemic models with distributed delays." Physica A: Statistical Mechanics and its Applications 467 (February 2017): 527–41. http://dx.doi.org/10.1016/j.physa.2016.10.034.

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20

Ferreira, P. M., L. Lavoura, and João P. Silva. "Renormalization-group constraints on Yukawa alignment in multi-Higgs-doublet models." Physics Letters B 688, no. 4-5 (May 2010): 341–44. http://dx.doi.org/10.1016/j.physletb.2010.04.033.

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21

Kelley, Jonathan, M. D. R. Evans, Jennifer Lowman, and Valerie Lykes. "Group-mean-centering independent variables in multi-level models is dangerous." Quality & Quantity 51, no. 1 (January 28, 2016): 261–83. http://dx.doi.org/10.1007/s11135-015-0304-z.

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22

Fan, Dejun, Pengmiao Hao, and Dongyan Sun. "Global stability of multi-group viral models with general incidence functions." Journal of Mathematical Biology 76, no. 5 (September 9, 2017): 1301–26. http://dx.doi.org/10.1007/s00285-017-1178-6.

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23

Artemov, V. G., and N. S. Nersesian. "Comparison between multi-group and few-group neutronic models of small gas-cooled fast reactors." Nuclear Propulsion Reactor Plants. Life Cycle Management Technologies., no. 2 (2022): 49–60. http://dx.doi.org/10.52069/2414-5726_2022_2_28_49.

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24

Guo, Ying, Wei Zhao, and Xiaohua Ding. "Input-to-state stability for stochastic multi-group models with multi-dispersal and time-varying delay." Applied Mathematics and Computation 343 (February 2019): 114–27. http://dx.doi.org/10.1016/j.amc.2018.07.058.

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25

Bennani, Saloua, Iliass Ait El Kouch, Mahmoud El Hamlaoui, Sophie Ebersold, Bernard Coulette, and Mahmoud Nassar. "A Formalization of Group Decision Making in Multi-Viewpoints Design." Computer and Information Science 13, no. 1 (January 14, 2020): 58. http://dx.doi.org/10.5539/cis.v13n1p58.

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Complex systems are typically designed collaboratively by stakeholders from different domains. This multi viewpoints paradigm promotes the separation of concerns since separate teams, from different business viewpoints, build partial models describing the system. These partial models are naturally heterogeneous. So, it is difficult to ensure their intermodel consistency if kept separately. For that, we propose a collaborative approach that combines Group Decision Making (GDM) and Model-Based Engineering (MBE). This paper highlights the GDM part of our approach and especially the concept of decision policy that enables coming up with collective decisions in group decision-making contexts.

26

Yang, Qingshan, and Xuerong Mao. "Extinction and recurrence of multi-group SEIR epidemic models with stochastic perturbations." Nonlinear Analysis: Real World Applications 14, no. 3 (June 2013): 1434–56. http://dx.doi.org/10.1016/j.nonrwa.2012.10.007.

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27

Liu, Yan, Pinrui Yu, Dianhui Chu, and Huan Su. "Stationary distribution of stochastic multi-group models with dispersal and telegraph noise." Nonlinear Analysis: Hybrid Systems 33 (August 2019): 93–103. http://dx.doi.org/10.1016/j.nahs.2019.01.007.

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28

Wang, Weize, Xinwang Liu, and Yong Qin. "Multi-attribute group decision making models under interval type-2 fuzzy environment." Knowledge-Based Systems 30 (June 2012): 121–28. http://dx.doi.org/10.1016/j.knosys.2012.01.005.

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29

Wang, Yi, and Jinde Cao. "Global dynamics of multi-group SEI animal disease models with indirect transmission." Chaos, Solitons & Fractals 69 (December 2014): 81–89. http://dx.doi.org/10.1016/j.chaos.2014.09.009.

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30

Chen, Tianrui, Zhenyao Sun, and Boying Wu. "Stability of multi-group models with cross-dispersal based on graph theory." Applied Mathematical Modelling 47 (July 2017): 745–54. http://dx.doi.org/10.1016/j.apm.2017.03.055.

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31

Guo, Ying, Xiaohua Ding, and Yingjian Li. "Stochastic stability for pantograph multi-group models with dispersal and stochastic perturbation." Journal of the Franklin Institute 353, no. 13 (September 2016): 2980–98. http://dx.doi.org/10.1016/j.jfranklin.2016.06.001.

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32

Noh, Kyoung Ju, Chi Yoon Jeong, Jiyoun Lim, Seungeun Chung, Gague Kim, Jeong Mook Lim, and Hyuntae Jeong. "Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets." Sensors 21, no. 5 (February 24, 2021): 1579. http://dx.doi.org/10.3390/s21051579.

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Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization.

33

Wang, Zhi-Gang, Rui-Mei Gao, Xiao-Ming Fan, and Qi-Xing Han. "Stability analysis of multi-group deterministic and stochastic epidemic models with vaccination rate." Chinese Physics B 23, no. 9 (September 2014): 090201. http://dx.doi.org/10.1088/1674-1056/23/9/090201.

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34

Wu, Zhibin, Shuai Huang, and Jiuping Xu. "Multi-stage optimization models for individual consistency and group consensus with preference relations." European Journal of Operational Research 275, no. 1 (May 2019): 182–94. http://dx.doi.org/10.1016/j.ejor.2018.11.014.

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35

Vacca, Gian Paolo, Alessandro Codello, Mahmoud Safari, and Omar Zanusso. "Multi-Critical Multi-Field Models: A CFT Approach to the Leading Order." Universe 5, no. 6 (June 13, 2019): 151. http://dx.doi.org/10.3390/universe5060151.

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We present some general results for the multi-critical multi-field models in d > 2 recently obtained using conformal field theory (CFT) and Schwinger–Dyson methods at the perturbative level without assuming any symmetry. Results in the leading non trivial order are derived consistently for several conformal data in full agreement with functional perturbative renormalization group (RG) methods. Mechanisms like emergent (possibly approximate) symmetries can be naturally investigated in this framework.

36

Liu, Guliu, Lei Li, Guanfeng Liu, and Xindong Wu. "Social Group Query Based on Multi-Fuzzy-Constrained Strong Simulation." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (June 30, 2022): 1–27. http://dx.doi.org/10.1145/3481640.

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Traditional social group analysis mostly uses interaction models, event models, or other social network analysis methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interactions between members of the group. Generally, Graph Pattern Matching (GPM) technique is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this article, first, a social group query problem that needs to consider the trust between members of the group is proposed. Then, to solve this problem, a multi-fuzzy-constrained strong simulation matching model is proposed based on multi-constrained simulation, and a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence is proposed. Aiming at the inefficiency of the NTSS algorithm when pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matching edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm.

37

Li, Yuhan. "COVID-19 Diagnosis and Detection Based on Deep Learning Models." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 744–51. http://dx.doi.org/10.54254/2755-2721/8/20230172.

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The COVID-19 pandemic has caused widespread illness and death since its emergence in 2019. This study examines various Artificial Intelligence (AI) techniques for diagnosing and predicting COVID-19. One such method is the Hust 19 model, which employs a hybrid learning architecture of CNN and DNN models. The model divides CT scans into three types that identify COVID-19-related imaging features and implements a deep learning framework based on the VGG16 architecture. Another group of researchers developed a deep learning-based COVID-19 diagnostic system using multi-class and multi-center data, segmenting lungs and identifying COVID-19 infection slices. They evaluated the model's accuracy using Receiver Operating Characteristic (ROC) curves. A third group developed a deep neural network based on DenseNet121, standardizing input CXR images through anatomical landmark detection and registration, and segmenting lung lesions to diagnose pneumonia. A final group developed a three-dimensional deep learning model called COVNet, which takes CT images as input, extracts features from each slice using the ResNet50 backbone, merges the maximum features obtained by the AI model, and generates classification predictions for the entire CT scan. They also proposed a multi-decoder split network to improve the model's accuracy and efficiency. Experimental results show that the Deep learning AI system model and COVNet model are relatively good, with average sensitivity and specificity. The remaining models, particularly Hust-19, show prominent specificity, but high specificity leads to low sensitivity, making the overall model imbalanced. These AI diagnostic models are just the beginning, and there may be more inventions and creations in the future.

38

Yusubov, N. D., and H. M. Abbasova. "Models for Machining Accuracy in Multi-Tool Adjustment." International Journal of Automotive and Mechanical Engineering 17, no. 3 (October 6, 2020): 8067–85. http://dx.doi.org/10.15282/ijame.17.3.2020.01.0605.

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The article discusses the technology capabilities of multi-purpose CNC machines, and possible options for implementing parallel multi-tool processing. It was revealed that the technological capabilities of these machines are used at best by 50% in factories. This is due to the lack of recommendations for the design and use of such adjustments for these machines. To this end, generalised lattice matrix models of the accuracy of multi-tool machining have been developed in order to fulfill the requirements of algorithmic uniformity models and their structural transparency. The use of lattice matrices greatly simplifies the error in model of multi-tool machining and makes it extremely visual. Also, full-factorial distortion models and scattering fields of the dimensions of multi-tool machining performed on modern multi-purpose CNC lathe machines have been developed to take into account the angular displacements of the workpiece when machining parts with prevailing overall dimensions. They take into account the flexibility of the technological system for all six degrees of freedom to identify the influence degree of complex of technological factors on the machining accuracy (structure of multi-tool adjustment, deformation properties of subsystems of a technological system, cutting conditions). A methodology has been developed for determining the complex characteristics of compliance of a technological system. On the basis of the developed accuracy models in spatial adjustments, it is possible to develop recommendations for the design of adjustments for modern multi-purpose machines in CNC turning group (creation of CAD of multi-tool machining). Thus, it is possible to achieve a number of ways to control multi-tool machining, including improving the structure of multi-tool adjustment, calculating the limiting cutting conditions.

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Wang, Jinliang, Xianning Liu, Toshikazu Kuniya, and Jingmei Pang. "Global stability for multi-group SIR and SEIR epidemic models with age-dependent susceptibility." Discrete & Continuous Dynamical Systems - B 22, no. 7 (2017): 2795–812. http://dx.doi.org/10.3934/dcdsb.2017151.

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Lu, Bin, Xin-Yuan Song, and Xin-Dan Li. "Bayesian analysis of multi-group nonlinear structural equation models with application to behavioral finance." Quantitative Finance 12, no. 3 (March 2012): 477–88. http://dx.doi.org/10.1080/14697680903369500.

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41

Shu, Hongying, Dejun Fan, and Junjie Wei. "Global stability of multi-group SEIR epidemic models with distributed delays and nonlinear transmission." Nonlinear Analysis: Real World Applications 13, no. 4 (August 2012): 1581–92. http://dx.doi.org/10.1016/j.nonrwa.2011.11.016.

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42

Iggidr, Aberrahman, Gauthier Sallet, and Max O. Souza. "On the dynamics of a class of multi-group models for vector-borne diseases." Journal of Mathematical Analysis and Applications 441, no. 2 (September 2016): 723–43. http://dx.doi.org/10.1016/j.jmaa.2016.04.003.

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43

Yuan, Chengjun, Daqing Jiang, Donal O’Regan, and Ravi P. Agarwal. "Stochastically asymptotically stability of the multi-group SEIR and SIR models with random perturbation." Communications in Nonlinear Science and Numerical Simulation 17, no. 6 (June 2012): 2501–16. http://dx.doi.org/10.1016/j.cnsns.2011.07.025.

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44

Rifhat, Ramziya, Kai Wang, Lei Wang, Ting Zeng, and Zhidong Teng. "Global stability of multi-group SEIQR epidemic models with stochastic perturbation in computer network." Electronic Research Archive 31, no. 7 (2023): 4155–84. http://dx.doi.org/10.3934/era.2023212.

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<abstract><p>In this paper, a class of multi-group SEIQR models with random perturbation in computer network is investigated. The existence and uniqueness of global positive solution with any positive initial value are obtained. The sufficient conditions on the asymptotic behavior of solutions around the disease-free equilibrium and endemic equilibrium of the corresponding deterministic model are established. Furthermore, the existence and uniqueness of stationary distribution are also obtained. Lastly, the analytical results are illustrated by the numerical simulations.</p></abstract>

45

Gniewosz, Burkhard, and Gabriela Gniewosz. "How to model intraindividual change in cohort data using Mplus’ multi-group approach." International Journal of Behavioral Development 42, no. 3 (January 9, 2018): 373–80. http://dx.doi.org/10.1177/0165025417750282.

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The present article aims to show how to model longitudinal change in cohort sequential data applying latent true change models using Mplus’ multi-group approach. The underlying modeling ideas are described and explained in this article. As an example, change in internalizing problem behaviors between the age of 8 and 13 years is modeled and predicted by gender. The example data stems from a large German cohort sequential study and comprises 806 children in three birth cohorts (2001, 2000, 1999). Finally, the advantages and disadvantages of this modelling approach are discussed. Annotated syntax is provided online for the models.

46

Gul, Rizwan, and Muhammad Shabir. "(α, β)-Multi-granulation bipolar fuzzified rough sets and their applications to multi criteria group decision making." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 2025–60. http://dx.doi.org/10.3233/jifs-210717.

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Pawlak’s rough set theory based on single granulation has been extended to multi-granulation rough set structure in recent years. Multi-granulation rough set theory has become a flouring research direction in rough set theory. In this paper, we propose the notion of (α, β)-multi-granulation bipolar fuzzified rough set ((α, β)-MGBFRSs). For this purpose, a collection of bipolar fuzzy tolerance relations has been used. In the framework of multi-granulation, we proposed two types of (α, β)-multi-granulation bipolar fuzzified rough sets model. One is called the optimistic (α, β)-multi-granulation bipolar fuzzified rough sets ((α, β) o-MGBFRSs) and the other is called the pessimistic (α, β)-multi-granulation bipolar fuzzified rough sets ((α, β) p-MGBFRSs). Subsequently, a number of important structural properties and results of proposed models are investigated in detail. The relationships among the (α, β)-MGBFRSs, (α, β) o-MGBFRSs and (α, β) p-MGBFRSs are also established. In order to illustrate our proposed models, some examples are considered, which are helpful for applying this theory in practical issues. Moreover, several important measures associated with (α, β)-multi-granulation bipolar fuzzified rough set like the measure of accuracy, the measure of precision, and accuracy of approximation are presented. Finally, we construct a new approach to multi-criteria group decision-making method based on (α, β)-MGBFRSs, and the validity of this technique is illustrated by a practical application. Compared with the existing results, we also expound its advantages.

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Zhang, Chunmei, Dan Xia, Huiling Chen, Hui Yang, Ran Li, and Nallappan Gunasekaran. "Identifying Partial Topological Structures of Stochastic Multi-Group Models with Multiple Dispersals via Graph-Theoretic Method." Fractal and Fractional 6, no. 7 (July 1, 2022): 371. http://dx.doi.org/10.3390/fractalfract6070371.

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In this paper, the partial topology identification of stochastic multi-group models with multiple dispersals is investigated. Based on adaptive pinning control and a graph-theoretic method, some sufficient criteria about partial topology identification of stochastic multi-group models with multiple dispersals are obtained. That is to say, the unknown partial topological structures can be identified successfully. In the end, numerical examples are provided to verify the effectiveness of theoretical results.

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Zhang, Chao, Deyu Li, Xiangping Kang, Yudong Liang, Said Broumi, and Arun Kumar Sangaiah. "Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information." Mathematics 8, no. 2 (February 9, 2020): 223. http://dx.doi.org/10.3390/math8020223.

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In plenty of realistic situations, multi-attribute group decision-making (MAGDM) is ubiquitous and significant in daily activities of individuals and organizations. Among diverse tools for coping with MAGDM, granular computing-based approaches constitute a series of viable and efficient theories by means of multi-view problem solving strategies. In this paper, in order to handle MAGDM issues with interval-valued neutrosophic (IN) information, we adopt one of the granular computing (GrC)-based approaches, known as multigranulation probabilistic models, to address IN MAGDM problems. More specifically, after revisiting the related fundamental knowledge, three types of IN multigranulation probabilistic models are designed at first. Then, some key properties of the developed theoretical models are explored. Afterwards, a MAGDM algorithm for merger and acquisition target selections (M&A TSs) with IN information is summed up. Finally, a real-life case study together with several detailed discussions is investigated to present the validity of the developed models.

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S, Mr ASWIN. "MULTI LINGUAL ASR WITH TRANSFORMERS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 27, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34841.

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Recent research has focused on developing multilingual automatic speech recognition (ASR) systems using Transformer-based models. These models aim to address challenges in training and deploying ASR systems for low- resource languages, adapting to multiple domains and languages, and reducing operational costs. Strategies such as locale-group multilingual Transformer language models, adaptable multi- domain language models, and configurable multilingual models have been proposed to improve the performance and efficiency of multilingual ASR. These advancements demonstrate a concerted effort to overcome the challenges of low-resource languages, domain adaptation, and multilingual speech recognition.

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Feng, Xiaomei, Zhidong Teng, and Fengqin Zhang. "Global dynamics of a general class of multi-group epidemic models with latency and relapse." Mathematical Biosciences and Engineering 12, no. 1 (2015): 99–115. http://dx.doi.org/10.3934/mbe.2015.12.99.

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