As one of the most effective ways to alleviate energy crisis and environmental pollution, the renewable energy sources (RESs) have received increasing attention. Different RESs enjoy different characteristics and are ...
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As one of the most effective ways to alleviate energy crisis and environmental pollution, the renewable energy sources (RESs) have received increasing attention. Different RESs enjoy different characteristics and are suitable for different scenarios, thus it is essential to evaluate them before installation. Due to the increasing complexity of reality, the RESs evaluation usually involves various risks and large-scale group decision makers. To manage these risks and decision makers, this paper proposes an interval type-2 fuzzy large-scale group risk evaluation method. First, the interval type-2 fuzzy sets (IT2FSs) are employed to encode the qualitative information provided by the decision makers. Then, a new clustering approach integrating consensus reaching model and risk measurement model is developed to manage the decision makers and enhance the evaluation efficiency. After the clustering process, the selection procedure is activated and an interval type-2 fuzzy centroid-based ranking method is presented to rank the candidate RESs. Finally, a case study in China is provided to illustrate the effectiveness of the proposed method and comparisons are also made to verify the advantages. (C) 2021 Elsevier B.V. All rights reserved.
High maternal and child deaths in developing countries are frequently linked to poor health services provided to pregnant women and children. To improve the quality of maternal, neonatal and child health (MNCH) servic...
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High maternal and child deaths in developing countries are frequently linked to poor health services provided to pregnant women and children. To improve the quality of maternal, neonatal and child health (MNCH) services, the government and other stakeholders in MNCH emphasize the importance of quality assessment. However, effective quality assessment approaches are mostly lacking in most developing countries, particularly in Tanzania. This study, therefore, aimed at developing a quality assessment approach that can effectively assess and report on the quality of MNCH services. Due to the need for a good quality assessment approach that suits a resource-constrained environment, machine learning-based approach was proposed and developed. K-means algorithm was used to develop a clustering model that groups MNCH data and performs cluster summarization to discover the knowledge portrayed in each group on the quality of MNCH services. Results confirmed the clustering model’s ability to assign the data points into appropriate clusters;cluster analysis with the collaboration of MNCH experts successfully discovered insights on the quality of services portrayed by each group.
Membrane computing, also known as P system, is a distributed and parallel computation framework models. Hierarchical clustering is one of the most basic and widely applied clustering algorithms among all clustering al...
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Membrane computing, also known as P system, is a distributed and parallel computation framework models. Hierarchical clustering is one of the most basic and widely applied clustering algorithms among all clustering algorithms. In this paper, the combination of membrane computing and hierarchical clustering algorithm is studied. A cell-like hierarchical clustering P system with priority evolution rules and promoters is designed by using the maximum parallelism of membrane computing. The feasibility and effectiveness of the designed P system are verified by the examples.
Many models for inference of population genetic parameters are based on the assumption that the data set at hand consists of groups displaying within-group Hardy-Weinberg equilibrium at individual loci and linkage equ...
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Many models for inference of population genetic parameters are based on the assumption that the data set at hand consists of groups displaying within-group Hardy-Weinberg equilibrium at individual loci and linkage equilibrium between loci. This assumption is commonly violated by the presence of within-group spatial structure arising from nonrandom mating of individuals due to isolation by distance (IBD). This paper proposes a model and simulation method implemented in a computer program to flexibly simulate data displaying such patterns. The program permits displaying of smooth spatial variations of allele frequencies due to IBD and more abrupt variations due to presence of strong barriers to gene flow. It is useful in assessing performance of various statistical inference methods and in designing spatial sampling schemes. This is shown by a simulation study aimed at assessing the extent to which IBD patterns affect accuracy of cluster inferences performed in models assuming panmixia. The program is also used to study the effects of spatial sampling scheme (e.g. sampling individuals in clumps or uniformly across the spatial domain). The accuracy of such inferences is assessed in terms of number of inferred populations, assignment of individuals to populations and location of borders between populations. The effect of spatial sampling was weak while the effect of IBD may be substantial, leading to the inference of spurious populations, especially when IBD was strong with respect to the size of the sampling domain. The model and program are new and have been embedded in the R package Geneland, for user convenience and compliance with existing data formats.
The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy pr...
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The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy projection clustering model, aimed at enhancing the efficacy of high-frequency trading systems. A composite distribution model of financial data is formulated to derive sequences of financial data activities. And cross-adaptive learning algorithm is employed to ascertain the interrelated attributes of financial data. Following this, the support vector machine algorithm is applied for the classification processing of these interrelated features, yielding a set of financial data feature vectors, which are then fed into the gray correlation-based information feature extraction model. Through extensive empirical evaluations with authentic trading data, the proposed intelligent financial system design framework exhibits commendable performance, furnishing a viable solution for the intelligent optimization of high-frequency trading systems.
A BS process involves building a model of the background and extracting regions of the foreground(moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background....
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A BS process involves building a model of the background and extracting regions of the foreground(moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. Video object extraction is a critical task in multimedia analysis and editing. Normally, the user provides some hints of foreground and background, and then the target object is extracted from the video sequence. In this paper, we propose a object segmentation system that integrates a clustering model with Markov random field-based contour tracking and graph-cut image segmentation. The contour tracking propagates the shape of the target object, whereas the graph-cut refines the shape and improves the accuracy of video segmentation. Experimental results show that our segmentation system is efficient.
A BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background...
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A BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. Video object extraction is a critical task in multimedia analysis and editing. Normally, the user provides some hints of foreground and background, and then the target object is extracted from the video sequence. In this paper, we propose a object segmentation system that integrates a clustering model with Markov random field-based contour tracking and graph- cut image segmentation. The contour tracking propagates the shape of the target object, whereas the graph-cut refines the shape and improves the accuracy of video segmentation. Experimental results show that our segmentation system is efficient.
A simple BS process involves building a model of the background and extracting regions of the foreground(moving objects) with the assumptions that the camera remains stationary and there exist no movements in the *** ...
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A simple BS process involves building a model of the background and extracting regions of the foreground(moving objects) with the assumptions that the camera remains stationary and there exist no movements in the *** object extraction is a critical task in multimedia analysis and ***,the user provides some hints of foreground and background,and then the target object is extracted from the video *** this paper,we propose a object segmentation system that integrates a clustering model with Markov random field-based contour tracking and graphcut image *** contour tracking propagates the shape of the target object,whereas the graph-cut refines the shape and improves the accuracy of video *** results show that our segmentation system is efficient.
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