Real-world data collected for computer-based applications are frequently impure. Differentiation of outliers and noisy data
from normal ones is a major task in data mining applications. On the other hand, elimination of noisy and outlier data from
training samples of a dataset may lead to over-fitting or information loss. A fuzzy support vector machine (FSVM) provides
an effective means to deal with this problem. It reduces the effect of the noisy data and outliers by using a fuzzy membership
functions. In this paper, a new formation for SVMs is introduced that considers importance degrees for training samples. The
constraints of the SVM are converted to fuzzy inequalities. The proposed method, RSVM, shows better efficiency in the classification
of data in different domains. Especially, using the proposed RSVM for multi-class classification of arrhythmia disease is
presented at the end of this paper as a practical case study to show the effectiveness of the proposed system. 相似文献
The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, there are relatively few studies on retrieval of Persian documents in the literature and one of the main reasons is the lack of a standard test collection. In this paper, we introduce a standard Persian text collection, named Hamshahri, which is built from a large number of newspaper articles according to TREC specifications. Furthermore, statistical information about documents, queries and their relevance judgments are presented in this paper. We believe that this collection is the largest Persian text collection, so far. 相似文献
This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three‐stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels. 相似文献
In the present study, Multi-objective optimization of composite cylindrical shell under external hydrostatic pressure was investigated. Parameters of mass, cost and buckling pressure as fitness functions and failure criteria as optimization criterion were considered. The objective function of buckling has been used by performing the analytical energy equations and Tsai-Wu and Hashin failure criteria have been considered. Multi-objective optimization was performed by improving the evolutionary algorithm of NSGA-II. Also the kind of material, quantity of layers and fiber orientations have been considered as design variables. After optimizing, Pareto front and corresponding points to Pareto front are presented. Trade of points which have optimized mass and cost were selected by determining the specified pressure as design criteria. Finally, an optimized model of composite cylindrical shell with the optimum pattern of fiber orientations having appropriate cost and mass is presented which can tolerate the maximum external hydrostatic pressure.
The widespread usage of image fusion causes an increase in the importance of assessing the performance of different fusion algorithms. The problem of introducing a suitable quality measure for image fusion lies in the difficulty of defining an ideal fused image. In this paper, we propose a non-reference objective image fusion metric based on mutual information which calculates the amount of information conducted from the source images to the fused image. The considered information is represented by image features like gradients or edges, which are often in the form of two-dimensional signals. In this paper, a method of estimating the joint probability distribution from marginal distributions is also presented which is employed in calculation of mutual information. The proposed method is compared with the most popular existing algorithms. Various experiments, performed on several databases, certify the efficiency of our proposed method which is more consistent with the subjective criteria. 相似文献
Separation of particles from liquid in the large gravitational tanks is widely used in mining and industrial wastewater treatment process. Thickener is key unit in the operational processes of hydrometallurgy and is used to separate solid from liquid. In this study, population balance models were combined with computational fluid dynamics (CFD) for modeling the tailing thickener. Parameters such as feed flow rate, flocculant dosage, inlet solid percent and feedwell were investigated. CFD was used to simulate the industrial tailing thickener with settled bed of 120 m diameter which is located in the Sarcheshmeh copper mine. Important factor of drag force that defines the rake torque of rotating paddles on the bed was also determined. Two phases turbulence model of Eulerian/Eulerian in accordance with turbulence model of k-ε was used in the steady-state. Also population balance model consists of 15 groups of particle sizes with Luo and Lehr kernel was used for aggregation/breakage kernel. The simulation results showed good agreement with the operational data. 相似文献
This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) can affect the ability of
neurons to deal with classification. Most of the common neuron structures are based on monotonic activation functions and
linear input mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation function and/or a nonlinear
input mapping to increase the power of a neuron. An MLP of these high power neurons usually requires a less number of hidden
nodes than conventional MLP for solving classification problems. The fewer number of neurons is equivalent to the smaller
number of network weights that must be optimally determined by a learning algorithm. The performance of learning algorithm
is usually improved by reducing the number of weights, i.e., the dimension of the search space. This usually helps the learning
algorithm to escape local optimums, and also, the convergence speed of the algorithm is increased regardless of which algorithm
is used for learning. Several 2-dimensional examples are provided manually to visualize how the number of neurons can be reduced
by choosing an appropriate neuron structure. Moreover, to show the efficiency of the proposed scheme in solving real-world
classification problems, the Iris data classification problem is solved using an MLP whose neurons are equipped by nonmonotonic
activation functions, and the result is compared with two well-known monotonic activation functions. 相似文献
We consider the problem of assuring the trustworthiness (i.e. reliability and robustness) and prolonging the lifetime of wireless ad hoc networks, using the OLSR routing protocol, in the presence of selfish nodes. Assuring the trustworthiness of these networks can be achieved by selecting the most trusted paths, while prolonging the lifetime can be achieved by (1) reducing the number of relay nodes (MPR) propagating the topology control (TC) messages and (2) considering the residual energy levels of these relay nodes in the selection process. In this paper, we propose a novel clustering algorithm and a relay node selection algorithm based on the residual energy level and connectivity index of the nodes. This hybrid model is referred to as H-OLSR. The OLSR messages are adapted to handle the cluster heads election and the MPR nodes selection algorithms. These algorithms are designed to cope with selfish nodes that are getting benefits from others without cooperating with them. Hence, we propose an incentive compatible mechanism that motivates nodes to behave truthfully during the selection and election processes. Incentive retributions increase the reputation of the nodes. Since network services are granted according to nodes’ accumulated reputation, the nodes should cooperate. Finally, based on nodes’ reputation, the most trusted forwarding paths are determined. This reputation-based hybrid model is referred to as RH-OLSR. Simulation results show that the novel H-OLSR model based on energy and connectivity can efficiently prolong the network lifetime, while the RH-OLSR model improves the trustworthiness of the network through the selection of the most trusted paths based on nodes’ reputations. These are the two different processes used to define the reputation-based clustering OLSR (RBC-OLSR) routing protocol. 相似文献