Online banking performance evaluation using data envelopment analysis and principal component analysis

This paper presents a hybrid approach to conducting performance measurements for Internet banking by using data envelopment analysis (DEA) and principal components analysis (PCA). For each bank, DEA is applied to compute an aggregated efficiency score based on outputs, such as web metrics and revenue; and inputs, such as equipment, operation cost and employees. The 45 combinations of DEA efficiencies of the studied banks are calculated, and used as a ranking mechanism. PCA is used to apply relative efficiencies among the banks, and to classify them into different groups in terms of operational orientations, i.e., Internet banking and cost efficiency focused orientations. Identification of operational fitness and business orientation of each firm, in this way, will yield insights into understanding the weaknesses and strengths of banks, which are considering moving into Internet banking.     

Finger-vein pattern identification using principal component analysis and the neural network technique

This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.     

A hybrid radial basis function and data envelopment analysis neural network for classification

We propose a hybrid radial basis function network-data envelopment analysis (RBFN-DEA) neural network for classification problems. The procedure uses the radial basis function to map low dimensional input data from input space ℜ to a high dimensional ℜ⁺ feature space where DEA can be used to learn the classification function. Using simulated datasets for a non-linearly separable binary classification problem, we illustrate how the RBFN-DEA neural network can be used to solve it. We also show how asymmetric misclassification costs can be incorporated in the hybrid RBFN-DEA model. Our preliminary experiments comparing the RBFN-DEA with feed forward and probabilistic neural networks show that the RBFN-DEA fares very well.     

基于PCA和神经网络的识别方法研究

在计算机集成制造系统环境下,质量控制图是统计过程控制的重要工具,实际应用中最困难的是识别出控制图中由于异常因素造成的不同异常模式。针对这一问题展开研究,用主成分分析法作为前处理过程进行样本集的选择与优化,提出了基于PCA_改进BP算法的控制图模式智能识别方法。     

Dynamic weight estimation using an artificial neural network

A Multi-Layer Perceptron Artificial Neural Network is employed to enable the mass that is applied to a weighing platform to be rapidly and accurately estimated before the platform has settled to the steady state. This is achieved through training the network on a set of waveforms resulting from applied masses over the operating range of the weighing platform. Results are given for both simulated and experimental data that confirm the success of the method.     

基于PCA的GABP神经网络入侵检测方法*

为克服BP算法易陷入局部最小的缺点,同时为减少样本数据维数,提出一种基于主成分分析(PCA)的遗传神经网络方法。通过降维和去相关加快收敛速度,采用改进的遗传算法优化神经网络权值,利用自适应学习速率动量梯度下降算法对神经网络进行训练。MATLAB仿真实验结果表明,该方法在准确性和收敛性方面都优于BP算法,应用于入侵检测系统中的检测率和误报率明显优于传统方法。     

Intelligent customer complaint handling utilising principal component and data envelopment analysis (PDA)

In this study, we consider customer to be a company's crucial asset. In order to have a fast, efficient decision-making process, it is vital that a customer relationship management (CRM) decision-maker condenses and abstracts the existing information. A questionnaire survey was conducted among respondents in order to obtain the required data. The questionnaire contains nine categories of satisfaction variables. To perform the analysis, we used principal component analysis (PCA) and data envelopment analysis (DEA). PDA has been utilised as an abbreviation for the integration of these two methods. To effectively analyse the procedure, PCA was utilised to assign a number to each category of questions related to each satisfaction variable. To achieve optimal precision, DEA was applied to the three categories of customers (‘most important’, ‘important’ and ‘ordinary’ customers) in order to determine the strengths and weaknesses of customer services from these customers’ perspectives. Customers were clustered and then DEA was used to determine their viewpoints. Using DEA, we have optimised our recognition of customers’ complaints and then provided recommendations and remedial actions to resolve the current issues in logistics and transport industry in general, and at Fremantle port in particular.SignificanceThe current study integrates soft computing and optimisation technique in order to build the CRM recommender system. It demonstrates the hybrid soft computing strengthens in area of CRM as the relevance solution. The significance of the proposed algorithm is three fold. First, it integrates soft computing and optimisation technique in order to build the CRM recommender system. Second, it utilises the most standard CRM variables in its decision making process. Third, it is an optimising algorithm because it integrates DEA with PCA technique.     

An evolving neural network to perform dynamic principal component analysis

Nonlinear principal component analysis is one of the best dimension reduction techniques developed during the recent years which have been applied in different signal-processing applications. In this paper, an evolving category of auto-associative neural network is presented which is applied to perform dynamic nonlinear principal component analysis. Training strategy of the network implements both constructive and destructive algorithms to extract dynamic principal components of speech database. In addition, the proposed network makes it possible to eliminate some dimensions of sequences that do not play important role in the quality of speech processing. Finally, the network is successfully applied to solve missing data problem.     

Cost estimation in road construction using artificial neural network

Neural Computing and Applications - Road construction projects on the territory of the Republic of Croatia are characterized by the overrun of planned costs. The experience of the contractor on...     

Intestinal broiler microflora estimation by artificial neural network

Microflora population of poultry was affected by various factors. Many methods and techniques were developed to study microflora population. But, most of them confronted some problems. Moreover, being costly, laborious, and time-consuming made it impossible to measure microflora population several times. In this study, we tried to estimate intestinal microflora population using artificial neural network (ANN). Lactic acid bacteria were used as model of microflora population. Time and lactic acid bacteria were used as input and output variables, respectively. The best model of ANN was determined based on coefficient of determination, root mean square error, and mean absolute error criteria. The results of current study have shown that ANN is appropriate, cheap, and reliable tools to estimate intestinal microflora population (lactic acid bacteria) of broiler at different ages.     

Using online textual data,principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms

In this paper we introduce a method that combines principal component analysis, correlation analysis, K-means clustering and self organizing maps for the quantitative semantic analysis of textual data focusing on the relationship between firms’ co-creation activities, the perception of their innovation and the articulation of the attributes of their product-enabled services. Principal component analysis was used to identify the components of firms’ value co-creation activities and service value attributes; correlation analysis was used to examine the relationship between the degree of involvement in specific co-creation activities, the online articulation of firms’ service value attributes and the perception of their innovativeness. K-means and self organizing map (SOM) are used to cluster firms with regards to their involvement in co-creation and new service development, and, additionally, as complementary tools for studying the relationship between co-creation and new service development.The results show that, first, there is a statistically significant relationship between firms’ degree of involvement in co-creation activities and the degree of articulation of their service value attributes; second, the relationship should be considered within the context of firms’ innovation activities; third, OS Software-driven firms are the best example in terms of co-creation and new product-enabled service development, i.e. the collaborative principles built in their customer participation platforms should be adopted by other (non-software) firms interested in enhancing their innovation capacity through involvement in co-creation and new product-enabled service development.     

基于主成分分析和BP神经网络的煤岩界面识别

针对现有煤岩识别方法由于提取的时域参数过多,存在识别速度慢、实时性差等问题,提出了一种基于主成分分析和BP神经网络的煤岩界面识别方法。该方法首先提取采煤机滚筒扭矩的时域信号,然后利用主成分分析方法对该时域信号进行压缩,最后将得到的最终信号输入到BP神经网络进行煤岩识别。仿真结果表明,该煤岩识别方法不仅满足了识别率,还提高了识别速度,为提高滚筒调高响应速度奠定了基础。     

A system to diagnose atherosclerosis via wavelet transforms,principal component analysis and artificial neural networks

In this study, Doppler ultrasound signals were acquired from carotid arteries of 82 patients with atherosclerosis and 95 healthy volunteers. We have employed discrete wave transform (DWT) of Doppler signals and power spectral density graphics of these decomposed signals using Welch method. After that, we have performed Principal component analysis (PCA) for data reduction and ANN in order to distinguish between atherosclerosis and healthy subjects.After the training phase, testing of the artificial neural network (ANN) was established. The overall results show that 97.9% correct classification was achieved, whereas two false classifications have been observed for the test group of 97 people.In conclusion we are proposing a complimentary expert system that can be coupled to software of the ultrasonic Doppler devices. The diagnosis performances of this study show the advantages of this system: it is rapid, easy to operate, noninvasive, inexpensive and making a decision without hesitation.     

一种基于PCA的MPSK信号码元速率估计方法

针对MPSK信号的码元速率估计问题, 研究了有限数据条件下循环谱的谱线特征受到背景色噪声干扰的现象, 提出了一种基于主分量分析(PCA)的循环谱特征码元速率估计方法。PCA变换抑制了信号循环谱中的背景色噪声, 提高了估计精度, 减小了估计方差。仿真表明, 该方法在有限数据条件下具有良好的估计性能, 适用于不同成形滤波系数的MPSK信号。     

基于改进二维主成分分析及神经网络的人耳识别方法

针对人耳识别特征提取阶段二维主成分分析算法(2DPCA)所提取的人耳特征维数较大,从而造成实时性差、数据存储空间不足等问题提出了一种改进方法。该方法首先对人耳图片进行预处理,然后采用改进的两级2DPCA算法,进一步压缩提取的人耳特征维数,最后采用BP神经网络进行分类识别。实验表明,将改进的两级2DPCA算法同BP神经网络相结合,具有较好的实时性,同时节约了特征数据的存储空间,并保持了较好的识别率。     

Filtering method of rock points based on BP neural network and principal component analysis

Filtering is an essential step in the process of obtaining rock data. To the best of our knowledge, there are no special algorithms for use in the point clouds of rock masses. Existing filtering methods remove noisy points by fitting the surface of the ground and deleting the points above the surface around a range of values. This type of methods has certain limitations in rock engineering owing the uniqueness of the particular rockmass being studied. In this paper, a method for filtering the rock points is proposed based on a backpropagation (BP) neural network and principal component analysis (PCA). In the proposed method, a PCA is applied for feature extraction, and for obtaining the dimensional information, which can be used to effectively distinguish the rock and other points at different scales. A BP neural network, which has a strong nonlinear processing capability, is then used to obtain the exact points of rock with the above characteristics. In the present paper, the efficiency of the proposed technique is illustrated by classifying steep rocky slopes as rock and vegetation. A comparison with existing methods indicates the superiority of the proposed method in terms of the point cloud filtering of rock masses.     

On the sensitivity of the neural network implementing the principal component analysis method

The efficiency of the proposed modification of the neural network implementing the principal component analysis (PCA) method is studied. A known neural network—the Hebbian filter—is chosen for the basic method. A test problem that allows varying the complexity of the input vectors is used to generate objects for testing both networks. Three series of experiments were conducted to compare the estimated efficiency of the Hebbian filter and the proposed architecture. The results of the experiments show the proposed modification to have an advantage for all the problems involved.     

A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty

To assess sustainability of power plants, this paper presents a novel hybrid method. To this end, self‐organizing map method of artificial neural networks is employed. Then, a double frontier data envelopment analysis is developed to rank power plants in each cluster of decision‐making units. Because outputs of power plants might be uncertain, a robust optimization approach is incorporated into proposed double frontier data envelopment analysis model to present ranks that are robust against different uncertainties. A case study is given to validate the proposed model. The case study shows that the proposed model can present improvement solutions that guide power plants towards efficient frontier and far from inefficient frontier. Given the results, decision makers can decide on which power plants should be closed and which power plants should be expanded.     

Personnel selection using analytic network process and fuzzy data envelopment analysis approaches

This paper develops a decision support tool using an integrated analytic network process (ANP) and fuzzy data envelopment analysis (DEA) approach to effectively deal with the personnel selection problem drawn from an electric and machinery company in Taiwan. The current personnel selection procedure is a separate two-stage method. The administration practice shows that the separation between stages 1 and 2 reduces the administration quality and may incur both the top manager’s displeasure and the decision-makers’ depression. An illustrative example by a simulated application demonstrates the implementation of the proposed approach. This example demonstrates how this approach can avoid the main drawback of the current method, and more importantly, can deal with the personnel selection problem more convincingly and persuasively. This study supports the applications of ANP and fuzzy DEA as decision support tools in personnel selection.     

Personnel selection using analytic network process and fuzzy data envelopment analysis approaches

This paper develops a decision support tool using an integrated analytic network process (ANP) and fuzzy data envelopment analysis (DEA) approach to effectively deal with the personnel selection problem drawn from an electric and machinery company in Taiwan. The current personnel selection procedure is a separate two-stage method. The administration practice shows that the separation between stages 1 and 2 reduces the administration quality and may incur both the top manager’s displeasure and the decision-makers’ depression. An illustrative example by a simulated application demonstrates the implementation of the proposed approach. This example demonstrates how this approach can avoid the main drawback of the current method, and more importantly, can deal with the personnel selection problem more convincingly and persuasively. This study supports the applications of ANP and fuzzy DEA as decision support tools in personnel selection.