OWA rough set model for forecasting the revenues growth rate of the electronic industry

Business operation performance is related to corporation profitability and directly affects the choices of investment in the stock market. This paper proposes a hybrid method, which combines the ordered weighted averaging (OWA) operator and rough set theory after an attribute selection procedure to deal with multi-attribute forecasting problems with respect to revenue growth rate of the electronic industry. In the attribute selection step, four most-important attributes within 12 attributes collected from related literature are determined via five attribute selection methods as the input of the following procedure of the proposed method. The OWA operator can adjust the weight of an attribute based on the situation of a decision-maker and aggregate different attribute values into a single aggregated value of each instance, and then the single aggregated values are utilized to generate classification rules by rough set for forecasting operation performance.To verify the proposed method, this research collects the financial data of 629 electronic firms for public companies listed in the TSE (Taiwan Stock Exchange) and OTC (Over-the-Counter) market in 2004 and 2005 to forecast the revenue growth rate. The results show that the proposed method outperforms the listing methods.     

Fusion ANFIS models based on multi-stock volatility causality for TAIEX forecasting

Stock market investors value accurate forecasting of future stock price from trading systems because of the potential for large profits. Thus, investors use different forecasting models, such as the time-series model, to assemble a superior investment portfolio. Unfortunately, there are three major drawbacks to the time-series model: (1) most statistical methods rely on some assumptions about the variables; (2) most conventional time-series models use only one variable in forecasting; and (3) the rules mined from artificial neural networks are not easily understandable. To address these shortcomings, this study proposes a new model based on multi-stock volatility causality, a fusion adaptive-network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price problems in Taiwan. Furthermore, to illustrate the proposed model, three practical, collected stock index datasets from the USA and Taiwan stock markets are used in the empirical experiment. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error, and further evaluation reveals that the profits comparison results for the proposed model produce higher profits than the listing models.     

Covering-based rough set classification system

Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

     

A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting

Stock market forecasting is important and interesting, because the successful prediction of stock prices may promise attractive benefits. The economy of Taiwan relies on international trade deeply, and the fluctuations of international stock markets will impact Taiwan stock market. For this reason, it is a practical way to use the fluctuations of other stock markets as forecasting factors for forecasting the Taiwan stock market. In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs a genetic algorithm (GA) to refine the weights of rules joining in an ANFIS model to forecast the Taiwan stock index. To evaluate the forecasting performances, the proposed model is compared with four different models: Chen's model, Yu's model, Huarng's model, and the ANFIS model. The results indicate that the proposed model is superior to the listing methods in terms of the root mean squared error (RMSE).     

Evaluating industry performance using extracted RGR rules based on feature selection and rough sets classifier

In strategy of investment, an important thing for investors is to correctly predict firm’s revenue growth rate (RGR), which is an effective evaluation indicator for them to see how big the potential power of future development is and measure how about the growth of future development for a target firm that may be selected to investment portfolios. However, conventional methods of forecasting RGR have some shortcomings such as statistical methods based on strict assumptions of linearity and/or normality limit applications in real world. Additionally, due to rapid changing of information technology (IT) today, some techniques (i.e. rough sets and data mining tools) have become important research trends to both practitioners and academicians. With these reasons above, a new procedure, using the feature selection method and rough sets classifier, is proposed to extract decision rules and improve accuracy rate for classifying RGR. In empirical study, an actual RGR dataset collected from publicly traded company of stock markets is employed to illustrate the proposed procedure. The experimental results of RGR dataset analyses indicate that the proposed procedure surpasses the listing methods in terms of both higher accuracy and fewer attributes, and the output of proposed procedure is to generate a set of easily understandable decision rules that are readily applied in knowledge-based investment systems by investors.     

AN ADAPTIVE EXPECTATION GENETIC ALGORITHM BASED ON ANFIS AND MULTINATIONAL STOCK MARKET VOLATILITY CAUSALITY FOR TAIEX FORECASTING

The stock market is a highly complex and dynamic system, and forecasting stock is complicated and difficult. Successful prediction of stock prices may promise attractive benefits; therefore, stock market forecasting is important and of great interest. The economy of Taiwan relies on international trade deeply and the fluctuations of international stock markets impact Taiwan's stock market to certain degree. It is practical to use the fluctuations of other stock markets as forecasting factors for forecasting on the Taiwan stock market. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models use only the last period of stock price in forecasting. In this article, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs an expectation equation method whose parameters are optimized by a genetic algorithm (GA) joined with an adaptive network–based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. To evaluate the forecasting performance, the proposed model is compared with Chen's model and Yu's model. The experimental results indicate that the proposed model is superior to the listing methods (Chen's model and Yu's model) in terms of root mean squared error (RMSE).     

Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach’s effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy.     

基于粗集的产品色彩预测系统研究

针对传统人工预测流行色方法效率低、费用高的问题,采用决策表知识表达技术和模糊集合方法构建了流行色知识仓库,结合可辨识矩阵理论和粗集理论提出流行色预测知识挖掘算法,该算法可根据流行色知识库建立条件属性和决策属性依赖关系,从而完成流行色的预测推理。开发了基于粗集理论的智能化流行色预测系统,并以服装产品为例预测流行色测,结果表明该系统可准确预测未来短期内的流行色。     

Retracted: A hybrid artificial intelligence sales‐forecasting system in the convenience store industry

Recently, there has been increasing interest in computer‐aided ergonomics and its applications, such as in the fields of intelligent robots, intelligent mobiles, intelligent stores, and so on. The operation of convenience stores (CVS) in Taiwan is facing a crossover revolution by providing multiple services, including daily fresh foods, a café, ticketing, and a grocery. Therefore, forecasting the daily sales of fresh foods is getting more and more complex due to the influence of both internal and external factors. Eventually, a reliable sales‐forecasting system will play an important role in improving business strategies and increasing competitive advantages. The purpose of this study is the development of an enhanced hybrid sales‐forecasting model of fresh foods, called ECFM (Enhanced Cluster and Forecast Model), for CVSs by combining a self‐organization map (SOM) neural network and radial basis function (RBF) neural networks. The model is evaluated for a six‐month sales data set of daily fresh foods at a chained CVS in Taiwan. Meanwhile, the performance of the proposed model is compared with that of fuzzy neural network (FNN) and cluster and forecast model (CFM). The result reveals that the proposed model is not only amenable but can also promise the fresh food sales forecasting for CVSs. © 2011 Wiley Periodicals, Inc.     

普适计算认证过程基于粗糙集的隐私保护策略

在普适计算环境中,用户要获得需要的服务,需要向对应的服务提供商提供一定的认证信息,而这些认证信息中往往包含有用户不希望泄漏的隐私信息。为了对这些隐私信息进行保护,本文提出了认证过程中基于粗糙集的隐私保护策略：用户将认证信息扩展成粗糙集提供给服务提供商;服务提供商根据策略从粗糙集中提取用户的真实认证信息对用户请求进行认证。该策略充分利用了粗糙集合的不确定性,能够有效地防止用户隐私泄漏。     

Assessing mathematics learning achievement using hybrid rough set classifiers and multiple regression analysis

Education is recognized as the key to individual success. Particularly, elementary education is vital for providing students with basic literacy and numeracy, as well as establishing foundations in mathematics, language, science, geography, history, and other social sciences. Mathematics is fundamental to numerous fields with real life applications, including natural science, engineering, medicine, and social sciences; therefore, student mathematics-learning achievement (MLA) in elementary school is valuable. This study aims to eliminate wastage of educational resources and seek suitable hybrid models for application to education. This study proposes an integrated hybrid model based on rough set classifiers and multiple regression analysis and provides a new trial of such a hybrid model to process MLA problems for elementary schools and their teachers. The proposed model is illustrated by examining a dataset from an elementary school in Taiwan. The experimental results reveal that the proposed model outperforms the listing methods in both classification accuracy and standard deviation. The rough set LEM2 (Learning from Examples Module, version 2) algorithm generates a set of comprehensible decision rules that can be applied in a knowledge-based education system designed for interested parties. Consequently, the analytical results have important implications for strategies related to mathematics teaching/learning and support to achieve teaching goals related to mathematics education.     

保正域的决策粗糙集属性约简

针对决策粗糙集模型,分析了它的正域随条件属性删除时的变化特点,即当条件属性集变小时,决策粗糙集的正域不但会变大,而且可能保持不变或变小。讨论了现有几种与正域相关的决策粗糙集属性约简定义的优缺点,在此基础上提出一种新的保正域不变的决策粗糙集属性约简。计算实例发现,现有基于差别矩阵的决策粗糙集属性约简方法不能求到它的所有保正域约简。上述研究结果说明,决策粗糙集模型与经典粗糙集模型的属性约简问题完全不同,因此不能简单地将经典粗糙集的方法平行推广到决策粗糙集模型上。该文的结论为将来系统研究决策粗糙集模型中的属性约简问题提供了很好的小结和理论基础。     

A forecasting method of forest pests based on the rough set and PSO-BP neural network

In order to improve the forecasting accuracy of the occurrence period of insect pests, this paper proposes a kind of forecasting method based on the combination of rough set theory and improved PSO-BP neural network. It takes insect pests of Euphrates poplar forests as the object of study. First, an attribute reduction algorithm of rough set is used to eliminate redundancy attributes. Input factors of the forecasting model of insect pests (temperature, humidity and rainfall) can be reduced from sixteen to eight. Then, particle swarm optimization (PSO) algorithm is improved using the inertia weight, and weights and thresholds of BP neural network are optimized using the improved PSO algorithm. Finally, the forecasting model of insect pests is established using rough set and an improved PSO-BP network. The test results show that rough set theory can effectively reduce the feature dimension and the improved PSO algorithm can reduce the iteration times, with an average accuracy of 94.8 %. This method can provide the technical support for the prevention and control of the insect pests of the Euphrates poplar forests.     

基于广义自适应多粒度的多源信息融合研究

多粒度粗糙集模型是一种有效的信息融合策略。利用该策略能从多个角度将多源信息进行融合,并转化成一致的信息表示。现有的大多数多粒度信息融合方法对每个知识粒度都采用相同的阈值,然而,众所周知,不同的信息源的来源和噪声都不尽相同,其对应的知识粒度的阈值也应不同。为此,首先在广义多粒度粗糙集中引入单参数决策理论粗糙集,提出了广义自适应多粒度粗糙集模型。然后,利用经典的融合策略设计了4种广义多粒度模型,所有模型都可以通过一个参数补偿系数$ \zeta $来自适应地获得知识粒度对应的阈值对,并讨论了这些模型的相关性质。最后,通过实验结果证明,所提模型在实际应用中灵活性更高,决策更为合理。     

Forecasting output of integrated circuit industry by support vector regression models with marriage honey-bees optimization algorithms

Integrated circuit (IC) is a vital component of most electronic commodity. IC manufacturing in Taiwan is booming, with revenues from the ICs industry having grown significantly in the recent years. Given the nature of technology, capital intensity and high value-added, accurate forecasting of IC the industry output can improve the competitivity of IC cooperation. Support vector regression (SVR) is an emerging forecasting scheme that has been successfully adopted in many time-series forecasting areas. Additionally, the data preprocessing procedure and the determination of SVR parameters significantly impact the forecasting accuracy of SVR models. Thus, this work develops a support vector regression model with scaling preprocessing and marriage in honey-bee optimization (SVRSMBO) model to accurately forecast IC industry output. The scaling preprocessing procedure is utilized to lower the fluctuation of input data, and the marriage in honey-bees optimization (MBO) algorithm is adopted to determine the three parameters of the SVR model. Numerical data collected from the previous literature are used to demonstrate the performance of the proposed SVRSMBO model. Simulation results indicate that the SVRSMBO model outperforms other forecasting models. Hence, the SVRSMBO model is a promising means of forecasting IC industry output.     

Tourism demand forecasting using novel hybrid system

Accurate prediction of tourism demand is a crucial issue for the tourism and service industry because it can efficiently provide basic information for subsequent tourism planning and policy making. To successfully achieve an accurate prediction of tourism demand, this study develops a novel forecasting system for accurately forecasting tourism demand. The construction of the novel forecasting system combines fuzzy c-means (FCM) with logarithm least-squares support vector regression (LLS-SVR) technologies. Genetic algorithms (GA) were optimally used simultaneously to select the parameters of the LLS-SVR. Data on tourist arrivals to Taiwan and Hong Kong were used. Empirical results indicate that the proposed forecasting system demonstrates a superior performance to other methods in terms of forecasting accuracy.     

A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting

In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have been found in many of the past forecasting models: (1) statistical assumptions about variables are required for time series models, such as the autoregressive moving average model (ARMA) and the autoregressive conditional heteroscedasticity (ARCH), to produce forecasting models of mathematical equations, and these are not easily understood by stock investors; and (2) the rules mined from some artificial intelligence (AI) algorithms, such as neural networks (NN), are not easily realized.In order to overcome these drawbacks, this paper proposes a hybrid forecasting model, using multi-technical indicators to predict stock price trends. Further, it includes four proposed procedures in the hybrid model to provide efficient rules for forecasting, which are evolved from the extracted rules with high support value, by using the toolset based on rough sets theory (RST): (1) select the essential technical indicators, which are highly related to the future stock price, from the popular indicators based on a correlation matrix; (2) use the cumulative probability distribution approach (CDPA) and minimize the entropy principle approach (MEPA) to partition technical indicator value and daily price fluctuation into linguistic values, based on the characteristics of the data distribution; (3) employ a RST algorithm to extract linguistic rules from the linguistic technical indicator dataset; and (4) utilize genetic algorithms (GAs) to refine the extracted rules to get better forecasting accuracy and stock return. The effectiveness of the proposed model is verified with two types of performance evaluations, accuracy and stock return, and by using a six-year period of the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) as the experiment dataset. The experimental results show that the proposed model is superior to the two listed forecasting models (RST and GAs) in terms of accuracy, and the stock return evaluations have revealed that the profits produced by the proposed model are higher than the three listed models (Buy-and-Hold, RST and GAs).     

Trust assessment of security for e-health systems

The expansive connectivity of emerging information systems has set the stage for pervasive access to healthcare services via e-health systems for selecting the best possible healthcare services. Emerging systems are expected to be highly dynamic open environments connecting diverse number of healthcare services and autonomous entities that are autonomous agents or software applications representing patients. Entities in such dynamic environments may have different security needs from e-health systems raising the challenge of trust computations regarding security. In this research, we proposed a trust assessment model of an e-health service from the viewpoint of an entity. The model contains a comprehensive architecture applicable to different types of entities, and a novel set of trust assessment metrics may be used to assess a specific property of a security system (i.e. partial metrics) or all properties (i.e. total metrics). The simulation based evaluation of proposed model in the context of a Hospital Online Appointment Service has shown that the proposed model provides better trust computation results than existing trust models for e-health systems. Furthermore, the entities are also able to assess the trust even with incomplete security information.     

Implementing support vector regression with differential evolution to forecast motherboard shipments

In this study, we investigate the forecasting accuracy of motherboard shipments from Taiwan manufacturers. A generalized Bass diffusion model with external variables can provide better forecasting performance. We present a hybrid particle swarm optimization (HPSO) algorithm to improve the parameter estimates of the generalized Bass diffusion model. A support vector regression (SVR) model was recently used successfully to solve forecasting problems. We propose an SVR model with a differential evolution (DE) algorithm to improve forecasting accuracy. We compare our proposed model with the Bass diffusion and generalized Bass diffusion models. The SVR model with a DE algorithm outperforms the other models on both model fit and forecasting accuracy.     

Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data

Sales forecasting plays a very important role in business operation. Many researches generally employ statistical methods, such as regression or auto-regressive integrated moving average model, to forecast the product sales. However, they only can consider the quantitative data. Some exogenous qualitative variables have more influence on forecasting result. Thus, this study attempts to propose a integrated forecasting system which is able to consider both quantitative and qualitative factors to achieve a more comprehensive result. Basically, fuzzy neural network is first employed to capture the expert knowledge regarding some qualitative factors. Then, it is combined with the time series data using an artificial immune system based back-propagation neural network. A laptop sales data set provided by a distributor in Taiwan is applied to verify the proposed approach. The computational result indicates that the proposed approach is superior to other forecasting methods. It can be used to decrease the inventory costs and enhance the customer satisfaction.