The mass appraisal of the real estate by computational intelligence

Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches – support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center.     

A case-based reasoning approach to the selection of comparable evidence for retail rent determination

Case-based reasoning is an artificial intelligence technique which utilizes past experience to solve current problems and in this respect it mirrors the process involved in real estate appraisal. This paper investigates its application as a computer-assisted valuation tool to the specific domain of retail rent determination. As property appraisal is goal orientated, it is essential that the most appropriate examples of previous rent determinations are selected. 1n exploring a case-based reasoning approach to the retail real estate domain five models are built namely; pure inductive, inductive (Q-model), inductive (prototype), inductive (Q-model and prototype) and nearest neighbour.     

房地产项目可行性研究辅助系统的建模

在房地产项目开发过程中,可行性研究是投资者决策成功的依据。目前,对可行性研究等工程项目的上游工作,缺乏有效的计算机辅助系统。文章根据目前可行性研究工作的现状,结合利用GIS、人工智能及CSCW等信息技术,提出了一个房地产项目可行性研究集成化辅助系统的模型,为相应系统的开发奠定了基础。该系统的成功开发将有力地支持房地产项目开发的前期工作。     

Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting

This study considers real estate appraisal forecasting problem. While there is a great deal of literature about use of artificial intelligence and multiple linear regression for the problem, there has been always controversy about which one performs better. Noting that this controversy is due to difficulty finding proper predictor variables in real estate appraisal, we propose a modified version of ridge regression, i.e., ridge regression coupled with genetic algorithm (GA-Ridge). In order to examine the performance of the proposed method, experimental study is done for Korean real estate market, which verifies that GA-Ridge is effective in forecasting real estate appraisal. This study addresses two critical issues regarding the use of ridge regression, i.e., when to use it and how to improve it.     

Autonomous power management in mobile devices using dynamic frequency scaling and reinforcement learning for energy minimization

Embedded systems execute applications that execute hardware differently depending on the computation task, generating time-varying workloads. Energy minimization can be reached by using the low-power central processing unit (CPU) frequency for each workload. We propose an autonomous and online approach, capable of reducing energy consumption from adaptation to workload variations even in an unknown environment. In this approach, we improved the AEWMA algorithm into a new algorithm called AEWMA-MSE, adding new functionality to detect workload changes and demonstrating why it is better to use statistical analysis for real user cases in a mobile environment. Also, a new power model for mobile devices based on k-NN algorithm for regression was proposed and validated proving to have a better trade-off between execution time and precision than neural networks and linear regression-based models. AEWMA-MSE and the proposed power model are integrated into a novel algorithm for energy management based on reinforcement learning that suitably selects the appropriate CPU frequency based on workload predictions to minimize energy consumption. The proposed approach is validated through simulation by using real smartphone data from an ARM Cortex A7 processor used in a commercial smartphone. Our proposal proved to have an improvement in the Q-learning cost function and can effectively minimize the average energy consumption by 21% and up to 29% when compared to the already existing approaches.     

房地产交易核价系统价格评估方法分析

对目前我国在房地产交易价格评估时常用的三种评估方法进行了分析研究,提出了一种基于市场法的房地产交易价格评估循环算法：该算法计算结果可靠,已应用于房地产交易税收核价系统中。     

Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.     

Integration framework for intelligent manufacturing processes

In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among artificial intelligence research. Our goal is to develop an integrated intelligent system for real time manufacturing processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system which manages the selection, operation and communication of these programs. A meta-system can be implemented in different language environments and applied to many disciplines. This new architecture can serve as a universal configuration to develop high performance intelligent systems for many complicated industrial applications in real world domains.To whom all correspondence should be addressed.     

基于运营商信息化平台的房产中介连锁行业综合解决方案

房产中介连锁行业对于信息化的需求日益明显,企业需要一个集成管理应用平台,以实现总部和加盟店对房产中介交易全过程的实时管控,满足集团企业跨级别、跨单位、跨地域的管理需求。此报告即提供了这样一个综合解决方案,从基础信息通信服务、基于中小企业信息化平台的综合信息服务以及房产中介信息平台服务三方面,进行了详细的论述并给出结构图。重点对企业级房源综合信息平台和运营商级房源综合信息平台进行了阐述,希望以此能够为电信运营商以及广大房产中介连锁企业提供一定的借鉴。     

Artificial neural networks for robotics coordinate transformation

Artificial neural networks with such characteristics as learning, graceful degradation, and speed inherent to parallel distributed architectures might provide a flexible and cost solution to the real time control of robotics systems. In this investigation artificial neural networks are presented for the coordinate transformation mapping of a two-axis robot modeled with Fischertechnik physical modeling components. The results indicate that artificial neural systems could be utilized for practical situations and that extended research in these neural structures could provide adaptive architectures for dynamic robotics control.     

Intelligent control of braking process

Intelligent modeling, prediction and control of the braking process are not an easy task if using classical modeling techniques, regarding its complexity. In this paper, the new approach has been proposed for easy and effective monitoring, modeling, prediction, and control of the braking process i.e. the brake performance during a braking cycle. The context based control of the disc brake actuation pressure was used for improving the dynamic control of braking process versus influence of the previous and current values of the disc brake actuation pressure, the vehicle speed, and the brake interface temperature. For these purposes, two different dynamic neural models have been developed and integrated into the microcontroller. Microcontrollers are resource intensive and cost effective platforms that offer possibilities to associate with commonly used artificial intelligence techniques. The neural models, based on recurrent dynamic neural networks, are implemented in 8-bit CMOS microcontroller for control of the disc brake actuation pressure during a braking cycle. The first neural model was used for modeling and prediction of the braking process output (braking torque). Based on such acquired knowledge about the real brake operation, the inverse neural model has been developed which was able to predict the brake actuation pressure needed for achieving previously selected (desired) braking torque value in accordance with the previous and current influence of the pressure, speed, and the brake interface temperature. Both neural models have had inherent abilities for on-line learning and prediction during each braking cycle and an intelligent adaptation to the change of influences of pressure, speed, and temperature on the braking process.     

Vision-based real estate price estimation

Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow’s estimates.     

A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach

This research uses a solution based on a combined grey-fuzzy DEMATEL method to deal with the objective of the study. This study is aimed to present a perception approach to deal with real estate agent service quality expectation ranking with uncertainty. The ranking of best top five real estate agents might be a key strategic direction of other real estate agents prior to service quality expectation. The solving procedure is as follows: (i) the weights of criteria and alternatives are described in triangular fuzzy numbers; (ii) a grey possibility degree is used to result the ranking order for all alternatives; (iii) DEMATEL is used to resolve interdependency relationships among the criteria and (iv) an empirical example of real estate agent service quality ranking problem in customer expectation is used to resolve with this proposed method approach indicating that real estate agent R₁ (CY real estate agent) is the best selection in terms of service quality in customer expectation.     

Using simulated annealing for computing cost-aware covering arrays

The configuration spaces of software systems are often too large to test exhaustively. Combinatorial interaction testing approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. In an attempt to reduce the cost of testing, standard t-way covering arrays aim to cover all t-way combinations of option settings in a minimum number of configurations. By doing so, they simply assume that every configuration costs the same. When the cost varies from one configuration to another, however, minimizing the number of configurations is not necessarily the same as minimizing the cost. To overcome this issue, we have recently introduced cost-aware covering arrays. In a nutshell, a t-way cost-aware covering array is a standard t-way covering array that “minimizes” a given cost function modeling the actual cost of testing. In this work we develop a simulated annealing-based approach to compute cost-aware covering arrays, which takes as input a configuration space model enhanced with a cost function and computes a cost-aware covering array by using two alternating neighboring state generation strategies together with a fitness function expressed as a weighted sum of two objectives: covering all required t-way option setting combinations and minimizing the cost function. To the best of our knowledge, the proposed approach is the first approach that computes cost-aware covering arrays for general, non-additive linear cost functions with multiplicative interaction effects. We evaluate the approach both by conducting controlled experiments, in which we systematically vary the input models to study the sensitivity of the approach to various factors and by conducting experiments using real cost functions for real software systems. We also compare cost-aware covering arrays to standard covering arrays constructed by well-known algorithms and study how fast the construction costs are compensated by the cost reductions provided. Our empirical results suggest that the proposed approach is more effective and efficient than the existing approaches.     

An integrated artificial neural network-computer simulation for optimization of complex tandem queue systems

This paper presents an integrated artificial neural network-computer simulation (ANNSim) for optimization of G/G/K queue systems. The ANNSim is a computer program capable of improving its performance by referring to production constraints, system's limitations and desired targets. It is a goal oriented, flexible and integrated approach and produces the optimum solution by utilizing Multi Layer Perceptron (MLP) neural networks. The properties and modules of the prescribed intelligent ANNSim are: (1) parametric modeling, (2) flexibility module, (3) integrated modeling, (4) knowledge-base module, (5) integrated database and (6) learning module. The integrated ANNSim is applied to 30 distinct tandem G/G/K queue systems. Furthermore, its superiority over conventional simulation approach is shown in two dimensions which are average run time and maximum number of required iterations (scenarios).     

Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks

In a modern machining system, tool condition monitoring systems are needed to get higher quality production and to prevent the downtime of machine tools due to catastrophic tool failures. Also, in precision machining processes surface quality of the manufactured part can be related to the conditions of the cutting tools. This increases industrial interest for in-process tool condition monitoring (TCM) systems. TCM supported modern unmanned manufacturing process is an integrated system composed of sensors, signal processing interface and intelligent decision making strategies. This study includes key considerations for development of an online TCM system for milling of Inconel 718 superalloy. An effective and efficient strategy based on artificial neural networks (ANN) is presented to estimate tool flank wear. ANN based decision making model was trained by using real time acquired three axis (F_x, F_y, F_z) cutting force and torque (M_z) signals and also with cutting conditions and time. The presented ANN model demonstrated a very good statistical performance with a high correlation and extremely low error ratio between the actual and predicted values of flank wear.     

Range estimation of construction costs using neural networks with bootstrap prediction intervals

Modeling of construction costs is a challenging task, as it requires representation of complex relations between factors and project costs with sparse and noisy data. In this paper, neural networks with bootstrap prediction intervals are presented for range estimation of construction costs. In the integrated approach, neural networks are used for modeling the mapping function between the factors and costs, and bootstrap method is used to quantify the level of variability included in the estimated costs. The integrated method is applied to range estimation of building projects. Two techniques; elimination of the input variables, and Bayesian regularization were implemented to improve generalization capabilities of the neural network models. The proposed modeling approach enables identification of parsimonious mapping function between the factors and cost and, provides a tool to quantify the prediction variability of the neural network models. Hence, the integrated approach presents a robust and pragmatic alternative for conceptual estimation of costs.     

Web-based services for real estate: model and implementation

E-commerce's impact on real estate is just as significant and multifaceted as in other areas. Its momentum springs from two factors: an increasing population of online customers and increased involvement and investment from the real estate industry. The online real estate business' growing demand for application development has created a new market for application service providers (ASPs) who developed standardized Internet application systems and information services supporting a specific set of business processes. E-commerce success stories have taught companies that every successful e-business must have a feasible business plan that complies with a model. CommRex (Commercial Real Estate Exchange, http://www.commrex.com), a Web-based real estate information system, offers a case study in one such service. CommRex's performance in terms of four characteristics - scalability, portability, operation ability, and availability - is satisfactory, owing to its multiorganization data allocation scheme. During this evolutionary process, the multilevel service model and multiorganization data management structure have been proven an effective choice for such an evolutionary process.     

The investigation of effects of digital proofing systems used in colour management on print quality with neural networks

The aim of this study is to assist users and to determine how digital proofing systems used in colour management effect print qualities. Also the main theme of this study is to determine the effects of digital proofing systems used in colour management on print quality by artificial neural network (ANN). The R² values are obtained 0.99702 and 0.99688 for training data as matte and cuated papers, respectively. Similarly, these values for testing data are 0.994707 and 0.99629, respectively. The ANN approach shows greater accuracy for evaluating colour management. Based on the outputs of the study, ANN model can be used to estimate the effects of digital proofing systems used in colour management on print quality with highly confidence.     

PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding

To simplify complicated traditional cost estimation flow, this study emphasizes the cost estimation approach for plastic injection products and molds. It is expected designers and R&D specialists can consider the competitiveness of product cost in the early stage of product design to reduce product development time and cost resulting from repetitive modification. Therefore, the proposed cost estimation approach combines factor analysis (FA), particle swarm optimization (PSO) and artificial neural network with two back-propagation networks, called FAPSO-TBP. In addition, another artificial neural network estimation approach with a single back-propagation network, called FAPSO-SBP, is also established. To verify the proposed FAPSO-TBP approach, comparisons with the FAPSO-SBP and general back-propagation artificial neural network (GBP) are made. The computational results show the proposed FAPSO-TBP approach is very competitive for the product and mold cost estimation problems of plastic injection molding.