基于遗传算法优化神经网络的房地产评估模型及实证研究

针对房地产价格和影响因素之间存在复杂且非线性的关系,用传统评估方法预测房地产价格精度不高的缺陷,提出一种基于遗传算法(Genetic Algorithm,GA)和BP神经网络(BP Neural Network,BPNN)的房地产评估预测模型。该模型利用BPNN确定房地产价格影响因子与评估价格之间的函数映射关系,并采用GA优化BPNN的权值和阈值来提高BPNN的收敛速度,解决了算法陷入局部极值点的问题。最后,通过对100组实际交易样本数据的计算机仿真实验,证明了该方法对房地产估价的有效性和准确性。     

基于RSA的网络安全及其在评估系统中的应用研究

RSA算法是一种公开密钥算法,它的安全系数取决于大素数分解的难度。其公开密钥和私人密钥是一对大素数(100到200个十进制数或更大)的函数。从一个公开密钥和密文中恢复出明文的难度等价于分解两个大素数之积。该文研究了RSA算法的实现过程,并对其中的一些重要步骤进行了详细的讨论。最后,实现了RSA算法在房地产评估系统中的应用。     

基于GIS的房地产批量评估数据库构建研究-以深圳市为例

与传统单宗房地产评估不同,房地产批量评估需要建立在强大详实的数据库基础上,如何构建高可靠性、高扩展性以及高集成性的批量评估数据库成为一项关键任务。在对房地产批量评估数据进行分析的基础上,基于GIS技术提出了房地产批量评估数据库构建的技术路线,设计了数据库的总体结构,并研究了房地产批量评估数据集成的方法,最后以深圳市为例说明了该方法的有效性。     

有关房地产司法鉴定评估的探讨

正房地产司法评估鉴定是鉴定人依法接受委托,运用评估学等专门知识和方法,依照法定程序,对与司法案件有关的涉讼资产进行估算、评定等;并对照法律法规的一定标准,作出专业判断的一种活动。法律诉讼涉及房地产价值不是近两年才出现,而是早就具有,因司法会计受技术限制无法做到涉讼资产的鉴定,故新兴了司法鉴定评估,因此司法鉴定评估作为一种新兴业务,有许多问题需要探讨:司法鉴定评估与正常的评估的性质、     

一种网络安全评估算法

对网络系统安全评估问题进行了研究,提出了一种评估网络系统安全的新算法。该算法基于代码分析、协议分析、脆弱性检测、认证检1测等四种网络安全缺陷发现技术,多角度查找网络漏洞,并用似然估计算法量化检测结果;同时算法中引入网络容忍度测试,以保证评估结果的可靠性,并根据该算法给出了一个完整的网络安全评估模型。     

不良数据检测与辨识算法的评估研究

针对算法评价的模糊性,提出一种基于模糊数学理论的算法综合评价模型,并用于状态估计中不良数据检测与辨识算法的综合评估。通过在不同系统配置条件下,对四种常用不良数据检测与辨识算法进行综合评估,比较出其优劣特性,验证了该评估系统的正确性,实现了算法评估中定性分析的定量化,为实际系统算法的选择提供了有效参考。     

一种目标身份识别算法的评估新方法

随着融合理论的发展,提出了多种目标身份识别算法,但是目前目标身份识别算法评估的研究相当有限,也还没有形成一种完整的、通用的评估理论．因此算法评估成为目标身份识别技术中一个蕈要的研究课题;为了对目标身份识别算法进行定量评估,提出了一种适合战场应用的评估新方法,利用该方法对某种目标身份识别算法进行了定量分析,结果证明该方法具有可行性．可以满足现代战场的需要。     

音频水印算法复杂性评估

数字水印技术具有鲁棒性、透明性、复杂性等特性,大部分文献对水印评估的阐述都是集中在鲁棒性上,实际上复杂性评估在数字水印评估领域也是非常重要的.因此,主要是针对数字音频水印算法嵌入函数的复杂性进行评估.给出了基本方案评估的概念,选取了两种典型的音频水印算法,应用此方案评估标准对算法进行评估,并同时论述了应用到基本方案上的嵌入参数和音频检验集.给出了两种算法复杂性评估的结果并将结果进行比较.     

基于搜索算法的测试用例优化问题研究

测试用例优化是软件测试领域的研究热点之一,已有优化算法的评估多以代码覆盖为基础,这并不能完全反应程序的错误状况,高代码覆盖率的测试用例并不一定具有高的揭错能力.本文结合变异测试,从新的角度-———错误覆盖出发,对两种贪心算法、一种启发式算法、及遗传算法的优化效果进行了评估,实验结果表明额外贪心算法优于其它三种算法.该评估结果的置信度更高,为此类算法的评估提供了新的研究思路.     

房地产评估办公自动化管理系统的设计与实现

根据房地产评估公司的业务流程,利用计算机软件开发出一套房地产评估办公自动化管理系统,从报单管理、勘察管理、预评管理、正评管理、业务统计等多个方面提供房地产评估公司办公自动化解决方案。已在公司运行多年,取得了较好的社会效益和经济效益。     

混合实时事务的延期单调速率调度算法及其可调度性分析

对于含有实时和非实时两部分的混合实时应用，传统的单调速率调度算法(RM)已不再适用.为此，该文引入“混合实时事务”的概念，并针对这类事务提出一种延期单调速率调度算法(DRM)；着重分析了DRM算法对混合实时事务的可调度性；进行了实验测试与性能分析比较．结果表明，事务集中混合实时事务占的比例越高，混合事务中非实时子事务占的比例越大，该算法的CPU使用率阈值就越高，且在各种情况下，DRM算法与RM算法相比性能都更优，最低情况也与之一样．     

普通住宅套型的面积优化及评价模型研究

为了更好的协调政府与房地产开发商之间的矛盾，不仅能满足政府小户型面积的比例要求，而且能使房地产开发商获得更好的利润，文章设计了小户型面积分配方案和开发商资金分配方案，通过计算证明这种方法是最优的。     

高效的关联规则快速更新算法

挖掘关联规则的两大经典算法Apriori和FP-tree算法都是以批处理方式处理所有事务。但在实际应用中,新事务频繁地出现,这就需要不断更新关联规则。为了提高更新效率,有效减少扫描原数据库的次数,基于次频繁项的概念,在快速更新频繁模式树（FUFP-tree）算法的基础上,提出了一种改进的算法。实验结果表明新算法具有良好的性能。     

基于灰色系统理论的财务风险评价方法

准确的财务风险评价是预防和化解企业财务危机的有效措施.文中提出了一种基于灰色系统理论的企业财务风险评价方法.该方法采用灰色系统理论构建财务评价模型,从企业财务指标和非财务指标两个方面生成评价指标体系.基于混沌模型对粒子群进行改进,采用改进后的粒子群算法对灰色系统权值进行优化.最后以某房地产企业数据进行了实验分析.结果表明,该方法能够有效完成企业财务风险评价,且具有较高的评价准确度.     

Sustainable market valuation of buildings by the single-valued neutrosophic MAMVA method

Traditionally, the real estate asset assessment is performed by experienced valuators, who take into account its economic, social, physical and locational aspects. Nowadays, the construction industry is becoming more and more influenced by the sustainability requirements. Therefore, the inclusion of the sustainability evaluation into real estate asset valuation is of utmost importance. The Neutrosophic Multi-Attribute Market Value Assessment (MAMVA) method developed by the authors of this article handles market value calculations by solving multiple criteria assessment problems, and the initial information vagueness is modelled explicitly. The supplementary novelty of the present paper is the inclusion of the sustainability aspects into the real estate market valuation. The sustainable market valuation of Croydon University Hospital (Emergency Department) is performed as the case study to present numerical capabilities of the proposed approach. Our research findings suggest that neutrosophic MAMVA is a rational approach for calculations of property market valuation and might be suitable for application worldwide.     

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.     

An integrated cost-based approach for real estate appraisals

Real estate appraisal information systems have been studied by many researchers in the past including those systems that have integrated geographic information systems, artificial neural networks, etc. This paper proposes a new integrated approach for real estate appraisals which can be used in real estate appraisal systems to improve efficiency and accuracy. Motivated by the identified limitations of existing cost approaches for real estate appraisals, we integrate some elements from sales comparison approach and income approach into the cost approach to improve the accuracy of the valuation of real estate appropriately. As a result, the new integrated cost-based approach is capable of taking all of the major factors into accounts; these factors are closely related to the assets of real estate in one way or another. In the implementation of the new approach: (1) the concept of replacements cost is revisited and expanded to consider dynamic, environmental, and cultural factors in real estate appraisals; (2) the conventional depreciation values and depreciation rates are replaced by adjustment values and coefficients to include both the positive and negative impact on the changes of real estate value; (3) the theory of technology economics is applied, six forces have been systematically analyzed to determine replacement costs; and finally, (4) different methods for value adjustments, including the algorithm based on artificial neural network, have been utilized to deal with the randomness and uncertainties of mass data for the determination of adjustment values and coefficients.     

不完全信息下开发商Markov博弈均衡的最优策略

房地产开发商对于资源的争夺存在零和博弈的特点,在信息不对称条件下对开发商经济博弈模型的研究具有重要价值。该研究考虑到市场上存在开发商Cartel联盟的情形,以Markov博弈模型为核心,针对不完全信息下的Cartel联盟与竞争者的Markov博弈均衡进行研究,得到了博弈双方的最优策略及演算方法。最后,利用实例对该模型的有效性和可行性进行了说明。     

A neural-based concurrency control algorithm for database systems

Concurrency control (CC) algorithms guarantee the correctness and consistency criteria for concurrent execution of a set of transactions in a database. A precondition that is seen in many CC algorithms is that the writeset (WS) and readset (RS) of transactions should be known before the transaction execution. However, in real operational environments, we know the WS and RS only for a fraction of transaction set before execution. However, optional knowledge about WS and RS of transactions is one of the advantages of the proposed CC algorithm in this paper. If the WS and RS are known before the transaction execution, the proposed algorithm will use them to improve the concurrency and performance. On the other hand, the concurrency control algorithms often use a specific static or dynamic equation in making decision about granting a lock or detection of the winner transaction. The proposed algorithm in this paper uses an adaptive resonance theory (ART)-based neural network for such a decision making. In this way, a parameter called health factor (HF) is defined for transactions that is used for comparing the transactions and detecting the winner one in accessing the database objects. HF is calculated using ART2 neural network. Experimental results show that the proposed neural-based CC (NCC) algorithm increases the level of concurrency by decreasing the number of aborts. The performance of proposed algorithm is compared with strict two-phase locking (S2PL) algorithm, which has been used in most commercial database systems. Simulation results show that the performance of proposed NCC algorithm, in terms of number of aborts, is better than S2PL algorithm in different transaction rates.     

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.