Optimisation to ANN Inputs in Automated Property Valuation Model with Encog 3 and winGamma

2013 ◽  
Vol 462-463 ◽  
pp. 1081-1086
Author(s):  
Nguyen Vo ◽  
Hao Shi ◽  
Jakub Szajman
Keyword(s):  
Design Methodology ◽  
Tax Revenue ◽  
Residential Property ◽  
Property Valuation ◽  
Valuation Model ◽  
Property Evaluation ◽  
Residential Properties ◽  
Property Model ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

An automated property model for prediction of the sales price of residential properties with optimized inputs was developed. Optimised inputs improve efficiency and speed of an Artificial Neural Network (ANN). Property appraisal ANNs have a great potential not only to save time and money but also help local government authorities to determine the tax revenue. While the criteria for the ANN’s number of hidden layer neurons are well known, there is no theory to support the optimisation to ANN inputs. The proposed optimisation to ANN inputs procedure aims to resolve some of the issues in using ANNs especially in the case of automated property valuation modelling (AVM). A brief review of ANNs and their applications is given, followed by the discussion of the ANN design methodology and optimisation. Details of ANN optimisation using Java based Encog 3 and winGamma are presented in this paper. It is shown that optimisation to ANN inputs can improve the accuracy in residential property evaluation using winGamma and Encog 3.

scholarly journals Brownfields Remediation: Impact on Local Residential Property Tax Revenue

2017 ◽  
Vol 19 (03) ◽  
pp. 1750013 ◽  
Author(s):  
Karen A. Sullivan
Keyword(s):  
Environmental Protection ◽  
Environmental Protection Agency ◽  
Property Tax ◽  
Tax Revenue ◽  
Residential Property ◽  
Property Value ◽  
Protection Agency ◽  
Single Tax ◽  
The Us ◽  
Residential Properties

The US Environmental Protection Agency (EPA) Brownfields Program provides grants to assess and clean up brownfields. There are few studies that estimate tax revenue impacts from cleanup beyond those generated directly from within the remediated site’s property lines. This study estimates the increased residential property tax revenue attributable to brownfields cleanup at 48 sites remediated between 2004 and 2011 under the EPA Brownfields Cleanup Grants Program. Findings from a previous study of a 5% to 15.2% property value increase following cleanup at these sites are applied to the assessed values of nearby residential properties along with local tax laws, assessment ratios, and rates to estimate tax revenue gained as a result of brownfields cleanup. The estimated increase in residential property tax revenue for a single tax year from remediating 48 brownfields properties was between $29 million and $97 million (2014 USD).

Western accounting reforms and accountability in wealth redistribution in patronage-based Nigerian society

2017 ◽  
Vol 30 (6) ◽  
pp. 1288-1308 ◽  
Author(s):  
Owolabi Bakre ◽  
Sarah George Lauwo ◽  
Sean McCartney
Keyword(s):  
Design Methodology ◽  
Accounting Standards ◽  
Fair Value ◽  
Public Officials ◽  
Content Type ◽  
Historical Cost ◽  
Property Valuation ◽  
Wealth Redistribution ◽  
Residential Properties ◽  
The Relationship

Purpose The purpose of this paper is to investigate the claim that Western accounting reforms, in particular the adoption of International Public Sector Accounting Standards (IPSASs) would enhance transparency and accountability and reduce corruption in patronage-based developing countries such as Nigeria. Design/methodology/approach The paper utilises the patron/clientelism framework to examine the dynamics of Western accounting reforms in the Nigerian patronage-based society, in which the institutions of governance and regulatory structures are arguably weak. The paper utilises archival data and interviews conducted with representatives of state bodies (elected politicians and officials) and professional accounting associations. Findings Results from two major reforms (the sale of government-owned residential properties in Lagos and the monetisation of fringe benefits for public officials) are presented. Despite the claim of the adoption of Western accounting standards, and in particular IPSAS 17, which requires full accrual accounting and the utilisation of fair value in property valuation, historical cost accounting appeared to have been mobilised to massively corrupt the process for the benefit of politicians, other serving and retired public officials and family members. Originality/value This study contributes to the current literature by providing evidence of the relationship between patronage, corruption and accounting in wealth redistribution in the patronage-based Nigerian socio-political and economic context.

scholarly journals Mapping and Estimation of Monthly Global Solar Irradiation in Different Zones in Souss-Massa Area, Morocco, Using Artificial Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
O. Nait Mensour ◽  
S. Bouaddi ◽  
B. Abnay ◽  
B. Hlimi ◽  
A. Ihlal
Keyword(s):  
Solar Energy ◽  
Solar Irradiation ◽  
Kriging Interpolation ◽  
Solar Pv ◽  
Energy Potential ◽  
Global Solar Irradiation ◽  
Interpolation Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Solar radiation data play an important role in solar energy research. However, in regions where the meteorological stations providing these data are unavailable, strong mapping and estimation models are needed. For this reason, we have developed a model based on artificial neural network (ANN) with a multilayer perceptron (MLP) technique to estimate the monthly average global solar irradiation of the Souss-Massa area (located in the southwest of Morocco). In this study, we have used a large database provided by NASA geosatellite database during the period from 1996 to 2005. After testing several models, we concluded that the best model has 25 nodes in the hidden layer and results in a minimum root mean square error (RMSE) equal to 0.234. Furthermore, almost a perfect correlation coefficient R=0.988 was found between measured and estimated values. This developed model was used to map the monthly solar energy potential of the Souss-Massa area during a year as estimated by the ANN and designed with the Kriging interpolation technique. By comparing the annual average solar irradiation between three selected sites in Souss-Massa, as estimated by our model, and six European locations where large solar PV plants are deployed, it is apparent that the Souss-Massa area is blessed with higher solar potential.

Analysing the Residential Market in Trójmiasto Using Self-Organizing Map

2021 ◽  
Author(s):  
Olgun Aydin ◽  
Krystian Zielinski
Keyword(s):  
Multidimensional Data ◽  
Residential Property ◽  
Self Organizing Map ◽  
Property Market ◽  
Residential Properties ◽  
Price Trends ◽  
Residential Market ◽  
Key Aspects ◽  
The Impact ◽  
Self Organizing

Although the residential property market has strong connections with various sectors, such as construction, logistics, and investment, it works through different dynamics than do other markets; thus, it can be analysed from various perspectives. Researchers and investors are mostly interested in price trends, the impact of external factors on residential property prices, and price prediction. When analysing price trends, it is beneficial to consider multidimensional data that contain attributes of residential properties, such as number of rooms, number of bathrooms, floor number, total floors, and size, as well as proximity to public transport, shops, and banks. Knowing a neighbourhood's key aspects and properties could help investors, real estate development companies, and people looking to buy or rent properties to investigate similar neighbourhoods that may have unusual price trends. In this study, the self-organizing map method was applied to residential property listings in the Trójmiasto area of Poland, where the residential market has recently been quite active. The study aims to group together neighbourhoods and subregions to find similarities between them in terms of price trends and stock. Moreover, this study presents relationships between attributes of residential properties.

scholarly journals A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 515 ◽  
Author(s):  
Sanjeev T. Chandrasekaran ◽  
Ruobing Hua ◽  
Imon Banerjee ◽  
Arindam Sanyal
Keyword(s):  
Breast Cancer ◽  
Design Methodology ◽  
Activation Function ◽  
Cancer Classification ◽  
Common Source ◽  
Fully Integrated ◽  
Breast Cancer Classification ◽  
Learning Classifier ◽  
Analog Machine ◽  
Artificial Neural Network Ann

We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 μ W power and occupies 0.003 mm 2 die area.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 35-43
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Multilayer Perceptron ◽  
Artificial Neural ◽  
Artery Disease ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

2018 ◽  
pp. 1-30 ◽  
Author(s):  
Jagan Jayabalan ◽  
Sanjiban Sekhar Roy ◽  
Pijush Samui ◽  
Pradeep Kurup
Keyword(s):  
Neural Network ◽  
Rock Mass ◽  
Elastic Modulus ◽  
Roughness Parameter ◽  
Jointed Rock ◽  
Jointed Rock Mass ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Hidden Layer ◽  
Intelligent Models

Elastic Modulus (Ej) of jointed rock mass is a key parameter for deformation analysis of rock mass. This chapter adopts three intelligent models {Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) and Generalized Regression Neural Network (GRNN)} for determination of Ej of jointed rock mass. MPMR is derived in a probability framework. ELM is the modified version of Single Hidden Layer Feed forward network. GRNN approximates any arbitrary function between the input and output variables. Joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (s3) (MPa), and elastic modulus (Ei) (GPa) of intact rock have been taken as inputs of the ELM, GRNN and MPMR models. The output of ELM, GRNN and MPMR is Ej of jointed rock mass. In this study, ELM, GRNN and MPMR have been used as regression techniques. The developed GRNN, ELM and MPMR have been compared with the Artificial Neural Network (ANN) models.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

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