Using neural network model to estimate the rental price of residential properties

2019 ◽  
Vol 24 (2) ◽  
pp. 217-230
Author(s):  
Olalekan Shamsideen Oshodi ◽  
Wellington Didibhuku Thwala ◽  
Tawakalitu Bisola Odubiyi ◽  
Rotimi Boluwatife Abidoye ◽  
Clinton Ohis Aigbavboa
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Predictive Accuracy ◽  
Content Type ◽  
The Neural Network ◽  
Residential Properties ◽  
African Context ◽  
Neural Network Algorithm ◽  
The Government

Purpose Estimation of the rental price of a residential property is important to real estate investors, financial institutions, buyers and the government. These estimates provide information for assessing the economic viability and the tax accruable, respectively. The purpose of this study is to develop a neural network model for estimating the rental prices of residential properties in Cape Town, South Africa. Design/methodology/approach Data were collected on 14 property attributes and the rental prices were collected from relevant sources. The neural network algorithm was used for model estimation and validation. The data relating to 286 residential properties were collected in 2018. Findings The results show that the predictive accuracy of the developed neural network model is 78.95 per cent. Based on the sensitivity analysis of the model, it was revealed that balcony and floor area have the most significant impact on the rental price of residential properties. However, parking type and swimming pool had the least impact on rental price. Also, the availability of garden and proximity of police station had a low impact on rental price when compared to balcony. Practical implications In the light of these results, the developed neural network model could be used to estimate rental price for taxation. Also, the significant variables identified need to be included in the designs of new residential homes and this would ensure optimal returns to the investors. Originality/value A number of studies have shown that crime influences the value of residential properties. However, to the best of the authors’ knowledge, there is limited research investigating this relationship within the South African context.

PID control of an omnidirectional mobile platform based on an RBF neural network controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Niu Zijie ◽  
Zhang Peng ◽  
Yongjie Cui ◽  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Mobile Platform ◽  
Content Type ◽  
The Neural Network ◽  
Omnidirectional Mobile Platform

Purpose Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced. Design/methodology/approach In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. Findings The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°. Originality/value In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.

Understanding and predicting the quality determinants of e-government services

2015 ◽  
Vol 10 (3) ◽  
pp. 325-340 ◽  
Author(s):  
Sujeet Kumar Sharma ◽  
Srikrishna Madhumohan Govindaluri ◽  
Said Gattoufi
Keyword(s):  
Neural Network ◽  
Service Quality ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Demographic Variables ◽  
Neural Network Models ◽  
Content Type ◽  
Government Services ◽  
The Neural Network

Purpose – The purpose of this paper is to investigate the quality determinants influencing the adoption of e-government services in Oman and compare the performance of multiple regression and neural network models in identifying the significant factors influencing adoption in Oman. Design/methodology/approach – Primary data concerning service quality determinants and demographic variables were collected using a structured questionnaire survey. The variables selected in the design of the questionnaire were based on an extensive literature review. Factor analysis, multiple linear regression and neural network models were employed to analyze data. Findings – The study found that quality determinants: responsiveness, security, efficiency and reliability are statistically significant predictors of adoption. The neural network model performed better than the regression model in the prediction of e-government services’ adoption and was able to characterize the non-linear relationship of the aforementioned predictors with the adoption of e-government services. Further, the neural network model was able to identify demographic variables as significant predictors. Practical implications – This study highlights the importance of service quality in the adoption of e-government services and suggests that an enhanced focus and investment on improving quality of the design and delivery of e-government services can have a positive impact on the usage of the services, thereby enabling the Oman Government in achieving the governance objectives for which these technologies were employed. Originality/value – Studies in the area of e-government typically focus either on technology adoption problems or service quality problems. The role of service quality in adoption is rarely addressed. The research presented in this paper is of great value to the institutions that are involved in the development of technology-based e-government services in Oman.

scholarly journals Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution

2020 ◽  
Author(s):  
Aravind Akella ◽  
Vibhor Kaushik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Network Model ◽  
Neural Network Model ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Artery Disease

AbstractThe development of Coronary Artery Disease (CAD), one of the most prevalent diseases in the world, is heavily influenced by several modifiable risk factors. Predictive models built using machine learning (ML) algorithms may assist healthcare practitioners in timely detection of CAD, and ultimately, may improve outcomes. In this study, we have applied six different ML algorithms to predict the presence of CAD amongst patients listed in an openly available dataset provided by the University of California Irvine (UCI) Machine Learning Repository, named “the Cleveland dataset.” All six ML algorithms achieved accuracies greater than 80%, with the “Neural Network” algorithm achieving accuracy greater than 93%. The recall achieved with the “Neural Network” model is also highest of the six models (0.93). Additionally, five of the six algorithms resulted in very similar AUC-ROC curves. The AUC-ROC curve corresponding to the “Neural Network” algorithm is slightly steeper implying higher “true positive percentage” achieved with this model. We also extracted the variables of importance in the “Neural Network” model to help in the risk assessment. We have released the full computer code generated in this study in the public domain as a preliminary effort toward developing an open solution for predicting the presence of coronary artery disease in a given population and present a workflow model for implementing a possible solution.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Linear Static Response of Suspension Arm Based on Artificial Neural Network Technique

2011 ◽  
Vol 213 ◽  
pp. 419-426
Author(s):  
M.M. Rahman ◽  
Hemin M. Mohyaldeen ◽  
M.M. Noor ◽  
K. Kadirgama ◽  
Rosli A. Bakar
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Network Model ◽  
Neural Network Model ◽  
Mesh Size ◽  
Maximum Displacement ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis

Modeling and simulation are indispensable when dealing with complex engineering systems. This study deals with intelligent techniques modeling for linear response of suspension arm. The finite element analysis and Radial Basis Function Neural Network (RBFNN) technique is used to predict the response of suspension arm. The linear static analysis was performed utilizing the finite element analysis code. The neural network model has 3 inputs representing the load, mesh size and material while 4 output representing the maximum displacement, maximum Principal stress, von Mises and Tresca. Finally, regression analysis between finite element results and values predicted by the neural network model was made. It can be seen that the RBFNN proposed approach was found to be highly effective with least error in identification of stress-displacement of suspension arm. Simulated results show that RBF can be very successively used for reduction of the effort and time required to predict the stress-displacement response of suspension arm as FE methods usually deal with only a single problem for each run.

Grey RBF Neural Network Forecasting on LV under the View of Government Responsibility

2012 ◽  
Vol 452-453 ◽  
pp. 700-704
Author(s):  
Feng Rong Zhang ◽  
Annik Magerholm Fet ◽  
Xin Wei Xiao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Development Policy ◽  
Rbf Neural Network ◽  
Good Method ◽  
Government Responsibility ◽  
Long Period ◽  
The Government

At present, the domestic research on the scale of macroscopic logistics has yet belonged to the blankness, therefore, this research tries using LV in circulation and LV in stock to measure the logistics volume and forecasting it in a long period. In order to overcome the phenomenon of “floating upward” in long-term period, this paper establish the improved Grey RBF to forecast the LV next 5-10 year in Jilin province of China. The results show that the increased circulation of goods is the main reason leading to increased logistics volume, and the simulation also shows that the improved gray RBF neural network model is a good method for the government to establish the logistics development policy.

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