scholarly journals Estimation of Residential Property Market Price: Comparison of Artificial Neural Networks and Hedonic Pricing Model

2020 ◽  
Vol 4 (6) ◽  
pp. 530-538
Author(s):  
Michaela Štubňová ◽  
Marta Urbaníková ◽  
Jarmila Hudáková ◽  
Viera Papcunová
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Estate ◽  
Learning Algorithm ◽  
Market Price ◽  
Residential Property ◽  
Pricing Model ◽  
Property Market ◽  
Price Estimation ◽  
Artificial Neural

The correct real estate property price estimation is significant not only in the real estate market but also in the banking sector for collateral loans and the insurance sector for property insurance. The paper focuses on both traditional and advanced methods for real estate property valuation. Attention is paid to the analysis of the accuracy of valuation models. From traditional methods, a regression model is used for residential property price estimation, which represents the hedonic approach. Modern advanced valuation methods are represented by the artificial neural network, which is one of the soft computing techniques. The results of both methods in residential property market price estimation are compared. The analysis is performed using data on residential properties sold on the real estate market in the city of Nitra in the Slovak Republic. To estimate the residential property prices, artificial neural networks trained with the Levenberg-Marquart learning algorithm, the Bayesian Regularization learning algorithm, and the Scaled Conjugate Gradient learning algorithm, and the regression pricing model are used. Among the constructed neural networks, the best results are achieved with networks trained with the Regularization learning algorithm with two hidden layers. Its performance is compared with the performance of the regression pricing model, and it can state that artificial neural networks can considerably improve prediction accuracy in the estimation of residential property market price. Doi: 10.28991/esj-2020-01250 Full Text: PDF

Web tension control of multispan roll-to-roll system by artificial neural networks for printed electronics

2013 ◽  
Vol 227 (10) ◽  
pp. 2361-2376 ◽  
Author(s):  
KyungHyun Choi ◽  
Muhammad Zubair ◽  
Ganeshthangaraj Ponniah
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Printed Electronics ◽  
Learning Algorithm ◽  
Network Control ◽  
Learning Rate ◽  
Roll To Roll ◽  
Roll System ◽  
Artificial Neural ◽  
Web Tension

The mass production of printed electronic devices can be achieved by roll-to-roll system that requires highly regulated web tension. This highly regulated tension is required to minimize printing register error and maintain proper roughness and thickness of the printed patterns. The roll-to-roll system has a continuous changing roll diameter and a strong coupling exists between the spans. The roll-to-roll system is a multi-input-multi-output, time variant, and nonlinear system. The conventional proportional–integral–derivative control, used in industry, is not able to cope with roll-to-roll system for printed electronics. In this study, multi-input-single-output decentralized control scheme is used for control of a multispan roll-to-roll system by applying regularized variable learning rate backpropagating artificial neural networks. Additional inputs from coupled spans are given to regularized variable learning rate backpropagating artificial neural network control to decouple the two spans. Experimental results show that the self-learning algorithm offers a solution to decouple speed and tension in a multispan roll-to-roll system.

Volume Learning Algorithm Artificial Neural Networks for 3D QSAR Studies

2001 ◽  
Vol 44 (15) ◽  
pp. 2411-2420 ◽  
Author(s):  
Igor V. Tetko ◽  
Vasyl V. Kovalishyn ◽  
David J. Livingstone
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Algorithm ◽  
3D Qsar ◽  
Qsar Studies ◽  
Artificial Neural

scholarly journals Diagnosing Diabetes Using Artificial Neural Networks

2020 ◽  
Vol 5 (2) ◽  
pp. 221-224
Author(s):  
Joy Oyinye Orukwo ◽  
Ledisi Giok Kabari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Success Rate ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Diabetes Diagnosis ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural

Diabetes has always been a silent killer and the number of people suffering from it has increased tremendously in the last few decades. More often than not, people continue with their normal lifestyle, unaware that their health is at severe risk and with each passing day diabetes goes undetected. Artificial Neural Networks have become extensively useful in medical diagnosis as it provides a powerful tool to help analyze, model and make sense of complex clinical data. This study developed a diabetes diagnosis system using feed-forward neural network with supervised learning algorithm. The neural network is systematically trained and tested and a success rate of 90% was achieved.

scholarly journals Enhancing the reusability and interoperability of artificial neural networks with DEVS modeling and simulation

2016 ◽  
Vol 07 (02) ◽  
pp. 1650005 ◽  
Author(s):  
David Ifeoluwa Adelani ◽  
Mamadou Kaba Traoré
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modeling And Simulation ◽  
Computational Models ◽  
Learning Algorithm ◽  
Discrete Event ◽  
Neural Network Modeling ◽  
System Specification ◽  
Ann Models ◽  
Artificial Neural

Artificial neural networks (ANNs), a branch of artificial intelligence, has become a very interesting domain since the eighties when back-propagation (BP) learning algorithm for multilayer feed-forward architecture was introduced to solve nonlinear problems. It is used extensively to solve complex nonalgorithmic problems such as prediction, pattern recognition and clustering. However, in the context of a holistic study, there may be a need to integrate ANN with other models developed in various paradigms to solve a problem. In this paper, we suggest discrete event system specification (DEVS) be used as a model of computation (MoC) to make ANN models interoperable with other models (since all discrete event models can be expressed in DEVS, and continuous models can be approximated by DEVS). By combining ANN and DEVS, we can model the complex configuration of ANNs and express its internal workings. Therefore, we are extending the DEVS-based ANN proposed by Toma et al. [A new DEVS-based generic artficial neural network modeling approach, The 23rd European Modeling and Simulation Symp. (Simulation in Industry), Rome, Italy, 2011] for comparing multiple configuration parameters and learning algorithms and also to do prediction. The DEVS models are described using the high level language for system specification (HiLLS), [Maïga et al., A new approach to modeling dynamic structure systems, The 29th European Modeling and Simulation Symp. (Simulation in Industry), Leicester, United Kingdom, 2015] a graphical modeling language for clarity. The developed platform is a tool to transform ANN models into DEVS computational models, making them more reusable and more interoperable in the context of larger multi-perspective modeling and simulation (MAS).

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