• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.11-23
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• 2021

House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.

• Transactions of Materials Processing
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• v.29 no.5
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• pp.257-264
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• 2020

This paper is concerned with the influence of the plastic property of the rivet on the numerical prediction of the Self-Piercing Rivet (SPR) Joining. In order to predict the plastic property of the rivet, a ring compression specimen was directly fabricated from the rivet used for the mechanical joining of dissimilar materials, and the FE analysis together with the ring compression test was iteratively carried out by changing the plastic property of the rivet. For reliable FE analysis, a friction coefficient was estimated based on a friction calibration curve, measuring the reductions in inner diameter and height of the ring specimen after the compression test. From each simulation result, the force-displacement curves were then compared from each other so as to obtain the rivet plastic property that shows good agreement with the experimental result. The SPR joining between GA590 1.0t and Al5052 2.0t was conducted, and the numerical prediction was performed with the use of the plastic property evaluated based on the inverse analysis and the one referred from Mori et al. [11]. Comparison of the experiment and the numerical predictions in terms of the interlock and bottom thickness revealed that the reliable evaluation of the plastic property of the rivet is necessary for the trustworthy numerical prediction of the SPR joining.

• Journal of Mechanical Science and Technology
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• v.19 no.6
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• pp.1229-1242
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• 2005

When natural rubber is used for a long period of time, it becomes aged; it usually becomes hardened and loses its damping capability. This aging process affects not only the material property but also the (fatigue) life of natural rubber. In this paper the aging effects on the material property and the fatigue life were experimentally investigated. In addition, several fatigue life prediction equations for natural rubber were proposed. In order to investigate the aging effects on the material property, the load-stretch ratio curves were plotted from the results of the tensile test, the compression test and the simple shear test for virgin and heat-aged rubber specimens. Rubber specimens were heat-aged in an oven at a temperature ranging from $50^{\circ}C$ to $90^{\circ}C$ for a period ranging from 2 days to 16 days. In order to investigate the aging effects on the fatigue life, fatigue tests were conducted for differently heat-aged hourglass-shaped and simple shear specimens. Moreover, finite element simulations were conducted for the specimens to calculate physical quantities occurring in the specimens such as the maximum value of the effective stress, the strain energy density, the first invariant of the Cauchy-Green deformation tensor and the maximum principal nominal strain. Then, four fatigue life prediction equations based on one of the physical quantities could be obtained by fitting the equations to the test data. Finally, the fatigue life of a rubber bush used in an automobile was predicted by using the prediction equations, and it was compared with the test data of the bush to evaluate the reliability of those equations.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.4
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• pp.55-60
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• 2018

The thermal property is one of the most important characteristics in the field of energetic materials. Because energy materials release decomposition heat, differential scanning calorimetry (DSC) is frequently used for thermal analysis. However, thermodynamic events, such as melting can interfere with DSC kinetic analysis. In this study, we use isothermal mode for DSC measurement to avoid thermodynamic issues. We also merge accelerating rate calorimetry(ARC) data with DSC data to obtain a robust prediction results for small scale samples and for large scale samples as well. For the thermal property prediction, advanced kinetics and technology solutions(AKTS) programs are used.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1155-1156
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• 2013

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.243-248
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• 1995

In this article we show that a class of random coefficient autoregressive processes including the NEAR (New exponential autoregressive) process has the strong mixing property in the sense of Rosenblatt with mixing order decaying to zero. The result can be used to construct model free prediction interval for the future observation in the NEAR processes.

• Journal of the Korean Society of Tobacco Science
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• v.29 no.2
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• pp.80-84
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• 2007

This study was conducted to evaluate the prediction of sensory property of smoke from the leaf chemical property and characterize leaf chemical components for the best tobacco taste's leaves in burley tobacco. For analytical and sensory evaluations, sixteen grades were used. The major leaf chemical components to predict the sensory property of smoke were ether extract for tobacco-like, chloride for impact and total nitrogen/nicotine for irritation. Within ${\pm}20\;%$ range of difference, the predictable probabilities of sensory property of smoke from the leaf chemical properties were 100 % for tobacco-like, impact and irritation. As a result of K-means cluster analysis on the basis of tobacco taste, the desirable leaf chemical component contents were $6.5{\sim}6.8\;%$ in ether extract, $0.25{\sim}0.30\;%$ in chloride and $1.26{\sim}1.54$ in total nitrogen/nicotine ratio. This study suggest that the some regression equations may be useful to predict the sensory components of tobacco smoke from a few selected leaf chemical properties in burley tobacco and to select the burley tobacco leaves for enhance the tobacco taste of cigarette.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.230-234
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• 2020

A system that uses deep-learning techniques to predict properties from molecular structures has been developed to apply to chemical, biological and material studies. Based on the database where molecular structure and property information are accumulated, a deep-learning model looking for the relationship between the structure and the property can eventually provide a property prediction for the new molecular structure. In addition, experiments on the actual properties of the selected molecular structure will be carried out in parallel to carry out continuous verification and model updates. This allows for the screening of high-quality molecular structures from large quantities of molecular structures within a short period of time, and increases the efficiency and success rate of research. In this paper, we would like to introduce the overall composition of the materiality prediction system using deep-learning and the cases applied in the actual excavation of new structures in LG Chem.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.23-34
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• 2011

Property auctions have become a new method for real estate investment because the property auction market grows in tandem with the growth of the real estate market. This study focused on the statistical model for predicting bid price rates which is the main index for participants in the real estate auction market. For estimating the monthly bid price rate, we proposed a new method to make up for the mean of regions and terms as well as to reduce the prediction error using a decision tree analysis. We also proposed a linear regression model to predict a bid price rate for individual auction property. We applied the proposed model to apartment auction property and tried to predict the bid price rate as well as categorize individual auction property into an auction grade.