Neural network hyperparameter optimization for prediction of real estate prices in Helsinki

Kalliola, Jussi; Kapočiūtė-Dzikienė, Jurgita; Damaševičius, Robertas

doi:10.7717/peerj-cs.444

Cited by 30 publications

(21 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In deep learning, there are several hyperparameters that become variables that will affect model output, determine training details and significantly influence model performance [39]- [41]. In this study, three hyperparameter tuning experiments were carried out and can be shown in Table 1.…”

Section: Results Of Hyperparametermentioning

confidence: 99%

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

Prasetyo

Desrony

Panjaitan

et al. 2023

IJECE

View full text Add to dashboard Cite

Classification on corn plants is used to classify leaf of corn plants that are healthy and have diseases consisting of Northern Leaf Blight, Common Rust and Gray Leaf Spot. Convolutional Neural Network (CNN) is one of algorithms from the branch of deep learning that utilizes artificial neural networks to produce accurate results in classifying an image. In this study, ResNet-9 architecture implemented to build the best model CNN for classification corn plant diseases. After that we doing comparisons of epochs have been carried out to obtain the best model, including comparisons of epochs of 5, 25, 55, 75 and 100. After the epoch comparison, the highest accuracy value was obtained in the 100 epoch experiment so that in this study 100 epochs were used in model formation. The number of datasets used is 9145 data which is divided into two, there are training data (80%) and testing data (20%). In this study, three hyperparameter tuning experiments were carried out and the results of hyperparameter tuning experiments where num_workers is 4 and batch_size is 32. This classification obtained an accuracy rate of 99% and the model is implemented into a web interface.

show abstract

Section: Results Of Hyperparametermentioning

confidence: 99%

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

Prasetyo

Desrony

Panjaitan

et al. 2023

IJECE

View full text Add to dashboard Cite

show abstract

“…The hedonic price approach is widely employed in the literature to relate anomalous events and price changes. Several techniques have also been employed in the literature to understand the marginal contribution that building features (construction and neighbourhood) bring to the formation of the market price, such as spatial analysis, GIS (geographic information system), and, again, hedonic price regressions and neural networks [43,44].…”

Section: A Diachronic Analysis Related To Major Anomalous Eventsmentioning

confidence: 99%

“Location, Location, Location”: Fluctuations in Real Estate Market Values after COVID-19 and the War in Ukraine Based on Econometric and Spatial Analysis, Random Forest, and Multivariate Regression

Gabrielli

Ruggeri

Scarpa

2023

Land

View full text Add to dashboard Cite

In this research, the authors aim to detect the marginal appreciation of construction and neighbourhood characteristics of property prices at three different time points: before the COVID-19 pandemic, two years after the first COVID-19 alert but before the War in Ukraine, and one year after the outbreak of the War. The marginal appreciations of the building’s features are analysed for a pilot case study in Northern Italy using a Random Forest feature importance analysis and a Multivariate Regression. Several techniques are integrated into this study, such as computer programming in Python language, multi-parametric value assessment techniques, feature selection procedures, and spatial analysis. The results may represent an interesting ongoing monitoring of how these anomalous events affect the buyer’s willingness to pay for specific characteristics of the buildings, with particular attention to the location features of the neighbourhood and accessibility.

show abstract

“…16. Jussi Kalliola, et al [78] Suggested an ANN optimization model for real estate price prediction. To handle the nonlinear problem of real estate price prediction without under-and over-fitting problems, a multilayer perceptron (MLP) NN model utilized, along with fine-tuning hyperparameters in Helsinki, Finland.…”

Section: Applications Used Hyperparameters Optimization Algorithmsmentioning

confidence: 99%

“…The second problem accurse is filling in local minimum the most active hyperparameter in this case is momentum coefficient just like problem that showed in [73]. In [4], [78], [81], [83] the batch size hyper parameter was improved since it represents the number of the samples that have been processed prior to updating the model and the number of the complete passes through the whole training dataset in cases of a large dataset. Furthermore, the learning algorithm's dynamics are influenced by an important hyperparameter.…”

mentioning

confidence: 99%

Artificial Neural Network Hyperparameters Optimization: A Survey

Kadhim¹,

Abdullah²,

Ghathwan³

2022

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics. Neural Networks (NNs) are one of the most es-sential ways to ML; the most challenging element of designing a NN is de-termining which hyperparameters to employ to generate the optimal model, in which hyperparameter optimization improves NN performance. This study includes a brief explanation regarding a few types of NN as well as some methods for hyperparameter optimization, as well as previous work results in enhancing ANN performance using optimization methods that aid research-ers and data analysts in developing better ML models via identifying the ap-propriate hyperparameter configurations.

show abstract

Neural network hyperparameter optimization for prediction of real estate prices in Helsinki

Cited by 30 publications

References 49 publications

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

“Location, Location, Location”: Fluctuations in Real Estate Market Values after COVID-19 and the War in Ukraine Based on Econometric and Spatial Analysis, Random Forest, and Multivariate Regression

Artificial Neural Network Hyperparameters Optimization: A Survey

Contact Info

Product

Resources

About