House Price Prediction Using Machine Learning and Neural Networks

Varma, Ayush; Sarma, Anil Kumar; Doshi, Sagar N.; Nair, Rohini

doi:10.1109/icicct.2018.8473231

Cited by 95 publications

(34 citation statements)

References 8 publications

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“…In addition, the impact of various attributes on specific model have also been evaluated and debated. [38] Locational and structural attributes - [3] Locational and structural attributes 0.267 [39] Locational attributes - [40] Locational attributes - [10] Economic attributes - [41] Locational, structural and neighborhood attribute - [11] Locational and structural attributes 0.3079 [7] Copyright Based on reviewing numerous papers, there are several attributes used by researchers in their work to forecast house prices. All of these attributes can be divided into 4 main categories which are locational, structural, neighborhood and economic attributes.…”

Section: Finding and Discussionmentioning

confidence: 99%

House Price Prediction using a Machine Learning Model: A Survey of Literature

Zulkifley¹,

Rahman²,

Ubaidullah³

et al. 2020

IJMECS

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Section: Finding and Discussionmentioning

confidence: 99%

House Price Prediction using a Machine Learning Model: A Survey of Literature

Zulkifley¹,

Rahman²,

Ubaidullah³

et al. 2020

IJMECS

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“…The Davies-Bouldin Index is used to evaluate cluster results by measuring the ratio of the spread of clusters and the distance between clusters. The Silhouette coefficient will be shown at (5), while the Davies-Bouldin Index will be shown at (6) to (8).…”

Section: The Measurement Of Cluster Validitymentioning

confidence: 99%

“…Based on many considered features in determining house prices, the housing data are classified as a high-dimensional data. In some previous studies, Neural Network can be used to predict the price of a house [4][5] [6] [7] [8]. Several approaches of regression techniques to predict the house prices also done by [9][10] which using the time-series data.…”

Section: Introductionmentioning

confidence: 99%

High Dimensional Data Clustering using Self-Organized Map

2019

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As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution.

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“…Access to extensive data sources and the increasing computing power of machines have enabled the development of this field of science. Nowadays, interest in using neural networks is still growing, which can be observed by analyzing scientific publications on various topics from the last few years-development of ITS (Intelligent Transport Systems) [7,8], prediction and evaluation of atmospheric phenomena [9][10][11], distinguish information tweets (containing relevant facts) from non-information ones (containing rumors or non-detailed information) [12] and predicting dynamic FX markets [13] and the real estate market [14]. In military sector, AI algorithms can be used, among others, for speech recognition systems [15] or object detection and recognition [16].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

Bistroń

Piotrowski

2021

Electronics

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The paper presents an overview of current and expected prospects for the development of artificial intelligence algorithms, especially in military applications, and conducted research regarding applications in the area of civilian life. Attention was paid mainly to the use of AI algorithms in cybersecurity, object detection, military logistics and robotics. It discusses the problems connected with the present solutions and how artificial intelligence can help solve them. It briefly presents also mathematical structures and descriptions for ART, CNN and SVM networks as well as Expectation–Maximization and Gaussian Mixture Model algorithms that are used in solving of discussed problems. The third chapter discusses the attitude of society towards the use of neural network algorithms in military applications. The basic problems related to ethics in the application of artificial intelligence and issues of responsibility for errors made by autonomous systems are discussed.

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House Price Prediction Using Machine Learning and Neural Networks

Cited by 95 publications

References 8 publications

House Price Prediction using a Machine Learning Model: A Survey of Literature

House Price Prediction using a Machine Learning Model: A Survey of Literature

High Dimensional Data Clustering using Self-Organized Map

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

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