Search

Continuous changing in house prices very rapid and observed in last years which depends on many factors like position, area, population in the city and another requirement for individual house price, there is a lot of articles studied and adopted traditional ways for machine learning in house price prediction accurately but few of them focused on individual performance for complicated models. To explore effected factors in prediction way in this paper we applied traditional machine learning theory to investigate many models due to many limited we take some of techniques like (random forest, Extreme Gradient Boosting and stacked generalization model) and applied on several models to obtain more accurate results for house price prediction. We take Baghdad as a sample for house price and impact factor on difference cities, services, population, area, building age and others we mentioned in our paper by taking and applying machine learning house price prediction. [ABSTRACT FROM AUTHOR]

Generative models try to obtain a probability distribution that is similar to that of observed data. Two different solutions have been proposed in this regard in recent years: one is to minimize the divergence (distance) between the two distributions by maximizing the variational lower bound, and the other is to implicitly reduce the distance between the two distributions through adversarial processes. One of the problems in generative adversarial networks (GANs) is the mode collapse. Mode collapse is a phenomenon in which, for various inputs, the generative model generates low variety or similar images. This paper tries to provide a solution to the mode collapse problem proposing a novel method called variational generative adversarial networks (VGANs). This method exploits variational autoencoders to initialize GANs. In other words, in addition to maximizing the variational lower bound, it also implicitly reduces the distance between the two distributions. Experimental results show that this method can deal with the mode collapse problem better than the state-of-the-art. Moreover, in the qualitative analysis, according to a survey of 136 people on the authenticity of the generated images, the proposed method can generate images more similar to real ones. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to provide a comprehensive and systematic review of published studies on the topic of developing ontologies based on folksonomies as a means of information management in electronic environments. Method: This study is conducted using the systematic review method. After designating the questions, input, and output criteria, 87 papers published during 2003-2019 were obtained through querying domestic and international research databases. After screening the results, 29 papers (27 papers in English and 2 papers in Farsi) were closely studied and systematically analyzed. The data was entered into Excel for further statistical analysis and visualization. Findings: This study verifies the positive results of using folksonomized ontologies in central areas, including information management, and recovery and machine learning. The purpose of organizing knowledge is the quick transfer and retrieval of information. The inefficiency of traditional information organizing tools has always made it difficult to access information quickly and easily. The growth and development of information organization over time indicates a twoway relationship between knowledge growth and organizational evolution. This mutual influence between human knowledge and its organization has been accepted as a principle and its importance has increased over time. It seems that along with the increasing development of the world of knowledge, continuous review and improvement of organizational systems are inevitable. Moreover, this study shows the increasing popularity of folksonomized ontologies among users for determining semantic relationships between tags benefiting from more extensive sets of data. Furthermore, this study demonstrates that the synthesis between ontologies and folksonomies can facilitate semantic web development by leveraging their differences. An important factor in emphasizing the use of the tacit meanings of folksonomy is the high potential of folksonomy in facilitating the creation of semantic relationships. Conclusion: Although studies of the application of folksonomy in the development of ontologies have been conducted since 2003, semantic limitations in folksonomy have remained a challenging issue over last decades. However, the vast majority of user-generated folksonomies offer a promising future for semantic web development. Considering the significance of synthesizing folksonomies and ontologies in improving the semantic relationships in information systems and attending to users' needs by obtaining useful data from tags, further studies are required to find effective solutions to the semantic ambiguities tags and to structure them. This study offers new perspectives to assist experts in the fields of information management and the semantic web. [ABSTRACT FROM AUTHOR]