Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis

Horvath, Sabine; Soot, Matthias; Zaddach, Sebastian; Neuner, Hans; Weitkamp, Alexandra

doi:10.1016/j.landusepol.2021.105475

Cited by 13 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Italy, an AVM model with machine learning capabilities is presented, where the most important tool to achieve this goal was the use of a structured real estate database [9]. Researchers from Germany proposed that a non-linear modelling method can be used in property valuation to value properties in an inactive market [10]. Researchers from Bosnia and Herzegovina adapted a market value estimation model where one of the input parameters is the age and expected life of the structure [11].…”

Section: Results Of the Study Of Methodologiesmentioning

confidence: 99%

Развитие Методологии Оценки Объектов Недвижимости С Использованием Ретроспективного Метода Корреляционно-Регрессионной Зависимости

Гареев,

Ахметгалиев

2024

reem

View full text Add to dashboard Cite

Проведенное исследование базируется на информационных базах данных объектов недвижимости, где важной проблемой является, что при наличии большого количества данных, они, с одной стороны, используются для исследования рынка и массовой оценки недвижимости, однако с другой, в связи с отсутствием достаточного количества информации или инструментов, не позволяют в полной мере отображать конъюнктуру рынка. Статья отображает анализ использования данных сбора и обработки информации объектов недвижимости в различных странах, результаты исследований и возможности при использовании и внедрении данных об объектах недвижимости в зависимости от ценообразующих факторов. В данной статье рассмотрены экономико-математические методы оценки массива данных при оценке объектов недвижимости, в том числе с использованием искусственного интеллекта. Проведено обоснованное подтверждение гипотезы о том, что использование ретроспективной информации наиболее аналогичных и сопоставимых объектов аналогов по ценообразующим факторам для оцениваемого объекта недвижимости позволяет определить текущую рыночную стоимость с использованием ретроспективного метода корреляционно-регрессионной зависимости. Обоснование приведено на примере расчета рыночной стоимости произвольно выбранного объекта недвижимости. Проведенное исследование частично отображает изменение стоимости объекта недвижимости от фактора времени на основе обработки массива данных. В качестве одного из ценообразующих факторов выступают реальные индексы изменения цен объектов недвижимости на основе нескольких источников данных. В заключении приведены итоги проведенного исследования и преимущества метода для оценки объектов недвижимости, в том числе для определения кадастровой стоимости методом массовой оценки.

show abstract

Section: Results Of the Study Of Methodologiesmentioning

confidence: 99%

Развитие Методологии Оценки Объектов Недвижимости С Использованием Ретроспективного Метода Корреляционно-Регрессионной Зависимости

Гареев,

Ахметгалиев

2024

reem

View full text Add to dashboard Cite

show abstract

“…Two different models were built and compared for each variable of interest; the results showed that the Keras/Tensorflow models resulted in higher accuracies than the H 2 O models, with 93.7% for the tickets' predictive model, and 95.8% for the accidents' predictive model. However, the accuracy of these models can be considerably improved by using a much larger dataset, as the bigger the dataset, the higher the accuracy of the ANN models' performance [94].…”

Section: Discussionmentioning

confidence: 99%

Driving towards Sustainability: A Neural Network-Based Prediction of the Traffic-Related Effects on Road Users in the UAE

Abuzaid,

Almashhour,

Abu-Lebdeh

2024

Sustainability

View full text Add to dashboard Cite

Transportation is fundamental, granting access to goods, services, and economic opportunities. Ensuring sustainable transportation, especially in vehicular modes, is crucial for the pillars of social, economic, and environmental sustainability. High-traffic countries, like the United Arab Emirates (UAE), grapple with significant challenges to this end. This study delves into the repercussions of traffic-related incidents on UAE road users and their intricate links to the social and economic dimensions of sustainability. To achieve this, this work examines the influential demographic factors contributing to incidents, utilizing artificial neural network models to predict the likelihood of individuals experiencing traffic tickets and accidents. Findings reveal associations between gender, driving frequency, age, nationality, and reported incident frequency. Men experience more accidents and tickets than women. Age exhibits a negative linear relationship with incident occurrence, while driving experience shows a positive linear relationship. Nationalities and cultural backgrounds influence road users’ adherence to traffic rules. The predictive models in this study demonstrate their high accuracy, with 93.7% precision in predicting tickets and 95.8% in predicting accidents. These insights offer valuable information for stakeholders, including government entities, road users, contractors, and designers, contributing to the enhancement of the social and economic aspects of road sustainability.

show abstract

“…For example, there is a research that employs artificial neural networks to predict the resistance of chloride ion penetration and the compressive strength of concrete, with the purpose of knowing the mechanical properties and durability of concrete (Mohamed et al 2021). There are real estate valuations through ANN to maintain market transparency (Horvath et al 2021). ANN is also applied in neural networks for more accurate electrocardiogram (ECG) data diagnosis (Bhanot, Peddoju, and Bhardwaj 2018).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

Huang,

Chen,

Kanga

et al. 2024

Preprint

View full text Add to dashboard Cite

In the past, most of traditional master craftsmen always adopted the acoustic actions to recognition the situation of machine. Along with the development of time and technology, the mode of industry has changed with the Fourth Industrial Revolution (Industry 4.0). The long been known for the mother of industry, mold industry, has been inevitably impacted by Industry 4.0. This research stems from the structure of the six-level IoT model, through Internet connecting sensors, data collection, and the appropriate implementation of human and machine interface to intellectualize the injection molding machine. This research has collected 130 times of audio frequency, and there were 53 effective data sets, in sum there were 34,030,640 datasets. There were 5 manufacturing actions of petroleum molding machines that were successfully identified. Due to the low accuracy of one of the manufacturing actions, the training of audio frequency is based on the other four. In the end, there are 93.64% of accurate AI intelligent identifying models. Concurrently, through labeling the audio characteristics of different manufacturing parameters, the model recognizing audio characteristics from injection molding machines under different injection speed and rotation speed parameters is successfully trained. It is expected that in the future, other researchers can use this research as a reference to further strengthen the correlation between audio characteristics and injection molding machines to engage a more in-depth and diverse application of this topic. JEL Classification: C80, C88, C90.

show abstract

Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis

Cited by 13 publications

References 16 publications

Развитие Методологии Оценки Объектов Недвижимости С Использованием Ретроспективного Метода Корреляционно-Регрессионной Зависимости

Развитие Методологии Оценки Объектов Недвижимости С Использованием Ретроспективного Метода Корреляционно-Регрессионной Зависимости

Driving towards Sustainability: A Neural Network-Based Prediction of the Traffic-Related Effects on Road Users in the UAE

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

Contact Info

Product

Resources

About