GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms—R Package and Web-Based Tool

Dağ, Osman; Karabulut, Erdem; Alpar, Reha

doi:10.2991/ijcis.d.190618.001

Cited by 12 publications

(7 citation statements)

References 20 publications

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“…The basic form of a relation between input and output data can be declared as a discrete type of the Volterra functional series, referred to as the Kolmogorov-Gabor polynomial. Equation (2) shows the underlying neural network map, which is also called the polynomial of Ivakhnenko [65,66].…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

et al. 2020

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Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex problems. Hence, in this research work, a serious challenge in the sustainable development process was investigated using the classification of confirmed cases of COVID-19 (new version of Coronavirus) as one of the epidemic diseases. Hence, binary classification modeling was used by the group method of data handling (GMDH) type of neural network as one of the artificial intelligence methods. For this purpose, the Hubei province in China was selected as a case study to construct the proposed model, and some important factors, namely maximum, minimum, and average daily temperature, the density of a city, relative humidity, and wind speed, were considered as the input dataset, and the number of confirmed cases was selected as the output dataset for 30 days. The proposed binary classification model provides higher performance capacity in predicting the confirmed cases. In addition, regression analysis has been done and the trend of confirmed cases compared with the fluctuations of daily weather parameters (wind, humidity, and average temperature). The results demonstrated that the relative humidity and maximum daily temperature had the highest impact on the confirmed cases. The relative humidity in the main case study, with an average of 77.9%, affected positively, and maximum daily temperature, with an average of 15.4 • C, affected negatively, the confirmed cases.

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Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

et al. 2020

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“…The GMDH type of neural network as a subset of artificial intelligence was proposed by Ivakhnenko [41], and it has a suitable capability for the training of network statistics. This method includes a set of neurons obtained from the connection of different pairs according to a quadratic polynomial that is also called Ivakhnenko polynomial equation [42][43][44]. The models are based upon the dataset and they are self-regulating models.…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

Morosini

Haghshenas

et al. 2020

Applied Sciences

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Investigation of Water Distribution Networks (WDNs) is considered a challenging task due to the unpredicted and uncertain conditions in water engineering. When in a WDN, a pipe failure occurs, and shut-off valves to isolate the broken pipe to allow repairing works are activated. In these new conditions, the hydraulic parameters in the network are modified because the topology of the entire system changes. If the head becomes inadequate, the Pressure Driven Analysis (PDA) is the correct approach to evaluate the performance of water networks. Hence, in the present study, the water distribution system was evaluated in pressure-driven conditions for 100 different scenarios and then using a type of neural network called Group Method of Data Handling (GMDH) as a stochastic technique. For this purpose, several most notable parameters including the base demand, pressure, and alpha (the percentage of effective supplied flow) were calculated using simulations based on a PDA approach and applied to the water distribution network of Praia a Mare in Southern Italy. In the second stage, the output parameters were used in a developed binary classification model. Finally, the obtained results showed that the GMDH algorithm can be applied as a powerful tool for modeling water distribution networks.

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“…The general form of GMDH works based on a map of input and output data which is a self-organized and a unilateral neural network, and also, it is called the polynomial of Ivakhnenko equation. The basic neural network map is based on Equation (1) [86,87].…”

Section: Group Methods Of Data Handling (Gmdh) Type Of Neural Networkmentioning

confidence: 99%

Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm

Guido

Haghshenas

et al. 2020

Sustainability

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Evaluating road safety is an enduring research topic in Infrastructure and Transportation Engineering. The prediction of crash risk is very important for avoiding other crashes and safeguarding road users. According to this task, awareness of the number of vehicles involved in an accident contributes greatly to safety analysis, hence, it is necessary to predict it. In this study, the main aim is to develop a binary model for predicting the number of vehicles involved in an accident using Neural Networks and the Group Method of Data Handling (GMDH). For this purpose, 775 accident cases were accurately recorded and evaluated from the urban and rural areas of Cosenza in southern Italy and some notable parameters were considered as input data including Daylight, Weekday, Type of accident, Location, Speed limit and Average speed; and the number of vehicles involved in an accident was considered as output. In this study, 581 cases were selected randomly from the dataset to train and the rest were used to test the developed binary model. A confusion matrix and a Receiver Operating Characteristic curve were used to investigate the performance of the proposed model. According to the obtained results, the accuracy values of the prediction model were 83.5% and 85.7% for testing and training, respectively. Finally, it can be concluded that the developed binary model can be applied as a reliable tool for predicting the number of vehicles involved in an accident.

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GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms—R Package and Web-Based Tool

Cited by 12 publications

References 20 publications

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm

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