Minimal neural network topology optimization for aesthetic classification

Carballal, Adrián; Cedrón, Francisco; Santos, Iria; Santos, Andréa Cynthia; Romero, Juan

doi:10.1007/s00521-020-05550-x

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…Suppose the determined ANN architecture does not match the needs, which results in under-fitting or over-fitting of the ANN, leading to the reduction of ANN performance. Some methods have been performed to determine the ANN architecture based on the rule of thumb [32] , [33] , input and output attributes [28] , [29] , trial and error [31] , and K-means clustering and principal component analysis [63] .…”

Section: Methodsmentioning

confidence: 99%

“…Nevertheless, arriving at the optimal architecture remains a complex and challenging problem. In previous studies, researchers have employed different methods to determine the topologies of ANNs, including approaches that rely solely on the number of input and output neurons [28] , [29] , trial and error [30] , [31] , and the rule of thumb [32] , [33] . However, these methods were solely conducted for one or two hidden layers.…”

Section: Introductionmentioning

confidence: 99%

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On predicting annual output energy of 4-terminal perovskite/silicon tandem PV cells for building integrated photovoltaic application using machine learning

Nguyen¹,

Ishikawa²

2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On predicting annual output energy of 4-terminal perovskite/silicon tandem PV cells for building integrated photovoltaic application using machine learning

Nguyen¹,

Ishikawa²

2023

Heliyon

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“…The metrics used by the system are metrics obtained through genetic programming [ 29 ], artificial neural networks (ANN) [ 30 , 31 ] and ad-hoc metrics [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Validation of an Aesthetic Assessment System for Commercial Tasks

Rodríguez-Fernández

Alvarez-Gonzalez

Santos

et al. 2022

Entropy

Self Cite

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Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system.

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“…Determining the topology that does not match the needs caused overfitting or underfitting in neural networks. Several researchers have conducted research to determine the neural network topology in various ways: methods based solely on the number of input and output attributes ( Sartori & Antsaklis, 1991 ; Tamura & Tateishi, 1997 ), trial and error ( Blanchard & Samanta, 2020 ; Madhiarasan, 2020 ; Madhiarasan & Deepa, 2016 ; Madhiarasan & Deepa, 2017 ; Şen & Özcan, 2021 ) , and the rule of thumb ( Bakhashwain & Sagheer, 2021 ; Carballal et al, 2021 ; Rahman et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction

Rachmatullah

Santoso

Surendro

2021

PeerJ Computer Science

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Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. Topology determination means finding the hidden layers number and the hidden neurons number for corresponding hidden layer in the neural network. The difference between this research and previous research is that the objective function of this research is regression, while the objective function of previous research is classification. Determination of the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used to predict wind speed. Then the performance of topology determination using PCA and clustering is then compared with several other methods. The results of the experiment show that the performance of the neural network topology determined using PCA and clustering has better performance than the other methods being compared. Performance is determined based on the RMSE value, the smaller the RMSE value, the better the neural network performance. In future research, it is necessary to apply a correlation or relationship between input attribute and output attribute and then analyzed, prior to conducting PCA and clustering analysis.

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Minimal neural network topology optimization for aesthetic classification

Cited by 5 publications

References 33 publications

On predicting annual output energy of 4-terminal perovskite/silicon tandem PV cells for building integrated photovoltaic application using machine learning

On predicting annual output energy of 4-terminal perovskite/silicon tandem PV cells for building integrated photovoltaic application using machine learning

Validation of an Aesthetic Assessment System for Commercial Tasks

Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction

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