Abalone Life Phase Classification with Deep Learning

Sahin, Egemen; Saul, Can Jozef; Ozsarfati, Eran; Yilmaz, Alper

doi:10.1109/iscmi.2018.8703232

Cited by 11 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of epochs was set at 20. We then compare it with the neutral network model (13) proposed by Sahin et al The neural network model consists of a total of 4 layers with 2 hidden layers and the number of nodes is 70, 65, 65 and 3 respectively. It also uses batch normalization, with 120 epochs, a batch size of 40, and a learning rate of 0.001.…”

Section: Fig3 Structure Of Neural Network For Abalone Ringmentioning

confidence: 99%

Validation Check of Neural Network using Monte Carlo Stochastic Optimization Method (MOST)

Hebishima

Inage

2023

The Proceedings of the 11th IIAE International Conference on Industrial Application Engineering 2023

View full text Add to dashboard Cite

Recently, artificial intelligence has been applied in various fields, and the core optimization methods are required as an indispensable tool in engineering design, and the selection of high-precision optimization methods has become important. However, traditional optimization methods present challenges such as guaranteed convergence, convergence speed, and include tuning constants. Therefore, we developed the Monte Carlo Stochastic Optimization (MOST), which is an optimization method using the Monte Carlo method. MOST first divides the search region into two parts and integrates them by Monte Carlo method. The integrals are then compared and the side with the smaller integral value is selected. The selected area is then split and integrated again in two, selecting the smaller side of the integration. By repeating the selection and integration in this way, the optimum solution is obtained. In this paper, we apply and validate the proposed method to optimize the weighting factors of neural networks for wine evaluation problem, used car identification problem, and abalone age determination problem.

show abstract

Section: Fig3 Structure Of Neural Network For Abalone Ringmentioning

confidence: 99%

Validation Check of Neural Network using Monte Carlo Stochastic Optimization Method (MOST)

Hebishima

Inage

2023

The Proceedings of the 11th IIAE International Conference on Industrial Application Engineering 2023

View full text Add to dashboard Cite

show abstract

“…Abalone rings (age) in 1-29 are related to 8 parameters of "sex", "length, diameter", "height", "whole weight", "shucked weight", "viscera weight", "shell weight" ( 22), (23) As described above, abalone rings are widely distributed up to 1 -29, and it is difficult to predict them directly, so Egemen Sahin et al classified them into 3 major groups of "< 9", "9 < or ≦ 18", and "18 or more" before calculating (12) . As shown in Table 1, for this problem, the hidden layer is one layer with nine nodes.…”

Section: Abalone Ring Classification Problemmentioning

confidence: 99%

“…The neural network achieved an accuracy rate of 73.8% for the teaching signal and 69.6% for the TEST signal, while the SiNG achieved an accuracy rate of 86%, which exceeded that of the neural network. This ring classification problem is rather a difficult problem to solve in neural networks (12) , and SiNG has achieved a useful accuracy rate.…”

Section: Abalone Ring Classification Problemmentioning

confidence: 99%

Verification of a New Machine Learning SiNG in Classification Problems by a Large-scale Benchmark Problems

Arakaki

Dozono

Hebishima

et al. 2023

The Proceedings of the 11th IIAE International Conference on Industrial Application Engineering 2023

View full text Add to dashboard Cite

This paper aims to propose and validate a new machine learning for classification problems. Classification problems are important in engineering applications of machine learning, including pattern recognition. Neural networks and other classification problem evaluation methods already exist and have been well validated. We propose a new classification method: SiNG, which has a simple configuration and a much lower computational load than neural networks. This paper applies the proposed SiNG to benchmark problems that consists of large input parameters and is of practical importance, and performs further verification of the SiNG. As a result, we confirmed the effectiveness of the SiNG even in large-scale benchmark problems.

show abstract

“…Some researchers have designed a set of automatic threshing, image acquisition, extraction of grain length, grain width and other data, and automatic bagging. By this device, grain characteristics can be automatically extracted, which is highly correlated with yield and can be used for damage prediction [ 32 ]. Lossless yield prediction is mainly based on panicle cutting, color feature extraction, and regression model with yield through RGB images.…”

Section: Deep Learning Algorithm and Its Application In Crop Yield Pr...mentioning

confidence: 99%

Research on Rice Yield Prediction Model Based on Deep Learning

Han

Liu

He³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people’s health level constantly, and the world population is constantly climbing to a new peak. With the continuous development of deep learning in recent years, its advantages are constantly displayed, especially in the aspect of image recognition and processing, it drives into the distance. Thanks to the superiority of deep learning in image processing, the combination of remote sensing images and deep learning has attracted more attention. To simulate the four key factors of rice yield, this article tries a regression model with a combination of various characteristic independent variables. In this article, the selection of the best linear and nonlinear regression models is discussed, the prediction performance and significance of each regression model are analyzed, and some thoughts are given on estimation of actual rice yield.

show abstract

Abalone Life Phase Classification with Deep Learning

Cited by 11 publications

References 7 publications

Validation Check of Neural Network using Monte Carlo Stochastic Optimization Method (MOST)

Validation Check of Neural Network using Monte Carlo Stochastic Optimization Method (MOST)

Verification of a New Machine Learning SiNG in Classification Problems by a Large-scale Benchmark Problems

Research on Rice Yield Prediction Model Based on Deep Learning

Contact Info

Product

Resources

About