Prediction of average daily gain of swine based on machine learning

Ham²,

Ban

et al. 2019

IEEE Access

Self Cite

Estimating the growth performance in pigs is important in order to achieve a high productivity of pig farming. We herein analyze and verify the machine learning based estimations for the growth performance in swine which includes the daily gain of body weight (DG), feed intake (FI), required growth period for growing/finishing phase (GP), and marketed-pigs per sow per year (MSY), based on the farm specific data and climate, i.e., temperature, humidity, initial age (IA), initial body weight (IBW), number of pigs (NU) and stocking density (SD). The growth data used in our work is collected from 55 pig farms which are located across South Korea for the period between October 2017 and September 2018. In the estimation of growth performance, four machine learning schemes are applied, which are the logistic regression, linear support vector machine (SVM), decision tree, and random forest. Through the evaluation, we confirm that the accuracy of estimation for growth performance can be improved by 28% using machine learning techniques compared to the base line performance which is obtained by the ZeroR classifier. We also find that the accuracy of estimation is heavily dependent on the pre-process of growth data.

Section: Growth Data Of Swinesupporting

confidence: 62%

Section: Growth Data Of Swinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of Growth Performance in Swine Based on Machine Learning

Ham²,

Ban

et al. 2019

IEEE Access

Self Cite

Estimation of best corrected visual acuity based on deep neural network

Kim

et al. 2022

Sci Rep

In this study, we investigated a convolutional neural network (CNN)-based framework for the estimation of the best-corrected visual acuity (BCVA) from fundus images. First, we collected 53,318 fundus photographs from the Gyeongsang National University Changwon Hospital, where each fundus photograph is categorized into 11 levels by retrospective medical chart review. Then, we designed 4 BCVA estimation schemes using transfer learning with pre-trained ResNet-18 and EfficientNet-B0 models where both regression and classification-based prediction are taken into account. According to the results of the study, the predicted BCVA by CNN-based schemes is close to the actual value such that 94.37% of prediction accuracy can be achieved when 3 levels of difference can be tolerated during prediction. The mean squared error and $$R^2$$ R 2 score were measured as 0.028 and 0.654, respectively. These results indicate that the BCVA can be predicted accurately for extreme cases, i.e., the level of BCVA is close to either 0.0 or 1.0. Moreover, using the Guided Grad-CAM, we confirmed that the macula and the blood vessel surrounding the macula are mainly utilized in the prediction of BCVA, which validates the rationality of the CNN-based BCVA estimation schemes since the same area is also exploited during the retrospective medical chart review. Finally, we applied the t-distributed stochastic neighbor embedding to examine the characteristics of CNN-based BCVA estimation schemes. The developed BCVA estimation schemes can be employed to obtain the objective measurement of BVCA as well as the medical screening of people with poor access to medical care through smartphone-based fundus imaging.

A mobile traffic load prediction based on recurrent neural network: A case of telecommunication in Afghanistan

Ahmadzai

2022

Electronics Letters

Self Cite

This paper investigates the prediction of mobile traffic load based on four variants of recurrent neural networks, which are the simple long short-term memory (LSTM), stacked LSTM, gated recurrent unit (GRU) and bidirectional LSTM. In the considered schemes, the mobile traffic load of 15 min ahead of time is estimated based on the previous mobile traffic load data. The performance of the proposed scheme is verified using realistic traffic load data collected from the base station located in Kabul city, Afghanistan, which belongs to the SALAAM telecommunication operator during December 2020 and January 2021. Through performance evaluation, the authors confirm that the traffic load can be predicted with high accuracy using considered schemes and the GRU-based scheme outperforms other schemes in terms of accuracy.