Analysis of Growth Performance in Swine Based on Machine Learning

Lee, Woongsup; Ham, Younghwa; Ban, Tae-Won; Jo, Ohyun

doi:10.1109/access.2019.2951522

Cited by 15 publications

(11 citation statements)

References 23 publications

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“…In addition to RNN-based traffic load prediction schemes, we also considered conventional machine learning-based mobile prediction schemes as benchmark schemes. Specifically, the random forest, linear regression and support vector machine (SVM) with the linear kernel are used for the mobile traffic load prediction [2,16]. The performance evaluation is conducted using a computer equipped with an Intel Core i7-8750H, an NVIDIA GeForce RTX 2080 Ti and 16 GB of memory, and the simulation codes is implemented using Python 3.7 and Tensorflow 1.4.1.…”

Section: Resultsmentioning

confidence: 99%

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

Ahmadzai

Lee

2022

Electronics Letters

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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.

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Section: Resultsmentioning

confidence: 99%

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

Ahmadzai

Lee

2022

Electronics Letters

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“…Providing optimal indoor environmental conditions provides optimum welfare and productivity in any livestock [ 7 , 12 , 46 ]. Pigs are highly sensitive to humidity more than temperature.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, ref. [ 10 ] utilized an ANN model to predict a swine building’s temperature and relative humidity, whereas the growth performance of swine was analyzed with decision trees and support vector machines by a previous study [ 12 ]. Likewise, ref.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi

Basak

Sihalath

et al. 2021

Animals

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Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence the homeostasis of livestock animals reared in closed barns. Further, predicting IAT and IRH encourages farmers to think ahead actively and to prepare the optimum solutions. Therefore, the primary objective of the current literature is to build and investigate extensive performance analysis between popular ML models in practice used for IAT and IRH predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. This study used accessible factors such as external environmental data to simulate the models. In addition, three different input datasets named S1, S2, and S3 were used to assess the models. From the results, RFR models performed better results in both IAT (R2 = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R2 = 0.9594; RMSE = 2.429; MAE = 1.47) prediction among other models particularly with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Overall, the current study demonstrates a better model among other models to predict IAT and IRH of a naturally ventilated swine building containing animals with fewer input attributes.

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“…To address this challenge, it may be feasible to use machine learning, which has previously been used to find hidden regularity in irregular data. For the present case, for example, the data may seem irregular when they are handled using conventional manual data processing methods, even though in reality they may have some regularity [11]- [13]. Machine learning could be an effective means of investigating these characteristics given its ability to yield appropriate parameters by considering various external environmental parameters together at the same time.…”

Section: Previous Studiesmentioning

confidence: 99%

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Byun

Cho

et al. 2021

IEEE Access

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Given the ever-increasing interest in the Internet of Underwater Things (IoUT), various studies are ongoing to solve some of the practical problems affecting the development of underwater wireless communication. The main problems are related to the use of acoustic waves in a water medium, in which extremely high propagation loss and drastic channel fluctuation are common. On the basis of hands-on experience and measurements made in real underwater environments, the conventional Adaptive Modulation and Coding (AMC), which uses the high correlation between SNR (Signal to Noise Ratio) and BER (Bit Error Rate), might not be affordable in underwater environments because the normal correlation between SNR and BER almost disappears altogether. This work therefore collectively takes into account multiple quality factors of communication at the same time by creating, analysing and validating the machine learning model to predict the most adequate communication parameters to solve the problem. The dataset of underwater wireless communication used in the learning models was obtained from measurements made in a real underwater environment near the Gulf of Incheon, South Korea, using a practical testbed designed and implemented by the authors. The estimated network throughput based on the communications parameters predicted using the machine learning models was enhanced by up to 25% compared with the conventional handcraft method.

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Analysis of Growth Performance in Swine Based on Machine Learning

Cited by 15 publications

References 23 publications

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

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

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

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