A neural network approach to the selection of feed mix in the feed industry

Pathumnakul, Supachai; Piewthongngam, Kullapapruk; Apichottanakul, Arthit

doi:10.1016/j.compag.2009.04.001

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…Apichottanakul et al [16] have forecasted the market share of Thai rice. Other studies [17] related to the feed mix industry also showed that the production rate and dust level enhance the mill's capability by using ANN predicted well. Some research showed that the hybrid ARIMA-ANN model improved the accuracy of forecasting the resource usage in server virtualization as compared to ARIMA and ANN separately [18].…”

Section: Introductionmentioning

confidence: 73%

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

Selvakumar¹,

Satpathi²,

Kumar³

et al. 2021

ujaf

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The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting third party claim amount for different categories of vehicles based on the chosen characteristics of the data. In actuarial research, predicting the insurance claim amount for different vehicle categories is a challenging task, and minimal empirical research studies were done to forecast the claims. In the present study, the annual time series historical data were collected for a period of 34 years. We had built the machine learning predictive models to modeling the claim amount with different categories of vehicles effectively. In this context, we exhibited the feasibility of using a statistical machine learning approach such as Linear regression Model, the Exponential Smoothing Model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict the various categories of vehicles claim amount. The data were analyzed, compared, and the empirical analysis showed that Artificial Neural Network is a better predictive model among the other time series models based on performance evaluation metrics RMSE and MAPE with lesser variance. Therefore, the machine learning approach for forecasting third party claim amounts will help the Insurance Companies in India to provide a better predictive model, which ensures better claims settlement and management for different categories of vehicles.

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Section: Introductionmentioning

confidence: 73%

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

Selvakumar¹,

Satpathi²,

Kumar³

et al. 2021

ujaf

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“…Moreover, the numbers of reprocessed batches also rise along with production completion time and back orders. The situation is likely to give rise to customer complaints and even a loss in sales if it persists (Pathumnakul et al, 2009). Pathumnakul et al (2009) suggested a way to prevent the reprocessing caused by such a situation by detecting the feed rations prior to starting actual production.…”

Section: Yellow Corn Rice Branmentioning

confidence: 99%

“…The situation is likely to give rise to customer complaints and even a loss in sales if it persists (Pathumnakul et al, 2009). Pathumnakul et al (2009) suggested a way to prevent the reprocessing caused by such a situation by detecting the feed rations prior to starting actual production. They offer a prediction model created by the artificial neural network (ANN) as a tool for the production manager to communicate with the formulation manager such that the problematic feed rations can be distinguished from the good ones.…”

Section: Yellow Corn Rice Branmentioning

confidence: 99%

How soaring agricultural prices will impact the way we do feed business

Pathumnakul

Piewthongngam

2010

R. Bras. Zootec.

Self Cite

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The rising price of agricultural products leads to frequent change of feed recipe, which can cause a high number of reprocessing batches, elevating the overall cost of production. In this study, we proposed an artificial neural network to predict production rate. The conversion of production rate to production cost, the tips for data collection as well as tips for implementation of new feed cost estimation are also discussed. Being able to estimate production rate enables feed mills to improve their operations. In this study, we elaborate its application to feed scheduling (although the applications can be extended to other aspects such as productivity improvement, which goes beyond the scope of this particular study).

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“…A key technological advancement shaping the landscape of animal feed formulation is the integration of computational models and artificial intelligence [8,9]. This transformative pair facilitates the analysis of vast datasets, considering factors such as animal physiology, genetics, and environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Animal Feed Formulation—Connecting Technologies to Build a Resilient and Sustainable System

Akintan,

Gebremedhin,

Uyeh

2024

Animals

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The unprecedented challenges presented by the increase in global population have placed substantial demands on the livestock industry for human nutrition, necessitating heightened animal productivity and leading to an increased demand for natural resources to produce animal feed. Feed producers are leading the charge, consistently refining formulations to adapt to the evolving needs of livestock, driven in part by the cost of over 50% associated with feed production. This paper critically analyses the pressing issues within feed formulation, addressing the requirement for environmentally sustainable practices amidst the challenges of climate change. The exploration extends to how advanced decision support tools can enhance formulation techniques and profitability and contribute to environmental sustainability. Through an in-depth review of current feed formulation technologies, encompassing their applications and limitations, this study aims to enhance the existing knowledge base. Additionally, we examined future trends, highlighting the essential role of connecting technologies to establish a resilient and sustainable system. The emphasis is on the potential of formulation techniques to positively impact the environment and enhance the overall quality and performance of the animals. This paper provides actionable insights to improve animal production by examining feed formulation models and decision support tools. The anticipated outcome is a more informed and sustainable decision-making process, addressing the multifaceted challenges confronted by the livestock industry and making contributions to global efforts in climate change mitigation and environmental stewardship in animal production agriculture.

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A neural network approach to the selection of feed mix in the feed industry

Cited by 11 publications

References 25 publications

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

How soaring agricultural prices will impact the way we do feed business

Animal Feed Formulation—Connecting Technologies to Build a Resilient and Sustainable System

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