Application of Artificial Neural Network (ANN) for Animal Diet Formulation Modeling

Saxena, Pratiksha; Parasher, Yaman

doi:10.1016/j.procs.2019.05.018

Cited by 12 publications

(8 citation statements)

References 6 publications

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“…ANN, according to several authors [3][4][5][6][7][8][9][11][12][13][14][15], is a promising tool in analyzing and predicting lots of complex issues that exist in animal studies. As indicated by Fernández et al [10], its advantage over traditional analytical methods is due to its accuracy of estimation as well as its ability to generalize even when less significant data is entered.…”

Section: Discussionmentioning

confidence: 99%

“…The application of artificial neural networks (ANN) has already become commonplace in modelling as well as in the optimization of several processes in technical studies [1,2]. It is also increasingly becoming a subject of interest to scientists engaged in issues concerning animal breeding and use and optimal nutrition [3][4][5][6][7][8]. This has been due, partly though, to the need to seek alternative methods with high potentials of application and offer reliable information (data) that enhances forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Topczewska

Kwater

2020

Sustainability

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The paper suggests the application of artificial neural networks (ANN) for the analysis of variables that significantly impact on the results of Hucul horses that participate at the National Breeding and Utility Championships for Hucul horses. The study exploits the results obtained during 2009–2015. The research material collected enabled the creation of a set of input data (for the artificial neural network), out of which independent learning and testing sets were isolated. The neural classification system in form of a multi-layered artificial neural network suggested in this paper was implemented in the programming environment Matlab, the 8.1.0.604 version. Each horse was described using features in three models. Experimental simulations were carried out separately for each model, conducting the learning and testing simulation process 10 times. In accepting the division of the evaluated group of horses into 10 classes for the analysis of the issue both the expert and network designated the classes, not without reservations due to imprecision of demarcations. The increase in class numbers would result in increased accuracy of selection (allocation to varied classes) of individuals. The average for 10 network responses which was 77% suggest an identical or a very similar horse class when compared with the expert’s value. Preliminary results of the application of artificial neural networks in predicting the utility value of Hucul horses, relying on a specific set of features seem promising.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Topczewska

Kwater

2020

Sustainability

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“…It is possible to apply mathematical models to forecast the ideal feed for animals blend for an animal, such as the best proportion of cost, nutrients, and water (Saxena and Parasher, 2019).…”

Section: Nutritional Optimizationmentioning

confidence: 99%

The Implementation of Artificial Intelligence in Veterinary Diseases and Practices: A Comprehensive Review

Gulzar,

Hussain

2023

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There currently exists no regulatory structure controlling the development and application of artificial intelligence (AI) tools in veterinary medicine and infectious diseases (viral, bacterial, and parasitic), in contrast to the heavily regulated field of the development of medical devices (which include AI) in human medicine. When introducing such potent technology that might affect the welfare of both humans and animals, we must acknowledge the significance of exercising prudence and accountability. As veterinary experts, it is therefore our responsibility to hold ourselves responsible for guaranteeing the security, precision, and dependability of any AI technologies we create or use. Effective outcomes in an area where data misunderstanding, even hostile misinterpretation, is commonplace depending on data transparency and use. The AI tools can be tricked by deliberate manipulation of data. It has been shown how image disruptions that the human eye interprets as slight changes can cause an AI tool to incorrectly show a tabby cat image as more likely to be guacamole. Although this is a humorous example, it raises a very important question regarding what happens if the self-driving car's camera is covered, causing the system to misinterpret blurry images of pedestrians. Expanding the use of an evidence-based approach to creating AI is necessary, as is supporting openness, continuous quality control, and a risk-benefit analysis. We think AI tools should only be used in clinical settings once they have undergone thorough validation. Once that is done, we think they should be continuously checked in clinical settings. Furthermore, it is imperative that veterinary practitioners receive precise instructions and guidelines for the best possible use of these tools.

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“…However, the focus of the study is to find feeds at a minimum cost so that the authors made use of the labor cost for the feed cost. Saxena and Parasher (11) , on the other hand, developed mathematical models for animal diet formulation using artificial neural network (ANN).…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Optimizing Nutrient in Daily Lunch Intake among College Students in Cagayan de Oro City, Philippines using Linear Programming

Namoco¹,

Abecia²,

Pailagao³

2021

IJST

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Objectives: This study aims to develop a decision support system to find food choices that will optimize nutrient intake among college students given their food choices and budget by formulating and solving linear programming models. Methods: Survey interviews among college students in various colleges and universities around Cagayan de Oro City were conducted to determine the foods which they usually eat as well as their usual budget for lunch. Moreover, cafeterias and fast-food chains located near school areas were also visited to ascertain the kind of foods they serve to students as well as the corresponding prices per serving of these foods. Using this information such as costs per food serving, nutrient content, student meal budget and their food preferences, linear programming models are then formulated and solved while making sure that individual nutrient requirements are met. The models are then incorporated into an excel-based program to present a userfriendly decision support system (DSS) for determining lunch menu options. Findings: Results of the study show various lunch food combinations for male and female college students. Recommended food menu costs vary according to sex, age, BMI classification and level of daily activity. For example, the lowest for normal BMI male between 14-18 years old is PhP 40 while the lowest food menu cost for 14-18 years old female with normal BMI is PhP 45. In particular, the DSS developed in this study allow students to input their preferences such as choice of drink, viand, number of servings preferred and budget allotted for a particular meal, as well key-in their personal attributes (such as height, weight, physical activities engaged in) which are then used as basis for computing the recommended lunch menu options. Novelty. This study provides students the guide for a more nutritionally-filled lunch menu that is within their budget.

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Application of Artificial Neural Network (ANN) for Animal Diet Formulation Modeling

Cited by 12 publications

References 6 publications

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

The Implementation of Artificial Intelligence in Veterinary Diseases and Practices: A Comprehensive Review

A Decision Support System for Optimizing Nutrient in Daily Lunch Intake among College Students in Cagayan de Oro City, Philippines using Linear Programming

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