Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System

Herbert Peter Wanga; Nasir Ghani; Khamisi Kalegele

doi:doi:10.11648/j.ajsea.20150403.13

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Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System

Herbert Peter Wanga, Nasir Ghani, Khamisi Kalegele

Published in American Journal of Software Engineering and Applications (Volume 4, Issue 3)

Received: 30 April 2015 Accepted: 8 May 2015 Published: 27 May 2015

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Abstract

Smallholder livestock keepers live in rural areas where there is poor Internet connectivity. Many mobile based system designed do not function well in such areas. To address these concerns, an Android Mobile Application will be designed and installed on a smartphone. The application will have an easy to use Graphical User Interface (GUI) and request resources from the server through the Internet. This Intelligent Livestock Information System (ILIS) will be able to provide and predict feedback to the livestock keepers. This solution will also collect livestock data from livestock keepers through mobile phones. The data will then be sent to the database if connectivity is available or through synchronization if connectivity is poor. Livestock experts will be able to view data and respond to any query from livestock keepers. The system will also be able to learn and predict the responses using machine learning techniques. The goal of the ILIS is to provide livestock services to anyone at anytime, overcoming the constraints of place, time and character. Overall, this is a novel idea in the field of mobile livestock information systems. Along these, this paper presents the software, hardware and architecture design of the machine learning based livestock information system. Overall this solution embodies an artificial intelligence approach which combines hardware and software technologies. The design will leverage the Android ADK operating system and Android mobile devices or tablets. Our main contribution here is the intelligent livestock Information System, which is a novel idea in the field of mobile livestock information systems.

Published in	American Journal of Software Engineering and Applications (Volume 4, Issue 3)
DOI	10.11648/j.ajsea.20150403.13
Page(s)	56-64
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Intelligent Information System, Machine Learning, Android ODK, Requirements, Modeling, Artificial Intelligence, Livestock App

References

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APA Style

Herbert Peter Wanga, Nasir Ghani, Khamisi Kalegele. (2015). Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System. American Journal of Software Engineering and Applications, 4(3), 56-64. https://doi.org/10.11648/j.ajsea.20150403.13

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ACS Style

Herbert Peter Wanga; Nasir Ghani; Khamisi Kalegele. Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System. Am. J. Softw. Eng. Appl. 2015, 4(3), 56-64. doi: 10.11648/j.ajsea.20150403.13

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AMA Style

Herbert Peter Wanga, Nasir Ghani, Khamisi Kalegele. Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System. Am J Softw Eng Appl. 2015;4(3):56-64. doi: 10.11648/j.ajsea.20150403.13

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@article{10.11648/j.ajsea.20150403.13,
  author = {Herbert Peter Wanga and Nasir Ghani and Khamisi Kalegele},
  title = {Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System},
  journal = {American Journal of Software Engineering and Applications},
  volume = {4},
  number = {3},
  pages = {56-64},
  doi = {10.11648/j.ajsea.20150403.13},
  url = {https://doi.org/10.11648/j.ajsea.20150403.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20150403.13},
  abstract = {Smallholder livestock keepers live in rural areas where there is poor Internet connectivity. Many mobile based system designed do not function well in such areas. To address these concerns, an Android Mobile Application will be designed and installed on a smartphone. The application will have an easy to use Graphical User Interface (GUI) and request resources from the server through the Internet. This Intelligent Livestock Information System (ILIS) will be able to provide and predict feedback to the livestock keepers. This solution will also collect livestock data from livestock keepers through mobile phones. The data will then be sent to the database if connectivity is available or through synchronization if connectivity is poor. Livestock experts will be able to view data and respond to any query from livestock keepers. The system will also be able to learn and predict the responses using machine learning techniques. The goal of the ILIS is to provide livestock services to anyone at anytime, overcoming the constraints of place, time and character. Overall, this is a novel idea in the field of mobile livestock information systems. Along these, this paper presents the software, hardware and architecture design of the machine learning based livestock information system. Overall this solution embodies an artificial intelligence approach which combines hardware and software technologies. The design will leverage the Android ADK operating system and Android mobile devices or tablets. Our main contribution here is the intelligent livestock Information System, which is a novel idea in the field of mobile livestock information systems.},
 year = {2015}
}

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TY - JOUR
T1 - Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System
AU - Herbert Peter Wanga
AU - Nasir Ghani
AU - Khamisi Kalegele
Y1 - 2015/05/27
PY - 2015
N1 - https://doi.org/10.11648/j.ajsea.20150403.13
DO - 10.11648/j.ajsea.20150403.13
T2 - American Journal of Software Engineering and Applications
JF - American Journal of Software Engineering and Applications
JO - American Journal of Software Engineering and Applications
SP - 56
EP - 64
PB - Science Publishing Group
SN - 2327-249X
UR - https://doi.org/10.11648/j.ajsea.20150403.13
AB - Smallholder livestock keepers live in rural areas where there is poor Internet connectivity. Many mobile based system designed do not function well in such areas. To address these concerns, an Android Mobile Application will be designed and installed on a smartphone. The application will have an easy to use Graphical User Interface (GUI) and request resources from the server through the Internet. This Intelligent Livestock Information System (ILIS) will be able to provide and predict feedback to the livestock keepers. This solution will also collect livestock data from livestock keepers through mobile phones. The data will then be sent to the database if connectivity is available or through synchronization if connectivity is poor. Livestock experts will be able to view data and respond to any query from livestock keepers. The system will also be able to learn and predict the responses using machine learning techniques. The goal of the ILIS is to provide livestock services to anyone at anytime, overcoming the constraints of place, time and character. Overall, this is a novel idea in the field of mobile livestock information systems. Along these, this paper presents the software, hardware and architecture design of the machine learning based livestock information system. Overall this solution embodies an artificial intelligence approach which combines hardware and software technologies. The design will leverage the Android ADK operating system and Android mobile devices or tablets. Our main contribution here is the intelligent livestock Information System, which is a novel idea in the field of mobile livestock information systems.
VL - 4
IS - 3
ER -

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Author Information

Herbert Peter Wanga

School of Computational and Communication Sciences and Engineering (CoCSE), Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania
Nasir Ghani

College of Engineering, Department of Electrical Engineering, University of South Florida, Florida, USA
Khamisi Kalegele

School of Computational and Communication Sciences and Engineering (CoCSE), Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania

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APA Style

Herbert Peter Wanga, Nasir Ghani, Khamisi Kalegele. (2015). Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System. American Journal of Software Engineering and Applications, 4(3), 56-64. https://doi.org/10.11648/j.ajsea.20150403.13

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ACS Style

Herbert Peter Wanga; Nasir Ghani; Khamisi Kalegele. Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System. Am. J. Softw. Eng. Appl. 2015, 4(3), 56-64. doi: 10.11648/j.ajsea.20150403.13

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AMA Style

Herbert Peter Wanga, Nasir Ghani, Khamisi Kalegele. Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System. Am J Softw Eng Appl. 2015;4(3):56-64. doi: 10.11648/j.ajsea.20150403.13

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@article{10.11648/j.ajsea.20150403.13,
  author = {Herbert Peter Wanga and Nasir Ghani and Khamisi Kalegele},
  title = {Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System},
  journal = {American Journal of Software Engineering and Applications},
  volume = {4},
  number = {3},
  pages = {56-64},
  doi = {10.11648/j.ajsea.20150403.13},
  url = {https://doi.org/10.11648/j.ajsea.20150403.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20150403.13},
  abstract = {Smallholder livestock keepers live in rural areas where there is poor Internet connectivity. Many mobile based system designed do not function well in such areas. To address these concerns, an Android Mobile Application will be designed and installed on a smartphone. The application will have an easy to use Graphical User Interface (GUI) and request resources from the server through the Internet. This Intelligent Livestock Information System (ILIS) will be able to provide and predict feedback to the livestock keepers. This solution will also collect livestock data from livestock keepers through mobile phones. The data will then be sent to the database if connectivity is available or through synchronization if connectivity is poor. Livestock experts will be able to view data and respond to any query from livestock keepers. The system will also be able to learn and predict the responses using machine learning techniques. The goal of the ILIS is to provide livestock services to anyone at anytime, overcoming the constraints of place, time and character. Overall, this is a novel idea in the field of mobile livestock information systems. Along these, this paper presents the software, hardware and architecture design of the machine learning based livestock information system. Overall this solution embodies an artificial intelligence approach which combines hardware and software technologies. The design will leverage the Android ADK operating system and Android mobile devices or tablets. Our main contribution here is the intelligent livestock Information System, which is a novel idea in the field of mobile livestock information systems.},
 year = {2015}
}

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