Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System
American Journal of Software Engineering and Applications
Volume 4, Issue 3, June 2015, Pages: 56-64
Received: Apr. 30, 2015;
Accepted: May 8, 2015;
Published: May 27, 2015
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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
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.
Herbert Peter Wanga,
Designing a Machine Learning – Based Framework for Enhancing Performance of Livestock Mobile Application System, American Journal of Software Engineering and Applications.
Vol. 4, No. 3,
2015, pp. 56-64.
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