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Towards a Framework for Enabling Operations of Livestock Information Systems in Poor Connectivity Areas
Herbert Peter Wanga,
Khamisi Kalegele
Issue:
Volume 4, Issue 3, June 2015
Pages:
42-49
Received:
11 April 2015
Accepted:
24 April 2015
Published:
7 May 2015
Abstract: Livestock farming is one of the major agricultural activities in the country that is contributing towards achieving development goals of the national Growth and Reduction of Poverty (NSGRP). Smallholder livestock keepers depend on the information from the livestock field officers for sound decision making. Mobile application based solutions, which are currently widely proposed to facilitate the process, fail to perform in poor connectivity areas. This study proposes a machine learning based framework which will enhance the performance of mobile application based solutions in poor connectivity areas. The study used primary data, and secondary data. The primary data were collected through surveys, questionnaires, interviews, and direct observations. Secondary data were collected through books, articles, journals, and Internet searching. Open Data Kit (ODK) tool was used to collect responses from the respondents, and their geographical positions. We used Google earth to have smallholder livestock keepers’ distribution map. Results show that smallholder livestock keepers are geographically scattered and depend on the field livestock officers for exchange of information. Their means of communication are mainly face to face, and mobile phones. They do not use any Livestock Information System. The proposed framework will enable operations of Livestock Information System in poor connectivity area, where majority of smallholder livestock keepers live. This paper provides the requirements model necessary for designing and development of the machine learning-based application framework for enhancing performance of livestock mobile application systems, which will enable operations of livestock information systems in poor connectivity areas.
Abstract: Livestock farming is one of the major agricultural activities in the country that is contributing towards achieving development goals of the national Growth and Reduction of Poverty (NSGRP). Smallholder livestock keepers depend on the information from the livestock field officers for sound decision making. Mobile application based solutions, which ...
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Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization
Issue:
Volume 4, Issue 3, June 2015
Pages:
50-55
Received:
21 April 2015
Accepted:
1 May 2015
Published:
13 May 2015
Abstract: Data missing usually happens in the process of data collection, transmission, processing, preservation and application due to various reasons. In the research of face recognition, the missing of image pixel value will affect feature extraction. How to extract local feature from the incomplete data is an interesting as well as important problem. Nonnegative matrix factorization (NMF) is a low rank factorization method for matrix and has been successfully used in local feature extraction in various disciplines which face recognition is included. This paper mainly deals with this problem. Firstly, we classify the patterns of image pixel value missing, secondly, we provide the local feature extraction models basing on nonnegative matrix factorization under different types of missing data, thirdly, we compare the local feature extraction capabilities of the above given models under different missing ratio of the original data. Recognition rate is investigated under different data missing pattern. Numerical experiments are presented and conclusions are drawn at the end of the paper.
Abstract: Data missing usually happens in the process of data collection, transmission, processing, preservation and application due to various reasons. In the research of face recognition, the missing of image pixel value will affect feature extraction. How to extract local feature from the incomplete data is an interesting as well as important problem. Non...
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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
Issue:
Volume 4, Issue 3, June 2015
Pages:
56-64
Received:
30 April 2015
Accepted:
8 May 2015
Published:
27 May 2015
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.
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 reques...
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