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.

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.

The paper provides a prototype for multimedia content delivery with reduced channel code rate from conventional Non-Transmittable Codewords Enhanced Viterbi Algorithm. The code rate reduction was simulated using VB.NET Viterbi simulator available at College of Informatics and Virtual Education-University of Dodoma. The study approximates Uplink and downlink speeds limits of the prototype using High Speed Packet Access Evolved technology by assuming all other parameters remain constant. The uplink and downlink of the prototype is clearly presented. The code rate of 1/3 was obtained by simulating different 8-bits patterns. This code rate of 1/3 enabled reduction of encoder output bits from 48-bits to 24-bits, therefore, few bits would be sent to the network and bandwidth conservation is attained. This makes the prototype to be the good choice for low network bandwidth channel. In addition, the reduced code rate will reduce the expenses of user internet bundles, because number of MBs to be charged will be smaller. This prototype for multimedia delivery over network has three benefits, high data transmission reliability due to adopted NTC Enhanced Viterbi, minimum network bandwidth utilization and satisfied uplink and downlink access speed.

In this paper, authors developed an intelligent subsystem which manages training set, finds high accuracy models, selects best model to be used, computes prediction, stores in the database, and sends to the user interface through internet during online mode, and in offline mode through developed log file and filtering method. The intelligent subsystem is one of solutions which support mobile phone systems to be executed offline, on mobile device. Prediction results can be locally stored in the database and log file while in presence of a fairly good connection environment. Thereafter, offline predictions are made available when a poor quality in connection comes. System development covers intelligent subsystem, MySQL database development, log file, filtering of information, and Android application. Apart from viewing predictions basing on ElasticNet algorithm, the system allows a user to register, login, and access livestock market, information portal, information request, information responses, and submit daily records.The filtering techniques are used to select part of information from the log file. The log file created on the last online activity is used to serve all the offline operations as follows: -1) Once the user has selected the offline option in the app's interface, he/she is directed to select the breed, sex and grade. 2) Upon submission of his/her input i.e. breed, sex and grade, the system reads the log and through the filtering algorithm, the predicted price is captured and displayed to the user.Overall, this paper makes the following six key contributions; (1) Creating a mechanism to select a subset of information between livestock keeper and server. The subset of information is used when internet fails. (2) To design and implement machine learning based sub-system that is able to select frequently asked requests and make them available to livestock keepers during the offline mode. (3) To develop a subsystem, this is ad hoc and performs model selection. (4) Model creation that evaluates more than one algorithm in our setting or context. Others compared algorithms in their settings. (5) Introduction of a smaller database that replicates information, which smallholder farmers requests online and stores them to be available during offline state. (6) Using database of the phone to store vital information.