Fostering Distance Training Programme (DTP) Students’ Access to Semester Examination Results via SMS at University of Rwanda-College of Education

This paper presents a situation analysis and implementation of Distance Training Programme (DTP) Semester Examination Results Access (SERA) through Short Message Service (SMS) available anytime and anywhere. ‘Texting’ or SMS mobile phone messaging is rapidly increasing communication in business and community service. The prompting scenario addressed in this paper is the release of semester examination results (marks) at one and only one place: The UR-CE main campus notice board, regardless of the geographical dispersion of intended audience: The DTP students. To study the DTP students’ access to semester examination results via mobile SMS implementation possibilities, analysis of available telecommunication infrastructures, and services coverage in the country (Rwanda) was done. Then a survey was conducted on the information system implementation status at UR-CE, and the DTP management staff and students perceptions toward mobile SMS to support DTP administration communications. In the paper we discuss the inclusion of SMS technology among the DTP administration communication channels to permit DTP students at UR-CE access the semester examination results through mobile SMS technology. The SMS pull method is proposed for implementation in regards to the SERA communication. The implementation success of DTP semester examination results access via SMS is likely to improve the communication to both DTP administration and students sides. Keywords: Distance Training Programme, DTP, UR-CE, Semester Examination Results Access, SERA.

Designing a Mobile SMS System to Support Offline Distance Training Programme Communications at University of Rwanda

This paper reports on a study carried out to establish the basis of designing an Offline Distance Training Programme Mobile Short Message Service Communication System (ODTPmSMSCS) intended to improve administrative communication within the Distance Training Programme (DTP) at University of Rwanda-College of Education (UR-CE). The paper outlines the ODTPmSMSCS structure, potential users and the associated implementation challenges such as lack of a policy for SMS to be among the official communication channels of the DTP. In addition to the implemented prototype, the paper presents some recommendations to be considered for implementation by the UR-CE managers to assure that users are committed to use and allow for sustainability of ODTPmSMSCS to take advantage of the technologies the DTP students already have. Keywords: DTP students, Distance Training Programme, UR-CE, University of Rwanda

A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies.

This research article published by Hindawi, 2019Characterization of smallholder farmers has been conducted in various researches by using machine learning algorithms, participatory and expert-based methods. All approaches used end up with the development of some subgroups known as farm typologies. The main purpose of this paper is to highlight the main approaches used to characterize smallholder farmers, presenting the pros and cons of the approaches. By understanding the nature and key advantages of the reviewed approaches, the paper recommends a hybrid approach towards having predictive farm typologies. Search of relevant research articles published between 2007 and 2018 was done on ScienceDirect and Google Scholar. By using a generated search query, 20 research articles related to characterization of smallholder farmers were retained. Cluster-based algorithms appeared to be the mostly used in characterizing smallholder farmers. However, being highly unpredictable and inconsistent, use of clustering methods calls in for a discussion on how well the developed farm typologies can be used to predict future trends of the farmers. A thorough discussion is presented and recommends use of supervised models to validate unsupervised models. In order to achieve predictive farm typologies, three stages in characterization are recommended as tested in smallholder dairy farmers datasets: (a) develop farm types from a comparative analysis of more than two unsupervised learning algorithms by using training models, (b) assess the training models' robustness in predicting farm types for a testing dataset, and (c) assess the predictive power of the developed farm types from each algorithm by predicting the trend of several response variables

Characteristics of smallholder dairy farms by association rules mining based on apriori algorithm

This research article published International Journal of Society Systems Science (IJSSS), Vol. 11, No. 2, 2019Characteristics of smallholder dairy farmers across regions are highly similar. However, introduction of improved farm management practices and extension support can be effective if specific constraints are identified for each farm typology. So far, approaches used to formulate farm types and characterise farming systems are not tailored to studying hidden patterns from farm datasets. Using the apriori association rules mining algorithm, characteristics of four smallholder dairy farm types are studied. Applying the power of the ArulesViz package, frequent items were visualised. These visuals which display some hidden attributes, solidified understanding on the key determinants for change in the studied farm types. The hidden smallholder farm characteristics were identified in addition to those given by cluster analysis in preliminary studies. Characterising smallholder farm data by using association rules mining is recommended in order to understand such systems in terms of what/how the majority practice rather than basing on cluster averages

Mobile application development framework to support farming as a business via benchmarking: the case of Tanzania

This research article published by the International Journal of Advanced Computer Research (IJACR), Volume-9 Issue-45 -2019Contributions from various researchers and scholars have made major advances relevant to a wide range of mobile applications at various scales. Although current agricultural and rural development (ARD) systems have features that are needed for farming as a business (FAAB). It is established that all of them have limitations in realising benchmarking as their basic principle. Common limitations across all systems, include 1) scarcity of data for modelling, evaluating, and applying benchmarking and 2) inadequate knowledge systems that effectively communicate benchmarking results to farmers. These two limitations are greater obstacles to developing useful mobile applications than gaps in conceptual theory or available methods for using “Farming as a Business via Benchmarking (FAABB)”. This paper presents reviews of the current state of mobile application development frameworks, focusing on their capabilities and limitations to support FAABB. The paper presents a new framework to support FAABB in the Tanzanian context, which is implemented through a FAABB cyber studio hosted at the Nelson Mandela –African Institution of Science and Technology (NM-AIST) in Tanzania. The framework promises to address not only the knowledge codification problem, but also the need for a cultural change among agricultural researchers to ensure that data for addressing the range of use-cases are available for future mobile application development. The FAABB framework has been tested in the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) and its initial results provides a useful starting point for developing m-apps for addressing ARD challenges in developing countries

Review of Agricultural and Rural Development System Models and Frameworks to Support Farming as a Business via Benchmarking: The Case of Tanzania

This research article published by the International Journal of Computing and Digital Systems, 2019This paper presents a review of the current state of agricultural and rural development (ARD) system frameworks, focusing on their capabilities and limitations to support farming-as-a-business via benchmarking (FAABB). Presented and discussed include the state of system models in relation to five modelling views of the ARD systems, namely: (i) defining factors for agricultural echo systems, (ii) farm characterization and management practices, (iii) simulation systems for predictable farm data, (iv) limiting factors for agricultural optimization, and (v) performance estimation through benchmarking. Also, the paper proposes a new framework to support FAABB in Tanzania that is being tested through various use-cases in the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) with a FAABB Cyber Studio hosted at the Nelson Mandela – African Institution of Science and Technology (NM-AIST) also in Tanzania. The FAABB setup at NM-AIST promises to address not only the agricultural knowledge codification problem, but also the need for cultural change among agricultural researchers to ensure that data for addressing a range of use-cases is available for future mobile application development. The proposed FAABB framework provides a useful starting point for addressing limitations of existing frameworks and considering a ubiquitous m-app development framework for targeted ARD research in developing countries

Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa.

This research article published by Springer Nature Switzerland AG., 2020This study is concerned with developing predictive models using machine learning techniques to be used in identifying factors that influence farmers' decisions, predict farmers' decisions, and forecast farmers' demands relating to breeding service. The data used to develop the models comes from a survey of small-scale dairy farmers from Tanzania (n = 3500 farmers), Kenya (n = 6190 farmers), Ethiopia (n = 4920 farmers), and Uganda (n = 5390 farmers) and more than 120 variables were identified to influence breeding decisions. Feature engineering process was used to reduce the number of variables to a practical level and to identify the most influential ones. Three algorithms were used for feature selection, namely: logistic regression, random forest, and Boruta. Subsequently, six predictive models, using features selected by feature selection method, were tested for each country-neural network, logistic regression, K-nearest neighbor, decision tree, random forest, and Gaussian mixture model. A combination of decision tree and random forest algorithms was used to develop the final models. Each country model showed high predictive power (up to 93%) and are ready for practical use. The use of ML techniques assisted in identifying the key factors that influence the adoption of breeding method that can be taken and prioritized to improve the dairy sector among countries. Moreover, it provided various alternatives for policymakers to compare the consequences of different courses of action which can assist in determining which alternative at any particular choice point had a high probability to succeed, given the information and alternatives pertinent to the breeding decision. Also, through the use of ML, results to the identification of different clusters of farmers, who were classified based on their farm, and farmers' characteristics, i.e., farm location, feeding system, animal husbandry practices, etc. This information had significant value to decision-makers in finding the appropriate intervention for a particular cluster of farmers. In the future, such predictive models will assist decision-makers in planning and managing resources by allocating breeding services and capabilities where they would be most in demand

A Framework for Timely and More Informative Epidemic Diseases Surveillance: The Case of Tanzania

This research article published by the Journal of Health Informatics in Developing Countries, 2018Background: A number of health facilities in the United Republic of Tanzania use different Hospital Management Information Systems (HoMISs) for capturing and managing clinical and administrative information for report generation. Despite the potentials of the data in the systems for use in epidemic diseases surveillance, timely extraction of the data for integrated data mining and analysis to produce more informative reports is still a challenge. This paper identifies the candidate data attributes for epidemic diseases surveillance to be extracted and analyzed from the Government of Tanzania Hospital Management Information System (GoT-HoMIS). It also examines the current reporting setup for epidemic diseases surveillance in Tanzania from the health facilities to the district, regional, and national levels. Methods: The study was conducted at the Ministry of Health, Community Development, Gender, Elderly, and Children (MoHCDGEC), Tumbi Designated Regional Referral Hospital (TDRRH), Muhimbili University of Health and Allied Sciences (MUHAS), and Mzumbe Health Centre, all in the United Republic of Tanzania. A total of 10 key informants (medical doctors, epidemiologists, and focal persons for various health information systems in Tanzania) were interviewed to obtain primary data. Data entry process in the GoT-HoMIS was also observed. Documents were reviewed to broaden understanding on several aspects. Results: All the respondents (100%) suggested patients’ gender, age, and residence as suitable attributes for epidemic diseases surveillance. Other suggested attributes were occupation (85.71%), diagnosis (57.14%), catchment area population (57.14%), vital status (57.14%), date of onset (57.14%), tribe (42.86%), marital status (42.86%), and religion (14.29%). Timeliness, insufficient immediate particulars on an epidemic-prone case(s), aggregated data limiting extensive analytics, missing community data and ways to analyze rumors, and poor data quality were also identified as challenges in the current reporting setup. Conclusion: A framework is proposed to guide researchers in integrating data from health facilities with those from social media and other sources for enhanced epidemic disease surveillance. Data entrants in the systems should also be informed on the essence and applications of data they feed, as quality data are the roots of quality reports

Leveraging peer-to-peer farmer learning to facilitate better strategies in smallholder dairy husbandry

This research article published by SAGE Journal, 2020Peer-to-peer learning paradigm is seldom used in studying how farmers can increase yield. In this article, agent-based modelling has been applied to study the chances of dairy farmers increasing annual milk yield by learning better farming strategies from each other. Two sets of strategies were considered; in one set (S), each farmer agent would possess a number of farming strategies based on their knowledge, and in a second set (S'), farmer agents would possess farming strategies that they have adopted from their peers. Regression models were used to determine litres of milk that could be produced whenever new strategies were applied. By using data from Ethiopia and Tanzania, 28 and 25 determinants for increase in milk yield were fitted for the two countries, respectively. There was a significant increase in average milk yield as the farmer agents interacted and updated their S'– from baseline data, average milk yield of 12.7 ± 4.89 and 13.62 ± 4.47 to simulated milk yield average of 17.57 ± 0.72 and 20.34 ± 1.16 for Tanzania and Ethiopia, respectively. The peer-to-peer learning approach details an inexpensive method manageable by the farmers themselves. Its implementation could range from physical farmer groups to online interactions

FEASIBILITY STUDY OF USING A DIGITAL COMPUTER AS A VARIABLE-AMPLITUDE VARIABLE-FREQUENCY OSCILLATOR

Previous studies have shown that a dual-excited synchronous generator (a machine which has two identical field windings on the rotor: one on the d-axis and the other on the q-axis) can run asynchronously at any speed and still generate voltage at constant frequency. This capability of the generator can be utilized for variable-speed constant-frequency operation if the windings are both excited by slip frequency alternating currents. The main problem in such schemes is the design of a source which can supply such control signals whose frequency changes with the slip. This thesis is a feasibility study of using a digital computer as a variable-amplitude variable-frequency oscillator; which generates the two-phase sinusoidal excitation control signals for the dual-excited synchronous gener­ator. In this thesis, the oscillator control methodology is formulated and ex­perimentally verified. The performance of the oscillator is experimentally investigated. The experimental results of the methodology verification and the oscillator performance tests are presented. The results show that it is feasible to use a digital computer as a variable-amplitude variable-frequency oscillator. The oscillator software-based design is flexible for generating different types of signal waveforms. By a single control variable, the software can be configured to vary the oscillator frequency range, and to greatly reduce (almost eliminate) the harmonic distortion of the output signals. Some recommendations for further research are included