Academic submissions (CoCIS)

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    Politics in the indigenization of library and information services : The way forward for East Africa
    (SAGE, 2010) Magara, Elisam
    This paper puts forward a case for the indigenization of LIS in the East African region. The paper recognizes that the politics of LIS in East African Region is influenced by both the global developments, agendas and goals in LIS (including World Summit on the Information Society (WSIS) and IFLA), and the political and socioeconomic environment of the indigenous society. This article emphasizes the need for the LIS profession to redefine itself in addressing the global needs in a knowledge society. This requires international cooperation and networking and a well-defined educational, research and development programme. For this to work within the East African economic and political blocks requires that a supportive environment is nurtured by the East African Community and the member countries.
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    Electricity theft in Kampala and potential ICT solutions
    (SPRINGER LNICST 147, 2014) Mbabazi-Mutebi, Ruth ; Sansa-Otim, Julianne ; Sebitosi, Ben ; Okou, Richard
    Electricity theft is the main source of non-technical losses in electricity distribution utilities. This paper presents data from an ongoing research to study the causes of electricity theft in Kampala, Uganda and people’s response to the efforts being made to reduce it. Our study reveals that electricity theft in Kampala is largely due to economic reasons and corruption within the utility company. It confirms that people perceive electricity theft as the utility’s problem and are not willing to report electric theft suspects. We propose ICT technologies to encourage consumer participation in reducing electricity theft.
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    Modeling scheduling of customers in a bank queue using residual time based Preemptive Priority Queuing
    (Makerere University, 2016-09-01) Nalwanga, Julian Doreen
    In a bank, tellers are usually reserved for different kinds of customers, that is standard customers who carry out the normal deposits/withdraws and corporate customers such as western union and corporate customers. Standard customers are usually very many hence their queues are long while the queue for corporate customers is usually almost always empty because they are few. To reduce on the queuing delay for standard customers without de grading that of the corporate customers, we propose a threshold based preemptive priority scheme where the teller reserved for corporate customers be allowed to serve standard customers but once the corporate customer comes, the service of the standard customer be paused. Pausing of the service of the standard customer depends on the amount of service time remaining for him or her to complete (also called residual service time). From our case study of a bank, we derived expressions for the average sojourn time that we used in the subsequent analysis. Average sojourn time is the total time spent by a customer in the queue and service. From the analytical results, we obtained the optimal threshold for the residual time to be 3 minutes and used it in the subsequent analysis. We observe that standard customers experience lower average sojourn time under preemption with threshold compared to under preemption without threshold regardless of the arrival rate and service rate of customers into the system. On the other hand, corporate customers are observed to experience higher average sojourn time under preemption with threshold than under preemption without threshold. We also observe that the gain experienced by standard customers due to the use of the residual time-based threshold is less than the degradation experienced by corporate customers. Therefore, using residual time-based threshold for preemption greatly improves the performance of standard customers with appreciable service degradation to corporate customers.
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    Towards a persuasive technology for electricity theft reduction in Uganda
    (Springer LNICST 250, 2017) Mbabazi-Mutebi, Ruth ; Sansa-Otim, Julianne ; Sebitosi, Ben
    Technology for changing attitude and behaviour, known as persuasive technology, has been applied to solve many challenges, ranging from personal health and finance, to environmental sustainability. In this paper, an application to persuade electricity consumers in Kampala, Uganda, to partner with the electricity utility company in fighting electricity theft is proposed. The persuasive application will implement a number of persuasive techniques including tailoring, reduction, notifications and suggestion. These techniques, along with the choice of technology, were derived basing on Fogg’s process of persuasive systems development.
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    Enhancing the interoperability of public health systems in Uganda using a data exchange module
    (Makerere University, 2017-10) Sekiwere, Samuel
    With an increasing adoption of digital health systems by the Ministry of Health in Uganda, adequate digital health systems interoperability has become a major concern. The need for these systems to coexist and complement each other has also increased the need for 1) secure and reliable health information exchange, 2) scalable message transfer and 3) simpli ed management and control for information being exchanged. In our project, we have developed a secure data exchange module (Dispatcher2) that can be used to integrate at least two disparate digital health systems. Dispatcher2 implements a simple commu- nication protocol that governs information exchange between any pair of applications it integrates. It relies on a persistent queue to o er message persistence and improved traceability for the information being exchanged. It also ships with management web interfaces that manage the message queue and con gurations for the systems it integrates. Our project has endeavored to follow existing interoperability standards and recommendations whenever possible. It has been simulated with 3 publically used health systems in Uganda, that is; mTrac, mTracPro and DHIS 2. The performance of the solution has also been analyzed and it was able to achieve a throughput of over 300 transactions per second and an average response time of about 9.78ms on laptop hardware. More results from our project are discussed in detail in this report.
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    Enhancing the DCFM node isolation attack mechanism for OLSR protocol in Android MANETs
    (Makerere University, 2017-11-23) Kawulira, Edwin
    Mobile Ad hoc Networks (MANETs) have recently gained wide adoption by their ability for communication amongst users without infrastructure for instance Wireless Mesh Community Networks that are setup not to rely on telecommunication infrastructure because they may be too expensive, damaged from natural disasters or simply nonexistent. However, major investigations have mainly focused on routing protocol problems with little progress in solving secure routing in MANETs. This in turn has led to the proliferation of threats and vulnerabilities like the Node Isolation attack against Optimized Link State Routing - OLSR one of the most widely used MANET protocols where a malicious node attacks by exploiting topological knowledge of the network to isolate the victim from the rest of the network and subsequently deny communication services to the victim. This project adopts the Denial Contradictions with Fictitious Node Mechanism (DCFM) which we enhance with Group Testing techniques that yield better and more efficient detection rates against node isolation attacks by employing the same tactics used by the attacker itself. This DCFM enhancement is achieved through modelling and construction of a Colored Petri Net (CPN) model of the mandatory parts of the OLSR protocol for formal verification of its behavioral correctness. The applications of Colored Petri Nets and state space analysis tool have been successful in modelling and performing analyses of the OLSR protocol with DCFM demonstrated success metrics of increase in detection rates of over 95 percent of attacks and a very high reduction in delay latency attributed to Group Testing’s disjunct matrices techniques and finally after demonstrating how the construction of executable formal models such as a CPN model can be a very effective way of systematically reviewing an industrial-size protocol specification for security verification and formal behavioral analysis which can be employed to other security attacks.
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    An agricultural knowledge-sharing framework for smallholder farmers and agricultural knowledge experts
    (Makerere University, 2018-04) Mwesigwa, Ezra
    Using Participatory Action Research (PAR) as our research methodology, we selected smallholder farming communities in the districts of Apac, Lira, Kumi and Bukedea with a major goal of developing an ICT based knowledge sharing framework between smallholder farmers and agricultural knowledge experts. In order to better understand and sufficiently study the engagement between smallholder farmers and agricultural knowledge experts, we built a mobile tool that was used as a medium for exchange of knowledge between the farmers and the knowledge experts. A combination of data collected from the field using questionnaires, narrative interviews and mobile tool meta data helped us design the knowledge sharing framework which was later implemented and validated for knowledge sharing. The final framework was achieved by extending a knowledge management framework developed by Heisig (2009). Through our developed framework, we emphasize that successful knowledge sharing is achieved by critically harnessing five (5) enablers to knowledge sharing. Chapter 1 of this thesis presents a comprehensive background on the growing information needs of smallholder farmers worldwide and in Uganda. It also presents justification for this study, the objectives and research questions that guided the researchers. Chapter 2 provides a grounded evidence through literature review about previous scholarly works to address the growing information needs of smallholder farmers. Within this chapter, we review a number of knowledge sharing models and frameworks and compare them to one another. We identified existing gaps in the literature reviewed at the end of the chapter. In Chapter 3, we show in detail how Participatory Action Research (PAR) was used to achieve the study objectives. We divided the study in four major stages i.e. Problem Diagnosis, Solution Design, Solution Implementation and finally Evaluation of the implemented solution. Chapter 4 covers the data analysis process. We used questionnaires and narrative interviews to collect data from which we drew meanings and conclusions. Lastly, Chapter 5 presents discussions, recommendations and conclusion of the study.
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    A real time revenue recognition tool
    (Makerere University, 2018-11) Bintubizibu, Fortunate
    Many Organizations receive money from customers for services or products for which the benefit is yet to be fulfilled. This arrangement creates a liability on the part of the seller equal to the revenue earned until delivery of the good or service. In case the seller ceases operations then the unconsumed service or product is refundable to the consumer, which is referred to as unearned revenue according to Generally Acceptable Accounting Principles (GAAP). Examples of such organisations or companies include Telecommunication companies selling prepaid services like airtime e.g., MTN, Digital Television subscriptions and Prepaid Electricity to mention but a few. Currently the challenge to most of the prepaid vendors is to accurately determine the liability of what is due to the customer in real-time or good time. Part of this challenge is due to architectural design and setup. It was necessary to carry out a study on the existing systems architecture of a prepaid organization to come up with an improved tool that could be used to quickly determine, accurately compute and manage unearned revenue. A study of the existing systems was carried out through a review of the various business processes related to the distribution, activation and usage of services and products, as well as interviews with various stakeholders within the prepaid environment. This led to a better understanding of the existing systems and the required system improvements. It is from these requirements that the system specifications were outlined, and the proposed system design developed. A prototype was then developed using PHP (5.5.38) scripting language and MySQL (5.6) database. Based on the prototype system and with the growth of the prepaid environment there is a need to have integration within the different systems in the environment to enable accurate and timely unearned revenue reporting and management. This system can be moderated for use in any related organization that needs to track, report and maintain its unearned revenue of prepaid products or prepaid services in general.
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    Application of random forest regressor algorithm to predict PM2.5 concentration levels in Kampala
    ( 2018-12-06) Wabinyai, Fidel Raja
    As it happens in every society, it is every body's wish to live in a clean and fresh environment. However, this might not be achieved in every daylife but at least the level of pollution can be controlled. Air pollution is one of the leading global public health risks but its magnitude in many developing countries is not known. As is in many African cities, fine particulate matters (PM2.5) is dangerously high in Kampala. This thesis uses data mining algorithms to build a predictive model for the following days PM2.5 concentration level. The prediction of concentrations of pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. This thesis presents a forecasting model to predict the daily average concentrationof PM2.5 for the next few days(i.e. 3 to 5days). The proposed model used in this thesis was Random Forests regression. Random Forests regressor was compared with 4 other regression models namely Extra Trees Regressor, Gaussian Process Regressor, XGBoost, and Elasticnet. The performance estimation is determined using the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE) and R-squared (R2). The results demonstrated that the Random Forests regressor algorithm outperformed other models. 6 pollution monitoring stations in Kampala measuring PM2.5 were selected. We found that the mean concentration of PM2.5 pollution was 3 times higher than the World Health Organization (WHO) recommended level.
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    Fracture detection in children using convolution neural network
    (Makerere University, 2019-07) Nanziri, Eunice
    Fractures in children are among the medical cases that take a lot of doctors’ attention and time while analyzing the images to identify the presence or absence of fracture. More so a tired doctor may fail to identify the presence of fracture after looking at many images with health bones which may result in poor treatment. Our method of fracture can help doctors in faster detection, meet their deadline but also can help in obtaining accurate results hence administer proper treatment. In this research, we used X-ray images which we obtained from Mengo hospital. We obtained 60,140 Images and with the help of a radiologist we were able to find 211 images of children from the entire data set and also, we were able to separate images with fracture from those with fracture. we split our data into 3 different sets (train set, validation set, and test set). We used a convolution neural network (CNN) a method of automatic detection that is mostly preferred for cases that require deep analysis. We trained our model on 211 x-ray images and used a kernel to generate different features of interest. we obtained 4 convolution layers while developing our model and a max-pooling layer was placed in between each 2d convolutions layers to record the weights of the parameters but also to reduce over-fitting. We used 3 different matrices to evaluate our model which include accuracy, Precision, and recall. On training the model, we obtained Test accuracy levels of 47%, 59% for test precision, and Test recall of 76%. while using our data-set and we noticed an over-fit because of the small data set used, we then applied data augmentation and obtained an accuracy level of 56%, test precision 49%, and test recall of 76%. We also trained our model on VGG Net a model and we obtained an accuracy level of 63%, test precision of 66%, and test recall of 79%.
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    Tree farming expert system: a case of eucalyptus & pine species
    (Makerere University, 2019-11-18) Musoke, Mike
    There is an increasing investment in tree farming business in Uganda by both Ugandan nationals and foreign investors, commonly grown tree species among others include; Prunus Africana , Eucalyptus, Tick Tree, however, due to its lucrativeness, there are many challenges facing commercial tree farming for example a lot of setbacks and great financial losses due to lack of technical knowledge about tree planting, poor quality seedlings purchased from private tree nurseries, unpredictable rain seasons, tree pests and diseases among others. Forests are of immense importance to Ugandans. The National Forestry Authority report of 2008 indicates that in 2004, the total economic value of Uganda’s forests, including all marketable and nonmarketable values, was estimated at Uganda shillings(Ushs) 593.24 billion (USD 304 million at the exchange rate of USD 1 = Ushs. 1920), equivalent to about 5.2% of the Gross Domestic Product (GDP). Forests and trees contribute Uganda shillings 332.3 billion (US$173 million) to the total annual incomes of the households in Uganda. The Forest Sector Review Report (Ministry of Water, Lands and Environment, 2001) indicates that wood and non-wood products removed from the forest for subsistence use are about Ushs. 210 billion (USD 109 million) or 2.75% of the GDP. Thus, the overall contribution of forests is about 6% There’s no state of the art Expert system in place to equip farmers with the best tree farming techniques/practices/knowledge to avert the challenges highlighted above most farmers hire Forestry experts – from National Forestry Authority which is very costly. Therefore, this study was conducted at National Forestry Authority (NFA) with a major aim of developing a Tree Farming Expert System that can be used by any tree farmer or technical expert to freely acquire knowledge about tree farming for better quality tree products (Obua, 2010). In this study, a contextual design methodology is employed as it allows tree farmers and technical experts to fully participate and contribute to the design, development, and implementation of the Expert System prototype. The farmers and technical experts are in the best position to improve how best they can access and avail this information easily respectively. To establish the user requirements the researcher will use; interviews, questioners, observation, and reviewing literature or writings from other sources.
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    Information seeking behaviour of rural women in Bungokho County, Mbale District of Uganda
    (Makerere University, 2019-12) Khanyalano, Justine
    The aim of the study was to investigate the information seeking behaviour of rural women involved in economic activities in Bungokho County, Mbale District in order to suggest strategies to enhance the information seeking. The following objectives guided the study; to examine the information needs of rural women of Bungokho County, to identify the information sources/resources used by rural women of Bungokho County to seek and satisfy their information needs, to establish the challenges faced by rural women of Bungokho County in seeking information and to suggest strategies to enhance the information seeking of rural women of Bungokho County. Mixed-Methods approach was used with survey design for quantitative and case study for qualitative. Questionnaires and focus group discussion were used to collect data. Data was analysed using thematic analysis and Statistical Package for Social Scientists (SPSS). The findings revealed that respondents’ commonly needed information regarding pests and disease management followed by income generating activities. The rural women consulted informal than formal sources of information and social-political meetings like burial ceremonies, weddings and harvest festivals were the most used channel of communication. The major challenges that respondents faced was lack of awareness and knowledge on where to get useful information and financial obstacles. The strategy seconded by the respondents was exposing and training the rural women in different income generating activities and women empowerment through education and enlightenment programs. The study recommended regular training of women in income generating activities, financial literacy programmes, making loan facilities available to the rural women for business investments, repackaging of information, introduction of adult education in different villages, deployment of extension workers and gender sensitization.
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    A framework for GIS-enabled public e-participation in municipal solid waste management
    (Makerere University, 2020-07) Arinaitwe, Irene
    Municipal solid waste is a serious environmental challenge that affects most urban authorities globally. Annually, more than two billion metric tons of wastes are generated globally. Municipal solid waste is especially a serious challenge to many developing countries because of a high waste generation stemming from high population growth and rapid urbanisation. In Sub-Saharan Africa alone, at least 62 million tons of waste are generated annually. The high rate of waste generation has made municipal solid waste management (MSWM) to be the single most crucial function of urban authorities in most developing counties, including Uganda. However, most urban authorities cannot cope with the high demand for MSWM because of weak infrastructure, limited funding, and weak legal and regulatory framework. Additionally, there is limited public engagement in MSWM amidst prevalent poor attitude towards waste management initiatives, by the public. Public participation is central to the achievement of sustainable waste management systems. However, there is minimal public participation in government administrative processes, including municipal solid waste management, in many sub-Saharan countries. Limited public participation in MSWM is attributed to the lack of effective public participatory platforms that ensure inclusive and broader stakeholder engagement. Although public participatory geographic information systems (PPGIS) have the potential to enhance public participation, there are inadequacies in theoretical foundations to guide their implementation. Therefore, geographical information systems (GIS) tools that enhance broader and effective stakeholder engagement have not been leveraged to address the challenges of MSWM in most developing countries. The implementation of PPGIS requires theoretical frameworks that support systematic analysis of not only the technical aspects but also the social-behavioural principles. Although several frameworks for public participation exist, they are also not tailored to address the contextual barriers to public participation, especially in the developing-country context. Therefore, this research aimed at developing a framework for GIS-enabled public e-participation in MSWM (coined as GPEP) in urban authorities, especially in developing countries. Pragmatism was selected as the philosophical underpinning for this research because this research aimed at finding a practical solution that is contextually suited to address MSWM challenges. The abductive approach research approach was used to maximize the strengths of both deductive and inductive research approaches. Pragmatism and the abductive approach would ensure a comprehensive yet contextually suited framework. In the development of GPEP, the design science research method was used. Design science research method supports the development of tangible and practical solutions to societal problems, including public eparticipation in MSWM. GPEP extended the Enhanced Adaptive Structuration Theory (EAST-2) that was developed by Jankowski and Nyerges to include constructs and aspects from Adaptive Structuration Theory (AST), Enhanced Adaptive Structuration Theory (EAST), Dynamic capabilities theory, and the institutional analysis and development framework. Also, GPEP includes factors that influence the uptake and use of PPGIS applications identified from the literature and contextual requirements derived from an exploratory study that was conducted in the greater Kampala area. GPEP was tested with a descriptive field study. Only the constructs that met the validity and reliability thresholds were included in GPEP. Data were analysed using the partial least squares (PLS) technique of structured equation modelling (SEM). The analysis involved an iterative stepwise forward factor selection process whilst determine the relationships between aspects and constructs. A two-tailed statistical significance of 0.05 was used and power set at 80%. The analysis found that technology influences and task influences have a direct bearing on conducting GIS-enabled public e-participatory processes (p<0.001 for all). Similarly, Social Institutional influence and participant influence had insignificant influence on GIS-enabled public e-participatory processes, contrary to the findings from prior research. GPEP was evaluated using structured walkthroughs and experimentation methods. GPEP was evaluated on a five-point Likert based on its Usability, Feasibility, Completeness, and Consistency attributes. The evaluation reported high usability scores (mean of the mean scores=3.7), feasibility (mean of the mean scores=3.9), and completeness (mean of the mean scores=3.70), and consistency (mean of the mean scores =4.4). This research achieved three objectives. (1) The requirements for GPEP were determined, (2) GPEP was designed, and (3) GPEP was evaluated to ascertain its practical utility. GPEP contributes to theory in three ways, (1) the factors that influence the adoption and implementation of PPGIS were identified, (2) a combination of methods-structured walkthroughs, experimentation and structured equation modelling were used evaluate the GPEP, and (3) the GPEP was derived through the integration of aspects and constructs from different theoretical frameworks. The study findings point to the need for a proper understanding of participant characteristics, consideration of organisational workflows and business processes, and task technology fit assessment before applying PPGIS in environmental public participation. Additionally, the implementation must be preceded by change management, feasibility studies, resource mobilisation, public awareness, and GIS infrastructure investments. Overall, GPEP should be used to inform the implementation of a PPGIS in Uganda. This study was cross-sectional, and the long-term benefits of this research cannot be determined at this stage, and this should be determined using a longitudinal study.
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    Detecting money laundering using a pattern matching approach based on Rete Algorithm
    (Makerere University, 2020-09) Kiwanuka, Patrick Ivan
    Money laundering is the criminal practice of filtering funds gotten from illegal activities through a series of transactions so that the funds appear as proceeds from legal activities. Money laundering is a diverse, complex process and basically involves three independent steps namely placement - placing, layering, and integration. It mainly occurs within financial institutions and it is difficult to detect [1]. All financial institutions in Uganda are mandated by the Central Bank to have mechanisms in place for detecting money laundering activities. The Parliament of Uganda passed an Anti-Money Laundering Act Law in 2013 which criminalizes these activities. In addition to the above laws, many institutions have developed policies internally that help them in detecting money laundering activities. Money laundering can have negative effects on the economy of the countries such as loss of government tax revenue, driving up the cost of government due to the need for increased law enforcement to fight the vice, undermining the legitimate private sector and integrity of the financial markets since Money launderers often use front companies which co-mingle the proceeds of illicit with genuine ones. These front companies have access to substantial illicit funds, allowing them to subsidize front company products and services at levels well below market rates. The objective of this project was to develop and validate money laundering detection rules using rete pattern matching algorithm. Through case studies with core banking systems of two local banks and related systems, requirements were established to inform the design of the patterns linked to potential money laundering activities. These patterns informed the design of rules such as the Daily transactions limit and extended the existing rules with a dynamic scoring system where a threshold score combines a net laundering score at run time. A banking system prototype was developed using python and SQLite for the database. The designed rules were implemented using Jess rule engine and integrated into the prototype banking core system. The system integrates dynamic rules making it hard for criminals to deduce the transactional limits. A scoring system has been introduced which evaluates each transaction against all the rules and categorizes it based on the overall score. This reduces on the number of false positives. Jess wrapped in Java was used to develop the improved rule sets and a scoring system that detected suspicious transaction patterns. Several transactions were carried out using the banking applications and suspicious transactions were highlighted by the system.
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    Modeling connectivity for vehicular adhoc networks under high traffic density with interference
    (Makerere University, 2020-12) Kamya, Michael
    Previous studies of connectivity in Vehicular Ad hoc Networks (VANETs) were done with slight traffic interference in low traffic road scenarios. These studies were done under the assumption that the interference were negligible. However, under high density traffic scenarios, nearby vehicles cause interference to the connectivity of the vehicles causing reduction in signal strength. This study developed analytical models that incorporates interference and can be used to investigate the communication interference of other vehicles' signals on connectivity under high density traffic in terms of connectivity probability as the performance metric. Under dense traffic scenarios, the inter-vehicle distance is assumed to follow the Nakagami-m model distribution unlike under light traffic scenarios where the inter-vehicle spacing is assumed to follow the exponential distribution. The numerical results obtained from the derived models show that increase in intereference leads to decrease in connectivity probability. It is also observed that increase in traffic density decreased the connectivity probability due to increased interference. Furthermore, it is noted that connectivity probability of the vehicles increased with increase in the communication range. It can be further observed that the connectivity probability decreases with the increase in the length of the road segment.
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    Developing a framework for measuring IT project success in developing countries : a case study of Uganda
    (Makerere University, 2021) Barigye, Innocent
    Over the past years, measuring project success had been concentrated on the performance of project management more especially on cost, time and quality but not considering product operation and involvement of the clients and the employees. In Organizations and Companies, Successes on a project means that certain expectations for a given participant were met, whether owner, planner, engineer, contractor or operator. However, these expectations may be different for each participant and the study of project success and project frameworks is often considered as one of the vital ways to improve the effectiveness of project delivery (Chan et al., 2004). It is reasonable to believe that, if we meet the quality, time and cost targets for a project, it will be considered successful. Apparently, there are other factors that stakeholders deem to be important in determining project success. If Project implementation methodology adopts other relevant criteria for successful projects, organizations/companies would experience improved project execution and delivery. The study used the National Information Technology Authority Uganda (NITA-U) and other IT project management firms like Government (UCC), Airlines, Banking, SMEs in order to widen the scope for our research focusing on IT project management. The main objective of this research is to develop a framework for measuring IT project success, the specific objectives for the study were, To determine the factors for IT project failure and success in the developed framework for developing countries, To identify the appropriate measures that can be used to achieve the IT project success, To assess the relationship between the framework criteria and IT project success in developing countries. The research design used here was a cross-sectional research design and involved quantitative and qualitative approach to data collection but also literature review approach was used to compare several project frameworks. The survey results showed that Time (74.4%), Cost (55.6%) and Quality (51.1%) still remain the important criteria for assessing the performance of IT projects in the minds of professionals. However, the users seemed to consider Time and Cost more important than Quality. Project should also target at satisfying the needs of the key stakeholders. The key stakeholders for an IT project are: the software users’ satisfaction (77.8%), and the customer satisfaction which stood at 55%% from the data got from the respondents for this study. From both the survey and the literature reviewed the researcher has made modifications to the four basic perspectives based on the following views: The I.T. projects are commonly carried out for the benefit of both customers and software users. Project personnel and project team should be as internal customers that should benefit from the project. If we give more attention to human resources, we will then have an excellent basis for improved results in the rest of the perspectives. From the study several measures were suggested in order to achieve the project success like good planning, adequate resources, Effective communication, Cost and time measures of control Deadlines & budgeting, Good risk management. Any project is only good if it is functional. Nothing else matters much if for example a software program is not accepted by its users. Therefore, having a clear definition of project success as the establishment of a set of success criteria is of utmost importance for every project-oriented organization. If an organization does not know early on the project how they are going to measure its business success, they will surely be faced with unpleasant situations in the long run.
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    A model for adoption of information systems for antenatal healthcare practice in Wakiso District
    (Makerere University, 2021) Nyakato, Judith Kyobutungi
    Globally, information systems have become ubiquitous and shown a potential to transform and benefit antenatal care delivery to expectant mothers in Uganda. The benefits associated with this include accessibility to information about antenatal care, when to start antenatal care, monitoring the progress of the mother’s pregnancy, following up with medical checkups and critical updates. The successful adoption of information systems in antenatal healthcare practice can lead to reduced maternal deaths and pregnancy-related complications among expectant mothers. However, despite the identified demonstrable benefits of information systems, their adoption by expectant mothers, healthcare practitioners and other concerned people have always been slow in Uganda. Thus, it has been difficult to realize the associated benefits. This is attributed to the fact that factors affecting the use of information systems in antenatal healthcare practice are not well understood in developing economies like Uganda. This study aimed to determine the factors that favor adoption of information systems for antenatal healthcare delivery in Uganda and to create a model that explains the intention to adopt information systems in antenatal care practice. A descriptive field study was carried out using questionnaires to identify the factors for successful adoption of information systems for antenatal Healthcare practice in Uganda. The results from the field study were used to extend the Technology Acceptance Model and Technology Task Fit models in order to design one that best explains the adoption of information systems in antenatal Healthcare practice in Uganda. The derived model was evaluated in a questionnaire-based study using Healthcare experts working in antenatal care sections. The results revealed that Task Characteristics, Technology Characteristics, Perceived Usefulness, Perceived Ease of Use, Perceived Trust, System Value and System Knowledge significantly influence the behavioral intention to use information systems for antenatal Healthcare practice in Uganda. The model combines the tested strengths of information system adoption and usage model with the factors fit for adoption of information systems in antenatal Healthcare practice in Uganda. The model can be adopted for use by other economic and technological developing countries with similar contexts as Uganda.
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    Predicting infectitious disease density in urban settings using Convolutional Neural Networks
    (Makerere University, 2021) Sanya, Rahman
    Rapid and unplanned urbanization is said to pose serious public health challenges to developing countries due to inequality in socio-economic wellbeing, decent housing, etc. Consequently, differential disease risk is experienced across even the same city. For example, overcrowded housing in high density neighborhoods do not only provide fertile ground for airborne infectious diseases to thrive, they also facilitate their rapid spread as a result of increased human contact. The close association observed between urban settings and infectious diseases raises important questions which have not received adequate research attention. For example, what is the nature of this association? What methods are available or are suitable for investigating this kind of association? Would existing methods for characterizing settlements as urban or rural be suitable for studying this kind of association? Furthermore, what can neighborhood characteristics tell us about disease occurrence in a population? With advances in deep learning and big data projected to shape the future of epidemiology and public health, this thesis attempts to answer some of the questions above by leveraging Convolutional Neural Networks (CNN) and using Tuberculosis disease (TB), an airborne infectious disease, as case study. The specific objectives include to, 1) determine potential of socio-economic data as predictor for infectious disease density, 2) determine potential of urban density data as predictor for infectious disease density, 3) build and evaluate a CNN model for identifying patterns in urban housing from satellite imagery, 4) build and evaluate a multimodal CNN model for predicting disease density from socio-economic and housing data, and 5) build and evaluate a siamese CNN model for predicting infectious disease density from housing image data. We developed a linear regression model to achieve each of objectives 1 and 2. CNN methods were developed in a variety of input modalities and architecture designs in both a regression and classification task formulation to fulfill objectives 3, 4, and 5. The TB data used was obtained from Uganda’s Health Management Information System, satellite imagery from Google Static Maps API, and socio-economic data from WorldPop. Socio-economic data was found to posses predictive power for estimating disease density. However, inherent limitations associated with data derived using current methods for quantifying urban density produced misleading results when used for the same purpose. On the other hand, CNN were found to be reasonable for detecting patterns in urban housing density. For example, we achieved 80% accuracy on a housing density detection task. Results from using CNN for inferring TB density from neighborhood characteristics were promising. For example, we attained reasonable accuracy (81.6%) on a task of predicting TB density with a single-input CNN model trained on housing data. The architecture of this overall best model was extended in a novel way inspired by the idea of siamese twins, what we call learning deep features over neighbor scenes. We achieved moderate improvement in prediction performance as a result of the proposed architecture. Despite these promising results however, the potential of CNN for inferring occurrence of a disease in a population requires further investigation. An interesting research direction would be exploring performance of deeper and larger multimodal network architectures using larger training sets. We expect DNN to play an important role in epidemiology of human infectious diseases in the future.
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    A mobile application for recommended fertiliser application rates of selected crops in Central Uganda
    (Makerere University, 2021) Mutyaba, Robert
    In order for farmers to realize acceptable yields and returns to their scarce resources under these poor soil conditions, they require to use improved soil fertility management practices that they always find unaffordable. Uganda is known as an agricultural country, where most agricultural practice recommendations are given by traditional methods. At present, fertiliser application rates recommendations for farmers are based on one to one communication between the farmers and the experts and these experts have different recommendation rates. The main objective of this project was to develop a mobile application that analyses soil nutrient’s results and provides recommended fertiliser application rates information to farmers after carrying out soil nutrient tests. The project objective was achieved through the use qualitative methodology for requirement gathering and the System Development Life Cycle (SDLC) for the development of the application through the guidance of the waterfall model. The field results analyzed showed that 80% of the farmers interviewed had been trained on the use soil testing kit (STK) to analyze soil nutrients that helped in requirements identification. They identified color interpretation, no access to the soil testing kits and lack of enough training as some of the reasons as to why they do not carry out soil tests by themselves. From the results analyzed farmers still indicated that they were faced with the following challenges while carrying out soil nutrient analysis; run out soil of reagents, it’s a long process, identification of color while others indicated that they had no idea since they had never attained any training. From the analyzed results, 80% of the farmers attained their fertiliser recommendations from extension staff. In addition to that, the commonly tested for soil nutrients were Nitrogen (N), Phosphorous (P), Potassium (K), Alkalinity (PH) and Organic matter. This guided and resulted into the development of the mobile application that interprets, analyzes and provides recommended fertiliser application rates to farmers after carrying out soil analysis. This mobile application would subsequently aid in improving turnaround time for delivery and attaining of recommended fertiliser application rates by farmers. This smart environmental choice allows growers to save money and increase yields, by using the right rate and type of fertilizers. The solution also addresses the global misuse of fertilizers that results in waste, soil damage, and groundwater contamination. It also enhances fertiliser management through provision of timely information concerning correct timing, placement and methods of fertiliser application and right source of fertiliser in order to maximize nutrient uptake and yield targets. The mobile application focused on the use of Information Communication Technologies (ICTs) as a tool through which recommended fertiliser application rates can be disseminated to farmers timely and in a cost saving way. This application can be used on farmer’s android-based mobile devices. Inorganic fertiliser use has a significant potential production increase in the agricultural sector that in return leads to increased income to the small-scale farmers although farmers are still faced with a challenge of interpreting soil test results and attaining timely onsite recommended fertiliser application rates in order to rejuvenate the soil fertility. It is highly recommended to use the fertiliser application rates mobile application in order to improve on fertiliser application decision making.
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    Malware detection using static analysis with PCA, mRMR and machine learning
    (Makerere University, 2021-02) Bukombi, Brian
    Malicious software (malware) is software that harbors malicious intent and is harmful to computer systems. The number of malware being developed is increasing rapidly, and despite the use of anti-malware software, the timely detection of malware still remains a challenge today, with disastrous consequences that may result into losses valued in millions of dollars. Most anti-malware software today uses signature based detection techniques to protect legitimate users from malware attacks. Signatures are byte sequences that uniquely identify malicious software. However, this method fails to detect new types of malware, and new variants of existing malware for which no signatures exist in the signature databases. To address the short comings of signature based detection, researchers have proposed the use of statistical based detection, utilizing statistical properties of program features, and dynamic based detection that monitors the behavior of programs during execution. These techniques are used in conjunction with machine learning models that are trained on the selected features. Selecting individually good features does not necessarily translate into optimal classification results. There is therefore need to select optimal sets of features to use in building the machine learning models used in the detection of unknown malware. In this research, we evaluate Principle Component Analysis and Maximum Relevance and Minimum Reduction dimensionality reduction algorithms for the selection of optimal feature sets to use in building the machine learning models for detection of unknown malware. We evaluate different sets of features to determine the most parsimonious model with the lowest classification error. We show that the highest area under a receiver operating curve was 91% and was achieved with the Decision Tree classifier using 20 features selected using Maximum Relevance and Minimum Reduction.