SU+ Digital Repository

SU+ is an online repository for the preservation and promotion of assorted digital content at Strathmore University

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Communities in DSpace

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Conferences / Workshops / Seminars + 474
Documents and Proceedings of Conferences, Seminars, Workshops (and more) held at Strathmore University
Digital Archives 178
Assorted collections of resources covering various subject themes contributed by Faculty and Library Staff
Reports / Policies + 10
Public reports and policy documents
Research / Researchers / Publications 4438
Researcher Profiles / Conference presentations / Published research articles / Faculty and Corporate research outputs
Strathmore Heritage Collection 41
A digital chronicle of the History of the University presented through a mix of pictures, videos and digitized publications

Recent Submissions

A Malaria early warning alert application for health officials (Imalaria)
(Strathmore University, 2024) Mwamsidu, C.
Malaria is a critical public health issue that must not be overlooked since it affects majority of the world’s population. The goal of the study was to determine how to use confirmed malaria case data, and current climatic conditions to forecast malaria epidemics and offer timely information to health officials. This research will help to inform and build a malaria early warning alert application that can offer public health experts with early warning signs and prediction of malaria outbreaks. The first step was selection of the variables that has a significant impact on malaria transmission. From literature review, malaria incidences, rainfall/precipitation, humidity, and temperature were selected. Data collection was done where malaria data was collected from Kenya Health Information System (KHIS) and incidence rates calculated based on cases and population. Weather data was also collected from historical data API accessed through this link: https://open-meteo.com/. Hourly data collected was aggregated and averaged for the month derived. Selection of the best machine learning algorithm for the malaria early warning alert system was conducted and six models including Decision Tree Regressor, Linear Regression, Random Forest Regressor, Ada Boost Regressor, Gradient Boosting Regressor, SVR and decision tree were tested. Random forest Regressor which had the best score was selected. Training of the model on the collected data to make predictions and provide alerts to health officials at facility level was done using 70% of the data. Evaluation of the performance of the model using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error RMSE was also conducted using 30% of the data. Deployment of the model was done using TensorFlow's Saved Model format which was integrated into the React application to create the malaria early warning alert system. The UNHCR model for emergency response was adopted to calculate the alert/epidemic threshold. This model considers the threshold for a malaria outbreak to be 1.5 times the baseline over the previous three weeks. Alert (or ‘epidemic threshold’) and action thresholds were used to provide information to the public health staff. The rest of the data was labelled as normal threshold.
Reliable and cost-effective long-distance transport in Kenya: a mobile-based technology carpooling solution
(Strathmore University, 2025) Ngaruiya, B. M.
Carpooling is a form of organisation of car use among several individuals that involve their sharing of costs. It reduces their overall transport costs while reducing the number of cars on the roads. The achievement of the latter is the major emphasis of this study. The study aims to develop a mobile application to promote carpooling and hence reduce the overall congestion on Kenyan roads. Several key objectives will guide the paper, including evaluating the modes of carpooling and their use of technology. Additional objectives include the development of a mobile-based prototype that addresses carpooling and evaluating the effectiveness of the prototype developed as far as carpooling is concerned. The prototype developed will enable users of the mobile app to share their private cars with others at a fee. Car users will be able to post a request on the system to access carpooling services.
A Machine learning model for human population forecasting: case for Kenya
(Strathmore University, 2024) Mete, M. O. O.
The growth of a country’s population can be a complex issue that has a significant impact on the development and sustainability of countries all over the world. In Kenya, the population is growing rapidly, which is putting a strain on the resources of the country, such as land, water, and infrastructure. The currently used methods of forecasting population growth, such as censuses and mathematical models, are costly, time-consuming, and not consistently accurate. The aim of this study is to develop a ML algorithm to forecast population growth in Kenya more accurately compared to the models currently being used. In this study, seven different machine learning models were examined Artificial Neural Networks, Random Forest, Logistic Regression, Support Vector Machines, Linear Regression, Decision Trees, and K-Nearest Neighbor to determine their effectiveness in predicting the population of Kenya. A variety of factors that impact population growth were considered, such as fertility and mortality rates, life expectancy, net migration, economic growth, access to healthcare and education, and gender equality. All models were built using the Scikit-Learn library and demonstrated impressive accuracy, but the top performers were Artificial Neural Networks, Random Forest, and Linear Regression. Of these, Linear Regression stands out as the best performer overall with a MAPE of 0.0179% and an accuracy of 0.9977% when tested with new data. This is a significant improvement over the other models, which showed slightly lower accuracies.
A Food recommendation system for weaning of children in Kenya using rule-based technique
(Strathmore University, 2024) Ndemo, D. M.
Childhood malnourishment is a key worldwide health concern that affects millions of children globally. It can lead to stunting, wasting, and underweight conditions, as well as micronutrient deficiencies. These conditions can have far-reaching consequences, including stunted growth and development, suboptimal academic performance, and compromised overall health. One major cause of childhood malnutrition is poor feeding practices, particularly during the weaning stage. Parents and caregivers often lack the knowledge and resources they need to provide infants and young children with the balanced diets they require. There is also a large human resources gap in the Kenyan healthcare system, where there aren’t enough nutritionists and dietitians to attend to the population and give professional child feeding advice. A way to solve this knowledge gap has been to develop food recommendation systems that help users in making more informed food choices based on their current health status. Many of these systems are tailored for adult populations such as patients with chronic diseases. This study developed a food recommendation system that is tailored to the specific needs of a child, considering factors such as age, weight and height. The system sought to utilize rule-based technique to develop a food recommendation system that would serve as a decision support system for parents and caregivers. The rule-based system was built using Experta, and contained in a full-stack web application that was developed using Flask and React. A comprehensive and diverse food database was adapted from the Kenya Food Composition Tables. The system provided tailored nutrition feedback on amount and frequency of feeding, as well as nutritionally balanced food recommendations in the suggested meal plan. This system not only considered calorie needs but also emphasized nutrient diversity to ensure that children are getting the key micronutrients they need. The system is also usable by healthcare workers to fill the nutrition technical skills gap in healthcare facilities. Keywords: nutrition, weaning, information science, informatics, rule-based technique, decision-support system, recommendation systems.
A Bi-lingual counselling chatbot application for support of gender based violence victims in Kenya
(Strathmore University, 2024) Mutinda, S. W.
Gender-based violence (GBV) remains one of the highest prevailing human rights violations globally, surpassing national, social, and economic boundaries. However, due to its nature, it is masked within a culture of silence and causes detrimental effects on the dignity, health, autonomy, and security of its victims. The prevalence of GBV is fueled by cultural nuances and beliefs that justify and promote its acceptability. The stigma surrounding GBV in addition to fear of the consequences of disclosure deter victims from seeking help. Additionally, the resources available for addressing GBV such as legal frameworks and recovery centers are limited. Technological approaches have been established to tackle GBV as intermediate and supplementary support for victims as part of UN-SDG 5. Conversational Agents such as Chomi, ChatPal, and Namubot have been developed for counselling of GBV victims who struggle with disclosing their predicament to humans. The existing chatbots, however, are not a fit for Kenyan victims because they utilize languages such as Swedish, Finnish, Isizulu, Setswana and Isixhosa in addition to incorporating referral services specific to their regions. This research addressed this gap by developing a chatbot application suitable for the Kenyan region for counselling of GBV victims using both Kiswahili and English, the languages predominantly used in the country, in addition to including contacts to referral services within the country. The methodology utilized involved the development of a chatbot application based on Rasa open-source AI framework by training a model using a pre-processed counselling dataset. The performance of the model was evaluated using NLU confidence score to determine the model’s certainty in its intent identification and a confusion matrix was generated which with 80% and 20% training and testing data split resulted in 100% classification threshold accuracy. Python’s Fuzzy Matching Token Set Ratio score was also used to determine the response which best matches the input with results indicating satisfactory performance of the model ranging between 63% and 92% for GBV queries input. The developed model was then integrated into a web application as the user interface for user access and interaction with the model hence achieving the research objective of developing a chatbot application to conduct counselling for GBV victims in Kenya using English and Kiswahili languages. Keywords: Gender-based Violence, stigma, chatbot, Rasa open source, NLU Confidence Score, Fuzzy Matching Token Set Ratio score