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 + 8
Public reports and policy documents
Research / Researchers / Publications 4329
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 Predictive model for blood demand and supply: a case of obstetric emergencies at Kenyatta National Hospital
(Strathmore University, 2025) Mutai, K. V.
Maternal health is still a very critical concern in Kenya, with obstetric emergencies posing considerable challenges to maternal health and mothers’ well-being. One key factor that contributes to maternal mortality is the unavailability of blood during obstetric complications, which is essential for saving lives during emergencies related to childbirth. Unfortunately, many lives have been lost under the circumstances due to a lack of timely information regarding blood requests and donations. These delays in finding blood to manage such situations more effectively cause loss of life. The study aimed to develop a predictive web-based model to monitor blood demand and supply and ensure blood is available to save the lives of mothers and newborn babies during obstetric emergencies using Kenyatta National Hospital (KNH) as a case study. This will be achieved through improved blood bank management, monitoring blood availability and providing timely information to blood stakeholders, minimizing delays in blood donation and supply requests during obstetric emergencies. This will also help reduce maternal and infant mortality cases, thus improving maternal health outcomes. If successful, the proposed solution will go a long way in improving blood donation practices in the healthcare system. The model leveraged modern application development technologies and tools, including Predictive Analytics, Artificial Intelligence, and Machine Learning libraries, to forecast blood demand and supply based on historical data. The research adopts an experimental design to gain in-depth insights into the current practices, challenges, and patterns in blood management. This approach facilitated the identification of critical variables influencing blood demand and supply, leveraging both qualitative and quantitative data to make correct blood demand versus supply predictions. By exploring existing systems, healthcare workflows, and historical data, the study will provide a foundation for constructing an effective predictive model tailored to the unique requirements of obstetric care. Keywords: Obstetric Emergency, Maternal Health, Blood Donation, Predictive Analytics.
Automated event attendance recording tool using facial recognition
(Strathmore University, 2025) Makhatsa, B. S.
Monitoring and measuring participant attendance at events is crucial for several reasons. In addition to offering organizers and sponsors insights into an event's popularity, it is essential for assessing the economic, social, and environmental impacts of the event. Various methods for recording attendance are currently in use, including manual methods such as signing a register which are often inefficient and prone to errors. While electronic attendance systems, such as NFC cards and fingerprint readers, exist, NFC cards are vulnerable to impersonation, and fingerprint readers may raise health concerns due to contact with shared devices. In contrast, facial recognition technology offers a promising solution for improving the accuracy of attendance records at events while mitigating hygiene risks associated with fingerprint readers. However, facial recognition systems tend to be computationally intensive. This research developed an automated event attendance recording tool that integrates Hash-Based Indexing with facial recognition algorithms to enhance the accuracy of attendance records while reducing computational load through record indexing. The study employed agile methodology for developing the automated attendance recording tool. Testing and evaluation utilized publicly available image databases to train the machine learning image recognition model and assess its performance. The primary evaluation metrics included the accuracy of image identification and the duration of transaction processing. The model achieved 95% accuracy in face recognition, with its performance further analysed using the confusion matrix and classification report. The developed tool provided an interface for event organizers to create events and record attendance offering utilizing the full capabilities of CNN for image recognition. The developed tool offered an interface that allowed event organizers to create events and record attendance, fully utilizing the capabilities of CNN for image recognition and hash-based indexing for faster retrieval of records. Keywords: Attendance, Agile methodology, Electronic attendance systems, Facial recognition, Fingerprint readers, Machine learning, Hash-Based Indexing, Record indexing
Automated feature engineering tool for fraud detection in financial transactions using deep learning
(Strathmore University, 2025) Buoro, S. O.
Financial fraud has become increasingly prevalent in modern corporate environments. It involves the deliberate use of deceptive tactics to achieve monetary benefits within various corporations and organizations. Conventional approaches such as manual verifications and inspections, although intended to detect fraudulent activities, frequently demonstrate shortcomings in terms of accuracy, cost-effectiveness, and efficiency. Existing automated fraud detection systems also have limitations. These includes inefficiencies, high costs of implementation, data imbalance, concept drift, false positives and negatives, limited generalizability, and difficulties with real-time processing. The quick and timely detection of fraudulent activities allows financial institutions to mitigate fraudulent conduct before it could lead to financial loss. This research developed an automated feature engineering tool for fraudulent detection. The developed solution involved utilizing deep learning (DL) techniques to analyse transactional data, thus revealing hidden trends that could indicate fraudulent activities. The developed MLP Classifier achieved an accuracy of 99.75%, surpassing the Logistic Regression and Decision Tree models. The model achieved perfect classification, with no errors in predicting fraud or non-fraudulent transactions. The significance of implementing effective fraud detection systems cannot be emphasized, as they serve as protectors of the security and integrity of the financial ecosystem. By providing protection to both financial institutions and cardholders against potential financial instability, these systems strengthen the fundamental basis of confidence on which transactions rely. Key Words: Automated Feature Engineering, Deep Learning, Financial Fraud Detection, Machine Learning.
Deep learning tool for higher learning scholarship award decisions: a case of higher institutions of learning
(Strathmore University, 2025) Nyambura, T. M.
In today’s highly competitive higher education environment, scholarship programs play a crucial role in attracting and retaining talented students, especially those from disadvantaged backgrounds. However, traditional scholarship award processes often rely on manual and subjective methods, which can lead to inefficiencies, biases, and inequitable distribution of financial aid. The lack of standardized criteria and the complexity of handling large volumes of applications further complicates the decision-making process. This study developed a deep learning-based tool to assist in higher education scholarship award decisions, utilizing convolutional neural network (CNN) to automate the selection process. Through a data-driven approach, the tool was designed to analyse student profiles across multiple dimensions, including academic performance, financial need, extracurricular involvement, and personal background. The deep learning model aims to provide more accurate, objective, and fair scholarship allocations by removing human biases and ensuring that all eligible students are assessed equitably. The developed tool was tested on a dataset of historical scholarship applications and awards, and the performance of the CNN model was evaluated based on accuracy, fairness, and efficiency compared to traditional manual methods. The model achieved an accuracy of 93% surpassing the performance of all the reviewed models at the end of fine-tuning. The findings of this study offer valuable insights into the feasibility and effectiveness of integrating AI-driven tools into scholarship management systems in higher education institutions. Ultimately, this study contributes to ongoing efforts to make scholarship distribution more equitable and aligned with institutional goals and societal needs. Keywords: Scholarship Allocation, Deep Learning, Convolutional neural networks (CNNs), machine learning, higher education, automated decision-making, data-driven scholarship distribution, AI in education
A Distributed computing prototype for climate change impact simulation: case of Nairobi, Kenya
(Strathmore University, 2025) Makungu, C.
Climate change is one of the most pressing issues facing the world in the 21st century, and Nairobi is no exception. Climate change in Nairobi impacts climate-sensitive variables, including unpredictable rainfall, rising temperatures, and increased frequencies of extreme temperature and precipitation events, significantly affecting current and future urban infrastructures, public health, transportation systems, and livelihood activities. This project sought to develop a distributed computing environment for detailed climate change modeling and effects assessment in Nairobi, Kenya. The prototype successfully processed climate data at 1km² resolution compared to previous 10-25km² models for Nairobi. Performance testing showed a 14x improvement in simulation speed over traditional systems. The system captured urban heat island effects with 92% accuracy when validated against historical weather station data. Temperature predictions achieved ±0.4°C accuracy with 95% confidence intervals. Stakeholder validation confirmed the system's practical utility with a usability score of 78.3/100. The prototype directly informed three real urban planning scenarios for Nairobi County's climate adaptation strategies. Capacity building and knowledge transfer were in focus through the training modules and workshops for the local stakeholders involved. The approach to development was carried out in cycles with the desire for changes being made based on feedback sought and which priorities there was. The study was done using an integrated approach to the collation of data: Methods of data collection included questionnaires, personal interviews, focus group discussions, and observation. The quantitative and qualitative data analysis techniques generated useful information for application in urban planning and policy. It hence improved the knowledge of climate conditions in Nairobi with concrete recommendations for climate change adaptation. Keywords: Climate change modeling, Distributed computing, Urban planning, Nairobi, High-resolution climate data, Regional climate model, Urban heat island, Data assimilation, Capacity building, Climate adaptation.