MSIT Theses and Dissertations (2022)

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    A Location-aware nutritional needs prediction tool for type II Diabetic patients: case Kenya
    (Strathmore University, 2022) Karega, Lulu Amina
    Diabetes is a chronic disease caused by a lack of insulin production by the pancreas or by poor utilization of the insulin that is produced, with insulin being the hormone that helps glucose get to blood cells and produce energy. Urbanization and busy day to day schedules mean patients tend to pay little or no attention to their dietary habits which results in a preference for fast foods and processed food. The prevalence of type II diabetes in the world, Kenya included, has been steadily rising over the years and is projected to keep growing at an alarming rate. Diabetes if not properly managed can result in long-standing, costly and time-consuming complications. Diabetes management and control of blood sugar levels are generally done by the use of medication, namely insulin and oral hypoglycemic agents. However nutritional therapy can also go a long way to boosting the general health of a patient and reducing risk factors leading to further complications. Personalised nutrition has been formally defined as healthy eating advice, tailored to suit an individual based on genetic data, and alternatively on personal health status, lifestyle, and nutrient intake. Diabetes management falls under the field of health informatics that can benefit from data analytics. Predictive analytics is the process of utilizing statistical algorithms, software tools and services to analyze, interpret and visualize data with the aim to forecast trends, and predict data patterns and behavior within or outside the observed data. This study sought to develop a location-aware nutritional needs prediction tool for type II diabetic patients in Kenya. The prediction tool would help both nutritionists and patients by providing accurate and relevant nutritional advice that would help in dietary changes to combat type II diabetes with the added benefit of being location aware. The tool will use pathological results from nutritional testing to support nutritional therapy. If any deficiencies are identified from the provided nutritional markers, food items likely to improve those nutrient levels will be recommended. The amount of nutrient available in a given food item are determined by the food composition table for Kenya as published by the Food and Agriculture Organization (FAO) in conjunction with the Kenyan government. The study used a simplistic implementation of matrix factorization to provide predictions of locally available food items, down to the county level.
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    A Snake classification model for snakebite envenoming management
    (Strathmore University, 2022) Mabinda, Mariam
    Snakebite envenoming is a potentially life-threatening disease caused by the injection of toxins through a bite or venom sprayed into the victim’s eyes by certain venomous snake species. WHO program dubbed Neglected Tropical Disease Program (NTD) of 2019 indicated that about 5.4 million snake bites occur each year, resulting in 1.8 to 2.7 million cases of envenoming. Of this about 81,000–138,000 deaths occur and approximately 400,000 people are permanently disabled annually. Kenya is approximated to have more than 15,000 bites annually. Correct identification of the snake species in question plays a critical role in the proper administration of the right first aid and suitable prescription of the anti-venom for the patient. Currently, there is no automated method of identifying snake species using images in Kenya. The usual practice is to, kill the snake and carry it along with the patient to the hospital or to give a visual feature description of the biting snake. Also, a blood test can be done to look for the presence of toxins associated with the described snake species. The challenge however is, that the time required for test results to be out can jeopardize patients' survival depending on the type of venom injected. Furthermore, the cost associated with the test is also punitive. The ability to correctly classify a snake species is a challenging task for both humans and machines mainly because of subtle differences between different snake species and strong variety within the same species. Existing studies used a combination of feature extraction methods and deep neural networks and yielded an accuracy of 90%. These models applied Principal component analysis (PCA) and Linear discriminant analysis (LDA) as feature extractors. However, the use of the Singular Value Decomposition (SVD) algorithm was not explored despite its apparent advantage. This research study solved the classification challenge by creating a Kenyan snake species dataset and developing a snake species classification model that predicts a snake species based on the image and classifies it according to its venom toxicity. The study carried out feature reduction of the images using the SVD algorithm and passed these features as input to a deep learning model using transfer learning. The model was trained using 4521 labelled snake images via supervised transfer learning using MobileNetV2. The model was trained, validated, tested, and achieved an outstanding classification accuracy of 96 %. The model surpassed the accuracy of the existing model.
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    A Machine learning model to predict non-revenue water with severely unbalanced classes
    (Strathmore University, 2022) Muriithi, Patrick Kimani
    Every household, industry, institution, organization needs clean water for existence. In Kenya, water is used for human consumption, production, and agriculture. The consumption of water, therefore, contributes to the overall growth of the economy through water bills. The term non-revenue water (NRW) is defined as water produced and 'lost' before it reaches the customers. NRW is also described as the difference in volume reaching the final consumer for billing and the initial volume released into the distribution network. Based on the assessment of the Public-Private Infrastructure Advisory Facility (PPIF), an organization that fosters inter-agency cooperation to curbing NRW, physical losses are the main causes of NRW. As per PPIF, most NRW emanates from physical losses, including burst pipes that are often a result of poor maintenance. Besides physical losses, PPIF notes other numerous sources of NRW, especially commercial losses arising from the manner billing data is handled throughout the billing process. The main issues related to this cause include under-registration of customers' meters’ reading, data handling errors, theft, and illegal connections. Other causes of NRW include unbilled authorized consumption such as water used for firefighting, utilities for operational purposes, and water provided to specific groups for free. Therefore, non-revenue water risks the country's revenue collection, which can lead to slow economic growth. This research proposes development of a machine learning model that will be used by water service providers. The model will be able to assist the WSP companies to reduce non-revenue water by predicting water consumption of different customers. To achieve these objectives, we intend to focus on providing tools and methods that will guide the WSPs on reducing the non-revenue water. Our model was trained with 2 years consumption dataset of Nairobi County. The model developed was able to predict customer monthly consumption with percentage accuracy of 95%.
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    An Intelligent chatbot implementation for employee exit auto-clearance using deep learning
    (Strathmore University, 2022) Kasera, Lawrence Mwakio
    As part of the employee exit in an organization, the clearance process is a mandatory requirement that guarantees that the employee leaves formally, returns all organization property, and gets the final paycheck. It is commonplace for this process to entail filling and submitting an exit clearance form. For each area of responsibility in the clearance process, an ascertainment of completion is marked by the use of signatures or clearance approvals from the requisite personnel. The review of literature showcased that this process often employs the use of physical forms, which means printing, filing, and tones of record keeping. On the other hand, some organizations use automated means, which are still largely human reliant, leading to delays, inconsistencies and lots of redundancies. The aim of this study was to develop an intelligent chatbot implementation for employee auto clearance using Deep Learning. A chatbot is an Artificial Intelligence (AI)-driven software tool that simplifies the interaction between humans and computers. Among many other advantages, a chatbot reduces the overall costs in mundane tasks, enhances the user experience and has greater availability. This research employed the qualitative design to explore the different ways and approaches that make up the clearance process, alongside their challenges, in formal organizations, within Nairobi, Kenya. The proposed deep learning chatbot model was developed using 2 hidden layers and trained on 2,000 epochs. The training data dictionary was categorized as tags, patterns and responses. The model was able to correctly match 99.91% of the input pattern data points to their corresponding response output data points, and where an input pattern seemed unclear, the model was able to respond accordingly. The model could successfully make the API calls to the web service, where digital signatures are appended, and finalize the exit clearance process with a complete and signed clearance form.
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    A Predictive analytics model for pharmaceutical inventory management
    (Strathmore University, 2022) Musimbi, Patience Musanga
    Inefficient inventory management is a factor that affects pharmacies in Kenya. The unpredictable nature of weather patterns during the traditional long and short rain seasons has resulted in seasons starting earlier or later than expected. Seasonal diseases such as flu may spike up when the temperatures decrease or when the rainy seasons begin, causing an increase in sales of drugs that cure and prevent the flu and vice versa. Due to this unpredictability, pharmacies may fail to stock up or down for different seasons due to unpreparedness and not knowing what to stock and when to stock. Ineffective drug management has a significant financial impact on pharmacies. Inventory management ensures that needed drugs or medicines are always available, in sufficient quantities, of the right type and quality, and are used rationally. An effective drug management process ensures the availability of drugs in the right type and amount in accordance with needs, thereby avoiding drug shortages and excesses. This research proposed a predictive analysis tool that would predict the required drugs or medicines prior to when they are needed, based on sales and seasonality. Another parameter for predictive analysis for this research was the period of the year when a certain disease could be common. This research discussed stocking and inventory management of pharmaceutical products and how predictive analytics with machine learning algorithms could be applied to improve the inventory management process in a pharmacy’s context. The purpose of the study was to examine the inefficient stocking of medicines in pharmacies and use predictive analysis to predict future stock. It reviewed various previous methods used for pharmaceutical inventory management and proposed the SARIMAX model with time series analysis for stock prediction. The result was a model that predicted the quantity of drugs to be stocked for the next six weeks. The six-week prediction model had a Root Mean Squared Error (RMSE) of 5.5.