MSc. CIS Theses and Dissertations (2019)

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    Mobile application for long distance vehicles booking of passengers in Kenya
    (Strathmore University, 2019) Chemutai, Nancy
    Making a booking for a journey has been one of the challenges affecting passengers who travel for long distances. Public transport is one field that is facing extreme pressures with customers demanding higher service levels at an affordable prices. Over years, public transport is supposed to facilitate movement of people from one location to next conveniently and in a cheaper way but this is not the case in Kenya where there are a lot of inconveniences affecting passengers using public service vehicles. To ensure a passenger makes a booking in advance, they are required to visit the booking office prior to their travel date and pay for the journey in form of cash causing inconvenience and thus making the passenger to incur an extra cost in order to make a booking in advance. Thus, this study aims at developing a mobile application that would assist passengers in making a booking at their own convenience by indicating their pick-up location so that they do not have to visit the booking office thus saving them time and travelling cost and reduced queues in booking offices and the number of staff employed leading to increased revenues. In addition, the passenger would be in a position to make payment via M-PESA or Credit Card. A simple web page is provided to add some booking details to the database to be used by the mobile application and at the same time to make a booking for few passengers who would visit the office to make a booking and for viewing reports. Data collection was achieved through questionnaires and review of existing data sources. The study was carried out in line with the ethical practices as specified by the University’s rules and regulations.
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    A Mobile-based image recognition system for identifying bird species in Kenya
    (Strathmore University, 2019) Nyaga, Gideon Mwangi
    Kenya is world-renown for its wildlife tourism, which attracts many foreigners and is a leading foreign exchange earner. The last few years has seen significant growth in the number of people, called birders, who observe birds for recreation or for citizen science. Kenya in particular is one of the leading birding destinations in the World with about 11% of the World’s bird species. A big challenge birders face is correctly identifying the bird species observed. Fine-grained visual classification, and in this case the ability to identify Kenyan bird species, is a challenging task for both humans and machines mainly because of subtle differences between different bird species and strong variety within same species. This research study solved this challenge by developing a mobile-based image recognition system that can identify Kenyan bird species from images. A deep neural network called a Convolutional Neural Network (CNN), inspired by the human visual cortex, is well suited for image recognition because it is able to handle shifts and distortions in an image well, has fewer trainable parameters and thus uses less memory and training time than a standard artificial neural network (ANN). Further a depth-wise separable CNN is suited for resource-constrained mobile devices as it has lower computational cost as compared to standard CNNs. This research developed a depth-wise separable CNN model, which was trained using 39,031 labelled bird images via supervised transfer learning. The model achieved a final test accuracy of 97.3%. Consequently, an Android mobile application was developed to consume the resulting model. The model was embedded into the mobile application. Therefore, the user did not need internet to make an inference. The mobile application was able to process an input image containing a bird and identify the bird species. The mobile application was also able to give more details of the identified bird species.
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    A Prototype for predicting energy consumption in buildings: a case of commercial office buildings
    (Strathmore University, 2019) Wachira, Paul Manasse Macharia
    Energy consumption remains the highest cost areas for businesses together with facilities, people and equipment but unfortunately, it is the only one that is not carefully monitored. For businesses to be able to manage energy consumption they must first be able to predict future energy consumption so as to aid in budgeting and planning for cost reduction strategies. This study proposed an energy consumption prediction prototype to help predict future consumption of energy in commercial office buildings thus aiding proper budgeting and cost reduction. To develop the prediction model the study used the 2012 CBECS (Commercial Buildings Energy Consumption Survey) dataset hosted by the Energy Information Administration (EIA) of the United States of America. After cleaning and reviewing the data set, 26 Features were selected for Features Engineering. Features Engineering enabled the research to choose the best 4 Features which were used for training and testing different Regression Based Machine Learning Algorithms. Using R2 (R Squared), MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) to determine performance, the study selected Gradient Boost Machines as the best algorithm for the prototype with an Accuracy of 97%. Python packages Pandas, NumPy, Matplotlib, Seaborn and Scikit-leam were used indata cleaning, descriptive statistics, features engineering, data visualization and training and testing the machine learning algorithms for the energy consumption prediction model. The prototype was developed using Flask (a Python micro web framework) to enable the Building owners provide the prototype with data via web browser related to the 4 features selected for energy consumption prediction. Usability Test was done with 48.1% of the users strongly agreeing and 44.2% agreeing to use the prototype in future for prediction of electricity consumption in their buildings.