MSc. CIS Theses and Dissertations (2020)

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    Centralized public parking management - case study of County Government of Nairobi
    (Strathmore University, 2020) Kang’ethe, Evans Mungai
    Commuting in Nairobi is part of life for anyone living within the city. This has seen the exponential growth of vehicles that operate within the county. The county government is in charge of controlling parking spaces which are limited. The methods used are mostly manual and several automated parking which also has limitations and is highly inefficient. Manual processes are lengthy with low accuracy of actual operations and accountability. This involves lots of manpower, which is costly, inconsistent, and inefficient. This has an effect on productivity of the economy since time and revenue are lost. This research will be aimed at evaluating the current system and finding ways to make it effective, efficient and convenient to both the county citizens and government. Information will be collected on the existing method used to parking management, analyzed to establish current gaps and a recommendation of the best approach will be presented. There is need for a holistic approach to managing parking in the count of Nairobi. A system that aggregates all parking slots centrally and identifies each uniquely. The system will be accessible from anywhere using a web based enabled interface. The drivers in the county will be able to log in and preserve slots at a defined time on a first come first serve basis. This will coordinate traffic flow in a more efficient way since the system is able to predict estimated number of vehicles expected in the city per unit of time apart from those that will be in transit and not stopping. It will improve the approach used to coordinate parking, people involved with the efforts required and the technology that is currently being used. Administration of the system will be centralized and accountability methods stringent.
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    A hybrid predictive prototype for portfolio selection using probability based quadratic programming and neural networks
    (Strathmore University, 2020) Muganda, Brian Wesley
    A portfolio is a collection of investments held by an investment company, hedge fund, financial institution or individual. This collection of investment features a combination of financial assets such as stocks, bonds or options. The designing of a portfolio (fund allocation to each asset and selection of the assets) is done according to the investor’s risk tolerance, investment time frame and investment objectives. Robo-advisors, which are automated algorithm-driven investment platforms that use quantitative algorithms to manage investors’ portfolios, are at times used to perform portfolio allocation for investors having defined their risk preferences and investment time frames. However a majority of these robo-advisors still rely on classical mean variance allocation techniques of modern portfolio theory. This research therefore, developed a robo-financial advisor prototype on a hybrid programming architecture by using artificial neural networks to predict portfolio returns and variances based on underlying multi-asset Uhlenbeck process (OU) and geometric Brownian motion (GBM) processes’ estimates. These results were subsequently used in the probability-based quadratic optimization algorithm to provide an optimal portfolio allocation strategy. This probability-based quadratic programming approach is novel and is based on return certainty probability and value-at-risk constraints acting as proxies for investor’s risk tolerance. The results showed that neural network algorithm performed averagely well forecasting being able to predict the correct level of 2 of 5 assets and to predict correctly the trends of the remaining 3 assets, it however yielded low standard deviations compared to the OU and GBM models. The quadratic optimization algorithm supported investment in shorter time horizons since portfolio risk was lowest. Diversified allocation was achieved in the shorter time horizons. Longer horizons allocations were biased towards asset with lower standard deviations. Lowest risk portfolio was the ones with a lower certainty probability of target return and vice versa. Also, it shows a hybrid programming paradigm is an effective approach to leverage on strengths, speed and functionality of different programming languages; an elixir for multifaceted dissociable programming problems.