MSIT Theses and Dissertations (2020)
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- ItemA Model for identifying vulnerabilities on critical infrastructures: case of cyber threats in Kenya(Strathmore University, 2021-11) Maina, Simon KuriaWith advancement in technology, industry-focused technological systems have over time faced the challenge of attacks given their vulnerabilities resulting in denial of services and catastrophic operations for countries. This study focused at analysing the risk exposure on Kenya’s Critical Information Infrastructure (CII). A model for identifying the vulnerabilities that critical infrastructures are exposed to by detecting anomalies in the set thresholds was developed. This study adopted the vulnerability system development lifecycle to develop the model. The model was developed following the Rapid Assessment Methodology and used the Common Vulnerability Scoring System (CVSS) to measure the severity of potential vulnerabilities against critical infrastructure. This allowed the model to prioritize responses and resources to remediate against the vulnerability identified. The study found that vulnerabilities pose a security threat on systems that are deemed critical and as such recommends that organisations should invest on vulnerability assessment tools. These will help them detect, remediate and monitor and evaluate vulnerabilities on CIIs.
- ItemApplication of permissioned block chain technology on population data consolidation and sharing(Strathmore University, 2020) Omoka, Richard Siang'aniPopulation registers should provide the single source of truth for data regarding each resident of a jurisdiction of the register, over the lifetime of the individual. This data can then be shared and used by government agencies and private organizations regarding matters concerning the individual. In Kenya, however, data regarding an individual is collected by multiple government agencies resulting in duplication (of effort and data) and data inconsistency. The multiple collections of population data result in an individual having multiple valid identification documents. The use of relational database management systems, which have shortcomings in support for temporal data as well as no inbuilt security and auditability capability makes relational database management systems ineffective in the storage of population data. Lack of clear policy and standards; interoperability issues and data security are among the challenges affecting data sharing among government agencies. Blockchain technology, a shared, immutable, distributed ledger that facilitates the process of recording transactions and tracking assets in a business network, is a promising technology in the management of population registers. Blockchain technology has inbuilt capacity to solve most of the problems inherent in the current systems especially duplication, tampering, and sharing of data. This research, therefore, through the development of a prototype based on permissioned blockchain technology, explores the viability and validity of permissioned blockchain technology, in storing, securing, auditing, and sharing of population data to achieve the single source of truth of the population register. The prototype, implemented using a local installation of hyperledger fabric, enabled consolidation of data since all invited participants on the permissioned blockchain network were able to write data to the single blockchain. The invited participants were also able to read data off the chain based on defined access control rules therefore achieving a uniform standard for data sharing. Provenance, a key quality of blockchain was leveraged to track an individual’s data changes over time, with the current block holding the latest records about the individual, yet still maintaining the historical chain of an individual’s data changes. This was a key outcome especially because it solves the inability of relational database systems to support temporal data. This model for data consolidation and sharing was found to be simple in design and implementation since it provided a standard way of reading and writing data to the chain through the use of RESTful APIs.
- ItemPrediction model for determining healthcare facility locations by the Kenyan County Government(Strathmore University, 2020-03) Isaboke, Edward NyakerumaHealth has been a key subject of interest to governments and non-governmental organizations. Health includes various building blocks with human resource being the core of health systems. Evidence globally shows a correlation between a country’s healthcare work force and health. Infrastructure relating to health is crucial for efficient and effective healthcare systems. A strategic location for a healthcare facility can improve facility utilization and save costs. Knowledge of methods and techniques need to be regularly updated for the location and establishment of healthcare facilities. This research investigates the criteria for decision-making of locations of healthcare facilities to ensure the most strategic location for the facilities is opted for. This research looks at the challenges that affect the location of healthcare facilities in detail to model the most appropriate locations for the facilities. Existing location models developed are many and discussed in detail to cater for special cases. The two most important criteria for healthcare services are cost and efficiency. They enable reduced distance to travel by patients to the health facilities. Unpredictability is identified as an unavoidable element of healthcare facilities location problem. The purpose of this research is to develop a model for determining strategic locations to establish healthcare facilities. The model was developed using the rapid application development methodology. This research proposed a strategy which enabled predictions according to the location factors that are suitable for establishment of healthcare facilities. This is after a set of locations are identified to be the target locations by the set cover location model. The model calculates and identifies the strategic locations within an area entered by a user. It is therefore important for historical data to exist for the model to work. Based on the provided data, the model then calculates the priority of the strategic locations identified hence a sequence is known on which location to establish the facility. Datasets from an online source were used as data inputs into the prediction model. The research recommended the model as a base for decision making for establishment of a healthcare facilities.
- ItemVegetation index based crop yield prediction model using convolution neural network - a case study of Kenya(Strathmore University, 2020-06) Chepngetich, JudithPredicting the crop yield is a vital food security strategy that can help a country take suitable measures and come up with policies that will help in crop production management. Such predictions will also support the farmers and industries involved in crop production for strategizing the logistics of their business or farming activities. Having sufficient production plans can improve food sufficiency and avoid situations of food emergencies. Climate change has had a huge impact on food production with variations in crop yields, creating uncertainty. Most of the studies on crop yield prediction have been done based solely on weather data, which is sometimes inaccurate due to scarcity of weather information especially in developing countries, where there is poor record keeping and insufficient resources to collect data. The use of vegetation indices derived from remote sensing data overcomes these challenges by providing data that is easily accessible and gives a comprehensive and multidimensional analysis. This study proposes a model that uses of vegetation index to predict crop yield using machine learning. Data from past crop yields in Kenya and vegetation greenness indices were the inputs applied to the algorithms. Various machine learning algorithms were applied and thereafter evaluated, so as to select the algorithm that gives better accuracy. To determine the accuracy level for the prediction model, the RMSE is calculated to compare actual and predicted values. The RMSE values obtained using convolution neural network for the three crops maize, rice and wheat were lower compared to those obtained using ridge regression, so it was selected as the optimal algorithm for the crop yield prediction model.
- ItemA Computer vision based model for tomato plant nutrient and disease classification(Strathmore University, 2020-06) Kiyegga, Raymond PaulDetermination of disease and nutrients in plants is still a new concept. Despite efforts from researchers to come up with improved techniques of detecting diseases and nutrients, many have been limited to only specific plant images and no other data such as weather, surrounding conditions to back up the decision. Plant disease identification is very crucial to food production and security, however current practices in Africa include visual identification and microscopy. Visual methods are greatly affected by cognitive error while microscopy is time consuming. It is difficult to detect plant disease unless one is guided by expert knowledge. Therefore, there is a need to apply machine learning techniques to make use of this expert knowledge. Current practices include use of spectral images to achieve this in fruits and other applications to help farmers without access to this knowledge to diagnose plant diseases. One notable challenge is determining nutrient content using images. Current applications require a farmer to look at the provided image and compare with what he sees on the plant. This research work proposes a machine learning model that can automatically detect the disease affecting a tomato plant as well as the nutrition level in the plant leaves. The farmer captures an image on their phone while in the plantation, based on the features from the leaf, the model analyses the image and returns the details of the classification in terms of type of disease and presence of deficiency of nutrients. The model was built on convolution neural network and achieved an accuracy of 85% using a learning rate of 0.001. It trained on 8000 samples using 30 epochs. The model was trained, validated and tested.