MSIT Theses and Dissertations (2016)
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- ItemAn application for career path decision making among high school students: case of Nairobi(Strathmore University, 2016) Njeru, Daniel NdwigaIn many developing countries, young people have and are embracing formal education as this has proven to be one of the main ways of alleviating poverty. In Kenya, the level of literacy has taken an upward trajectory for more than a decade now. On the other hand, studies show that majority of Kenyan Students do not receive proper and adequate career guidance in their early ages so as to make informed decisions on which career best fits their preferences. In addition, students neither have adequate information of the available career paths and occupational opportunities neither are they well informed of which opportunity they best fit. Furthermore, Information communication and technology has not been adequately leveraged in education sector to facilitate students in making careers that they best fit. The purpose of this study is to develop an application that can facilitate students in making informed decision about their career aspirations. The study was guided by the objectives: To establish the specific data and information necessary for determining a career path of a student, to review the challenges that exist in the choice of a career path among students, to review the existing techniques that are used in determining the career path of a student, to develop an application for career path decision making and to test the application. To achieve these objectives, a thorough review of the scholarly literature was carried out, the researcher also carried out a pilot study to establish the viability and validity of the proposed solution, and spiral model of system development lifecycle was used to further define, model, develop and validate and implement the system. The researcher further reviewed the solution developed in comparison with the others that exist. Finally, recommendations were made and suggestion of further research work was proposed.
- ItemArtificial neural network model for inflation forecasting in Kenya(Strathmore University, 2016) Mwangi, Carolyn Naomi WanjaForecasts are important in decision making and entail prediction of a future state of a particular subject of interest. These forecasts depend heavily on historical data and the assumption that the past behaviour of forecast inputs will replicate itself in the future. Current linear and macroeconomic theory forecasting models used in Kenya lack reliable accuracy when predictors are futuristic and subject to changes over time. Artificial Neural Network (ANN) allow for the model to be more versatile in incorporating new predictors without altering the structure of the model. They work exceptionally well in environments that are nonlinear and where data is noisy and sometimes unavailable. The structure for the proposed model is a Neural Network with Back Propagation learning algorithm incorporating rainfall and M-Pesa use effects as additional inflation variables. The Backpropagation Neural Network was selected as a useful alternative due to the non-linear data used and to facilitate forecasting of future values. The adaptability of ANNs makes them most suitable for dynamic forecasting and classification problems. The results obtained from the model indicated that the back propagation was an appropriate algorithm that can be implemented in the process of inflation forecasting. The forecasting was done based on inflation variables identified as true inputs to the process of inflation forecasting. The model accuracy performance at 71.4286 % showed that the model is reliable as a tool for inflation forecasting. The study found that the optimum learning rate for the model was 0.5 while the momentum was at 0.9 for the training and 0.7 for the testing and validation data. Total iterations varied between the train, test and validate phases.
- ItemA geographic information based parking management prototype: a case of Nairobi(Strathmore University, 2016) Ogenche, Joan RaberaThe purpose of this research paper is to identify a solution to parking in Nairobi’s central business district having factored in all the spatial and non-spatial elements. The problem is that a new wave of people are flooding the city to live, work and do other activities and many arrive on four wheels thus creating massive headaches for city planners and drivers in reducing parking issues that cause street congestion and wastage of time in searching for parking spaces. The methodology that will be used to achieve the overall objective is the software development lifecycle. This involves sequential phases, with some overlap and splash back of activities between phases in development of the final product. A Geographic Information based parking management prototype that will factor in spatial and non-spatial data is the solution that is proposed herein to facilitate good quality secure parking in maintaining the vitality and viability of town centres in enabling retail and leisure uses to flourish. The output is a seamless mobile-web interface that will enable the county government as well as key players to access and monitor space availability which the users will be able to access from their mobile phones. Parking being a major use of land and the supply is frequently a factor that influences trip generation, the implications of this research is that all spaces available will be tapped into to strike a balance between encouraging new investments in the town centres and increasing parking spaces. The application created was able to reap parking space information from the map server which will greatly reduce the trial and error involved while in search of a parking space thus increasing a driver’s confidence, cutting down on the amount of parking time and the emotional stress associated with finding a parking space.
- ItemInternet of things for monitoring environmental conditions in greenhouses: a case of Kiambu County(Strathmore University, 2016) Kanake, James MainaEfficient management of greenhouse farming is a challenge to ensure high yield production. This is a great challenge to farmers who do not have a reliable mechanism to ensure the optimum environmental conditions for their crops. Farmers are opting to look for solutions from technologies such as Machine to Machine and Internet of Things. Machine to Machine Communication refers to solutions that allow communication between devices of the same type and a specific application through wired or wireless communication networks. Moreover, Internet of Things is a connection of physical things to the internet which makes it possible to access remote data and control the physical world from a distance. These types of solutions allow end-users to capture data about events and transfer it to other devices but they do not allow broad sharing of data or connection of the devices directly to the Internet. In this thesis, the researcher investigated the use of machine to machine communication by having small electronic devices equipped with sensors that when deployed in a farm they can record the environmental conditions and communicates the information to the farmers. Moreover, the different types of crops grown in greenhouses at Kiambu County. Thereafter, the information was analyzed and sent to relevant end users such as the farmer and a metrological department that will enable them to monitor and adapt to the environmental conditions. The research used applied method of research, interviews and questionnaires to gather data. Therefore, an IoT prototype was developed to gather the critical environmental conditions in a greenhouse. The recorded data was transmitted by wireless networks using machine to machine (M2M) technologies from the sensors to the cloud platform, Intel IoT analytics dashboard, for real-time predictive analysis of the environmental parameters. An email notification was sent to alert the farmers when the parameters exceeded the threshold which were preset. This IoT prototype was used in small to large commercial indoor operations as well as small personal gardens.
- ItemNear Field Communication (NFC) based card payment prototype : case of SMEs in Nairobi(Strathmore University, 2016) Yegon, Denis KipkorirThe card industry has been around for decades, evolving in tandem with the banking industry. Initially used exclusively for ATM transactions, cards have gone past that and are now used to perform payments in a society moving towards becoming a cashless society. The benefit of cashless transaction includes less opportunity for fraudulent and criminal activity (Wishart 2011). Despite this, the card industry has still been affected heavily by fraudulent activity; in Kenya for example, losses of in excess of 1.49 billion were reported between April 2012 and April 2013. This necessitated a move from magnetic stripe cards to EMV cards in a bid to cub the vice. Despite this measure being enforced, the SME sector in Kenya was largely unaffected as the utilization of card payments is very low. Research has shown that this can be attributed to the challenges merchants face in using card payment. It is no surprise therefore that most merchants in Kenya still prefer and take only cash payments as opposed to card payments. The major card service providers, VISA and MASTERCARD are directing additional focus in the form of investment to the East Africa in the hopes of increasing card payments in the market. The secret to this been a success could lie in the success of mobile payments in Kenya. MPESA has been adopted widely by merchants across SMEs in Kenya indicating that the merchants are not facing the same challenges they face in using mobile payments as when using card payments. If the principles applied to mobile payment‟s success can be applied in the card sector, it is likely adoption of card payment will also enjoy success in the SME sector. One of the ways this can be implemented it through Near Field Communication technology (NFC). The research will sort to understand the challenges that the merchants in SMEs face in utilizing card payments resulting in low adoption of the card payment systems. In addition, secondary data on implementation of mobile payments will be analyzed to understand the concepts that led to their success. Existing NFC based systems around the world will also be analyzed so that this combined knowledge can guide the researcher in designing and developing a prototype of NFC-based card payment system for business transactions for SMEs in Kenya. Research design to be adopted will be qualitative research design. The study targets SMEs spread in sectors of trade (wholesale and retail) in Nairobi County. The study will use primary data which will be collected through self-administered questionnaires and interviews.
- ItemA Prototype for secure data sharing amongst organizations in Kenya: case of broadcast media(Strathmore University, 2016) Aunyasi, Ambrose Ebale
- ItemA prototype for tracing missing children : a case of Nairobi County(Strathmore University, 2016) Ndeto, Martin NdithiTracing missing children has been quite hectic for parents and care givers. A missing child is vulnerable to risks associated with drugs; poor health; involvement in criminal activities for survival, assaults, murder, rape and infection with killer diseases. Currently, there is lack of coordination in departments dealing with issues concerning children and no timeliness for the police department in handling this process. In addition, there is no convergence among the agencies involved in child protection. Several solutions have been proposed among them, the “App for the loved ones”; which has a central database and uses short messaging services (SMS) to send search terms that must have an exact match. Social media has been another approach capable of mobilizing volunteers to spread the information concerning the missing child at a fast rate. However, it lacks credibility since any one can author the information. A “CodeSearch” application was introduced in Canada which uses global positioning system (GPS) to send geo-targeted alerts to its subscribers. However, most people tend to have their GPS turned off unless when in use. It is also limited to employees of the CodeSearch partners. This research aims at introducing an expert system that uses ID3 algorithm to populate its knowledge base and an interactive search using the same algorithm. This allows users to interactively search the database, enter details about their missing loved ones if not yet found and notify them whenever the person is found. The search is based on the person’s phenotypes as they cut across the human race. The research is a form of applied research. The sample size was computed through convenience non-probability sampling. Most of the respondents recommended a proper system hence the reason for creating this prototype. The prototype is developed using V-Process methodology since the clarity of the user requirements was high and the technical expertise needed was readily available. The prototype produced 99% accuracy in tracing the missing children in the sample used.
- ItemReal – time sentiment analysis for detection of terrorist activities in Kenya(Strathmore University, 2016) Ngoge, Lucas AchukuTerrorism has become a subject of concern to many people in Kenya today. Majority of people are worried lot because they don’t know when they will become victims of terrorists’ activities. Corruption, porous border and luck of government in the neighboring Somali, have made Kenya a potential target for terrorists’. The advancement in technology has brought a new era in terrorism where Online Social Networks such as Twitter, Facebook has driven the increase use of the internet by terrorist organizations and their supporters for a wide range of purposes including recruitment, financing, propaganda, incitement to commit acts of terrorism and the gathering and dissemination of information for terrorist activities. Although the Kenya government improved its ability to fight terrorism but the changing pattern of terrorist activities, human errors and delayed crime analyses have given criminals more time to destroy evidence and escape arrest. The evolution of computerized systems has made tracking of terrorist’ activities easier. This has helped the law enforcement officers to speed up the process of solving crimes. In this research data was collected from twitter then followed by sentiment analysis on tweets collected to derive rules for the real-time classifier. Geographic analysis was done to reveal a correlation between the tweets and the terrorist’ activities as portrayed by the map. The main objective of this research is to develop a model that will be used to establish crime patterns associated with terrorist activities using sentiment information deduced from twitter data. To achieve this objective, 346 tweets related to terrorism were collected, cleansed and stored in a database for a period of 7 days. This data was then used as features for training and development of the model which will then be used to carry out real time sentiment analysis on twitter data. The model was tested and it was able to classify text correctly into positive, negative and neutral classes with an accuracy score of 73%.
- ItemReal-time solution for automated inventory monitoring of antiretroviral medicines: case of Nairobi County(Strathmore University, 2016) Alick, Raymond StephenCases of stockouts and expiry of Antiretroviral Therapy medicines are occurring and this is due the fact that the current inventory monitoring systems for Antiretroviral Therapy Medicines and commodities are manual despite the use of electronic systems for the ordering and issuing of the same. This research proposed a real-time inventory control and monitoring model to address the challenges. The model uses fuzzy logic through a fuzzy inference engine to predict the appropriate quantity level on when to place an order so as to reduce the probability of a stockout occurring. The model uses a continuous review technique to monitor the quantity of the medicines in real-time as they are being issued or received. The model gives immediate notification when a reorder point for a particular inventory item is reached. The model is also able to monitor product lifetime of the medicines to ensure those concerned are informed when medicines in inventory have expired and need to be replaced. The model is validated through the implementation of a web-based system which is then tested against the propositions of model to confirm it works as expected. The tests on the system yielded a positive validation of the model. The fuzzy inference engine’s accuracy of predictions was tested using the Random Mean Square Error method and it yielded a standard deviation error result of 4.3% from a set of actual test data sets. The model was then compared against other existing models and it was proven that the model developed in this research is the most appropriate for the monitoring of Antiretroviral Therapy medicines and commodities.
- ItemVision-based model for maize leaf disease identification : a case study in Nyeri County(Strathmore University, 2016) Maina, Christine NjeriBiotic stress which includes pest and diseases affect crop productivity due to either death of affected crops or reduced yield per crop. Abiotic stress such as water and temperature also contribute to lower yields. Maize is Kenya’s staple food with most households having limited choices of other foodstuffs thus increasing their reliance on maize. Diseases affecting maize in Kenya include: Maize Grey Leaf Spot disease, Maize stem borer, Maize Lethal Necrosis Disease, Ear Rot, Stem Borers, and Maize Streak Virus. Currently, the human visual examination is the most commonly used method for classifying diseases. The method gives room for a lot of errors as the diagnosis is based on the experience of the farmer or the extension worker. The method also takes a great deal of effort and time to identify crop diseases based on the visually observable characteristics. Different experts diagnose the same disease as a different disease due to their varied experiences leading to erroneous identification of diseases. Introduction of artificial intelligence in various aspects of agriculture has gained momentum in today’s world. Artificial intelligence has seen its application in predicting soil organic matter based on remote sensing data as well as in prediction of crop yield based on factors of production and in identification of crop diseases. The research sought to propose use of an artificial intelligence model for identification of maize leaf diseases. In the proposed model, images of maize leaves were acquired and extracted color features used to identify the specific disease. Artificial Neural Network was used to identify the disease by implementing a back propagation learning algorithm. The data obtained was segmented into training and test data for the model. The algorithm was preferred due to its strengths in adaptive learning, its fast processing speed and the accuracy of its output. The performance evaluation of the model was based on the accuracy of the classification, the precision, recall ratio and the F- Measure. The model was proven to be significantly accurate with an accuracy of 78.94 % while the precision obtained was 0.778. The recall ratio from the neural network was 1 and an F-measure of 0.875.