MSIT Theses and Dissertations (2023)

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http://hdl.handle.net/11071/13518

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A Smart water management system for detecting household water wastage
(Strathmore University, 2023) Gichuhi, J. M.
Water scarcity in Kenya is a significant issue that cannot be overlooked. Despite numerous efforts made by Water Service Providers in delivering water services to Kenyan residents, the high demand for accessible water across the country remains unmet. In addition, the provider’s ability to meet this demand is further impeded by various obstacles including inadequate control over water usage, insufficient resources to manage and conserve water, and the dilapidated water infrastructure within the Nation. As global water resources rapidly decline, due to the effects of climate change and overuse, it is crucial to take action in conserving this resource. This study looks into the development of a smart water management system using water flow sensors connected directly to the water appliances, Node MCU microcontrollers and a cloud-based application. The system focuses on monitoring household water usage frequency to inform users of their consumption and alert them via mobile notifications of potential water leaks to reduce wastage. Management and control of household water consumption is a positive step towards impacting water conservation efforts. Keywords: Internet of Things, Machine Learning, Usage Detection, Leakage Detection, Smart Water Management System
An Online stock market recommender system using machine learning
(Strathmore University, 2023) Muoki, S. M.
Investing in the stock market presented significant challenges for novice investors, primarily due to the overwhelming volume of available data. Consequently, novice investors often made irrational decisions and experienced unfavourable investment outcomes, particularly in the context of Kenya. This project aimed to address this issue by developing an innovative online stock market recommender system that utilised machine learning techniques. By leveraging these techniques, the system aimed to facilitate informed decision-making based on reliable data. Novice investors frequently encountered difficulties in accessing sufficient and well-organised information about the companies they intended to invest in, resulting in suboptimal returns. Traditional research methods often failed to adequately address the complexities and vastness of the stock market data. However, incorporating machine learning into the investment process held promise for analysing historical data, identifying patterns, and providing valuable insights to support informed decision-making. To comprehensively achieve the research objectives, a mixed-methods approach was employed, which integrated both quantitative and qualitative data collection and analysis in a sequential design. The Object-Oriented Analysis and Design (OOAD) technique was systematically and logically adopted to develop the software system. Additionally, the Dynamic System Development Methodology (DSDM) served as a guiding framework to address the identified problem and facilitate the development of the online stock market recommender system. This research project identified the information challenges faced by individual investors in the stock market, highlighting issues such as limited access to critical information, lack of necessary skills, and reliance on inaccurate media reports. Moreover, the study identified specific factors that significantly influenced stock market investments, including earnings per share, firm fundamentals, market trends, and news sentiment. To overcome these challenges, the project focused on developing an advanced online stock market recommender system that effectively leveraged machine learning techniques to provide personalised investment recommendations. The system underwent rigorous testing, demonstrating superior performance by offering accurate recommendations and comprehensive investment performance reports. Keywords: Investing; Stock market; Novice investors; Poor investment outcomes; Online stock market recommender system; Machine learning techniques; Informed decision-making; Information challenges; Mixed-methods approach; Quantitative and qualitative data; Object-Oriented Analysis and Design (OOAD); Dynamic System Development Methodology (DSDM); Earnings per share (EPS); Market trends; News sentiment; Investment performance reports
Customer churn prediction tool using deep learning: a case of an ecommerce business operating in Kenya
(Strathmore University, 2023) Narina, P.
The problem of customer churn poses significant challenges for businesses, particularly in the e-commerce sector. With a high level of competition and low customer retention rates, businesses face the risk of losing customers and experiencing a decline in revenue. Previous research in customer churn prediction exhibited limitations in terms of accuracy and the lack of consumer-facing tools for predictions. Moreover, existing studies were often conducted before the COVID-19 era, failing to capture the impact of recent changes in consumer behavior and market dynamics. This research aimed to develop an effective customer churn prediction model for e-commerce businesses in Kenya. The study aimed to address the limitations of existing research by leveraging advanced machine learning techniques and considering the specific challenges faced by businesses in the post-COVID-19 era. To achieve the objective, an agile software development methodology was adopted. This approach allowed for continuous iterations and refinements during the model development process. Customer dataset was obtained from Kaggle an online platform for sharing datasets. The dataset included customer demographic information, transaction history, and customer engagement metrics. The data was carefully pre-processed to handle missing values, outliers, and ensure data quality. The multilayer perceptron model (MLP), a powerful deep learning algorithm, was employed to train the customer churn prediction model. The dataset was split into training and testing sets, with an 80-20 ratio, to assess the model's performance. The results of the study indicated that the developed customer churn prediction model achieved high accuracy, with precision, recall, and F1 scores of 94%. This demonstrated the model's effectiveness in identifying potential churners and enabling businesses to take proactive measures for customer retention. The findings of this study have significant implications for e-commerce businesses, providing them with a valuable tool to predict customer churn and implement targeted retention strategies. By leveraging the power of advanced machine learning techniques, businesses could enhance customer satisfaction, optimize resource allocation, and drive sustainable growth in a highly competitive market. Keywords: Customer Churn, Deep Learning, Multi-Layer perceptron, E-commerce, Big Data.
A Web application to detect phishing links using machine learning approach
(Strathmore University, 2023) Kimani, B. M.
Covid-19 fast tracked the transition of ways of working and learning into remote working models and with it the rise of Cyber Crime. Phishing is one of the most effective cybercrimes where attackers deceive users into falling for baits that enable them to steal personal information including identification or social security number, password, credit card details, debit card details and usernames which they then use to commit crimes mostly associated with financial losses and or identity thefts. These attacks are frequently carried out through malicious emails, texts, and phone calls. Even in the presence of robust antivirus software and phishing detection tools, phishing has continued to be rampant and widespread as adoption of IT across the world goes up. Several methods have been implemented to deal with phishing attacks including, blacklisting phishing Uniform Resource Locators (URLs), heuristic rule based and machine learning models. The blacklist anti-phishing approach is limited in its ability to detect new phishing URLs due to its reliance on a pre-compiled list of phishing URLs, whereas most Machine Learning (ML) methods for detecting phishing websites have been reported with very decent detection accuracy but significant false alarm rates. Attackers are continuously re-inventing methods of attacks which as well makes heuristic rule-based methods vulnerable to failed detection. This study has provided further information on phishing attacks to create awareness, designed and implemented a web-based phishing links detection tool using ML deep learning model which achieved an accuracy of 94.27 complemented by heuristic rules and blacklist capabilities. Further improvement areas including continuous models training and features re-engineering for improved prediction accuracy, and equipping Internet of Things (IoT) gadgets with developed Application Programming Interface (API’s) capability to determine what endpoints to or not to interact with. Keywords: Remote working, Phishing, Identity thefts, Machine Learning, Detection tool.
A Model for sign language recognition for Kenyan sign language
(Strathmore University, 2023) Wanjala, G. K.
Computer vision aids in increasing tech accessible for communities that are underserved, such as the disabled community. This study demonstrates how artificial intelligence via computer vision helps to bridge the communication gap between those with hearing problems and the general population. The purpose of this paper is to bring forth an artificial intelligence solution to cater to this targeted group to aid in communication. Artificial intelligence has come a long way to solve this problem of enabling sign language notations to be translated into readable form that can be easily understood. This is in accordance with the fact that there is a collective duty to ensure that the deaf can be part of our society on an equal basis with others, free from discrimination even when it comes to speech and communication. There is a great need for this interpretation so that communication is sped up through translation. Understanding between the deaf and hearing people can be fostered as well as costs associated with training individuals in sign language communication in sign language training centers are minimized. To answer this question, the research work collected and analyzed photos and videos in a quasi-experiment consisting of target photos of Kenyan sign language notations. The model is trained on 9100 Kenyan sign language (KSL) notations of varied gestures spanning from health and wellness to common day to day basic notations such as greetings, expressing feelings among others. Transfer learning through Tensor Flow object detection model, Open CV framework for image processing and python was used to actualize this sign language translation model in this research work. A trained machine learning model organizes the input photos and videos, analyses them and produces text that maps to the corresponding sign language notation used. Individual users can use the model to translate Kenyan sign language notations into readable English text. The model gave performance levels of 85% accuracy on a 20,000 training steps for 40 epochs. This gave a perfect balance of training duration and accuracy levels on the dataset given. One of the notable findings was that notations that involved movement of the hands and other body parts to express gestures were harder to detect and translate due to the motions involved. A lot of training data on such notations is needed to train the model further in detecting them. Keywords: Artificial Intelligence, computer vision, machine learning, sign language, disability

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