MSIT Theses and Dissertations

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Find here Theses and Dissertations from for the award of Master of Science in Information Technology (MSIT). These works have been scanned and passed through the OCR. We do not hold liablity for correctness of content.

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A Blockchain-based prototype for cybersecurity threat intelligence sharing: a case of Kenyan banking and insurance financial institutions
(Strathmore University, 2021) Kibuci, Wanjohi Stephen
Cybersecurity threats to financial institutions have become more sophisticated and challenging to deal with. The growing dependence of financial institutions on cyberspace makes cybersecurity preparedness against threats important to achieve a financial institution's mission and vision. In this context, cybersecurity preparedness is the process in which a financial institution can protect against, prevent, mitigate, respond to, and recover from cyber threats. Traditionally, most organizations share threat intelligence through ad hoc methods such as emails and phone calls but there is a need to automate threat intelligence sharing where possible to improve cybersecurity preparedness. To address this issue, and enhance cybersecurity and trust, a blockchain-based approach can be employed to share threat intelligence. This study aims to leverage blockchain technology by developing a prototype to automate cybersecurity threat intelligence sharing in financial institutions. The study used a quantitative approach in data collection using structured online questionnaires with close-ended questions and open source datasets and data analysis using several analytic tools. The prototype has been developed using the Rapid Application Development software development methodology using open-source Oracle Virtual Box that runs on Linux Operating System
A Framework for evaluating ICT use in teacher educ...
Oredo, John Otieno
Teachers are under increasing pressure to use Information and Communication Technology to impart to students the knowledge, skills and attitudes they need to survive in the 21st Century. The teaching profession needs to migrate from a teacher centered lecture based instruction, to a student-centered interactive learning environment. To attain this aspiration, an ICT enabled teacher education is fundamental. Towards this end, international and national authorities have been spending huge amounts of money to facilitate the implementation of ICT teacher education. This work attempts to evaluate the ueage of the available ICT facilities in Kenyan Public primary teacher colleges focusing ion the quantity of computer use,and the levels attained in terms of using ICT's support.
A Fraud investigative and detective framework in the motor insurance industry: a Kenyan perspective
Kisaka, George Ngosiah; Onyango-Otieno., Vitalis
Insurance fraud is a serious and growing problem, with fraudsters’ always perfecting their schemes to avoid detection by the basic approaches. This has caused a rise in fraudulent claims that get paid and increased loss ratios for insurance firms thereby diminishing profitability and threatening their very existence. There is widespread recognition that traditional approaches to tackling fraud are inadequate. Studies of insurance fraud have typically focused upon identifying characteristics of fraudulent claims and putting in place different measures to capture them. This thesis proposes an integrated framework to curtail insurance fraud in the Kenyan insurance industry. The research studied existing fraud detection and investigation expertise in depth. The research methodology identified two available theoretical frameworks, the Bayesian Inference Approach and the Mass Detection Tool (MDT). These were compared to comprehensive motor insurance claims fraud management with respect to the insurance industry in Kenya. The findings show that insurance claims’ fraud is indeed prevalent in the Kenyan industry. Sixty five percent of claims processing professionals deem the motor segment as one of the most fraud prone yet a paltry 15 percent of them use technology for fraud detection. This is despite the fact that significant strides have been made in developing systems for fraud detection. These findings were used to determine and propose an integrated ensemble motor insurance fraud detection framework for the Kenyan insurance industry. The proposed framework built up on the mass detection tool (MDT) and provides a solution for preventing, detecting and managing claims fraud in the motor insurance line of business within the Kenyan insurance industry.
A GIS decision based model for determining the best path for connection to a power distribution network a case study of Kenya power and lighting company limited
Kinuthia, Augustine Muturi; Kimani, Stephen
The purpose of this study is to present a GIS based decision model for determining the best path for connection to a power distribution network. The model was derived from studies that consider the design of the power distribution system and the GIS field of network analysis along with the method used by KPLC for connecting premises to the distribution network. A digital map of the study area and the distribution network was generated and taking into account the distributors and distribution transformers the best path between the premises and the transformer was derived. In this study it is demonstrated that the distributors’ length and size and the distribution transformers’ capacity, load and location influence the connection of premises to the distribution network. The results also show that combining geospatial methods with the power distribution network enables engineers to visualize the spatial distribution of data in maps which yields better insight into the nature of the power distribution network.
A HDF5 data compression model for IoT applications
(Strathmore University, 2022) Chabari, Risper Nkatha
Internet of things has become an integral part of the modern digital ecosystem. According to current reports, more than 13.8 billion devices are connected as of 2021 and this massive adoption will surpass 30.9 billion devices by 2025. This means that IoT devices will become more prevalent and significant in our daily lives. Miniaturization in form factor chipsets and modules has contributed to cost-effective and faster running computer components. As a result of these technological advancements and mass adoption, the number of connected devices to the internet has been on the rise, leading to the generation of data, in high volumes, velocity, veracity, and variety. The major challenge is the data deluge experienced which in turn makes it challenging to visualize, store and analyse data generated in various formats. The adoption of relational databases like MySQL has been majorly used to store IoT data. However, it can only handle structured data because data is organized in tables with high consistency. On the other hand, NoSQL has also been adopted because of its capabilities of storing large volumes of data and has no reliance on a relational schema or any consistency requirements. This makes it suitable for only unstructured data. This outlines a clear need of adopting an effective way of storing and data managing IoT heterogeneous data in a compressed and self-describing format. Furthermore, there is no one- size all approach of managing heterogeneous data in IoT architecture. It is in the paradigm that this research solved this challenge by creating a tool that compresses heterogeneous data while saving it in a HDF5 format. The format of the data used was in .csv datasets. These data was parsed in the storage tool and data tool of the HDF5 for compression and conversion. The tool managed to achieve a good compression ratio percentage of 89.34% decrease from the original file. The output of the compressed file was represented on an external interactor called hdfview to validate that the algorithm used was lossless.
A Loan default prediction and loan amount recommendation tool for SACCOs in Nairobi: a case of Okoa Management SACCO
(Strathmore University, 2023) Mwalozi, P. M.
SACCOs loan delinquency is a severe danger to the organization's capacity to continue availing loans to loan applicants and to grow. SACCOs are unable to collect what they have lent out to loan beneficiaries as the default rate rises gradually. This research project aimed at using the analysis of the different factors that determine loan defaults in microfinance institu-tions, microlending institutions and SACCOs in Kenya with a focus on Okoa Management Ltd. and how the same factors can be used to predict the likelihood of a loan borrower to default in the repayment process by applying machine learning algorithms. Credit risk assessment pre-cision is important to the functioning of lending institutions. Traditional and most existing credit score models are developed and designed using demographic characteristics, historical payment data, credit bureau data and application data, with most of them not suitable for de-veloping countries such as Kenya which consider the employment type (casual, temporary, contractual or permanent) and the fact that we can lend up to 3 times as much as the borrower’s savings. With these factors being constantly changing and dynamic, credit risk models based on machine learning algorithms provide a higher level of accuracy in predicting default as they can be continuously trained with new data sets should the variables that are used change. Risk management has been an increasing issue for credit lending institutions as the need to deter-mine the likelihood of defaulting by borrowers is becoming more evident. By using machine learning, we can be able to reduce the uncertainty that comes with borrowing and even go further to recommending lower amounts for borrowers who we predict are likely to default in the repayment of the loan amount they have in mind. The research focused on three main al-gorithms: logistic regression, decision trees and tensor flow on the prediction. The algorithm that provided the best accuracy was the decision tree. The results of the research showed that people with little or no collateral (home-ownership/car ownership) were more likely to default and that there was a low correlation between months since last delinquent and the loan predic-tion default likelihood status. Keywords: Loan default prediction, machine learning, credit lending
A Location-aware nutritional needs prediction tool for type II Diabetic patients: case Kenya
(Strathmore University, 2022) Karega, Lulu Amina
Diabetes is a chronic disease caused by a lack of insulin production by the pancreas or by poor utilization of the insulin that is produced, with insulin being the hormone that helps glucose get to blood cells and produce energy. Urbanization and busy day to day schedules mean patients tend to pay little or no attention to their dietary habits which results in a preference for fast foods and processed food. The prevalence of type II diabetes in the world, Kenya included, has been steadily rising over the years and is projected to keep growing at an alarming rate. Diabetes if not properly managed can result in long-standing, costly and time-consuming complications. Diabetes management and control of blood sugar levels are generally done by the use of medication, namely insulin and oral hypoglycemic agents. However nutritional therapy can also go a long way to boosting the general health of a patient and reducing risk factors leading to further complications. Personalised nutrition has been formally defined as healthy eating advice, tailored to suit an individual based on genetic data, and alternatively on personal health status, lifestyle, and nutrient intake. Diabetes management falls under the field of health informatics that can benefit from data analytics. Predictive analytics is the process of utilizing statistical algorithms, software tools and services to analyze, interpret and visualize data with the aim to forecast trends, and predict data patterns and behavior within or outside the observed data. This study sought to develop a location-aware nutritional needs prediction tool for type II diabetic patients in Kenya. The prediction tool would help both nutritionists and patients by providing accurate and relevant nutritional advice that would help in dietary changes to combat type II diabetes with the added benefit of being location aware. The tool will use pathological results from nutritional testing to support nutritional therapy. If any deficiencies are identified from the provided nutritional markers, food items likely to improve those nutrient levels will be recommended. The amount of nutrient available in a given food item are determined by the food composition table for Kenya as published by the Food and Agriculture Organization (FAO) in conjunction with the Kenyan government. The study used a simplistic implementation of matrix factorization to provide predictions of locally available food items, down to the county level.
A Machine learning model for support tickets servicing: a case of Strathmore University ICTS client support services
(Strathmore University, 2022) Maina, Antony Koimbi
Customer service is a highly vital part of any business. How satisfied your customers are can make or break a company. One of the greatest contributors to customer satisfaction is the ability to respond to their issues efficiently and effectively. Many businesses therefore opt to establish a customer service department that handles customers’ services, this includes receiving phone calls and replying to emails. Customers are expected to call with issues such as, “How do I reset my password?” “How do I access the Student Information System?” “Are the student’s marks out yet?” and the like. Often, the issues reported by customers are similar and tend to get similar resolutions. These requests can be overwhelming at times, for example in cases where the users/customers are accessing an online resource and the system goes down, the number of inquiries can be in the order of thousands depending on the number of system users. This means a human agent may not be able to service all these requests on time. This research aims to develop an intelligent chatbot model for a support ticketing system using machine learning to deliver an exceptional customer experience. This research specifically proposes to develop a machine-learning model that can be used to service customer tickets in the context of a university or learning institution. The Rapid Application Development methodology was used to produce a working prototype of a chatbot to test the model to be developed. Machine learning and natural language processing were used to extract a user’s intent from a message and by leveraging pre-trained frequently asked question models from the DeepPavlov library, the model was trained on 80% of the data and 20% for testing. All 37 sessions tested on Dialogflow were successful, translating to a 100% success response rate. The prototype was tested by integrating the WhatsApp messaging platform to send messages to the chatbot. The chatbot was able to respond to the user in a fraction of a second. The average response time was less than one minute during testing.
A Machine learning model to predict non-revenue water with severely unbalanced classes
(Strathmore University, 2022) Muriithi, Patrick Kimani
Every household, industry, institution, organization needs clean water for existence. In Kenya, water is used for human consumption, production, and agriculture. The consumption of water, therefore, contributes to the overall growth of the economy through water bills. The term non-revenue water (NRW) is defined as water produced and 'lost' before it reaches the customers. NRW is also described as the difference in volume reaching the final consumer for billing and the initial volume released into the distribution network. Based on the assessment of the Public-Private Infrastructure Advisory Facility (PPIF), an organization that fosters inter-agency cooperation to curbing NRW, physical losses are the main causes of NRW. As per PPIF, most NRW emanates from physical losses, including burst pipes that are often a result of poor maintenance. Besides physical losses, PPIF notes other numerous sources of NRW, especially commercial losses arising from the manner billing data is handled throughout the billing process. The main issues related to this cause include under-registration of customers' meters’ reading, data handling errors, theft, and illegal connections. Other causes of NRW include unbilled authorized consumption such as water used for firefighting, utilities for operational purposes, and water provided to specific groups for free. Therefore, non-revenue water risks the country's revenue collection, which can lead to slow economic growth. This research proposes development of a machine learning model that will be used by water service providers. The model will be able to assist the WSP companies to reduce non-revenue water by predicting water consumption of different customers. To achieve these objectives, we intend to focus on providing tools and methods that will guide the WSPs on reducing the non-revenue water. Our model was trained with 2 years consumption dataset of Nairobi County. The model developed was able to predict customer monthly consumption with percentage accuracy of 95%.
A Model for costing information technology services in public organizations : case study of the Kenya Revenue Authority
Osiro, Yvonne Wafula; Sevilla, Joseph
Public organizations are increasingly embracing technology as a means of achieving operational efficiency and in the process reduce the cost of doing business. It is necessary for organizations to have a clear financial visibility into their Information Technology operations. However, it is frequently observed that IT continues to drain financial resources without providing any insight on its consumption. This is partially due to the intangible nature of IT and partially due to lack of standard IT service costing frameworks. IT Managers need financial and non –financial information to have proper insight of their operations. Having a costing model enables IT departments to determine the cost of providing an individual or group of services. The objective of this research was the development of a service oriented costing model for IT services offered by public organizations, with the following specific objectives: 1. To investigate the available IT service costing frameworks and models. 2. To establish the policies and factors that determine IT service costs. 3. To develop an IT service costing model for use in public organizations. 4. To validate the effectiveness of the IT Service Costing framework developed. Using a qualitative approach, this thesis presents an IT service cost model to methodically guide public organizations in determination of costs associated with provision of an IT service. The research used a descriptive design to obtain information concerning the current status of the phenomena. A target population of thirty four senior and middle level managers from the ICT, Finance and Administration departments of the Kenya Revenue Authority were considered. Purposive sampling was used to select twenty two target respondents. Semi-structured interviews consisting both open-ended and closed questions to provide greater depth were the primary data collection method used, in addition to scholarly journals, books and websites. The model presented provides an approach to cost estimating that can simplify the determination of costs associated with an IT service. The application of suitable abstraction principles in terms of cost categories, cost types, cost activities, cost elements and cost drivers yields a modified IT service specific cost model. The approach was verified using a real service as an example to provide insight into cost structures and potential cost drivers. It was applied to a case study of the email service at the Kenya Revenue Authority and in this way the flexibility and adaptability of the model to given service-orientated scenarios was demonstrated.
A model for estimating network infrastructure costs : a case for all-fibre LAN networks
Maina, Anthony Mbuki; Ateya, Ismail Lukandu
The 21st century is an era that has been characterised by phenomenal growth in data rates at the local area network (intranet), extranet and the Internet.This trend has been pushed by the widespread deployment in organisations of “bandwidth-hungry” applications such as VoIP, security surveillance systems, video conferencing and streaming of multimedia content. Due to demand placed on network resources by these applications and services,physical layer cabling solutions have had to evolve to support faster, improved LAN technologies such as Gigabit Ethernet.Although new network architectures (such as Centralised Fibre networks) address current and long term demands of the modern networking environment, concerns have been raised about its cost viability. The key problem identified in this study was an inadequacy of suitable tools that aid decision making when estimating the cost of a network infrastructure project. Factors of importance in this regard were collected in a survey and used in development of a cost model. The model is aimed at being a tool to assist network planners in estimating LAN infrastructure costs. A network was designed based on two architectures – centralised fibre (allfibrenetwork) and hierarchical star (UTP for horizontal cabling and optical fibre for backbone cabling). Thereafter, cost of implementing these two architectures was calculated using the model. Based on the results computed from the cost model, the all-fibre network (centralised fibre architecture) was found to be more cost effective compared to the hierarchical star network
A Model for mapping crime hotspots using neural networks: a case of Nairobi
(Strathmore University, 2024) Echessa, R. G.
Since the inception of the first modernized police agency, the primary objective of police organizations has been to prevent crime. Law enforcement, police, and crime reduction agencies commonly used hotspot mapping, an analytical technique, to visually determine the locations where a crime was most prevalent. This assisted in decision-making to determine the deployment of resources in target areas. This study aimed to investigate crime mapping techniques in crime analysis and suggest ways to enhance the implementation of crime mapping in Nairobi. Beginning with a historical analysis of GIS and crime mapping, the study then moved on to a consideration of the significance of geography in dealing with crime concerns. Neural Networks and K-Means machine learning models were used, and data was collected through quantitative and qualitative means in two phases. X was utilized in the first phase to collect information from the general public and important informants. The second phase involved collecting crime hotspot coordinates using a participative Geographic Information System. The study focused on utilizing social media data and machine learning techniques, particularly the KMEANS with NN (Neural Network) model, to identify and map crime hotspots in Nairobi. By analyzing crime-related tweets and categorizing them as either positive, negative or neutral using this NN (Neural Network) model then clustering them as either high risk or low risk using K-Means, the study achieved high accuracy, precision, recall, and Fl-Score, suggesting the effectiveness of this approach for crime prediction and prevention. Keywords: Hotspot mapping, X, Machine learning, crime, Neural Networks, K-Means
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
A Model to measure information security awareness level in an organization : case study of Kenya commercial bank.
Mugo, Eric Anderson Kabugu; Wekesa, Cyrus
Information Security awareness forms a key basic part of Information Security Strategy within most organizations. Organizations that tend to be more conscious regarding Information Security will invest more than organizations that are less conscious. This can be seen in Financial and Telecommunications industry as compared to Agricultural industries. Information Security awareness is an investment that organizations make to ensure that the human aspect of Information Security is taken care of. Majority of organizations that invest in Information Security awareness do not measure the levels of awareness among their staff to identify the impact of their investment. Measurement of Information Security results in value add such as positive change in staff attitudes towards Information Security, respective increase in Information Security knowledge and a more secure organization. The value add comes with other added benefits such as reduced Information Security incidents and frauds, a more knowledgeable staff and an Information Security team with visibility into the general organizations predisposition to Information Security challenges as well as general awareness. This study aims at expounding on the various techniques used to impart awareness. The study aims at proposing a model that can be used to measure Information Security awareness levels in a Local financial institution. Achievement of specific objectives of the research was done through qualitative technique. Collection of data required is done from local Members of Information Security Profession who possess the required data in the area of Information Security. Following analysis of responses from the local Information Security professionals, the model developed was based on the Kruger and Kearney Model awareness measurement model with specific modifications to suit the local financial institutions' requirements. The models' modifications were based on a local banking institution for purposes of testing and validating the mode!. The modifications are as a result of the findings from the survey.
A Predictive analytics model for pharmaceutical inventory management
(Strathmore University, 2022) Musimbi, Patience Musanga
Inefficient inventory management is a factor that affects pharmacies in Kenya. The unpredictable nature of weather patterns during the traditional long and short rain seasons has resulted in seasons starting earlier or later than expected. Seasonal diseases such as flu may spike up when the temperatures decrease or when the rainy seasons begin, causing an increase in sales of drugs that cure and prevent the flu and vice versa. Due to this unpredictability, pharmacies may fail to stock up or down for different seasons due to unpreparedness and not knowing what to stock and when to stock. Ineffective drug management has a significant financial impact on pharmacies. Inventory management ensures that needed drugs or medicines are always available, in sufficient quantities, of the right type and quality, and are used rationally. An effective drug management process ensures the availability of drugs in the right type and amount in accordance with needs, thereby avoiding drug shortages and excesses. This research proposed a predictive analysis tool that would predict the required drugs or medicines prior to when they are needed, based on sales and seasonality. Another parameter for predictive analysis for this research was the period of the year when a certain disease could be common. This research discussed stocking and inventory management of pharmaceutical products and how predictive analytics with machine learning algorithms could be applied to improve the inventory management process in a pharmacy’s context. The purpose of the study was to examine the inefficient stocking of medicines in pharmacies and use predictive analysis to predict future stock. It reviewed various previous methods used for pharmaceutical inventory management and proposed the SARIMAX model with time series analysis for stock prediction. The result was a model that predicted the quantity of drugs to be stocked for the next six weeks. The six-week prediction model had a Root Mean Squared Error (RMSE) of 5.5.
A Prototype for detecting procurement fraud using data mining techniques: case of banking industry in Kenya
(Strathmore University, 2022) Muriithi, Francis W.
Fraud is a million-dollar business, and it is increasing every year. The numbers are shocking, all the more because over one third of all frauds are detected by 'chance' means. Given that the procurement process is part of the expenditure cycle that culminates with the payment of cash, it is rife with potential for exposing an organization to fraud and embezzlement. Today, whistle blowing, is the most common fraud detection method. However, this method does not proactively search for misconduct. As a result, a fraud detected through this means tends to be caught too late and after the organization has already lost millions of dollars. In this study, we propose a data driven fraud detection prototype to reduce the duration and cost of procurement fraud in Kenya’s banking industry. To achieve this, electronic data from the HR and ERP systems was analysed by the prototype using data mining techniques to identify potential fraud misconduct. The data mining techniques applied included rule-based, fuzzy string-matching, and z-score outlier analytics to crossmatch the data against procurement fraud red flag indicators. Thereafter, the prototype generated potential frauds notifications to the organization’s audit, risk, or forensic department for further investigation. The outcome of the investigation done by the audit team was also captured by the prototype to increase the accuracy of fraud detection and reduce future false positive alerts.
A Prototype for securing non-digital assets using non-fungible tokens
(Strathmore University, 2022) Kabiru, Brian Mwangi
In the capitalistic world, there are many people acquiring and transacting assets. While many of them go about it the legal way, there is also substantial number of those that use any form of trickery to acquire the said assets in the form of forgery. Normal citizens as well as government institutions and financial institutions deal with non-digital asset documents during their day-to-day operations. The analysis of the said documents is not only time consuming but also stressful for the human resources to go through. While there exist many methods to analyse the documents, there is also an affinity to doctor the documents or bypass the process of verification which has many ripple effects. Due to this and other factors, there is need to develop a prototype to protect the integrity of non-digital assets in an automated form that is accessible to both the individual and the institution. Furthermore, to avoid tampering, the prototype must be free from mutable changes and the said changes must be public and open for viewing and verification. This research aims to explore the existing strategies deployed in Kenya and other countries to protect non-digital assets, their merits and their challenges after which a prototype based on the Flow Blockchain Network will be developed for the purpose of protecting, tracking and authenticating non-digital assets.
A Rainfall prediction model using long short-term neural networks for improved crop productivity: a case of maize planting in Machakos County
(Strathmore University, 2022) Wangome, Brian Mwathi
Climate variability is a factor that affects crop productivity in Kenya. The unpredictable nature of weather patterns during the traditional long and short rain seasons has resulted in the rains starting earlier or later than expected. This unpredictability results in rainfed agriculture farmers experiencing losses on capital, fertilizers, and labor input and consequently declined agricultural productivity. The decline in food production also poses an existential threat to our nation’s food security and farmers’ incomes. Weather forecasts are aimed are reducing this uncertainty however, the sparse distribution of synoptic weather stations in Kenya that collect and monitor surface level meteorological conditions makes it hard for the Kenya Meteorological Department to guarantee a high spatial and temporal resolution. Therefore, the current forecast data disseminated to farmers is ‘coarse’, at the county and town level, which is of less significance to the smallholder farmer since this data does not factor in the topographical nuances within locations. The format of the weather forecasts is also technical for the farmers hence they resort to traditional methods in terms of planning for planting. The study proposed the use of deep learning techniques to build a rainfall forecasting model that accepted historical weather data and returned forecasted rainfall values in millimeters. The historical weather data was satellite data sourced from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2). The historical data was used to train a Long Short-Term Memory neural network. An experimental approach was used to determine the number of epochs used in training the model and the number of timesteps/days into the future in which the most optimal model would forecast. In this study, the model forecasts 30 days into the future by looking at the past 60 days observed. The 30-day prediction model had a Root Mean Squared Error of 2.45 millimeters. Therefore, given the farmer’s Global Positioning System coordinates, the system can fetch past 60-day weather data and forecast the rainfall for the coming 30 days to help farmers to determine when to sow.
A requirements elicitation process model for health management information systems: case of Kenyatta National Hospital
Gikura, Mary Wambui; Marwanga (Dr.), Reuben; Orero, Joseph Onderi; Kiraka, Ruth
Requirements Elicitation (RE) is about learning and understanding the needs of users and stakeholders with the aim of communicating these needs to the system developers. Requirements Elicitation is an important stage in Information Systems development (ISD), and has substantial impact on software costs.RE has remained a key topic of interest for researchers and they have stated that a large number of Information Systems development (ISD) projects fail resulting in high costs to organizations. One of the reasons that these projects fail is the inability of the Information System to precisely satisfy user 's requirements which is a result of inaccurate and incomplete requirements collected in the Requirements Elicitation (RE) stage. Considering the importance of the RE stage in information systems development projects , this stage therefore becomes a critical area for IS research. This research focused on the process of RE in the development of the Heath Management Information Systems (HMIS) in Kenyatta National Hospital. Using data collected from the developers and users in the hospital the study presents a Requirements Elicitation Process model for Health Management Information systems. The results showed that the greatest challenge in the RE process was communication and the study suggests requirements prototyping to solve communication challenges. The implementation was conducted in Kenyatta national Hospital 's Comprehensive care centre. In conclusion the study elaborates a RE model that incorporates communication and requirements prototyping as key elements in the model.
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

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