ghanaopendata/ghanaian-research-english huggingface dataset:
Non-intrusive load monitoring device (NILM) for household load disaggregation

Shared electrical connection is a cheap and easy method of providing houses with electricity access. However, the problem with this approach is the electricity bill, which doesn't set clear distinctions between the tenants who consumed more or less than the others, which creates a problem; that is who gets to pay more or less than the other. Our capstone project, using NILM, will be able to divide total power consumption into its individual constituents, and IoT to display power consumption and billing, will allow the tenants to track and monitor their consumption.

Source: Ashesi
Non-intrusive monitoring of individual pig-feeding behavior

Pig farming is one of the largest endeavours in the animal farming industry. Pork consumption, especially in Africa, has significantly increased over the years. Amidst this rise, current-day pig farming activities in Africa rely heavily on human intervention and intuition to determine the health of pigs. The approaches used are also time-consuming and very inaccurate, primarily relying on the farmer's intuition, who uses past experiences to determine a pig's health. Our proposed solution involves using machine learning and Internet of Things technology to monitor the individual feeding behaviour of individual pigs in a closed pen. The pen's environmental parameters, such as temperature and humidity, are also measured.

Source: Ashesi
Oil-Guard: Intelligent oil pipeline and tanker monitoring system

The oil and Gas industry uses crude oil pipelines and oil trucks in the transportation of oil either from upstream (exploration and production of oil and natural gas) to midstream (transportation, storage, and processing of crude oil, natural gas, and other petroleum products) and from midstream to downstream (refining, processing, and distribution of petroleum products). In this project, an intelligent oil pipeline and tanker monitoring system was developed to address the growing need for improved safety and security in the oil and gas industry especially in Nigeria. The system leverages different technologies, including pressure and flow sensors, Internet of things, data analytics, and machine learning, to provide real-time monitoring of oil pipelines and tanker movements. The system's key features include automated leak detection and notification, early warning of pipeline and tanker anomalies. In addition, the system provides real-time tracking of tanker movements as well as the oil level in the tanks, allowing for improved supply chain management and more efficient logistics planning. The development of this comprehensive oil pipeline and tanker monitoring system represents a significant step forward in improving safety, security, and efficiency in the oil and gas industry. The system's features and capabilities will help operators better manage risks and respond quickly to any incidents, ensuring the safe and reliable transportation of oil and gas products. This project seeks to provide a solution that will curb the incidences of vandalism and sabotage of oil pipelines, oil theft and hijacking of oil tankers in Nigeria.

Source: Ashesi
OnBoard: A Tailored Project and Task Management System for Enhanced Collaboration in Small Businesses

Organisational success depends on efficient project management and teamwork, especially in settings where task visibility and real-time coordination are crucial. Nevertheless, a lot of the project management tools available today can be complicated, counterintuitive, or ill-suited to dynamic settings and small teams. By investigating how technology can be used to promote improved task accountability, clearer communication, and more transparent progress tracking within teams, this project aims to address these issues. The project aimed to create a platform that would improve team productivity and expedite project organisation by utilising collaborative work management and real-time information sharing principles. The focus was on creating an environment that was simple and easy to use so that users could assign tasks, create projects, and track progress without needless complexity. In order to guarantee that the finished product matched the requirements of real-world teams, the project adhered to a structured design and development process that included iterative user feedback. A straightforward, real-time approach to project management can greatly enhance task visibility, coordination, and user satisfaction, according to evaluations conducted through testing and user studies. The work lays the groundwork for future research into easily accessible, lightweight collaboration tools and emphasises the significance of striking a balance between functionality and usability.

Source: Ashesi
Onboard: A Tailored Project And Task Management System For Enhanced Collaboration in Small Businesses

Mining operations in remote areas encounter significant equipment maintenance and security challenges, mainly due to limited connectivity, data privacy concerns, and the critical need for real-time decision-making. This study proposes a hybrid system that integrates edge computing, federated learning, and blockchain technology to enhance predictive maintenance and security in these settings. By enabling local data processing through edge devices, the proposed system reduces dependence on constant internet connectivity, allowing real-time analysis and timely maintenance interventions. Federated learning will be utilized to ensure that model updates are performed locally on devices, hence preserving sensitive operational data and reducing data transfer requirements. This study simulates the effectiveness, performance, and scalability of a hybrid system integrating edge computing, federated learning, and blockchain technology in remote mining environments. By showcasing the benefits of this integrated approach, the study aims to advance smart mining practices and provide insights applicable to other industrial sectors. The results will establish a framework for utilizing advanced technologies to enhance operational efficiency, improve data security, and refine predictive maintenance strategies in the mining industry.

Source: Ashesi
Online article management system

This is an applied group project done by Joshua Tabu Shehe and Ernest Musyoki Museve. This report looks at the processes by which the web-based article management system called WriteWell is developed and implemented for the userside (front-end). This application will allow Eldoret Writers Centre writers to manage their own articles in an efficient way than what they are using currently. This report will look at the background information about Eldoret Writers Centre based in Eldoret which will assist in looking at the methodologies, system design, implementation and testing involved. Lastly, we will look at the experiences gained while working on this project.

Source: Ashesi
Online restaurant order system

The use of computers has become one of the most common activities in Ghana today. With the wide variety of possible uses that exists for these complex electronic systems, it is no wonder that most of the commercial industries that exist today implement its uses one way or another. There however exist other alternatives to PC use which if explored could pave the way to greater productivity. In most restaurants that exist today, various methods are used to receive orders from customers. One of such modern day methods is the use of an online medium through which orders are made by customers through the internet, from any location. With this method, orders are normally received and managed at the restaurant on desktop computers. Using desktop computers to manage these orders however often come along with problems such as the high cost involved with the acquisition of computers, maintenance of these computers and of electricity used to power these computers. Some employees also sometimes use these computers for unproductive activities such as playing games, chatting online and sometimes viewing pornographic sites. This document reports on a project undertaken to develop an alternative system that could replace the desktop computers used to manage orders in such restaurants and also curb the problems that come with using desktop computers. It provides documentation on how the designed system works, the architecture behind it, and the procedure undertaken to arrive at the final product.

Source: Ashesi
Operations plan for Gym Ike Fitness Centre

With the world moving towards a more health conscious lifestyle, people have become increasingly aware of the health implications of eating the right food as well as exercising. This has provided numerous opportunities for fitness gurus to establish fitness centres and offer their services as well as their guidance and expertise. Gym Ike Fitness Centre is located at Adenta Frafraha in the Greater Accra Region and has been operating for the last ten years. During the needs assessment it was discovered that despite its many efforts, the fitness centre has continued to have problems with its operations structure. Related literature revealed that the application of operation management strategies have been ignored when the fitness industry is involved. However, there is a strong need for operations because of its relationship with the customer experience. The proposed solution for this project is an automation of the gym’s processes using the process chart – a process and capacity design model to increase its efficiency, reduce time and resources spent undertaking these processes, saving funds and keeping processes simple and scalable.

Source: Ashesi
Optimising Organ Donation Through Machine Learning and Blockchain: A Secure and Efficient Matching System

The shortage of organs for transplants is a significant healthcare issue, highlighted by the imbalance between available organs and the increasing demand for them.[l4] These problems result in extended wait times, avoidable fatalities, and disparities in healthcare provision.[38] In many parts of Africa, these challenges are further exacerbated by cultural beliefs such as the concept of bodily continuation after death, limited awareness and inadequate healthcare infrastructure. This project suggests creating an Enhanced Organ Donation Matching System using machine learning and blockchain technology to improve organ matching efficiency, increase transparency in record-keeping, and ultimately, save lives. Machine learning algorithms are set to improve the matching process by accurately predicting donor-recipient compatibility. Simultaneously, blockchain technology will enhance trust and transparency by offering secure and unchangeable donor and recipient information records. This system seeks to establish a fairer system for organ allocation by tackling issues like low donor registration rates, ethnic disparities in organ availability, and family consent concerns.[40] Incorporating predictive analytics will enhance the utilisation of accessible organs, while blockchain technology will guarantee secure and unchangeable records of donors' consent. This new strategy aims to improve matching effectiveness and increase community confidence in organ donation procedures.

Source: Ashesi
Optimising traffic flow in urban Accra through machine learning-based traffic light management

Traffic congestion is a phenomenon that happens around the world, including urban Accra. Despite the construction of new roads, not much has been done to reduce traffic congestion in the city. This paper presents the development of an Intelligent Traffic Management System with the aim of optimising traffic flow and reducing traffic congestion in Accra. The system consists of a web interface to control traffic, a simulation environment to model traffic in urban Accra, and a machine learning model to predict when to switch to the optimal traffic signal. The machine learning model, based on Deep Q-Networks (DQN), predicts when to switch traffic phases in a given situation. The simulation environment was implemented using SUMO, modelling real-world traffic scenarios in selected areas of Accra to test and validate the system. The web interface provides a way to interact with the simulation for traffic control.

Source: Ashesi
Optimization of epoxy-based coir fibre composites for roofing sheets in Ghana

Contemporary roofing materials in Ghana such as aluminium and zinc are quite costly. Metal roofing sheets require large amounts of energy to manufacture which affects the sustainability of them. Also, coastal areas in Ghana that rely on metal roofing sheets are subjected to corrosion and loud noise during rains. Traditional roofing materials such as palm leaves and straws need periodic maintenance and are also labour intensive to install. Thus, a sustainable alternative to current roofing methods is needed in Ghana. In this project, coir fibre reinforced eposy composites were created as substitute roofing sheets. The coir fibres were first treated with sodium hydroxide solution. The fibres were cut into lengths while considering the critical fibre length. The coir fibres were mixed at different weight ratios with eposy-hardener matrix and allowed to fully cure. The samples were loaded with thermal cycles to mimic the effect of thermal degration induced by the sun. The mechanical properties were analysed as well. Thermal conductivity, corrosion studies, and sound absorption tests were also performed on the composites while varying the coir composition in the epoxy matrix.

Source: Ashesi
Optimizing Customer Retention: Comparing the Efficacy of Multiple Machine Learning Techniques in Predicting E-commerce Churn

The rapid growth of e-commerce has intensified competition, making customer retention a critical priority for businesses. As acquiring new customers remains significantly more costly than retaining existing ones, accurately predicting customer churn has become essential for sustaining profitability. This capstone project investigates and compares the effectiveness of various machine learning (ML) algorithms in predicting customer churn within the e-commerce domain. The study explores a broad range of models, including traditional classifiers (Decision Tree, Logistic Regression, K-Nearest Neighbors), ensemble methods (Random Forest, Gradient Boosting, XGBoost, LightGBM), and deep learning architectures (Convolutional Neural Network and Long Short-Term Memory network), using a structured, feature-rich customer dataset. Models were evaluated using accuracy, F1 Score, and the Matthews Correlation Coefficient (MCC), with additional computational time and space complexity analysis to assess real-world deployment feasibility. Among all models tested, XGBoost emerged as the best performer, achieving a test accuracy of 98.40%, F1 Score of 0.9516, and MCC of 0.9424, while maintaining efficient training and prediction times. LightGBM and Random Forest also demonstrated strong performance, whereas deep learning models underperformed due to the static and tabular nature of the dataset, highlighting their limited suitability in such contexts. The study further validated its findings through statistical significance testing and comparative literature analysis, establishing alignment with prior high-impact studies while offering new insights into model performance in class-imbalanced, structured data environments. The results underscore the practical value of ensemble models for customer retention strategies and advocate for model selection approaches that balance predictive accuracy with interpretability and computational efficiency. This work contributes to both academic discourse and industry practice by providing a data-driven roadmap for mitigating churn in the evolving e-commerce landscape.

Source: Ashesi
Optimizing Meme-Based Social Media Marketing: An Algorithmic Approach to Enhancing Viewer Engagement and Conversion Rates

This study addresses the challenge of systematically leveraging memes for social media marketing by introducing MemeCreator, an algorithmic framework designed to enhance viewer engagement and conversion rates. Grounded in computational advertising theory, the framework integrates natural language processing (NLP) for caption generation, computer vision for template and visual-feature analysis, and real-time trend monitoring to adapt content to evolving audience interests. A dataset of 1,050 publicly sourced memes annotated with engagement metrics, textual sentiment, visual attributes, and contextual tags served as the basis for model training and evaluation. Through a controlled A/B testing campaign delivered via a Telegram bot interface, five AI-generated memes per day were compared against human-curated memes over a two-week period. Engagement outcomes measured by average likes (122 vs. 109), shares (54 vs. 48), and positive reaction rates (76% vs. 71%) demonstrate that algorithmically optimized memes can match or surpass traditional meme content. Semantic analysis identified humor and relatability as key drivers of interaction, while visual analyses highlighted the impact of high-contrast palettes and face-centric layouts. Sentiment analysis of user feedback further confirmed higher positive reception for Al-generated content. Beyond empirical validation, the study offers practical contributions: (1) a modular, scalable meme-creation system combining NLP, computer vision, and trend analytics; (2) data-driven guidelines for ethical meme marketing, including bias mitigation and privacy safeguards; and (3) evidence of the role of meme-driven campaigns in bolstering brand perception and customer loyalty. These findings suggest that integrating algorithmic creativity with human oversight can deliver dynamic, culturally resonant marketing assets. Future work will explore cross-platform deployment, video-based memes, and advanced multi-modal models to deepen personalization and contextual relevance.

Source: Ashesi
Optimizing Meme-Based Social Media Marketing: An Algorithmic Approach To Enhancing Viewer Engagement And Conversion Rates

This study examines the impact of Electronic Tax Filing Systems (ETFS) on tax compliance among Small and Medium-Sized Enterprises (SMES) in Accra, Ghana. With SMES contributing significantly to Ghanaprove compliance among SMES. Recommendations include simplifying the system

Source: Ashesi
Optimizing Sign-to-Speech and Speech-to-Sign Language Models for the Ghanaian Sign Language

The goal of the study was to build and assess machine learning models for real-time translation of Ghanaian Sign Language (GSL) to speech, with an emphasis on performance optimization and deployment. Two models were investigated: a hybrid sequential model composed of Long Short-Term Memory (LSTM) and Transformer layers; and a vision-based detection model using YOLOv5. The study also compared model architecture in terms of real-time translation performance, computational efficiency, and ease of implementation in a web platform. Experimental results showed that the YOLOv5 model had an 88.2% precision rate, 95.8% recall, and a mean Average Precision (mAP) of 95.4% at an IoU value of 0.5, showing significant advantages over the LSTM-Transformer model, particularly in inference speed and ease of implementation for a real-time scenario. The YOLOv5 model maintained about 40 frames per second (FPS) at inference, making it possible for smoother real-time sign translation. Based on comparative analysis, the YOLOv5 model was selected to be incorporated into a web application, built with Streamlit, to offer inclusive, real-time communication for the Deaf and Hard of Hearing (DHH) community in Ghana. These findings demonstrate the strength of vision-based models to overcome some of the challenges facing real-time sign language translation in developing more inclusive digital communication technology.

Source: Ashesi
Optimizing User Interface Rendering for Enhanced User Experience on Low-End Android Devices: Using ML and AUIs

UI elements and animations are critical in delivering a good user experience (UX) on mobile applications. The problem is that low-end devices have limited resources and often struggle to maintain smooth animations, leading to poor UX when users engage with an application. This project investigates how machine learning (ML) models and adaptive user interfaces (AUI) can dynamically optimize animation rendering based on real-time device resource availability. A quantitative experimental approach was adopted, collecting data such as CPU usage, CPU core count, frames per second (FPS), and junky frames from various device configurations. The training dataset was used to develop and evaluate multiple machine learning models, including a deep learning model, Gradient Boosting Regressor, XGBoost, LightGBM, and Support Vector Regression (SVR). After evaluation, a lightweight TensorFlow Lite model was deployed into an Android gallery application to adjust animation complexity dynamically. Testing across low-end, mid-range, and high-end devices showed that the ML-enabled version reduced CPU usage and janky frames compared to the regular and lite versions. The findings demonstrate that machine learning can effectively enhance UX on low-end devices by adaptively controlling animation rendering and balancing performance improvements with resource efficiency. This project contributes towards making mobile applications more accessible and responsive across a broader range of hardware capabilities

Source: Ashesi
Options for affordable rural broadband connectivity: a focus on TV White Space technology

There appears to be a coordinated effort the world over to achieve digital switch over (DSO) from analogue based to digital technology. Ghana signed the Geneva agreement which set 17th June 2015 as the deadline for the DSO. This switch over will create new spectrum opportunities for many wireless technologies due to the abundance of radio spectrum the switch over provides. This paper focuses on the opportunity for using White Spaces (TV frequencies allocated to a broadcasting service but not used locally) for internet connectivity in the rural and underserved areas of Ghana, using TV White Spaces in Koforidua as a case study. The paper investigates the challenges associated with lack of internet connectivity in rural Ghana and determines if the infrastructure necessary for the implementation of White Space technology in the rural areas was present and adequate. It also documents the user experience of Africa’s first commercial TV White Space network services in Koforidua, Ghana. A combination of qualitative and quantitative data collection approaches was used. Purposive and simple random sampling techniques were used in selection and administering of questionnaires. Lack of internet in the rural areas was found to be mainly due to perceived risk by the commercial network operators because of value for money considerations of existing technology options. Negative incentives for the common ISP to go rural included technical and geographic/landscape challenges. The relevant infrastructure necessary is still undeveloped in Ghana but various infrastructural projects are being undertaken by the Ghana Investment Fund for Electronic Communication (GIFEC) to bridge the infrastructure gap in these areas. Lastly, the user experience of Africa’s first commercial service network using TV White Spaces in Koforidua, Ghana were described and documented. Keywords: TV White Space, Digital switch over (DSO), Internet connectivity and Ghana Investment Fund for Electronic Communication (GIFEC) JEL classifications: O1, O32, O34, O38, O14, O15

Source: Ashesi