ghanaopendata/ghanaian-research-english huggingface dataset:
kNect: A computer assisted career guidance system for children in junior high schools in Ghana

The current education system of Ghana is designed in such a way that recent graduates of Junior High Schools are required to choose only one track (Science, Business, General Arts, Visual Arts or Home Economics) to pursue in traditional Senior High Schools, regardless of whether or not they are sure of what kind of career they want to pursue. This decision determines the kind of courses they can pursue at University and ultimately affects their course options at University, limiting their possible career choices. In order to solve this problem, this project provides a platform for students to learn about more diverse career paths and interact with mentors in the careers they desire to pursue. The project also recommends good career paths to students based on their given skills and interests.

Source: Ashesi
Kwanalytics: A geographic information system for crowdsourcing and aggregating road surface quality information from smartphones

Road surface quality information is critical to road users when navigating road networks, and road authorities when making decisions on road infrastructure. However, not many systems exist that readily provide this information. This work connects prior work and proposes a geographic information system for crowdsourcing and aggregating probabilistic road surface quality information collected from users’ smartphones from various times and at different geographic locations.

Source: Ashesi
LAMBER: An emergency response system for ambulance requests and first aid guide

Emergency Medical Services operations in Ghana are posing a challenge to the health sector in Ghana. Many people do not have access to emergency response services, let along good amenities to secure their good health. Unfortunately, there is no digital application in Ghana that will allow people to request emergency response services to make life easier. LAMBER is an emergency medical service software application that has three main applications for patients, clinics, and paramedics. The system offers two major services namely ambulance requests and First-Aid guides for potential patients. The system will allow patients to create requests and link them to the nearest ambulances based on their location. The patients are given the luxury to choose their ambulances and can send a general message to all available ambulances in case of any unforeseen emergencies. Future work includes developing an AI-powered first aid guide.

Source: Ashesi
Lamber: an Emergency Response System for Ambulance Requests and First Aid Guide

Emergency Medical Services operations in Ghana are posing a challenge to the health sector in Ghana. Many people do not have access to emergency response services, let alone good amenities to secure their good health. Unfortunately, there is no digital application in Ghana that will allow people to request emergency response services to make life easier. LAMBER is an emergency medical service software application that has three main applications for patients, clinics, and paramedics. The system offers two major services namely ambulance requests and First-Aid guides for potential patients. The system will allow patients to create requests and link them to the nearest ambulances based on their location. The patients are given the luxury to choose their ambulances and can send a general message to all available ambulances in case of any unforeseen emergencies. Future work includes developing an AI-powered first aid guide.

Source: Ashesi
Land Management System Using Blockchain-Asaase Aban

The Asaase Aban project presents a blockchain-based land administration system designed to enhance transparency, security, and efficiency within Ghana's dual land governance framework. By integrating Ethereum smart contracts, a PostgreSQL/PostGIS database, decentralized file storage through IPFS, and an intuitive web interface, the system provides a robust platform for land registration, ownership verification, and property transfers. Key features include tamper-proof transaction records, dynamic geospatial visualization, and role-based access control. Comprehensive testing across smart contracts, APIs, database services, and user interfaces validated the system's functionality, security, and usability. Despite challenges like blockchain transaction delays and GeoJSON parsing issues, iterative development and user feedback informed continuous improvements. Asaase Aban demonstrates the transformative potential of blockchain technology in land governance and establishes a scalable foundation for future innovations, including mobile applications, Al-driven insights, and integration with national land registries.

Source: Ashesi
Laser application of nanocomposite hydrogels on cancer cell viability

Nanocomposite hydrogels of poly-n-isopropyl were prepared by incorporating gold and magnetite nanoparticles. The nanocomposite-based hydrogels formed were geometrical, ~7.3 mm in diameter and 5 mm thick (in the swollen state). Morphological analysis was characterized by a scanning electron microscope. Drug-loaded hydrogels were subjected to laser heating at 1 W, 1.5 W and 2 W for 20 min in each laser cycle. The metabolic activities of the cells were analysed. The photothermal conversion efficiency of the nanocomposite hydrogels was also evaluated for P(NIPA)-AuNP-PG and P(NIPA)-MNP-PG to be 36.93 and 32.57 %, respectively. The result was then discussed for potential applications whereby metalbased hydrogels can be employed in microfluidic devices for targeted cancer drug delivery.

Source: Ashesi
Latent fingerprint identification system for crime scene investigation

The traditional means of criminal investigation used in Nigeria is often unreliable and leads to innocent people's wrongful detention and a lack of justice for deserving offenders. The poor record-keeping and weaknesses in Nigeria's investigation process significantly contribute to the high levels of crime and insecurity in Nigeria. To tackle these issues, this project provides an implementation of a fingerprint identification system to improve criminal investigation in Nigeria. Three image processing algorithms and a Convolutional Neural Network classification algorithm were explored for matching performance. The Convolutional Neural Network classification model performed better than the three image processing algorithms with an accuracy of 64.44%. The final system provides a web interface with database interaction to send a fingerprint image and meta data to receive match results and potential suspect (criminal) information.

Source: Ashesi
Legal management software

As the world increasingly progresses towards a technology driven environment, the legal sector is not to be left behind. In the past years, clients got to know about lawyers through recommendation and at the same time lawyers had a challenge reaching out to their clients. Work was slow and tedious as research was conducted on dusty and old textbooks. The basis of this project is to bridge the technology gap between the lawyers and advocates in Africa. Firms that deal with legal services will most likely handle huge quantum of inventory and have to make sure that these files are readily available at the point of request by the court. It has therefore become necessary for these firms to employ a method that will securely handle this large data and readily avail it to more than one person at a time even though they are in different locations. The goal of this project is to develop an application that will aid lawyers to access the files from wherever they are, and quickly generate reports.

Source: Ashesi
Leveraging AI Technologies In Developing An Intelligent Personal Document Management System

Philanthropic Corporate Social Responsibility (CSR) initiatives have become vital for companies aiming to address societal needs while enhancing brand image and consumer trust. and supporting youth development has gained prominence. However, the extent to which these initiatives influence consumer perceptions, attitudes and behaviours remains underexplored. This study addresses this gap by examining how consumers in Ghana respond to CSR efforts by bottled water companies and how such perceptions shape their brand-related decisions. Anchored in Stakeholder Theory and Social Exchange Theory, this research employed a qualitative design using semi-structured interviews with twenty participants aged 18-35. Inductive thematic analysis revealed seven key themes: emotional impact of CSR, visibility and communication channels, authenticity and consistency, youth participation and voice, trust and brand perception, purchase decision influence, and economic reality as a limiter. Findings suggest that emotionally resonant and consistently delivered CSR initiatives significantly shape consumer trust, brand loyalty, and advocacy. However, financial constraints often limit the extent to which positive perceptions translate into purchasing behaviour. Moreover, younger consumers expressed a strong desire to be included in CSR planning, reflecting a shift toward participatory brand engagement. This study contributes to the growing body of CSR literature in emerging markets and offers practical insights for companies seeking to align social impact with consumer expectations in economically diverse contexts.

Source: Ashesi
Leveraging AI Technologies in Developing an Intelligent Personal Document Management System

Managing personal documents can be a frustrating and time-consuming task, including retrieving, storing, and organising them. Individuals struggle to keep track of important files scattered across physical folders and digital devices, making finding or organising documents difficult, leading to wasted time searching for and extracting important details from these documents. To solve these problems, Sortify, an intelligent document management system designed to tackle these everyday challenges, was introduced. Sortify is built using a scalable modular monolithic architecture and combines artificial intelligence with user-friendly approaches to automate document categorisation, power smart search features, and improve user interaction with their documents. Sortify was shaped with a research-oriented approach focused on understanding user needs. This project aims to make personal document management simpler and smarter, leading the way for future developments in this space.

Source: Ashesi
Leveraging Al to Predict Acute and Chronic Pain Crises in Sickle Cell Disease Patients Based on Symptoms and Vital Signs

Addressing the critical challenge of predicting acute and chronic pain crises in Sickle Cell Disease (SCD) due to the lack of effective predictive measures, this study investigated whether Artificial Intelligence (AI) can predict these episodes based on patient symptoms and vital signs, hypothesizing that AI is a viable tool for this purpose. Given the limited access to real-world patient data, the research utilized a large, clinically informed synthetic dataset comprising approximately 55,000 unique records to simulate real-world complexity. The methodology involved developing both an ensemble machine learning model using the Random Forest, Gradient Boosting and XGBoost algorithms, and a mechanistic Ordinary Differential Equation (ODE) model based on engineered features derived from the synthetic data. The key findings demonstrated high predictive performance for both the ensemble ML model and the mechanistic ODE model, achieving accuracy scores around 0.97 and excellent discriminatory power with ROC AUC scores around 0.99 on test sets. These results supported the potential of AI to reliably predict SCD pain crises , offering a tool for early identification and prompt treatment, which could reduce pain intensity, hospital admissions, and costs. Future work necessitates validating these models using real-world patient data from institutions like Korle Bu Teaching Hospital.

Source: Ashesi
Leveraging Artificial Intelligence to Passively Record Sales in Micro and Small Businesses in Ghana

Micro and Small Enterprises (MSEs) play a vital role in Ghana, as in many other African countries. In this paper, we denote MSEs to refer to a diverse group of enterprises involved in activities ranging from petty trading to grocery store operations [6]. Over the years, MSEs in Ghana have been struck by many challenges including but not limited to poor managerial and record keeping skills, to mention a few. They also rely heavily on manual shop operations, slowing down their growth. For these MSEs, however, recording sales have numerous benefits such as providing customer insights and helping them get access to finance from financial institutions. Recognising that Artificial Intelligence is gaining importance in our world today, this paper seeks to explore how to use AI to help improve the sales recording process in MSEs. This paper aims to contribute to the body of knowledge on intelligent systems for efficient records keeping in MSEs and also create a wealth of knowledge about AI and its applications in the retail industry in Ghana and by extension, Africa.

Source: Ashesi
Leveraging Cataract Detection Algorithms to Assess the Effectiveness of Remote & Automatic Cataract Screening Within the Ghanaian Context

Cataracts are a condition of the eye where the eye lens becomes cloudy and difficult to see through. They can be managed through early detection and surgery. However, they are the leading cause of blindness globally due to factors such as ignorance and lack of access to screening facilities or technology. This study aims to assess the feasibility and potential effectiveness of remote cataract screening via a smartphone application. We modified a pre-existing cataract detection Convolutional Neural Network (CNN) model to better suit it to the dataset on which we trained it. We integrated the model into a Flutter mobile application which we used to assess user perceptions on the application’s usefulness and overall effectiveness among Ghanaian eyecare specialists and other potential users. We found a generally positive reaction to the concept among both categories of users albeit with a few concerns and suggestions for improvement to better suit the needs of the Ghanaian population. These findings highlight the promising future that mobile-powered cataract screening has in Ghana and its potential to revolutionise eyecare provision, especially in settings with limited resources.

Source: Ashesi
Leveraging Machine Learning for Precision Medicine In Ghana

Precision medicine, empowered by machine learning, has made substantial strides in developed countries, yet its implementation in Ghana faces substantial challenges due to limited research and data on African populations. This thesis addresses the critical need to establish systems for delivering personalized treatments using machine learning in Ghana’s healthcare context. Hence, it investigates the question, “can machine learning be leveraged to develop personalised plans in the context of Ghana’s personalized healthcare system?” This study combines Node2Vec graph embeddings with cosine similarity calculations to predict novel drug-disease association, focusing on drug repurposing as a cost effective strategy for personalized medicine. The study employs a two step prorcess, first, applying machine learning techniques to the well-established PrimeKG dataset, then comparing these approaches with a database contextually representative of Ghana. Experiments are conducted on both foreign data (focusing on bupropion) and Ghanaian data (focusing on AMG-510 or Sotorasib), utilising molecular profiling data from breast and colorectal cancer patients. Results demonstrate the potential of the approaches in identifying drug repurposing opportunities relevant to the Ghanaian population. The foreign data experiment reveals new potential indications and contraindications for bupropion in cancer related fatigue and in seizures respectively, whilst the Ghanaian data experiment uncovers promising associations between AMG-510 and NRAS-related cancers, particularly gastric adenocarcinoma. These findings provide a foundation for leveraging machine learning in Ghana’s healthcare system, potentially reducing drug development costs and improving access to personalized treatments.While challenges remain, this study marks a significant step towards implementing precision medicine in Ghana.

Source: Ashesi
Leveraging Natural Language Processing to Enhance User Comprehension of Privacy Agreements for Informed Consent on Online Platforms

In today’s digital age, data privacy has become a critical discussion, with users becoming increasingly curious about what their data is being used for. Online platforms are legally required to present privacy agreements, also known as cookie agreements,to inform users about how their data is collected, used, and stored and what rights the user and the platform have over the data. However, these documents are typically lengthy and complex, leading many users to agree to these conditions without understanding the implications. The research investigates whether an NLP-based interactive privacy agreement summarization tool,Koios, in the form of a web extension, can enhance user comprehension and engagement with privacy agreements. The web extension was developed using a combination of abstractive and extractive summarization to generate user-friendly summaries of privacy policies.The summaries of the tool was evaluated through a two-part experiment involving 100 participants. Results revealed that while only a few participants thoroughly read the traditional agreement, majority skimmed or skipped the policy, behaviour which is in line with prior research around user behaviour with privacy agreements. However, with the summarized version was fully read by majority of participants,who also performed well in answering associated questions, with many achieving perfect scores. Furthermore, most participants expressed interest in using a web extension that summarized agreements like Koios.Notably, there was also minimal differences in performance between users with prior knowledge of data privacy and those who do not, suggesting that the summaries are broadly accessible and easy to understand, thereby promoting informed consent across all categories.

Source: Ashesi
Leveraging Retail And Supply Chain Technologies For Sustainability in The Ghanaian Fashion Industry

The global fashion industry, one of the world's largest polluters, generates significant environmental and social challenges, particularly in Ghana, the largest market for second-hand apparel. This project explores the integration of Artificial Intelligence (AD), Blockchain, and Internet of Things (IoT) technologies to enhance sustainability in the Ghanaian fashion industry by addressing inefficiencies in textile waste management and supply chain transparency. The proposed system automates clothing categorization through an IoT-enabled image capture system and an Al-driven classification module, identifying fabric types, garment conditions, and recycling or upcycling potential. Blockchain technology ensures secure, immutable records of clothing data, promoting accountability and circular fashion practices. Testing revealed high accuracy in AI classification and stakeholder satisfaction with automation, though challenges such as blockchain integration complexity and processing delays were noted. Recommendations include refining AI models, optimizing blockchain APIs, and enhancing user interfaces for broader adoption. The project demonstrates the feasibility of leveraging digital technologies to reduce textile waste, improve supply chain transparency, and foster sustainable practices in Ghana's fashion industry, offering a scalable model for similar contexts. Future work may explore lightweight blockchain alternatives and expanded AI capabilities for greater efficiency and impact.

Source: Ashesi
Leveraging Retrieval-Augmented Generation In Large Language Models for Effective Learning: A Data Structures & Algorithms Learning Assistant

In the rapidly evolving field of artificial intelligence, integrating Retrieval-Augmented Generation (RAG) in Large Language Models (LLMs) provides an innovative approach to reshaping education. This paper explores the design and development of a learning assistant based on an LLM, specifically for Data Structures and Algorithms (DSA), using the RAG technique. According to [1], DSA is a core course that students need help grasping due to the abstractness of its concepts. With the advent of LLMs, students are now adopting these tools to support their learning [2]. However, LLMs have been shown to exhibit some issues of bias, hallucinations and generating irrelevant output, which may not be helpful to the students [3]. Therefore, this research seeks to address these problems using RAG to tailor the LLM's responses and better support students in learning data structures and algorithms. It seeks to answer the question: "How can RAG be leveraged to develop an effective DSA learning assistant?" The technology acceptance model and task technology fit provide the theoretical frameworks for evaluating the effectiveness and usefulness of the learning assistant. Previous research has demonstrated the value of RAG in customising LLMs for different domains; however, research has yet to be conducted on its application to data structures and algorithms. Therefore, further research is needed to bridge this gap and contribute to the ongoing revolution of AI in education. Results show that the RAG technique is effective in performing the task of generating accurate and relevant content.

Source: Ashesi
LiDAR based autofocus system for manual lenses and older camera systems

Being a creative in my early years showed me the importance of having good equipment and tools in your arsenal. Having to shoot an educational YouTube video solo and struggle to get the camera to just focus on my face was a huge hurdle for me. A few years down the line I met a number of creatives in high school and college, and they all shared stories of how much more they could have done if they had good autofocus in their old and budget cameras. Studies show that the media and entertainment industry is worth more than a trillion dollars in 2023 and young talents need the capable tools to create and add to this ever-growing sector [36]. The engineering project in this report addresses the problem of high cost and limited accessibility of fast autofocus lenses and autofocus cameras. While smartphone cameras are currently the most popular cameras in the world, they have small sensors that perform poorly in less-than-ideal lighting conditions and lack special characteristics of larger sensors such as optical blur due to higher apertures, less artificial sharpening to compensate for less detail and better performance in low light. The best options for large sensor cameras are budget DSLRs or cameras with manual lenses, but this combination does not have access to good autofocus. The motivation for this project is to provide a truly cost-effective alternative to passive autofocus systems by designing an active camera autofocus system using LIDAR distance detection. This system aims to cut costs and provide a faster and more precise autofocus solution for manual lenses and older autofocus systems.

Source: Ashesi
LiteGuard: Efficient And Secure Offline Language Model on Edge Devices For Health Professionals

This study explores how augmented reality (AR) can enhance the appreciation of contemporary art among Ghanaian audiences. A web-based AR application was created using A-Frame.js and MindAR to provide interactive overlays for Ghanaian artworks that include cultural context and artist insights. The evaluation, which involved 40 participants at an Accra gallery exhibition, found that there was a moderate baseline interest in art (mean = 3.6/5) and that AR significantly improved comprehension (80% reported enhanced under- standing) and engagement (dwell time increased by 180.9 seconds, p ; 0.00001). Despite usability issues like tracking jitter, the audience

Source: Ashesi