ghanaopendata/ghanaian-research-english huggingface dataset:
Empowering Accessibility Through Voice: Developing a Twi-Speaking Assistant to Enhance Digital Access for Person with Mobility and Sensory Disabilities

Yekasa tackles the digital gap experienced by Twi-speaking Ghanaians with severe visual or upper-limb impairments, who are up to 34% less likely to own or use smartphones. Instead of relying on touch, users speak commands in Twi to make calls, send texts, search the web, take notes, and adjust settings. A custom wake word starts recording, and the audio is sent over secure APIs for Twi speech-to-text, AI-driven intent understanding, and Twi text-to-speech playback. Raw audio is deleted immediately to protect privacy. Behind the scenes, Yekasa uses fuzzy matching to find the right contact, schedules reminders, and carries out actions without any screen taps. Lab and user tests show fast, reliable wake-word detection, responses in under five seconds, accurate name matching, and strong approval from visually impaired participants. By restoring independence and dignity to users left out by standard interfaces, Yekasa also enriches Ghana's tech landscape. This privacy-first, native-language voice assistant supports UN goals for innovation and reduced inequality and offers a model for bringing accessible voice technology to other low-resource language communities.

Source: Ashesi
Enabling Independent Development in Collaborative Software Projects: A Framework for Decoupled Systems

This thesis explores the development of a framework designed to enable independent development within collaborative software projects, focusing on creating decoupled systems. The shift from solitary to collaborative development has introduced challenges such as tight coupling of software components, leading to increased complexity and interdependencies. This research addresses these challenges by proposing a novel framework that supports the independent development and seamless integration of decoupled components, particularly suited for smaller organizations. The study investigates two primary research questions: (1) Which layers of an application need to be modified to achieve effective independent development? (2) Can independently developed decoupled components of an extensive application be deployed as a unitary application? Key findings indicate that the proposed framework mitigates dependency conflicts, manages database inconsistencies, and supports modular development, leading to streamlined development processes and reduced conflicts. The framework's architecture, including a developer key system for managing database interactions, ensures that independently developed components can function cohesively when integrated. This research contributes to the field of software development by suggesting a practical solution for decoupling in smaller collaborative environments that may not be suited for current decoupled architectures.

Source: Ashesi
Enabling sign language instruction with technology: the case of developing a computerized learning tool for Ghanaian Sign Language (GhSL)

Ghanaian Sign Language (GhSL) is a developing language in the sense that not much is known about it within or outside Ghana. The fact that GhSL is closely-related to American Sign Language (ASL) means that, knowledge of ASL can help one communicate with the Ghanaian Deaf. However, there exist Ghanaian-specific signs that are not available in ASL. This paper presents the Ghanaian Interactive Sign Language (GISL) Tutor, the first computer-based tutor for GhSL designed to teach GhSL vocabulary of Ghanaian-specific signs. Ghanaians who tested the tutor during its iteration stage expressed that they want more Ghanaian signs to be available on the tutor. The purpose of the GISL Tutor is therefore to make Ghanaian-specific signs accessible to anyone interested in learning GhSL for reception and expression.

Source: Ashesi
Energy monitoring for smart homes using Internet of Things

The increasing demand for electricity in Ghana has led to concerns about energy conservation, to reduce the rising energy costs as well as the carbon emissions produced from energy generation. Popular energy conservation methods used in Ghana include turning off devices when not in use and using Light Emitting Diode (LED) lamps in place of incandescent bulbs which consume more power. However, these methods do not have very significant results and are not very efficient. Additionally, the use of energy management systems or smart home systems is not very common in Ghana, although proven to be effective. Most approaches to smart energy management for households encompass sensor based or application-based control of devices and monitoring of energy consumption. However, in this project, the development of a smart home operated using both sensor-based and application-based control is explored while monitoring energy consumption. This is achieved using Internet of Things.

Source: Ashesi
Enhancing College Admission Decision-Making Using Machine Learning

This research explores the use of machine learning techniques to enhance the college admission process. The use of traditional frameworks in evaluating student applications for college admissions has been heavily criticised for their inefficiencies, subjectivity, and disparities. This research addresses these challenges by proposing using machine learning to aid the admissions departments of colleges in Ghana in making their decisions. The study investigates two research questions: (1) How do different machine learning models perform in predicting college admissions for Ghanaian students? (2) Which part of the college admissions process can be effectively automated using machine learning? The results show that the three algorithms (multiclass regression, multilayer perceptron, and random forest classifier) implemented performed well in predicting admissions outcomes; however, they should not be used as a standalone, but they could be used to work together with the admissions department to aid in college admission predictions. The study also showed that machine learning could automate the processing of students' grades with minimal human effort, significantly reducing the workload of admissions officers and ensuring consistency in the evaluation process. This was done by building an Automated Student Grade Processing and Scoring System as proof of concept.

Source: Ashesi
Enhancing equitable resource allocation: Integrating banker's algorithm with machine learning prediction

Global organizations play a pivotal role in tackling some of the world's socio-economic challenges. With well-defined processes, organizations such as UNICEF (United Nations International Children Emergency Fund) make well-informed decisions pertaining to their resource allocation approaches. Although these approaches have recorded some level of efficiency by ultimately allocating resources, they are heavily centered on traditional approaches that fail to reflect the dynamic changes to the growing needs of society due to their static nature. Just like the Public Finance toolkit used by UNICEF, such approaches rely on a paper-like streamlined, human-generated index that determines how allocation works, and this erases the overarching goal of resource allocation, fairness. This capstone focuses on optimizing resource allocation within organizations like UNICEF through the integration of various scheduling algorithms with Banker's algorithm. The goal is to streamline the process of resource allocation right from handling funding requests from the public and subsequently distributing available resources based on pre-defined conditions of the algorithm. The web application relies on PHP-implemented algorithms to manage requests and perform resource allocation to achieve more efficient and transparent results. The system allows organizations to input total available resources and select from multiple scheduling algorithms, including First Come First Serve (FCFS), Priority-Based and Round Robin. The Banker's algorithm is employed to determine the feasibility of each funding request, dynamically updating the request status to either "Accepted" or "Rejected" based on the allocation results. The backend integrates robust database operations to store, update, and retrieve request statuses and allocation details, ensuring data integrity and consistency. User authentication and session management are implemented to secure the application and personalize user experience. This project aims to provide a scalable and efficient solution for resource management in organizations, facilitating better decision-making and enhancing operational efficiency. The system's design and implementation address key challenges in resource allocation, offering a comprehensive tool for managing and optimizing funding requests.

Source: Ashesi
Enhancing Mobile Money Security Using Deep Learning Models For Fraud Pattern Recognition In Sub-Saharan Africa.

The rapid adoption of mobile money in Sub-Saharan Africa, particularly Ghana, has bolstered financial inclusion but also increased exposure to sophisticated fraud. Traditional detection methods, such as rule-based systems, struggle to address evolving threats. This study proposes a neural network-based anomaly detection system to enhance mobile money security by identifying fraudulent transactions in real time. Using a Long Short-Term Memory (LSTM) model trained on transactional data, the system achieves high accuracy (94.2%), precision (89.7%), and recall (87.8%), outperforming conventional approaches. Designed as a micro services architecture, it ensures scalability, real-time processing, and seamless integration with existing platforms. Key features include real-time monitoring, anomaly detection, alert management, and reporting. To address data imbalance, the Hybrid Cluster-Based Sampling Technique (HCBST) improves model sensitivity and reduces false positives. By leveraging deep learning, this research advances financial security in mobile money ecosystems, offering actionable insights for policymakers and service providers. The findings highlight neural developing regions.

Source: Ashesi
Enhancing Multilingual Sentiment Analysis Performance Using Hybrid And Ensemble Methods: A Focus On Twi And English Languages

This study describes how traditional machine learning techniques were used to develop a sentiment analysis system for Akan, a language that is widely spoken in Ghana. Using a carefully selected Akan text dataset, the system investigates the use of Naive Bayes, Random Forest, and Logistic Regression models. To improve classification robustness, a Voting Ensemble technique was also used. With an accuracy of 71%, the top-performing model showed that sentiment analysis for low-resource languages can be accomplished with conventional techniques. Class imbalance was carefully controlled during training, and feature extraction combined contextual embeddings with conventional text representations. The study incorporated ethical considerations such as informed consent, user privacy, and bias mitigation. Using FastAPI, Python, and Google Cloud services, the finished system was made available as an intuitive web application. Despite being constrained by the size of the dataset and the complexity of the model, this study establishes a basis for further investigation of deep learning techniques and practical implementation in multilingual and informal text environments.

Source: Ashesi
Enhancing Multilingual Video Accessibility for Diverse Audiences

With the rise of the internet and the evolution of digital communication, video content has emerged as a dominant communication medium. The widespread use of video content has led to multiple accessibility challenges, including those faced by individuals with hearing impairments and those with language barriers [1]. This project addresses these challenges by developing an automated software solution to enhance the accessibility of video content through translation, transcription, subtitling, and dubbing. The system employs a microservices architecture to ensure modularity, scalability, and maintainability. Each microservice handles specific tasks such as video transcription, subtitle generation, and translation. In the implementation phase, key functionalities were developed and rigorously tested. The subtitling feature allows users to upload videos of various lengths and sizes, which are then transcribed and subtitled. Transcribely supports over 120 languages, enabling users to access video content in their preferred language. Additionally, a chatbot feature powered by large language models allows users to interact with the video content, providing text summaries and answering specific questions based on the video's content. Comprehensive testing, including unit tests, component tests, and user tests, was conducted to verify the reliability and robustness of the system. Despite its capabilities, Transcribely has limitations. It relies entirely on internet connectivity, which limits accessibility in regions with poor internet infrastructure. Furthermore, while the system supports a wide range of languages, the quality of translation and transcription can vary depending on the quality of the video's audio.

Source: Ashesi
Enhancing proof of delivery systems with QR codes and GPS

Proof of delivery systems have been developed to enhance business operations in the case of businesses that carry out delivery services. Key issues that necessitate the usage of such systems include the need to enhance customer service, monitor delivery personnel, reduce the tendency of losing paper receipts as well as verifying that customers have received their goods. The main purpose of this project is to develop a proof of delivery system to streamline service delivery for businesses. Other features include a mobile and web application for customers to place orders as well as for managers to monitor operations. The proof of delivery system, was implemented on an Android platform for delivery personnel with managers monitoring activities from a web interface. When all modules are implemented and tested it is expected that business’ operations will be enhanced through a system that assists in rating the efficiency of delivery personnel, ensuring that customers have actually received their goods and helping managers make more informed decisions.

Source: Ashesi
Enhancing Retail Decision-Making Through Data Mining Techniques

In an era where a deep understanding of consumer behaviour increasingly drives retail decision-making, this research explores how data mining techniques can transform the retail sector. According to [3], millions of transactions are made, and data is generated and stored in the software of those retailers, waiting for further analysis to become useful for decision-making. This thesis ventures beyond traditional analysis, employing advanced algorithms to examine transactional data from a selected supermarket. The research aims to reveal hidden patterns in supermarket transactional data. The main objective is to collect, pre-process, and analyse supermarket transactional data. This thesis employs algorithms such as Apriori, FP-growth, and NOVA classification. The Apriori algorithm revealed frequent co-purchases and significant associations between products. NOVA classification provided insights into the processing levels of purchased foods, revealing a predominance of ultra-processed foods, which has implications for consumer health and marketing strategies. The study concludes that data mining techniques can significantly enhance retail decision-making by providing actionable insights into consumer behaviour in Africa.

Source: Ashesi
Enhancing Ride-Hailing Passenger Safety And Comfort For Women In Ghana Using Artificial Intelligence

Women in Ghana frequently encounter verbal and physical harassment during ride-hailing trips, which makes them feel unsafe and limits their freedom to move confidently. To address this, we developed a smartphone application that uses on-device Artificial Intelligence to detect early warning signs of danger without constant internet access. While some safety tools exist, they often require user action or stable connectivity, delaying response times. The app has three core modules: a route-based geofencing module that checks if the vehicle follows the planned path using GPS and Google Maps Directions API, alerting if it deviates; an audio-monitoring module that analyses one-second sound clips using mel spectrograms and a lightweight neural network to detect distress signals like screaming; and a facial expression recognition module that uses the front camera and a lightweight Convolutional Neural Network (CNN) to recognize expressions of fear or discomfort. Each module runs on-device, keeping the app fast and reliable. The app adjusts activity checks to conserve battery, scaling back when idle and increasing during motion. All personal data, such as audio, video, and location, is encrypted and automatically erased post-trip, aligning with Ghana

Source: Ashesi
Enhancing trust in peer-to-peer lending in Ghana

In the past years, financial institutions, mainly in Ghana, have witnessed a lot of innovations regarding how they operate and serve their customers. The recent introduction of financial technologies (fintech) includes blockchain, cryptocurrency, NFTS, web3, smart contracts, mobile banking, crowdfunding, and peer-to-peer lending. Traditional banks have seen parts of their core business from payment services and credit to advisory services encroached upon by digital competitors. In the case of credit and loans systems, we have realized how customers no longer have to file long loan documents in order to secure loans. There has been the introduction of the peer-to-peer ecosystem where one can just acquire a loan by the click of a button. The problem with these systems is that there have been reports with regards to nonrepayment of loans. Projecting fintech (automated forms of traditional financial services) as the future, how can we possibly solve this issue of trust in the peer-to-peer ecosystem? The main objective of this thesis is to explore the probable ways of building private, safe, and sensitive peer-to-peer applications. Specifically, this paper focuses on how to enhance trust in peer-to-peer lending systems by exploring credit score ratings, biometrics and smart contract technology. This thesis recommends a framework on how these systems can be built to enhance trust during transactions.

Source: Ashesi
Evaluating and choosing a machine learning algorithm for classifying road surface quality data

Considering the importance of roads to a community, stakeholders (Governments, Motoristsetc) needup-to-dateinformation about the state of roads for decisionmaking. This problem inspired Vorgbe’s (2014) work in implementing a machine learning classifier that could accurately classify roads as “good”, “fair” or “bad”. This information can then be visualised on Google Maps. However, with his algorithm failing to accurately classify some roads, this project seeks to evaluate five classification algorithms to determine which one is best for classifying road surface quality data. To do this, we collected x, y, z acceleration and location data, extracted the desired features from it, performed a 10-fold cross-validation trainingonthedatatochoosethe best model and then tested on a new set of examples to determine the model that accurately classifies the data. Fromthedataavailable,thedecisiontreemodelproduced the best performance with true positives of 97% accuracy for bad roads,81%accuracy for fair roads and 93% accuracy for good roads. The overall accuracy on the test set is 92% with a precision of 92% and recall of 90%. This means that, this model is more likely to accurately predict a new data point as belonging to its true class. The other algorithms (Logistic Regression, Random Forests, Support Vector Machines and Nearest Neighbour) performed well when classifying the “good” and “bad” road data but instead classified the “fair” road data as “good” road.

Source: Ashesi
Evaluating interference in Bluetooth classic and Bluetooth low energy technology

Wireless communication is one of the dominant discoveries to mankind. Bluetooth, a wireless technology, is a crucial technology enabling wireless communications and more importantly, the internet of things. A recognized challenge in Bluetooth communication is the overlap of frequencies for specific time slots causing decreased throughput and delay of signals. This is described as Bluetooth interference. In order to improve communications in the internet of things, there is the need to evaluate and improve the Bluetooth technology. In this project, Bluetooth interference is evaluated using simulation. The two technologies involved are Bluetooth Classic and Bluetooth Low Energy. Simulation results are to help make informed decisions and recommendations in the usage of Bluetooth devices in coexistence. Findings in this study, confirm the presence of interference in the two technologies, and their relationship with the number of communicating pairs. Moreover, the incidence of interference is found to be relatively high in the Bluetooth Low Energy technology.

Source: Ashesi
Evaluating Machine Learning Algorithms In Detecting And Predicting Antimicrobial Resistance

The rise of antimicrobial resistance poses a significant threat to global health, warranting the need for solutions that allow for rapid and accurate detection and prediction of resistant organisms. This study aims to evaluate machine learning algorithms that can be used in detecting and predicting antimicrobial resistance by comparative analysis of their output on genomes. The informed and uninformed search approaches were employed in this comparative analysis. The informed search involved the use of string matching and genetic algorithms while uninformed search involved the use of machine learning and deep learning algorithms techniques such as Random Forest (RF), Convolutional Neural Network (CNN), K-Nearest Neighbours (KNN) and Long-Short Term Memory (LSTM) in detecting patterns indicating antimicrobial resistance. This study revealed three unique genetic patterns indicating the possibility of causing antimicrobial resistance from whole bacteria genomes using the informed search approach. It also showed the possibility of using machine learning algorithms to detect these patterns directly from sequences, showing an exciting future research exploration in the field of computational biology and genetic modelling for drug discovery.

Source: Ashesi