ghanaopendata/ghanaian-research-english huggingface dataset:
An Intelligent Web based System for Translating Scientific based concepts to Ghanaian Language

This applied project analyzes the design and development of an intelligent webbased system that will interpret science education content into Twi, one of Ghana's predominant languages. The system addresses a core barrier in Ghana's science education system: the intellectual barrier students face in understanding scientific concepts in English, especially grades 4 12. In the majority of rural and disadvantaged areas, prevalence of autochthonous languages like Twi brings about a language gap among learners and English-medium scientific education. The gap is reflected in poor comprehension, low motivation, and underachievement in science subjects. Through local languages and modern educational technology, the project proposes a biliterate e-learning platform that supports inclusive and culturally relevant science learning. The site integrates machine learning translation models, interactive multimedia content, and curriculum-aligned learning interface. Key features include scientific word real-time translation, vocabulary assistance, quizzes, user forums, and performance insights. Hugging Face and Firebase microservices are employed in architecture for scalability and language model execution. Evaluation revealed increased student engagement, comprehension, and recall in learning Twi. The platform provides a model for addressing disparities in language-based learning and could influence broader regional educational reform. Since technical and infrastructural limitations exist, the project lays groundwork for future multilingual science education, content creation, and longitudinal studies of bilingual learning impacts in Ghanaian schools.

Source: Ashesi
An interactive web platform to foster accountability and assess performance of Ashesi University student government

Effective democratic governance is still gaining its roots in Ghana. With time, people have become more aware of their societies and the effort put in by their leaders to make it better. For a leader, being accountable to the people is taking steps to ensure that his or her actions and promises are binding. Accountability remains one of the challenging factors in governance because the people can assess the performance of the incumbent. This project therefore seeks to provide a system centered on Ashesi University College, for assessing student government and fostering accountability at each stage of the electoral process. The development of this system went through several stages, including data collection through surveys from members of the Ashesi community to get their input on what a potential system could look like, to several iterations of the website itself to test its functionality. This paper assesses the current state of governance on a national level as well as on the Ashesi campus, pointing out the downfalls as well as areas that could be improved while outlining how the system is built and the coding languages that went into the different facets of its structure. It also speaks about current systems that are tackling accountability issues in governance. Finally, the limitations and potential future work that the system could be used for is discussed and conclusions are given to the work overall.

Source: Ashesi
An introduction to Mozilla’s Firefox OS: Customization and application development

The smartphone market is steadily increasing all over the world, as such, most mobile device users are gradually shifting from feature phones to smartphones, tablets and portable computing devices. However, in emerging markets, majority of the mobile device users still use their feature phones, presenting an opportunity for OEM’S and operating system manufacturers to produce a phone that would suit this market. Mozilla, the company that popularized the open source browser Firefox, has developed a mobile operating system. Hopefully, this operating system would provide smartphones at affordable prices to consumers who cannot afford the high-end devices that are currently sold on the market. This paper seeks to explore Firefox operating system to give an in-depth understanding on how the operating system works.

Source: Ashesi
An inventory management and accounting system (IMS)

Inventory management and control with its associated financial books preparation/management has been a major source of concern for a good number of businesses over the past years. For most businesses, the inability to effectively monitor and control has been a source of preoccupation with the businesses inevitably incurring huge loses, breaking even, let alone making profits. As a key element in overhead management, inventory management is a major factor in the realization of net profit of these businesses. The rise in the scale of such small scale businesses has made it imperative for a system or mechanism that helps merchants to effectively keep an eye on and control stock hence a way of ensuring profitability. The project, an inventory management system allows the average business person to monitor and control inventory as well as act as a point of sale system. The system is developed using Microsoft.NET utilizing Seagate Crystal Report and with a MySQL database for storage. It is also networked to enable different users running the application connect to and use a shared database. Overall, the system is designed to increase the user's confidence.

Source: Ashesi
An investigation into the buying behavior of young women in tertiary institutions for fashionable clothing: case of Ashesi University College and University of Ghana

The aim of the research was to investigate into the buying behavior of fashionable clothing amongst young women in the tertiary institutions. Women are however considered to have a unique self-image and the drivers of fashion in the world (Tamboli, 2008). The theoretical frameworks used for this study were the five-stage model decision process (need recognition or problem recognition, information search, evaluating alternatives, product choice and outcomes) and the major factors that influence consumer behavior. Surveys based on these theories were used in collecting data from respondents from Ashesi University College and University of Ghana. This research adopted an exploratory and descriptive research where data was collected through interviews and the use of questionnaires. Family and Friends were found to be the major factors that influence a young Ghanaian woman to purchase fashionable clothing. Three most important criteria for these young women in selecting fashionable clothing are Comfort, Style and Style. Findings from this study revealed that though young women are not trendsetters in fashion, they like to buy fashionable clothing.

Source: Ashesi
An Investigation On The Feasibility Of Machine Learning Models In Predicting And Preventing Credential Stuffing Attacks

Account Takeover Attacks (ATOs), fueled in large part by credential stuffing, are increasing at an alarming pace, presenting dire risks to individuals and organizations. Legacy security solutions are hard-pressed to match the escalating automation and innovation of credential stuffing attacks, especially with high-risk, low-tech users remaining exposed through password re-use and security unawareness. Despite the promise of Machine Learning (ML) in cybersecurity, its potential in predicting and blocking credential stuffing, specifically for this at-risk population, is unknown. The present research explores the performance of ML models in predicting and blocking credential stuffing attacks against high-risk, non-technical users. A two-phased approach involved the generation of synthetic datasets simulating credential stuffing attempts with clean and noisy data. Behavioral and network features were meticulously crafted to differentiate between human users and automated bots. Machine learning sequences incorporating Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional LSTM (ConvLSTM) were established and experimented with. Although the models worked exceptionally well with clean data, their work with noisy data recorded approximately 80% accuracy, with the RNN being slightly better. Real-time deployment of the trained RNN model in the simulated environment caused severe degradation in its performance, achieving 52% accuracy with a bias towards labeling login attempts as attacks. The key findings are that ML models have modest effectiveness in this domain, with significant limitations in transitioning from controlled training to simulated real-world settings. Simulated data usage, class imbalance problems, and dynamic nature of actual attacks created limitations. Nonetheless, the research provides a proof-of-concept that machine learning-based user behavior profiling is a promising avenue for proactive security but requires strict validation, ongoing model updating, and robust real-world defenses in order to be feasible in practice.

Source: Ashesi
An investigative study of sponsorship in the Ghana Premier League

For many football leagues all over the world, sponsorship is a very lucrative source of income. This is especially true in Germany and USA. Given that the sponsorship in the Ghana Premier League has not really been stable, this study seeks to understand how exactly, sponsorship works in the Ghana Premier League and also recommend solutions or ideas to improve sponsorship in the league based on the findings of this study. From the research, I was able out that the Ghana Premier League is not independent of the Ghana Football Association. Concerning sponsorship, sponsors are found using an external marketing body. The Ghana Football Association is therefore not involved in the process of finding sponsors but they are involved in negotiating the sponsorship fee. All the money that is gained from sponsorship is invested back into the league but the Ghana Football Association still funds the league regardless of sponsorship.

Source: Ashesi
An IoT based battery management system for electric vehicles

A prototype of an IoT based electric vehicle battery management system was designed to improve the health of cells in a battery pack of an electric vehicle. An IoT system was deployed to allow users and producers of these battery packs to view the status of the cells in the battery pack from the cloud. The design uses various resistor dividers to monitor the individual cells' voltages, current, and temperature. A passive balancing circuit is also implemented to ensure the individual cells charge and discharge at the same voltage level. The voltages, current and temperature of individual cells were monitored successfully. A database was designed to keep records of the individual batteries using phpMyAdmin, and the data was successfully displayed on the website.

Source: Ashesi
An IoT fall detection and alert system for elderly persons with stroke and heart attack

Abrupt falling of the aged population is a rising concern among other chronic sicknesses faced by older people in the world. Elderly patients are disposed to falling abruptly due to heart-related diseases, muscle weakness, high blood pressure, and balance-related diseases such as labyrinthitis – inflammation of the delicate balance regulating parts of the ear. This project focuses on the design and development of simple, low-cost fall detection, and smart alert system. The system detects the fall of a patient using the device's gyroscopes and accelerometers to send an alert message via mail to the caretaker for immediate intervention. The device also possesses a panic push button that can be used by the patient to call for urgent help. The device's buzzer is used to make an alert sound for caretakers who are around the patient's vicinity to respond urgently. Data collected from the device are stored in a database and further aggregated to give the patient's fall history in a web application. The web application displays the patient's fall history by showing the patient’s name, age, state, time of fall, and their condition.

Source: Ashesi
An IoT system for recycling organic food waste into compost/fertiliser supplement

In recent times, the issue of food wastage has slowly become one that is of great concern. The Internet of Things is an evolving technology that provides solutions to problems. This work implements an IoT hardware system that allows for the biological fermentation of organic food waste into compost/fertilizer. The main objective of the project is to monitor conditions in the IoT system through a mobile and web application. Using statistical, descriptive and predictive analysis methods, the project was able to implement a 91.1% accurate system. Results show that data from the system are consistent and high levels of waste induces higher temperatures and humid conditions. This project will help curb the issue of food waste in homes by providing an alternative use for organic food waste.

Source: Ashesi
An IoT-based remote health monitoring system for remote locations in Ghana

Life is one of the most valuable things to mankind. The mere fact of losing one’s life or the life of a loved one is an unbearable experience. The health of each individual is very important. The risk of inappropriate access to proper healthcare poses a serious risk to an individual’s life. Access to healthcare today in our urban areas has been improved greatly by our health industry. But what about our distant members in rural areas? Can they boast of the same access to health care or attention? Valuable lives are being lost daily due to the lack of access to essential healthcare in rural areas. With the rate at with technology is evolving, accessing remote areas should be made relatively easy. This capstone report highlights focuses on the problem of the inability for individuals to have access to medical attention due to the problem of long distances particularly in the rural areas. In this capstone report, I attempt to solve the problem of distant healthcare by using the technology of internet of things and machine learning to help provide some level of medical attention to individuals in rural areas. At the end of this capstone report, patient’s health are monitored using their vitals. Results of patient’s vital signs are posted to a remote server for which a medical facility has access to for further analysis from which feedback and attention can be given to the patient without the presence of the patient at the hospital.

Source: Ashesi
An online stock brokerage platform for African markets

Stock trading is a vibrant industry around the globe. The advent of the internet on the other hand has resulted in the birth of online payment systems and electronic commerce, thus enabling a lot of businesses to carry out transactions online. However, a lot of stockbrokers in African countries are yet to take advantage of these technological developments to enable their clients to buy and sell shares online. This project therefore seeks to facilitate this transition by developing an online brokerage platform that can be easily replicated to meet the needs of online investors in African stock markets and uses the Ghana Stock Exchange as an example in the development process. This paper explains in detail the development process for the online brokerage platform intended to facilitate online buying and selling of stocks. Key advantages of such a platform include allowing investors to buy and sell shares at any time, and it is time and cost effective as it eliminates the need to go through a stockbroker in order to buy or sell shares. The platform also provides educational material to give investors better insight in their trading activities. The end product of this project is a web application where users can easily create trading accounts and start the process of buying and selling shares.

Source: Ashesi
An online system that connects farmers to buyers

The introduction of agriculture technology in any country improves the annual production. Agriculture technology changes farming by reducing the amount of man power needed and increasing yield Connecting farmers from any farming community to buyers at any place can be very challenging for both parties (farmers and buyers). Farmers after every farming season run at a loss due to lack of buyers of their farm products. Also, poor prices offered by few buyers who have been transacting business with these farmers affect them as well. Finally, the product gets spoiled when there is bumper harvest and they cannot sell all at a goal. For instance tomatoes and gardens easily get spoiled if not sold at the right time. As a result, the essence of this project is to develop an online system that will connect the crop farmers to the buyers of their farm products. The application, therefore, considers the various challenges described above to help the farmer get the instant buyer of his or her product in order to maximize profit.

Source: Ashesi
An SMS-to-Burro database gateway system to enable Burro to access information from their central database via SMS

Marathon is the nickname of this project. The aim of the project is to automate the process of providing some of the most common information Burro Staffers and hundreds of Burro Resellers throughout Ghana normally call to request from the Burro office. It aims to provide information such as prices, contact info, and discounts from the Burro central database, Fodder, via SMS text message queries even to those in the remotest parts of the country. For example, if a Burro Reseller who resides in a no internet zone wants to look up a certain Burro product’s price, the person sends the question in plain language to Marathon’s number (MTN sim card in a GSM module sitting in the Burro server room) and Marathon will process this request and sends back the answer right away. Marathon aims to save time and resources for Burro as wells as to make this process easier.

Source: Ashesi
Analysing the power signature of electronic appliances using machine learning

In settings where multiple appliances operate simultaneously, identifying the electricity use of specific appliances in use within the household poses a challenge. This difficulty is exacerbated in compound houses with shared meter systems, making it hard to accurately determine individual tenant energy consumption. This project aims to develop a system for detecting characteristics of an electrical device non-intrusively. It monitors the use of electrical appliances within a compound house by collecting and processing AC signals of appliances in use. The processed data is used subsequently with Machine Learning to identify devices used and for billing.

Source: Ashesi
Analysis of credit card fraud detection methods

Technological advancements in the field of finance have brought about several ways to buy and sell via the internet. With the rampant increase in digitization worldwide, more people are leaning towards the use of the internet as the main medium for conducting transactions. Credit cards serve as one of the modes of payment for these transactions and fraudulent activities in the use of these cards have caused a great deal of harm to several institutions in the online market space. Unfortunately, detection of this menace is not a straightforward task as it has two main issues associated with it: (1) The problem of data imbalance in credit card fraud data (2) The profiles of fraudulent and genuine users being dynamic. This projects tackles the problem of data imbalance in credit card fraud data by adopting a resampling approach in combination with three different classification algorithms to detects instances of credit card fraud. The dataset used contained 284,315 genuine transactions and 492 fraudulent transactions, making it highly imbalanced. Such data may cause classification algorithms to be biased towards the majority class (the class with genuine users) since they are designed with the assumption that they are working with a fairly equal number of examples for each class. Hence, different resampling techniques were used to resample the data before Logistic Regression, Naive Bayes and the K-Nearest Neighbor algorithm were used to predit if a transaction was fraudulent or not. The K-Nearest Neighbor classifier obtained the best performance in terms of f1score and Precision-Recall arca under curve (PR AUC) score when it was used with the Neighborhood Cleaning Rule for undersampling. The values obtained for these performance metrics were 82.5% and 81% respectively.

Source: Ashesi
Analysis of data mining techniques and algorithms for healthcare application using cervical cancer as a case study.

Cervical cancer is the most common cause of cancer among African women. It is a preventable disease and can be treated if identified at early stages. Given the lack of adequate health care services and the costly nature of colposcopies in Africa, it is difficult to get an early diagnosis. The development of smartphone-based diagnostic tools like MobileODT – with which pictures of the cervix are taken and sent to doctors for diagnosis – promises to address the expensive nature of colposcopy and Pap test; still, the diagnosis of these images is prone to human errors. This project aimed to recommend an algorithm that best classifies cervical images into cancerous and non-cancerous, in order to aid medical officials to give a better diagnosis. K-Nearest Neighbour (KNN), Convolutional Neural Network (CNN) and Support Vector Machine (SVM) were analyzed and compared based on their classification accuracy, sensitivity and specificity and how these results varied after applying Principal Component Analysis (PCA) on the dataset. KNN, CNN, and SVM models obtained classification accuracies of 68.75%, 83.3%, and 66.37% respectively while PCA-KNN and PCA-SVM models had classification accuracies of 78.12% and 62.7% respectively.

Source: Ashesi
Analysis of Ghana's EV Policy And Readiness For Adoption

Ghana's Electric Vehicle (EV) policy, though well-intentioned and aligned with global sustainability goals, faces significant hurdles in implementation. The policy demonstrates strong planning and a long-term vision for sustainable transportation, but practical challenges such as high costs, limited charging infrastructure, insufficient financing options, and weak accountability mechanisms are impeding progress. This study, employing document analysis and expert interviews, assesses Ghana's EV policy and evaluates the country's readiness for widespread EV adoption. The findings reveal that despite a carefully designed policy and active stakeholder engagement, issues of affordability and inadequate implementation remain major barriers. The study recommends implementing clear accountability measures and promoting equity and environmental protection to accelerate EV adoption in Ghana.

Source: Ashesi