ghanaopendata/ghanaian-research-english huggingface dataset:
Developing a distribution strategy to improve the availability of Recyclon Ghana products to customers

Recyclon Ghana LLC is a privately owned company, which was established in September 2014. The company recycles waste into functional products to generate revenue. The central purpose of this project is to identify and define a major problem faced by Recyclon Ghana LLC and develop a functional solution to curb the problem. Presently, Recyclon Ghana LLC does not have a strategized distribution system, which will assist the company to make its products always available to its customers. The founder chiefly and exclusively does most of the activities pertaining to distribution. Accordingly, when the founder is absent from operations or highly engaged, distribution stops. To solve this issue, a Two-level distribution model was developed to strategically assist Recyclon Ghana LLC to improve its supplies to its customers. Also, it will assist Recyclon Ghana to reduce cost, retain its customers and maximize profit. This distribution strategy was developed based on interviews with the founder and co-partner, customers and insights obtained from distinguished writers in the field of product distribution. Cost-benefit analysis, SWOT and PESTLE, and other analysis done shows that the use of this strategic distribution strategy by Recyclon Ghana LLC would improve its ability to reach its customers. This is because customers will have access to an online shop and other sales avenues where they can make regular purchases.

Source: Ashesi
Developing a functional Natural Language Processing system for the Twi language with limited data

Language is a basic characteristic of human beings. It does not only play the vital role in the transmission of information, but it is also pivotal in the establishment of social connection and emotional bond. For centuries, machines had never possessed the ability to exchange ideas with mankind in an intelligent or intuitive way. However, in recent years, due to breakthroughs in the field of Artificial Intelligence and the rise of computational power, machines have made significant and quite impressive gains in the goal of understanding human language and interacting with it. The branch of Artificial Intelligence which is responsible for enabling machines to understand human language is known as Natural Language Processing. Natural Language Processing involves the utilization of statistical and mathematical models to create algorithms that can train machines to learn and understand human language. The major problem with the algorithms that are created in Natural Language Processing is that they require huge amounts of data to train. Unfortunately, this implies that Natural Language Systems cannot be created for languages that do not have large amounts of readily available data. These kinds of languages are called “low resource languages” and most Ghanaian languages, including the Twi language, fall into this category. This research would explore how a functional Natural Language System may be created for the Twi Ghanaian local language with limited language data.

Source: Ashesi
Developing a GPS Based Application to Help Users Create Safety Groups When Going Out and Keep Track of All Group Members

One of the reasons why many people prefer to participate in social activities with groups of friends is for security purposes. The presence of other people that you know and who care about you gives you confidence that should something happen to you, someone would know and do something about it. This is the premise of the buddy-up system where different group members lookout for other members of the group. Whether it's checking in on your friends when they take too long in the bathroom, calling them when they had gone to buy a snack at an event and are taking too long or checking in to ensure that they reach home safely, we all practise this system. This project borrows from the buddy system and will involve the construction of a GPS based application called Salama to help users keep track of each other when in unsafe locations and will alert the other group members when a user moves too far away from the rest of the group. The application will also be useful in helping users check other group members locations should they be trying to locate them.

Source: Ashesi
Developing a machine learning model for malaria diagnosis in rural areas

Medical diagnosis of diseases like Malaria and tuberculosis still use microscopy as a standard, but this procedure is usually very tiring for pathologists and health workers as it imposes much stress on their vision. Due to the fatigue that health workers get from this process, they might end up misdiagnosing a case. In most Rural areas of Cameroon and Ghana, there are no qualified personnel to do these diagnoses. Moreover, according to the World Bank, malaria still kills millions of people every year in Sub-Saharan Africa. To solve this problem, we used a machine learning approach; transfer learning to retrain an already existing model to perform binary classification on malaria blood smear images. The pretrained model was already optimized for devices with low memory, therefore this project’s model can work on low memory devices with no network connectivity. This project also explored Generative Adversarial networks as an alternative way of training a classifier for scenarios with data scarcity. This project shows how a model trained on a different task can be retrained to solve a similar task and shows a technique for developing a classifier in scenarios of data scarcity.

Source: Ashesi
Developing a micropatterned surface to prevent bacterial adhesion

Hospital Acquired Infections (HAIs), also commonly referred to as Nosocomial infections, present a significant problem to hospitals around the world. The problem is more pronounced in African hospitals due to the lack of thorough and efficient sanitation and hygiene practices. They present a substantial danger to infants in neo-natal care who do not have well developed immune systems. Hospital acquired infections also pose a significant economic and social problem in urban areas. Although a wide range of pathogens can cause nosocomial infections, bacteria is responsible for a larger number of infections. With this in mind, a safe, effective and cost-effective solution is essential to minimize the occurrence of nosocomial infections. The strategy employed will be to try to reduce bacterial colonies in hospitals to prevent infection by bacteria. Natural fibres have been documented to exhibit anti-microbial action and they are also very cheap. The creation of a mat using such fibres that can be used in hospitals to reduce bacterial growth will form the basis of the solution.

Source: Ashesi
Developing a mobile based application for feature phones to teach children in rural area how to code

It is important that while the world moves towards a digital age, people around the world build fluency in communicating with computers. In order to remain relevant, programming skills are highly necessary in the 21st century. In order to create such a culture, starting from the children is a step in the right direction. Teaching programming to children in schools has become just as necessary as teaching them Mathematics, English or Science. In developing countries however, children are being left behind for two reasons; educational curriculums that do not support this new wave and systems that are not well structured or resourced to tackle this issue. Nevertheless, it is necessary that children learn to program, however, it is even more important that children are taught to program in the way that they learn best, not the way that may be traditionally accepted. The paper discusses a tool that has been developed on feature phones to teach children in rural areas, in the upper primary level how to code.

Source: Ashesi
Developing a monitoring systerm for early detection of LPG leakages for gas filling stations in Ghana

Following the dreadful gas explosion at Atomic Junction, Accra-Ghana in the year 2017[2], the innocent public and owners of gas filling stations across the country continue to face fear in managing Liquified Petroleum Gas (LPG) filling stations. Gas explosions have caused loss of precious lives and huge economic loss to owners of gas filling stations and the government. In the past four years, the rate of fire explosion in Ghana has plummeted steadily and if measures are not put in place, the nation will be thrown into another series of gas explosions. To avert this situation, this project presents a monitoring system for the early detection of LPG leakages using the concept of Internet of Things (IoT). The proposed solution presents the design and implementation of an integrated circuit unit which employs sensors, LCD display, buzzer and some other components all connected to ATMega8-p microcontroller. The sensor data obtained from these devices are sent from the microcontroller to a web application via GSM. The main features of the web application include data collection and analysis, database and tracking of gas stations. The impact of the LPG monitoring system is high. The results presented show that gas explosions are minimized if prompt precautions and safety knowledge about gas leakages are given to stakeholders involved in managing gas filling stations.

Source: Ashesi
Developing A ROS 2-Based Software Architecture For The Ashesi Robot Assistant Platform (Arap)

Robotics offers transformative opportunities in education and research, yet many African institutions face challenges accessing affordable and customizable robotic platforms. To address this gap, the Ashesi Robot Assistant Platform (ARAP) project aims to develop a locally relevant robotics platform tailored to educational environments. This capstone project focuses on designing and implementing a modular, scalable software architecture for ARAP based on ROS 2. The architecture follows a layered approach, made up of three distinct layers: User Interface, Logic, and Hardware Interface Layer, and a vertical integration Middleware Layer, to promote modularity, ease of customization, and system extensibility. Core functionalities such as autonomous navigation, obstacle avoidance, and real-time mapping and localization, were developed and tested within simulated environments using Gazebo and RViz. Through a combination of reusable ROS 2 nodes, parameterized configurations, and modular launch structures, the system enables streamlined deployment and supports future expansion by researchers and students. The architecture

Source: Ashesi
Developing A User-Friendly Digital Platform To Streamline Artisanal SME Operations In Ghana

The adoption of digital platforms by artisanal SMEs in Ghana presents a significant barrier to operational efficiency despite the huge economic opportunity it presents. This research investigates the challenges contributing to this and proposes a practical solution through the design and evaluation of Domo, a tailored application for Ghanaian artisans. It uses a mixed-methods sequential exploratory approach built on an experimental phase where the research first explores artisans' challenges through qualitative research and then develops a digital solution influenced by the conceptual framework designed by adapting the IS Success Model with HCI principles of usability, credibility and utility. Subsequent survey results reveal that Domo effectively enhances services discovery, trust and transaction transparency, addressing barriers such as poor usability, credibility concerns, and lack of service record-keeping in the traditional artisanal system.

Source: Ashesi
Developing a waste segregation assistant (mobile application) for households in Ghana

As the slowly continues to deteriorate, many organizations and sovereign states are responsible for innovating sustainable measures to reduce the production of harmful substances and repurpose materials by adding value to them. Ghana is a rapidly developing country. With this development comes a large amount of waste production, the majority of which ends up in the landfills, subsequently destroying the soil and releasing harmful gases into the air due to its poor waste management infrastructure. This project focuses on using deep learning and mobile application technology to take advantage of its massive privatized informal waste collection sector to redirect waste from landfills to treatment plants, thereby addressing specific Using transfer learning with a pretrained Keras model, a waste classification model with a 91% test accuracy was developed. This enabled the average household user to sort their waste visually. Results of the classification analysis are shared with waste collectors and recyclers registered on the mobile application to enable them to engage in the specialized collection. This approach would sensitize household users on the amount of waste they generated daily and prevent the contamination of waste items when transported to treatment plants because waste collectors would be collecting one type of waste from different locations at once.

Source: Ashesi
Developing a web-based system for inferential data analytics

The rapid generation of data by businesses has created a unique and exciting opportunity for management and employees to explore relationships in their data and make informed decisions based on these insights. However, limited sets of tools exist for businesses with no programming and data analysis skills for them to explore this opportunity. This paper proposes a new and improved web-based platform based on a desktop application, INFER, developed by Songhai Group. This application takes into consideration the features and limitations of software tools for data analytics that are currently available. The proposed solution is an end-to-end data analytics platform that enables users to build inferential models from observed data.

Source: Ashesi
Developing Adaptive Machine Learning Models For Real-Time Malware And Online Threat Detection

The current cybersecurity systems experience challenges in monitoring rapid security threats, including phishing attacks, malware, and zero-day exploits. A system has been developed to solve the void in real-time adaptive threat detection through machine learning and threat intelligence connections. The main research question was how to design a system that can detect and respond to emerging threats as they happen. The system includes two detection pipelines: one for analyzing URLs and another for analyzing text content in uploaded files. The system trained Random Forest and XGBoost models through a combination of engineered features and TF-IDF representations while supported by external verification from VirusTotal, Google Safe Browsing, and Gemini AI APIs. The system contained a training adaptation feature that enables continuous learning through new user-submitted data. The results show that combining machine learning with external threat intelligence significantly improves real-time cybersecurity detection.

Source: Ashesi
Developing an Ai-Based Predictive Model for Early Detection of Diabetes Complications

Globally, the number of people diagnosed with diabetes grows by the day at alarming proportions. The condition of diabetes leads to long-term complications if not detected early or if the blood sugar is not controlled properly. This research explores how to use machine learning techniques for early prediction of diabetes-related cardiovascular heart disease (CHD) and Nephropathy using relevant patient data. The paper highlights how various data preprocessing methods and feature extraction techniques, such as Oversampling using SMOTE and dimensionality reduction by PCA and LDA, impact the performance of machine learning models. The best model for predicting CHD achieved over 87% accuracy, whilst the best model for predicting Nephropathy achieved over 81% accuracy. The proposed research approach involved designing and implementing a mobile application to streamline the prediction and monitoring of diabetes-related complications. The implemented system will solve the problem of predicting diabetes-related complications, which can result in better patient outcomes, decreased costs of healthcare, and enhanced quality of life for individuals with diabetes.

Source: Ashesi
Developing an innovative course in design and entrepreneurship for an African university

Developing the next generation of ethical entrepreneurial leaders, without exposing all students to formal training in entrepreneurship, was a shortfall at Ashesi University. With an existing capstone entrepreneurship option which ensures active, experiential and experimental pedagogical approach, Ashesi still saw it expedient to extend this opportunity to students of all majors. In 2013, the journey to start a course, that helped the University better achieve its mission, commenced at an Executive Committee meeting. Within a curriculum that was full, and overflowing with core and elective courses waiting to be deployed, a unified community of administrators, Heads of Departments and faculty, brainstormed how this can become a reality. This paper discuses the journey Ashesi took to arrive at what is currently running as Foundations of Design and Entrepreneurship (FDE), a maiden creative problem solving and basics of business course, heavily drawing on design thinking, design and entrepreneurship. The course was started in September 2015, after two years of planning and development. FDE seeks to inspire and equip all freshmen regardless of their majors, with foundational skills in entrepreneurship. Lessons this paper seeks to share include how a purposed institution can be innovative, the processes necessary for developing new programmes, and failure points to be mindful. This will inform peers and institutions who want to innovate entrepreneurial education in the country, by developing innovative courses to shape graduates ready to transform the continent, and represent Africa on the world platform.

Source: Ashesi
Developing And Deploying An IOT Based Air Quality Monitoring Device

Air quality significantly influences health, cognitive performance, and general wellbeing, yet monitoring remains limited in many educational institutions. This project addresses the lack of a sustainable, real-time air quality monitoring solution at Ashesi University by designing a solar-powered, IoT-based system. The developed system measures key pollutants humidity. An ESP32-based microcontroller collects sensor data and transmits it wirelessly to a cloud database, while a mobile application visualizes real-time and historical readings for users. A Random Forest machine learning model was also trained and integrated to predict the Air Quality Index (AQI) from collected sensor data, enh Testing demonstrated high accuracy in data collection, successful cloud synchronization, and strong predictive performance with an Riovers a low-cost, sustainable, and intelligent air quality monitoring solution, positioning Ashesi University as a leader in smart campus innovation and providing a model for broader adoption across educational institutions.

Source: Ashesi
Developing Lightweight Algorithm For Autonomous Plastic Waste Management Using Low-Cost Edge Devices Over Ghana

This capstone focuses on addressing the growing problem of waste pollution in Ghanaios exploring or implementing object detection in identifying specific categories of marine debris, including Paper, Cardboard, Plastic, Metal, and Glass. This will contribute to more precise data collection on prevalent waste types and inform the development of more targeted and efficient waste management strategies in resource-limited settings.

Source: Ashesi
Development of a biometric authentication voting system for senior high schools in Ghana

Voting is practised in almost every Senior High School in Ghana to elect school leaders in various schools. In Ghana, several issues are facing senior high schools during voting processes. Some of these issues include: The voting process being costly, taking long hours to finish the voting process, Issues of election fraud (election manipulation or vote rigging), and generally the voting process being tedious, not secured, and trusted. It is required for every voting process to be transparent and able to withstand a variety of fraudulent behaviours. This project presents the design of a highly secured, cost-effective voting system designed with Flask python web framework running on a raspberry pi 3B+ (sever). This system includes a fingerprint authentication as a form of security to prevent fraudulent behaviours. The results from the voting are displayed on an admin page, together with some other voting statistics.

Source: Ashesi
Development Of A Controlled Drying System For Terracotta Tiles: A Case Study Of Sbo Farms

This project focused on the design, construction, and preliminary testing of a solar-assisted polycarbonate dryer intended for the controlled drying of ceramic tiles. The primary goal was to improve drying consistency and efficiency compared to traditional open-air methods, which are often unreliable due to weather variability. Static, thermal, and flow simulations were conducted to validate the mechanical integrity and thermal performance of the system. Static analysis of the drying rack showed a maximum von Mises stress of 5.97610 7 MPa against a yield strength of 6.20410 * MPa, resulting in a minimum Factor of Safety (FOS) of 1, indicating adequate but critical structural performance under a 500 N load. Thermal simulations at 100C confirmed uniform heat distribution, while flow simulations at 70ioose dried in open air. Specifically, enclosed samples (D, E, F) recorded an average moisture loss rate of up to 27.5 g/hr, compared to a maximum of 24.75 g/hr for open-air samples (A, B, C). This represents a 10-12% improvement in drying rate due to the controlled environment. Achievements include the successful completion of Phase | testing, full construction of the dryer and drying racks, and validation of the design via simulation. Limitations encountered included a restricted sample size for experimental testing. Future work will focus on Phase 2 testing with a larger variety of samples, improved airflow optimization, and integration of automatic environmental controls to further enhance dryer efficiency and product quality.

Source: Ashesi