ghanaopendata/ghanaian-research-english huggingface dataset:
Unified Feedback and Insight Management Platform for Ashesi University

This project presents the design, development and evaluation of the Unified Feedback and Insight Management Platform (UFIMP), branded as AshForms, developed for Ashesi University. The platform addresses the systemic fragmentation in the university’s current feedback processes, which rely on third-party tools like Microsoft and Google Forms. These tools result in data silos, low student engagement, and inefficient feedback analysis. Through stakeholder surveys, interviews, and benchmarking studies, key user requirements were identified, including the need for centralized survey access, intuitive form creation, automated reminders, and enhanced data transparency. Built using a XAMPP stack and following a three-tier architecture and MVC design pattern, AshForms provides role-based access, dynamic form creation, real-time analytics, and responsive interfaces for both administrators and students. Testing confirmed the system’s reliability, usability, and cross-browser compatibility. The platform successfully consolidates mechanisms into a unified, user-friendly system that fosters student participation, supports administrative decision-making, and strengthens the culture of continuous improvement at Ashesi University.

Source: Ashesi
Unified Feedback And Insight Management Platform For Ashesi University

This research examines the pressing need for energy efficiency and self-sufficiency to foster sustainability in future cities. Against the backdrop of global challenges such as climate change, rapid urbanisation, and rising energy demand exacerbated by post-COVID dynamics, the study emphasises the critical role of sustainable urban development in addressing these issues. As cities are projected to house seventy per cent of the global population by 2050 and consume seventy-five per cent of global resources, this research explores strategies for balancing urban growth with environmental sustainability driven by a perception-based study. The study integrates a comprehensive literature review to synthesise existing theories, frameworks, and empirical findings. Key themes and gaps in current knowledge are identified, focusing on how cities can reduce their environmental footprint while meeting the energy needs of growing populations. Drawing on diverse perspectives, the research highlights the importance of innovative approaches to urban planning, particularly in aligning efficiency and self-sufficiency with sustainable development goals. Methodologically, the research adopts a systematic approach to investigate the energy challenges and opportunities. A detailed analysis of data gathered via semi-structured interviews providing a structured inquiry into urban energy systems provides actionable insights into improving energy practices in future cities. The findings contribute to the broader discourse on sustainable urbanisation, offering practical recommendations for policymakers, urban planners, and stakeholders committed to advancing efficiency and self-sufficiency in energy consumption.

Source: Ashesi
Unmanned aerial vehicle for mining exploration

The mining exploration stage in mining is the process where the investigation of the presence of a mineral is done. There are so many mining exploration methods that are being used worldwide. However, in Africa, the exploration methods being used are costly, time-consuming, and detrimental to the environment. This project looks at a way to address this problem in a way that reduces sample collection, gives large area interpretation, and does not require drilling. To accomplish this, an Unmanned Aerial Vehicle (UAV) that can carry a laser and a camera was designed and fabricated. The main aim of the project is to design a UAV that carries a laser that allows visualization of the response from a biologically engineered organism and a camera that captures the response from the organism. In the study, computational fluid analysis, fatigue and static stress analysis were carried out on the UAV. The results from the study showed that lasers and cameras can be coordinated to work together for imaging together with a drone.

Source: Ashesi
Unsupervised Segmentation of Sign Language Videos for Scalable Dataset Creation

Developing effective sign language recognition systems depends heavily on large, well-annotated datasets. However. manual annotation is time consuming. expensive, and requires sign language expertise making data collection a major bottleneck, especially for low-resource languages like Ghanaian Sign Language (GSL). Witham such datasets, few systems exist to bridge the communication gap between signers and non-signers. This study addresses the dataset creation challenge by exploring unsupervised methods to segment sign language videos into distinct sign units, enabling scalable, cost-effective annotation and supporting the development ofmore inclusive sign language technologies.

Source: Ashesi
Use of drones in agriculture: A bird deterrent system

Pest birds damage to commercial crops is a serious problem for farmers in Zimbabwe, especially for high-value crops such as maize, wheat and millet. The lack of a universally costeffective bird deterring method has led to food insecurities in Zimbabwe. There is a need to develop a new cost-effective solution to curb bird pest problems using modern technologies complemented by bird psychology that reduce habituation. This project focuses on pest bird control in a 1-hectare maize field because of the extremely high value of the maize crop to the food security of Zimbabwe. The recent development of drone technologies has provided an autonomous pest bird management system as a potential solution. There are now available offthe- shelf bird deterring UAVs. However, the issue is to detect the presence of birds in the first place. Moreover, birds quickly habituate to simple scaring techniques, and these methods lose their effectiveness before crops are harvested. This project designed a cost-effective bird detective system using HB100 radar motion sensors dotted around the field. Different radar modules were investigated to determine their effectiveness at detecting birds at 15m from the sensor. Radar modules investigated were HB100, CMD324 and K-LCa1. HB100 proved to be better at detecting birds than other radar modules. Different prerecorded sounds for the bird deterring system were played from an mp3 player and Atmega328P. The mp3 played produced higher quality audio, which the birds can perceive as a real threat. However in terms of loudness a 5mW piezo horn twitter provided a sound that can be heard 500m away. Different UAVs were investigated for their effectiveness in carrying the predation risk system, and a UAV with autopilot performed better under windy conditions. Autopilot stabilized the drone and reduced the risk of crashing the drone. A prototype system was constructed and tested in a number of flight tests to verify its capability of deterring the birds from the field. The experiments demonstrated that the designed prototype system could detect and deter birds in the field with delayed habituation.

Source: Ashesi
Use of Machine Learning for the Optimization of Genetic Circuits in Synthetic Biology: Focusing On Promoter Prediction for Gene Expression in Escherichia Coli

This study explores the application of Machine Learning (ML) in optimizing genetic circuit design in Synthetic Biology, particularly focusing on predicting specific promoter for gene expression in Escherichia Coli. Despite the potential of ML, current methods rely heavily on trial-and-error, which is inefficient and costly. The research employs various ML models, including Genetic Algorithms, Support Vector Machines, and Neural Networks, alongside computational algorithms like Boyer-Moore-Horspool’s algorithm, to predict promoter efficacy and identify optimal promoter-gene configurations. Utilizing datasets from databases such as RegulonDB, EcoCyc, and PRODORIC, the study validates its findings through a combination of literature cross-checks and model performance metrics. The resulting model achieved a high accuracy in predicting promoter efficacy, with a 92% success rate in identifying optimal configurations. The findings suggest that incorporating transcription unit data significantly improves prediction accuracy, demonstrating the potential of ML in advancing synthetic biology towards more precise and efficient genetic circuit design.

Source: Ashesi
User involvement in home network configuration and network performance

Today's world is experiencing a steady growth of home networks. Numerous issues also develop as a result of its expansion. Troubleshooting, configuration, network performance, and security are just a couple of examples. This study focuses on configuration and network performance. In this study, we investigate whether user configuration and network performance are associated. An experimental quantitative research approach is used in this study. In this research, the network performance of a user's home network before and after user configuration is measured and compared using a web application known as Fusion Connect. The network performance indices are jitter, latency, upload and download speed. The results show that the only performance metric associated with user configuration among the chosen performance metrics is upload speed. Router producers and network providers can narrow their area of focus in effectively involving home network users in configuring their home network routers based on the findings of this research.

Source: Ashesi
Using a PRP robot manipulator for 3d printer print bed replacement

Ashesi University, within the past few years of starting its Engineering programme has deemed it a necessity to have 3d printers available to students to encourage rapid prototyping and testing. However, these 3d printers are not explored to their maximum capabilities due to lab hour restrictions. In this paper, a constructive mechatronic process is undertaken to design the best solution for students to used the 3d printer as often as possible regardless of lab hour restrictions. A PRP robot manipulator, after a series of design iterations is designed and a suitable controller is selected for the manipulator to function efficiently. The PRP manipulator design was solely based on simulations using the appropriate software for each section of the design process. Based on the results from the controllers, the PID controller was the most suitable controller for the the PRP manipulator as it met the user requirements. Using a PRP robot manipulator for 3d printer print bed replacement

Source: Ashesi
Using a PRP robot manipulator for 3d printer print bed replacement

Ashesi University, within the past few years of starting its Engineering programme has deemed it a necessity to have 3d printers available to students to encourage rapid prototyping and testing. However, these 3d printers are not explored to their maximum capabilities due to lab hour restrictions. In this paper, a constructive mechatronic process is undertaken to design the best solution for students to used the 3d printer as often as possible regardless of lab hour restrictions. A PRP robot manipulator, after a series of design iterations is designed and a suitable controller is selected for the manipulator to function efficiently. The PRP manipulator design was solely based on simulations using the appropriate software for each section of the design process. Based on the results from the controllers, the PID controller was the most suitable controller for the the PRP manipulator as it met the user requirements.

Source: Ashesi
Using analytical CRM system to reduce churn in the telecom sector: A macine learning approach

Customers are considered to be the most valuable assets of any business, and thus their loyalty is key to profitability as they indulge in repeat purchases and attract their colleagues through word-of-mouth. In competitive markets such as telecommunications, customers have a lot of flexibility due to the variety of service providers available and the introduction of mobile number portability (MNP) thus they can easily switch services and service providers. Customer churn is, therefore, a major problem among telecommunication companies hence their quest to reduce customer churn rate and retain an existing customer. Customer relationship management systems have been used over the years to track patterns within the customer data, but this could be improved notably with the technological advances hitting the universe on a daily basis. We have moved past the age of innovations around steam engines, electricity, computers, mobile, internet to the current technology trends in artificial intelligence and big data. We are at the cusp of a new wave where enterprises have embraced the application of machine learning in streamlining different business processes. Telecom companies have the advantage of mining large customer datasets that can be leveraged on for predictive analysis using data science. This project explores the use of analytical CRM system in reducing customer churn in the telecom industry using machine learning algorithms to predict customer behavior in order to retain them. Its goal is to analyze all relevant customer data and develop focused customer retention programs. This is on the focus that if you could somehow predict in advance which customers are at risk of leaving, you could develop focused customer retention programs to reduce customer churn.

Source: Ashesi
Using commercial FPGAs as external accelerators for artificial neural networks in embedded applications

Artificial Neural Networks (ANNs) is a branch of Machine Learning that has seen recent widespread adoption for solving computational problems that seem impractical to solve with traditional algorithmic approaches. ANNs have achieved high accuracy on tasks such as facial recognition, object detection and speech recognition. And recently, ANNs have also seen applications in embedded systems, where it has been used to train robots to learn from their environment and cameras to detect faces in a crowd. However, achieving reasonable performance on a traditional microcontroller can be difficult since ANNs are computationally expensive. This paper investigates the possibility of using a Field Programmable Gate Array (FPGA) as an external accelerator for a microcontroller unit. The aim is for the combined performance of the FPGA and the microcontroller, for running the ANN, to be better than just the microcontroller. For the tested neural network, the results show that the combined system with the FPGA and microcontroller runs at more than twice the speed of a system with just a microcontroller.

Source: Ashesi
Using FPGAs as accelerators on MCUs that perform floating-point arthmetic

Machine learning (ML) is a field under Artificial Intelligence (AI) that focuses on training software applications to make accurate predictions without being explicitly programmed by a human being. Because of the advantages of edge computing, such as reduced latency, improved security, and effective use of bandwidth, it is desirable to execute machine learning algorithms on embedded devices. However, embedded devices such as microcontrollers are constrained in processing power and memory. It may be beneficial to use the FPGA as an accelerator to the microcontroller unit, where computationally heavy operations will be offloaded to the FPGA to boost performance. This paper will explore the performance gains that can be attained when an FPGA is paired with a microcontroller that does floating-point arithmetic. From the tests, the system with the accelerator run at approximately twice the speed of the system comprising the MCU alone.

Source: Ashesi
Using geospatial analytics to find prospective locations for water harvesting in Northern Ghana

Access to potable water is a difficulty for about a quarter of the world’s population. The people living in the northern regions of Ghana, which have arid to semi-arid vegetation cover, form a significantly large percentage of that number in Ghana. Rainwater harvesting has been found to be an appropriate measure to mitigate the effects of water shortages, but its practice has also been fraught with various challenges. Advancing technology has encouraged much research into the best ways to optimise water catchment and distribution in Ghana, but little documented evidence exists on the application of Geographic Information Systems and Remote Sensing. This study investigates the potential of geospatial analytics in selecting suitable locations to channel resources for optimised rainwater harvesting. Adopting a Multi-Criteria Overlay Analysis model, suitability maps were created to be used as a visualisation tool for the classification of places as suitable or not.

Source: Ashesi
Using IoT to assist monitoring of the methane gas extraction at Lake Kivu

Methane gas is a powerful greenhouse gas with global warming potential. The current techniques being used to monitor the leaks are expensive and likely onerous and demands for trained operators. There are available solutions tried by the space agencies such as National Aeronautics and Space Administration (NASA) and European Space Agency (ESA) using satellites to better understand the distribution of greenhouse gases on regional and global scales. Those are ENVISAT, GOSAT, OCO-2, and the recently launched TROPOMI instrument on the Sentinel 5P satellite, but all these, regardless of the advanced technology associated cannot pinpoint the source of emissions. In this study, the performance of low-cost Internet of Things (IoT) sensors and isolation forest anomaly detection machine learning technique was implemented. Isolation Forest is one of the outstanding outlier detectors in the real-time DataStream for faulty detection, and money laundering in banking industry. It was tested in this system to improve the accuracy in detecting the methane gas leak. According to the experimental results, the anomaly detection based on isolation forest achieved an excellent performance in terms of accuracy of outlier detection while minimizing the false positives. Decarbonization is an essential component in the climate system, and this plays a key role in reducing methane emissions. Finally, the study presents future research directions to carry out research on the machine learning with Internet of Things (IoT).

Source: Ashesi
Using machine learning to improve public health surveillance in low-income countries - Disease forecasting and disease outbreak detection using open-source technology NUPIC by Numenta

Public health surveillance is a challenge across the globe. Unexpected disease outbreaks occur even though we have vast knowledge in the field of medicine. Also not being able to forecast the next set of diseases to be reported keeps our medical practitioners less prepared. This problem is heightened in low-income countries that cannot afford the infrastructure that comes with ubiquitous computing methods including Machine Learning on a national scale. There is also a notion that such computing approaches by huge companies such as Google and IBM are too expensive to implement. This research seeks to investigate if Machine Learning can be used in low-income countries to provide an effective public health surveillance regardless challenges in funding and availability of ubiquitous infrastructure. Disease Outbreak Detection and Disease Forecasting are the two branches of public health surveillance considered in this research. A prototype application called MATE is built based on Numenta’s implementation of Hierarchical Temporal Memory (HTM) Algorithms. Experiments were conducted with MATE proved 75% to 95% accuracy for forecasting next 20 diseases, and an instant recognition of anomalies for Disease outbreak detection.

Source: Ashesi
Using Model-View-Controller architectural pattern in building a new PHP microframework

This project is about building a minimal web framework in PHP for developers who use framework for building complex application. I had the opportunity to work with PHP and some of its framework in production level. Some of these frameworks are awesome to work with but depending on the kind of project a developer will be working on, you end up tweaking the internals of the framework which a developer with less knowledge of the framework will find it very difficult to do. There is also much learning curve in using this these frameworks This project seeks about building a minimal web framework in PHP where developers start from small and many functionalities are needed, developers can make use existing libraries on top of the framework to achieve those tasks. In this way, libraries are added only when they are demanded by the developers rather than coming with the frameworks in a bundled form. A feasibility study would be carried out to find if this framework would be feasible. Stakeholders would also be made to confirm functionalities and suggest new functions as well. Upon completion of this project, it’s the aim of the developer that the framework would help other developers to build applications with it.

Source: Ashesi
Using neural network in context awareness to improve email management

This paper details a context awareness application which can be used in personal computers to enhance user experience. Users are distracted frequently and lose focus because there is so much information accessible through the computer. The users have to sift through for the information they really needed at that moment in time. Contexto application address this problem by analysing the information which comes to the system and decide whether it falls in the current user’s current context or not. It applies a neural network which uses sum of the weights assigned to each input to make the decision if a given threshold. The application addresses the problem by filtering emails received in outlook mail client. Each email which comes to the inbox will be analysed to determine which context it falls in and it is flagged for that context. The user can now read emails intended to the context he/she is working in efficiently without unnecessary disturbance. This is done by analysing the email’s sender and subject. The sender and words contained in the subject will have their own weights in a database. Each input’s weight is multiplied by the weight of input category; whether sender or subject keywords. The sum is then compared to a threshold and if it is more than the threshold, the algorithm will determine which context the email best falls in. The experiment demonstrated that it is possible to determine information context using neural network analysis and rearrange it to improve user experience. It is possible to extend the implementation for the email client to file system and browser to make a holistic user experience.

Source: Ashesi
Using OLPC laptop technology as a computer science case study teaching tool

The One Laptop Per Child project has succeeded in developing a novel computer that is tailored to the particular needs of the developing world. The use of this example of a modern computer systems design challenge can be a valuable case study as a teaching tool in computer science. The OLPC laptop is a multiple-purpose computer that is rugged, low-cost, low-power, mesh- networked, and inexpensive. An overview of the technology in the laptops is given, as well as curricula suggestions for using the laptops in an undergraduate computer science curriculum.

Source: Ashesi