ghanaopendata/ghanaian-research-english huggingface dataset:
Content vs metrics: Using language modeling to evaluate in-line source code comments for Python

Documentation is vital to the understanding, maintenance and, ultimately, survival of software projects . And yet, a lot of software projects either lack documentation, or are very poorly documented. This results in a gradual decline in the quality of the code and may require complete overhauls in extreme cases. It is therefore important to evaluate documentation to ensure that it conveys clear and meaningful ideas. While existing methods of evaluating documentation are metrics based and look at the structure of documentation examples, this paper explores the possibility of evaluating documentation by assessing its contents. There is, however, a lack of an existing corpus of documentation for natural language processing tasks. A corpus of Python function/method comments is assembled, and a language modeling experiment is performed on them. The results of this experiment are mixed. While they show that it is possible to evaluate documentation by looking at its content as opposed to structure, they also show that this approach may not necessarily be more accurate, with lower quality comment examples having higher probability than those of higher quality.

Source: Ashesi
Country branding: A developing economy perspective

Country branding, has become a strategic tool for attaining country competitiveness. Emphasizing country branding as a social construction, this paper presents a conceptual branding model for Ghana, based on the identity brand management approach. Focusing on the role of the country citizenry, the first construct involves articulating country brand identity to purposefully affect country macro leadership, governance structures, country and product brand value delivery and communication. The second construct involves developing country brand mind-set and citizenship behaviours through purposeful brand knowledge and commitment. The third construct explains the country brand equity and citizens well being likely to be engendered through purposeful brand supporting behaviours, conscious creation of supporting country realities, and coordinated and harmonized nation and product communication. This approach emanates from and is supported by insights generated through exploratory interviews and group discussions involving Ghanaians as well as identity based brand management literature.

Source: Ashesi
Cowpea sorter: An alternative to the manual cowpea sorting process

Cowpea traders find the manual sorting process of cowpeas laborious and time-consuming. Based on the volume of cowpeas and the proportion of damaged cowpeas, the process can span a time interval of about 6 hours. This project integrates the power of computer vision and convolutional neural networks to develop a solution for the cowpea trader to effectively segregate good cowpeas from the damaged ones.

Source: Ashesi
Creating a peer-to-peer food vending, ordering and delivery ecosystem in colleges

Motivated by the current state of technological advancement, this project seeks to modernize the traditional methods of food vending, ordering and delivery by creating a unique yet satisfying mobile experience for users. It is with no doubt that some tools and technology exist that may be employed. However, in the case of student run food business, the default platform used to receive orders is via an embedded google form in an email broadcasted to the entire Ashesi University College community. In several cases, most student food businesses set up at a location on campus and advertise as much as possible to gain a larger reach of customers and traffic to their store/location. Although these methods solve the problem of getting students to patronize their products, there are several efficient ways such methods could be improved to gain a larger reach of customers. This project seeks to develop a peer to peer mobile application that grants users the choice and convenience of ordering, vending and delivering food items using their smart phones.

Source: Ashesi
Creating financial inclusion for the informal sector and small and medium-sized enterprises in Ghana

The Informal sector of Ghana together with the Small and Medium-Sized Enterprises (SMEs) contributes the most to the Gross Domestic Product of the country. Many countries in sub-Saharan Africa rely heavily on this sector of the economy. Think of the street hawkers, shop owners, bus drivers, etc. These are the drivers of our economy. Alas, they are also the most poorly financed sector of the economy. For a sector that could be termed as the backbone of the economy, it is unfortunate that they are not equipped financially enough to continue to provide support to the economy. This paper focuses on the reasons for the poor financing of this sector. The most prevalent concern was found to be the poor record-keeping system that exists which makes it difficult for financial institutions to make decisions to give credit to them. Thus, this project seeks to solve the problem of poor record-keeping in this sector as a means to create financial inclusion for this sector of the economy.

Source: Ashesi
Creating tools for machine learning acceleration at the edge

Abstract Machine learning at the edge has grown in popularity due to its low latency and cost and its efficiency for loT applications. Field-programmable gate arrays (FPGAs) are often used as edge devices to perform inference; however, the development process is not easy to complete quickly due to the required knowledge of hardware description language and Digital System Design. This paper proposes a solution to enable the use of Long Short-Term Memory Recurrent Neural Networks on FPGAs without extensive HDL and Digital System design knowledge by generating the HDL code for users based on their LSTM parameters.

Source: Ashesi
Crescendo: Developing a smartphone based hearing system

The ability to hear is an integral part of being a human being. It plays a vital role in various facets of the human experience, communication, listening to music and even being aware of one’s environment. Unfortunately, not everyone is born with this gift, and the hard of hearing or deaf are thus isolated from society; simply because they cannot communicate and build social connection in the same way as the hearing population. The development of hearing aids was the attempt to bridge that gap; however, the considerable expense and lack of production mean that these assistive technologies are not accessible to those that really need it. Although this has continuously been an issue, the adoption of smartphones across the globe means that technology is more accessible to people no matter what part of the world they live in. Discoveries in digital signal processing algorithms also suggest that sound can be manipulated to augment users hearing. The combination of these accessible devices and improvements in technology means that there should be a way to provide low cost, accessible hearing devices to all individuals, even those from low-income countries. This research will explore how a functional application may be created both to test a users hearing and function as a hearing aid.

Source: Ashesi
Crop Lab: Crops disease detection using deep learning integrated with a mobile application

The farming sector has primarily sourced Ghana's economy. Its importance can never be underestimated, from feeding households to serving manufacturing industries. The agricultural sector suffers a continuous annual disease attack which predominantly affects crop output. However, most large- and small-scale farmers in Ghana fail to devise ways of automating the detection and control of disease infection. Instead, they rely on a manual approach to diagnose their crops. The manual process is time-consuming, and the outcome is mostly incorrect observations and interpretations. It results in low-quality production of food crops. Therefore, to address these challenges, Crop Lab Mobile Application is designed and integrated with an efficient deep-learning model to aid farmers in identifying and treating various diseases affecting their crop plants quickly. Crop Lab provides a rapid first-aid kit for farmers to diagnose and treat crop diseases without needing external agricultural officers.

Source: Ashesi
Cross-Modal Transformer With Spatio-Temporal And Audio Fusion

The rise of deepfakes, realistic synthetic media generated via deep learning, poses significant threats to information integrity and public trust. Existing detection approaches often rely on spatial artifacts and struggle to generalize across diverse manipulation techniques. This paper presents a multimodal deepfake detection framework that integrates RGB frames, optical flow, and raw audio using a three-stream architecture with Transformer-based cross-modal fusion. Spatial and temporal features are extracted via 3D CNNs, while voice inconsistencies are captured through Wav2Vec2. Tested on the AV-Deepfake1M dataset, the proposed model outperforms recent baselines across all key metrics, achieving an accuracy of 0.9899 and an F1-score of 0.9952. These results show the effectiveness of joint spatio-temporal-audio modelling. Future work will include ablation studies to assess modality contributions and extended cross-dataset benchmarks.

Source: Ashesi
Cryptocurrency wallet for virtual currencies

This project s eeks to demonstrate the use of virtual and cryptocurrencies. In recent years, they have taken on various forms; from mediums of exchange to stores of investment value. This unique function is made secure through the security and reliability of Blockchain technology. This project employs Stellar, a Blockchain implemented network to construct a payment and money exchange technology that has the capability of easing transactions and lowering the cost of money transaction across global distances. The project has demonstrated that virtual currencies held on digital ledger networks on the Blockchain can be practically implemented as a money wallet for every day use.

Source: Ashesi
CyberShield: An AI-Powered Ransomware Detection System for Smes in Ghana

Small and Medium Enterprises (SMEs) play a pivotal role in Ghana's economic development, contributing substantially to employment and GDP. However, their increasing dependence on digital technologies has made them prime targets for cyber threats, particularly ransomware attacks, which can cripple operations and cause significant financial losses. Despite the growing risk, most SMEs lack access to affordable, easy-to-use cybersecurity tools tailored to their unique constraints. This project introduces CyberShield, an AI-powered Intrusion Detection System (IDS) designed specifically for SMEs. CyberShield detects ransomware threats at the network level, providing early warnings and protection through a browser-based interface that requires minimal technical expertise. The system bridges the gap between complex enterprise solutions and the simplicity SMEs need, delivering a scalable, cost-effective, and privacy-conscious security solution. By equipping SMEs with proactive defenses against ransomware, CyberShield advances SDG 8 (Decent Work and Economic Growth) by enhancing business continuity and Sustainable Development Goal 9 (Industry, Innovation and Infrastructure) which fosters resilient digital infrastructure and inclusive innovation for small businesses.

Source: Ashesi
Data mining and public health surveillance

The ability to observe current trends in health information and predict the future health status of a country is an important goal of health service institutions. Health information systems are used to accumulate, validate and examine data in order to provide information which is used in the formation of health policies. The fast growth and integration of databases means health scientists now have a huge resource that can be studied to make inferences and reveal valuable trends. Developing countries like Ghana often lack such systems, with high cost of software and IT infrastructure being the major issues. However, due to its low hardware requirements, an open-source system like DHIS2 solves these problems. There is also the need to develop systems which simulate the human thought process and data mining is one element of this stimulating area of adaptive behaviour and machine learning. The main research objective of this thesis is to examine how medical data are being used currently and explore better ways of analyzing them to deliver an improved quality of health service.

Source: Ashesi
Data Privacy Literacy On Social Media: An Assessment Of The Ashesi Student Community Through Web-Based Tools

This research assesses data privacy literacy among the Ashesi student community regarding social media usage. Examining the relationship between privacy awareness and online behavior, the study employed a quantitative approach through a web-based survey distributed to three academic year groups. The resulting data privacy literacy score incorporated weighted components of awareness (40%), knowledge (30%), and practices (30%), with scores ranging from 1.957 to 4.854 (mean = 3.105, SD = 0.673). Analysis revealed no statistically significant relationships between demographic factors (gender, age, academic year) and privacy literacy. Academic major approached significance (p = 0.06347), with Computer Science students demonstrating the highest mean literacy scores (3.683) compared to MIS (2.996), Business Administration (2.888), and Engineering (2.678). Notably, hours spent on social media showed no significant correlation with literacy scores (r = -0.047, p = 0.823), failing to reject the null hypothesis that no relationship exists between privacy literacy and social media activity. The study's limitations include a small sample size (n=25) and reliance on self-reported data. Future research should expand the sample size, incorporate qualitative methods, and examine platform-specific behaviors rather than general usage patterns. These findings establish a baseline understanding of privacy literacy within the Ashesi community and highlight potential disciplinary differences in privacy awareness that merit further investigation, ultimately contributing to the discourse on data privacy in academic settings.

Source: Ashesi
Deep fake speech and automatic speaker verification systems in the African setting

Automatic Speaker Verification (ASV) systems authenticate anyone who interacts with a digital system using speech in a seamless manner. ASV systems are widely utilised in the banking industry to validate customer identities. Deep neural network (DNN)- based voice synthesis systems, on the other hand, have enabled the cloning of the human voice in the form of deepfake audio, which can fool most humans and ASV systems. If these deepfake audios are used maliciously, they jeopardise people's identities and impede the security of ASV systems. This study documents efforts and findings from an exhaustive experimental study on the impact of deepfake audio generated on African accents on ASV systems. We found that deepfake audio generated on African accents is less likely to fool modern ASV systems. These findings highlight the importance of deepfake audio systems that can simulate convincing African accents to ensure that current technologies are used to tackle modern challenges in Africa.

Source: Ashesi
Demalvertising: Enhanced Detection and Mitigation of Malvertising on Android Devices

Web Advertisements are the bedrock for the internet economy, having global ad spending projected to reach $645.80 billion by the close of 2024. However, this blessing can turn into bane as this lucrative industry faces peril from malvertising – the injection of malware or malicious code within web advertisements. This study looked at the application of machine learning techniques to detect malvertising threat, with particular focus on Android devices. The study used the CCCS-CIC-AndMal 2020 dataset which contained a collection of benign and malicious android applications. The study also evaluated several machine learning models such as Decision Trees, Random Forest, XGBoost, Convolutional Neural Networks (CNN). The findings indicated that ensemble method, XGBoost and Convolutional Neural Networks (CNN), showed superior performance in comparison with the other models. Performing machine learning models were integrated with a web-browser extension infrastructure to provide an enterprising detection against malvertising threat. The study discussed limitations and future directions, including the need for a more dynamic feature analysis and potential platform expansion.

Source: Ashesi
Dengue fever prediction and analysis

Dengue fever is a mosquito-borne disease usually found in tropical areas such as Africa, South American and Asia, and the current annual global estimate for dengue-related cases has risen from $8.9 billion and will only keep rising from here. Given the lack of information and a predictive model for finding out the number of cases within a specific geographical location. This paper aims to develop a model for predicting dengue fever based on the climate conditions of a specific location. This is done using dengue fever data and climate data from Peru to train and test our prediction model. We perform exploratory data analysis on our model to see the relationships between the various variables and how they affect prediction capabilities. This paper explores the impact of climate change and vegetation indices on the spread of dengue. Analysis on the climate variables such as temperature, dew point, precipitation and rainfall as well as vegetation indices from the north, south, east and west of both San Juan and Iquitos. The project uses time series plot to predict the number of future cases and uses a series of descriptive plots to explain the correlation between climate, vegetation and the number of cases. The final model with the best error rate had a Mean Absolute Error of ).071 and was the combination of Principal Component Analysis and the Light Gradient Boosting model. The final analysis showed that only climate variables had an impact on the prediction of the disease.

Source: Ashesi
DERMAPAL: Your AI-based dermatologist mobile application for skin disease diagnosis

This project aims to explore the potential of artificial intelligence (AI) in serving as a supplement to diagnosing skin diseases, which is a significant challenge in the field of dermatology. The problem is that skin diseases are often neglected by patients because of the low mortality rates in comparison to other diseases [1]. This can lead to heavy emotional and psychological burden caused by the increase in consciousness especially among the youth of their body and beauty further leading to anxiety [1]. The purpose of this project is to develop an AI-based system that can accurately and efficiently diagnose skin diseases, using deep learning algorithms and computer vision techniques. The objective is to serve as a pre medical diagnostic supplement capable of recommending medically approved home remedies, enhancing the overall quality of patient care, and preventing the worsening of these skin conditions. The project will involve collecting and annotating large datasets of skin images, training and fine-tuning deep learning models, and evaluating the performance of the AI system against human dermatologists. The outcomes of the project will have implications for the future of dermatology and healthcare, where AI can be used to improve patient outcomes, reduce healthcare costs, and enhance the overall quality of care.

Source: Ashesi
Design and analysis of a low cost, deployable unmanned aerial vehicle for environmental surveillance

Surveillance aircraft require long flight endurance and range to perform their task fully. An aircraft's flight endurance can be increased by lowering the aircraft's weight and increasing the UAV's wingspan. However, the challenges that arise with a long wingspan are increased weight and costs due to the addition of materials to the wing. Most importantly, it results in large volumes that take much storage space resulting in difficulties in storing and deploying multiple UAVs. This project discusses the design and analysis of a low-cost micro-UAV with collapsible wings made from lightweight, flexible fabric. The UAV designed in this paper weighs less than 300g and flies at an altitude of 200m and a flight endurance of approximately 45 minutes. Size optimisation was done in guidance with the mission and design requirements. Flight endurance baseline was established by deriving a mathematical endurance model together with power sizing. The shape of the UAV was defined using configuration selection. This was followed by 3D modelling of parts and were assembled using SolidWorks software. To wrap the design, an XFLR5 software was used to analyse and select aerofoils and analyse the UAV's aerodynamic performance, Cl, Cd and Cl/Cd. The coefficient of lift of the aircraft when cruising is 0.455. Results from XFLR5 were compared with the analytically predicted values. Lastly, structural analysis (Finite Element Analysis (FEA)) was performed numerically (using SolidWorks) to determine the structural performance of the wing hinge to avoid failures due to static and fatigue torsional stresses. The critical point on the hinge had 0.74% damage and a safety factor of 2.258, showing that the hinge is unlikely to fail. Keywords: UAV Design, Aerofoil, XFLR5, Flight Endurance

Source: Ashesi
Design and analysis of a water filtration system for the removal of heavy metals due to "galamsey" activities: Using agricultural waste products

Illegal mining is one of the major causes of water pollution in Ghana. Activities of illegal miners has led to the pollution of water bodies by Heavy Metals (H.M.) including other waste products, making it difficult for people, especially those in the rural areas to access clean waters for their various use. Computer Aided Design (CAD) software (SolidWorks 2018 package) was used to obtain conceptual designs for the water filtration system. A final proposed system was designed to fit the set requirements. The filtration system contains sub-systems, which contains agricultural waste products such as orange peels, for the heavy metals removal; carbon filters to remove impurities and improve the taste of the water. Ceramic filter was also proposed for the removal of the sediments from the wastewater. Furthermore, computer simulations were done on various part of the filtration system. Stress, strain, and deformation analysis were carried out on possible key parts and likely affected parts of the filtration system that may fail due to mechanical errors. The implication of the results was discussed the fabrication of the water filtration system.

Source: Ashesi
Design and Analysis of SAE Mini BAJA Suspension System

All-Terrain vehicle as the name implies refers to a car that has been designed to handle a more extensive variety of terrains than most other cars. ATVs are used for several purposes which include recreational purposes and others ranging from military to jungle safaris and desert rides. ATVs are rated highly based on their ability to sustain irregularities of the terrain with ease. The central system involved in supplying the damping characteristics of the vehicles is the suspension system. This paper discusses the development of the front and rear suspension systems for an off-road vehicle, mainly the mini Baja, which will be used to compete in the Baja SAE competitions.

Source: Ashesi