ghanaopendata/ghanaian-research-english huggingface dataset:
OSF-Hub: An online architectural project management system with an interactive page

Given the vast increase in technological advancement, almost everything is done online which includes the collecting, processing, organizing and storing data. The security measures put in place with a proper data management system far surpass the physical storage in terms of technology and space control. This is why a data management must be employed in every establishment. This project explores the benefits of a data management system for OSF Architectural Company, who could manage their company data better. However, it not only seeks to provide for them a better management system but also aims to use OSF as a base to expand the knowledge of architecture in Africa through an architectural interactive website. The goal is to ensure OSF is embodied in this technological advancing world.

Source: Ashesi
Paperless ticketing using mobile vision: An Ashesi case

The bus is used by most staff and faculty to get to and from school the campus daily during the weekdays. The current system for paying for fares involves paying for a paper ticket with cash and does not allow for any other payment options. In this project, this problem has been addressed with the implementation of a Mobile Vision Android application and the accompanying Web application, which has proven useful in helping the finance team better monitor, transactions and generate reports. This project is another step to moving the Ashesi community to a cashless one.

Source: Ashesi
Performance of a Parallel Hybrid Electric vehicle with Liquefied Natural Gas

Fossil Fuels are currently classified as some of the leading producers of greenhouse gases which are major agents of Global warming. This study establishes the benefits Liquified Natural Gas (LNG) would have when used as a fuel in a hybrid-electric vehicle. The study establishes a Parallel-Hybrid vehicle model equipped with a control system to determine the mode of operation of the vehicle. Simulations using the vehicle models developed were performed in AVL Cruise. The results generated from the simulations the vehicle’s fuel consumption and CO2 emissions are compared to that of a traditional gasoline/diesel Internal Combustion Engine. The results show a lower acceleration time and better fuel consumption in the LNG hybrid vehicle operation. Further tests are needed to properly ascertain the CO2 emission and overall efficiency of the LNG hybrid vehicle model.

Source: Ashesi
Phishing in the Era of AI: Evolution, Creation, Detection and Countermeasures

As the digital world evolves, so does the complexity of cyber threats, with phishing attempts being a constant and developing concern. This thesis investigates the complex dynamics of phishing attacks in the era of artificial intelligence (AI), examining the trends, strategies, and countermeasures connected with this malicious behaviour. By exploring the interplay between AI-driven offensive tactics and defensive techniques, this research paper hopes to contribute to the continuing efforts to secure individuals and organisations against the risks of phishing attacks.

Source: Ashesi
Piezoelectric glove sensor for early detection

Breast cancer is the top cancer in women in the developed and the developing world. Globally, about 25% and 15% of all new cancer cases and cancer deaths, respectively, among females were due to breast cancer[2]. According to the American Cancer Society, when breast cancer is detected early and is in the localized stage, the 5-year relative survival rate is 99% [3]. Early detection is enhanced by conducting monthly breast self-exams. However, these existing screening methods, such as MRI, Mammography, and Ultrasound, are expensive, making it difficult for the masses to conduct self-examination regularly. As a result, women in low and middle-income countries suffer the most due to breast cancer. This project seeks to develop a cost-effective system for detecting breast cancer cells in stages 0 and 1(early-stage detection) through tissue stiffness, which contrasts with the traditional mechanical or optical means of detection. It has been identified that the stiffness of a normal breast tissue is less than that of a malignant tissue. Therefore, by determining the elasticity of the breast tissue, it can be deduced if a tumor has been detected in the breast. The piezoelectric glove sensor combines the traditional method of self-examination: clinical breast examination (CBE) to conduct examinations. Three tests were conducted on three breast phantoms containing four tumors of different sizes using the glove sensor. The system was able to detect some of these tumors placed in the breast phantom. The sensitivity and specificity of the system were 84.6% and 83.3% respectively, concluding that the piezoelectric glove has a sensitivity and specificity more significant than a Mammogram and CT scan, and therefore is a suitable pre-screening device for early detection.

Source: Ashesi
Playground design for Kayayo Day Care Centre

Playgrounds are easily the most common and important forms of entertainment for young children. They provide a means for children to develop physically, through the movements made during play, and socially, through their interactions with other children while they play. These activities are all done within a safe environment that has been engineered to look aesthetically pleasing to children and ensure their safety while they have fun. However, despite their apparent benefits, the playgrounds available in Accra and other West African cities are very few, lack design creativity, and are often poorly maintained. This paper showcases a creative design for a set of playground structures for young children and the analysis done to ensure its safety. The playground was designed for the Kayayo day-care centre.

Source: Ashesi
Pounding mechanism of an automated fufu pounding machine

Fufu is a dish widely eaten in various forms across West Africa. This cuisine plays a significant role in the cultural landscape of most West African nations. Preparing fufu is a very laborious process as it requires significant exertion of force from individuals engaging in the pounding of fufu. This, coupled with the poor sanitary conditions accompanying the traditional fufu pounding process, has led to significant innovation in automating the fufu pounding process. Much headway has been made to improve and automate the fufu preparation process. However, most of this upcoming technology strays from the basic founding principles of the traditional preparation process. In this project, a fufu pounding machine that replicates the pounding of traditional fufu preparation is designed, and a prototype is developed as a step towards more innovation in fufu preparation machines.

Source: Ashesi
Power optimal inverse kinematics for quadruped robot

Our world of today has become complex and sophisticated, and so have our problems. One of the eye-opening solutions to these problems has been made available through science and technology. Science and technology have given humans the opportunity to engineer life changing solutions. One of these life-changing solutions is a robot. This project is aimed at making a power optimal quadruped robot using inverse kinematics. The MSP430 was used in the embedded systems of this project, along with SG90 servo motors which served as the joints between the body, upper limb and lower limb of my 8DOF quadruped robot. The Inverse Kinematics of the robot was achieved usig the Denavit-Hartenberg convention and with this there was a successful achievement of two gait processes, walking and trotting.

Source: Ashesi
Power quality measurements and monitoring with smart technology: design and analysis with a single-phase power meter.

Electronic devices comprising of non-linear components (diodes, transistors, transformers, etc.) produce some number of harmonics in our power systems creating various voltage distortions [7]. As a result, several technical problems may arise for the device such as overheating and damage to sensitive equipment which would most likely lead to lifetime reduction to the customer’s devices. To reduce the likelihood of these undesired happenings, this project seeks to develop a digital power quality analyzer that would aid customers to efficiently monitor the power flow into their devices. This would help them detect some upcoming power system errors beforehand.

Source: Ashesi
Power quality monitoring of a low voltage distribution network

A rising population creates a high demand for electricity which leads to network failures, the Low Voltage (LV) network to be precise since is directly impacted by the consumer loads. As the usage of LV networks continues to grow, LV monitoring needs to become more widespread and detailed so that network operators can see accurate and real-time information. Research has been done over the years to find efficient ways of monitoring and assessing the power quality of LV networks. This project explored the use of software development tools for power quality monitoring and analysis. Models were designed to create the disturbances of interest in power systems. Simulated power systems were decomposed using the wavelet transform toolbox in MATLAB to attain their energy densities. The obtained energy densities were input into a trained neural network in MATLAB to detect and classify fault types with respect to the parameter deviation in the power quality. An accuracy of 92.3% correct classification was achieved for the neural network training process. In MATLAB Server Pages (MSP), once all processes were completed, the results of the analysis were sent to a database. The results were then made accessible via the internet by creating a login system for users.

Source: Ashesi
Predicting alarms in manufacturing industries

This paper introduces a predictive algorithm designed for managing alarms in manufacturing industries. By gathering data from various sources, such as machine sensors and maintenance records, the algorithm can anticipate potential alarms and notify operators ahead of time. Unforeseen downtime can have substantial negative consequences for businesses, including revenue loss, decreased productivity, and diminished customer satisfaction. As reported by the Association for Manufacturing Technology, the average cost of unexpected downtime for a manufacturing facility amounts to $22,000 per minute, which can rapidly accumulate and significantly affect a company's profitability. This study aims to evaluate the performance of multiple machine learning algorithms, such as decision trees and random forests, in predicting alarms and identifying potential issues. The insights gained from this research will shed light on the potential advantages and limitations of employing machine learning algorithms in the manufacturing sector.

Source: Ashesi
Predicting digital engagement from social media

The purpose of this research is to explore the adoption of a predictive machine learning model for business digital engagement. However, the focus aims to test the hypothesis of whether or not machine learning regression models can be used to effectively predict social media engagement metrics. The exploratory research this project undergoes consists of three data analysis experiments, filtered by factors such as the size of the data set and the inclusiveness of outlier data elements. At the start of the project, Instagram and Twitter were both considered as data sources, but revised data privacy policies prevented a data collection process within scope, for Instagram specifically. The following models: Decision Tree Regressor, Random Forest Regressor, Support Vector Regressor and Artificial Neural Network was trained and tested on Twitter data. Of the three experiments conducted, the third experiment consisting of the larger data set and removed outliers proved to be the most effective. Though the predicted results are not accurate enough to be replicated across several edge scenarios.

Source: Ashesi
Predicting injury in football using pitch quality, player’s function, player’s age and match intensity: A case study of the 2017 African Cup of Nations

Football is the most popular sport in the world, and many individuals have taken advantage of it to earn a living and improve upon their standards of living. Injuries are also unfortunate incidents that occur in daily life and in sports, which affect an individual’s ability to make good use of his sporting talent to earn a living for himself and his family. In this project, modifiable risk factors that affect a player’s likelihood of getting an injury are identified, and their individual contributions to injury of a player is assessed. A predictive model for determining important risk factors for determining injuries in football is generated using the identified risk factors: pitch quality, match intensity, player function and player age.

Source: Ashesi
Principles and guidelines for developing web applications for children: A Ghanaian perspective

Children, in Ghana, between the ages of 5 and 10 have a good amount of knowledge on the use of web applications and browsing for information on web sites. However, knowledge on how to develop web applications for these children is very limited. The process for establishing principles and guidelines for the development of web application for these children has been started, though, in countries like the United States of America. The aim of this dissertation is to start the process for establishing such principles and guidelines for Ghanaian children as well. By combining the study approaches used by a number of researchers discussed in this paper and analyzing the results, findings proved that the use of color and images should be minimal and the display of instructions should be visible and straight to the point. The facts mentioned above in addition to many others have been discovered. It is therefore essential that web developers, who seek to develop for Ghanaian children within the given age range, consider these during development.

Source: Ashesi
Privacy-by-design for Internet of Things (IoT): Implementing user autonomous options in a smart home scenario

In traditional Internet of Things (IoT) systems, users are unable to authorize and/or deauthorize the collection of user data. Hence, the problem of the absence of user autonomy in IoT systems. The project aims to tackle this problem by suggesting a human-centered privacy-by-design option in the design and implementation of IoT systems. It aims to prioritize the need for the privacy of the user in designing IoT systems. It proposes to do this through the provision of user autonomous commands that enable the user to opt out of the collection of a particular data type and restore the collection of that data type at will. A Smart Home was built and designed, as the IoT system, for the proof of concept. Various data types were collected at the edge level and three of them (audio, image and temperature) were selected to be transmitted to a remote NoSQL database. Through the user web application, the user was able to authorize and/or deauthorize the transmission of data types by choice. In addition, the user was given access to view a user-friendly presentation of the cloud database, to validate the execution of their autonomous actions taken. This was demonstrated in several use case scenarios. The results shown from this research illustrate that user autonomous actions for privacy can successfully be implemented in the design of IoT systems.

Source: Ashesi
Private Sector Employment Preferences Among Recent Graduates in Ghana: A Study of Gen-Z Graduates From Ashesi University

Youth Unemployment and shifting career expectations among Generation Z are reshaping Ghanaced with private sector preference while concerns about bureaucracy and political instability deter public sector employment. Most respondents preferred high-paying roles despite poor work-life balance. These results operationalize the hypothesis that extrinsic motivators such as financial reward and growth potential significantly influence Gen Z's employment choices. The study highlights the urgent need for public institutions to reform recruitment practices and for universities to provide balanced exposure to both sectors. It offers practical recommendations for policymakers, educators, and recruiters aiming to attract and retain top Gen Z talent.

Source: Ashesi
Prodigiosin-loaded electrospun nanofibers scaffold for localized treatment of triple negative breast cancer

Hybrid composite nanofibers, with the potential to enhance cell adhesion while improving sustained drug release profiles, were fabricated by the blend electrospinning of poly(d,l-lactic-co-glycolic acid) (PLGA), gelatin, pluronic F127 and prodigiosin (PG). Scanning Electron Microscopy (SEM) images of the nanofibers revealed diameters of 1.031 ± 0.851 μm and 1.349 ± 1.264 μm, corresponding to PLGA/Ge-PG and PLGA/Ge-F127/Ge, respectively. The Young's moduli were also determined to be 1.446 ± 0.496 kPa and 1.290 ± 0.617 kPa, while the ultimate tensile strengths were 0.440 ± 0.117 kPa and 0.185 ± 0.480 kPa for PLGA/Ge-PG and PLGA/Ge-F127/Ge, respectively. In-vitro drug release profiles showed initial (burst) release for a period of 1 h to be 26.000 ± 0.004% and 16.000 ± 0.015% for PLGA/Ge and PLGA/Ge-F127 nanofibers, respectively. This was followed by 12 h of sustained release, and subsequent slow sustained release of PG from the composite nanofibers. The cumulative release of PG (for three days) was determined to be 82.0 ± 0.1% for PLGA/Ge and 49.7 ± 0.1% for PLGA/Ge-F127 nanofibers. The release exponents (n) show that both nanofibers exhibit diffusion-controlled release by non-Fickian (zeroth order) and quasi-Fickian diffusion in the initial and sustained release regimes, respectively. The suitability of the composite nanofibers for supporting cell proliferation and viability, as well as improving sustained release of the drug were explored. The in-vitro effects of cancer drug (PG) release were also studied on breast cancer cell lines (MCF-7 and MDA-MB-231 cells). The implications of the results are discussed for the potential applications of drug-nanofiber scaffolds as capsules for localized delivery of chemotherapeutic drugs for the treatment of triple negative breast cancer.

Source: Ashesi
Production of corrugated roofing sheets from epoxy-based matrix reinforced with treated coir fibers.

Green roofing is a relatively new approach toward constructing environmentally friendly and sustainable roofing structures. Not only are these structures environmentally friendly, but they can be manipulated for the purposes of energy harvesting, thermal insulation or cooling while saving cost in the long run. Much like the green roof approach, this project seeks to create, for an African setting, with a case study in Ghana, a less costly alternative to durable roofing options such as roofing shingles and composite tiles. Coir fibers were chemically modified at different concentration and at different durations to establish the kinetics of potassium hydroxide (KOH) in the fibers. Pullout test confirmed the length of the fiber to be used in the composite material. Epoxy-based structures were then formed (by casting into fabricated molds) with reinforced treated coir fibers. Mechanical characterization was carried out with a universal testing machine. From the result, a composite with 10 wt% of coir proved to serve the same purpose as the roofing tiles and shingles. Thermal and optical characterizations were also investigated. The implications of the results were discussed for the production of a relatively lower cost roofing sheets.

Source: Ashesi
Project Efua: A user trained, mobile-first image classification AI

Machine learning and the development of Artificial Intelligence (AI) has grown nearly exponentially over the past few years. However, growing fears over the nature of AI and the use of user data by large companies has put an air of distrust over the ML community. This makes it hard to collect more data with better user representation is needed to train more useful and accurate models and creates a unique problem space that my project seeks to tackle. It raises the question; how might researchers improve public understanding of AI while creating more representative datasets? To this question I propose the solution, Project Efua; a user taught mobile AI application meant to bridge the gap between the developer community and the wider public.

Source: Ashesi
Prototyping a CAN bus node for predictive vehicle maintenance

The modern-day automobile is no longer just an analog and mechanical entity. Currently, the most basic of vehicular functions have been computerized. The dedicated hardware assigned to these tasks are electronic control units (ECU). Automobiles consist of a number of ECUs networked together to ensure proper functioning of the vehicle. The overall safety of the vehicle relies on real-time communication between the ECUs. Intra-vehicular communication is possible because of the Controller Area Network (CAN). ECUs are responsible for detecting skids, performing anti-lock braking and providing vehicle diagnostic information. Access to CAN bus could prove useful to mechanics, replacing the trial and error method of identifying vehicle faults. Described in this paper is a hardware and software design of a prototype system that provides real-time CAN bus data. Leveraging on the available CAN bus data, the prototype system will provide vehicle performance data over time. This information should aid in the detection of early detection of vehicle irregularities.

Source: Ashesi
Psyche: A mobile application connecting students to school counselors

This paper explores the problem of depression among university students and provides a means by which the students can be linked to a mental health professional, in order to receive guidance. This system is made up of a mobile application and an electrical system. The mobile application includes a chat system that allows the students to contact available counselors, and also, includes a virtual reality interface that allows the user to relax in a virtual environment of choice while listening to an audio of choice. This could be an instrumental, or natural sounds like the wind blowing, rain drops and othersa. The electrical system takes in the user’s temperature and heart rate and sends these data to the cloud database, to be displayed to the counselor in the mobile application as per the counselor’s request.

Source: Ashesi
Pulse diagnosis using signal processing and machine learning techniques

For many centuries, pulse diagnosis has been a technique studied and applied in determining the state of health of the human body. However, its subjective nature puts it at a disadvantage in presenting an accurate diagnosis of the human body. In this project, practical research is done to standardize pulse diagnosis by acquiring body signals and applying signal processing and machine learning techniques for analysis. A prototype acquisition system is designed to obtain the body signals such as the pressure pulse waves from arteries and ECG signals. The system's efficiency was verified using cross-correlation analysis between the data acquired from the system and the standard data from Lei Zhang’s database and MIT physio net database. For the diagnostic system for the signal analysis, the time domain, frequency domain, and time-frequency domain of the signal processing techniques are adopted to extract features such as the power spectral density to be further used for classification and distinction between different signal groups. Applying the different techniques showed a distinction between the different groups of signals that aided the Support Vector Machine and K-Nearest Neighbour classification models to achieve above 90% and 80% accuracy. The experimental process and results provided insight into ways pulse diagnosis could be standardized for healthcare services.

Source: Ashesi