ghanaopendata/ghanaian-research-english huggingface dataset:
Ai-Driven Personalised Recommendation System For Ghanaian Fashion

Despite Ghanas vibrant fashion scene and the growing number of talented local designers, the country imported nearly 249 million dollars worth of clothing in 2019, flooding the market and overshadowing homegrown creativity. As the fashion industry undergoes a digital transformation, artificial intelligence is emerging as a game-changer, offering the potential to personalize consumer experiences and elevate local fashion. This paper explores the development of an Al-driven fashion recommendation system tailored to the unique needs of Ghanaian consumers, aiming to bridge the gap between imported goods and local design innovation. By integrating personality-driven recommendations with local fashion products, including traditional fabrics such as Kente, Batik, and Ankarang experience. Additionally, the platform supports local fashion brands by allowing them to upload product images and descriptions, which are then recommended to users based on their preferences. The paper highlights the impact of AI in transforming fashion e-commerce in Ghana, addressing the need for more personalised, culturally aware shopping experiences while promoting local brands.

Source: Ashesi
AI-enabled intrusion detection and cybersecurity in smart meters to detect false data injection attacks

A smart meter is an electronic device that records energy consumption values and provides real time monitoring and also allows remote communication by transmitting data to utility companies. Incorporating an Artificial Intelligence Intrusion Detection System into a smart meter is beneficial in detecting and mitigating false data injection attacks in real-time ensuring accuracy and reliability of energy consumption values. In this research paper, both hardware and software modules were designed, developed and deployed. The smart meter prototype used an ESP32 microcontroller in conjunction with a ZMPT101B voltage sensor and a SCT013 current sensor to measure energy consumption values. For the software aspect of this project, machine learning models were developed and trained to be deployed unto a web application built with Django REST-framework for the real time detection of false data injections. The developed machine learning models included Logistic Regression, Decision Tree, KNN, SVM, Gaussian Naïve Bayes, Bernoulli Naïve Bayes and Artificial Neural Network. The training data used to train the models were obtained from the smart meter prototype with a light bulb as a load. For training, a Principal Component Analysis (PCA) was employed during the preprocessing stage for accuracy. By utilizing techniques such as anomaly detection, pattern recognition and adaptive learning the machine learning models were able to distinguish between normal and malicious data effectively. The performance metrics used in determining which model performed the best were F1 score, Recall score and accuracy and precision. After using the performance metric to find out which model performed the best, the support vector machine with the kernel 'rbf' had the highest accuracy, f1 score and recall score. The proposed solution illustrates significant improvements in detecting and responding to false data injections, thereby safeguarding the integrity of smart meter data and enhancing the smart grid.

Source: Ashesi
AI-Enhanced Video Editing: User-Driven Video-To-Video Style Transfer

With the growing demand for more expressive and flexible video editing tools, this research presents a video-to-video style transfer framework that allows users to apply the dynamic visual style of any reference video onto another. Moving beyond conventional editing software and AI systems confined to static or limited styles, the proposed method supports personalized, user-driven stylization while tackling the technical challenge of maintaining temporal consistency. By leveraging neural style transfer techniques optimized for video, the framework minimizes visual artifacts such as flickering and improves coherence across frames, contributing to a smoother and more intuitive editing experience. This work advances intelligent media creation by blending automation with individual creative control.

Source: Ashesi
AI-Powered Dynamic Pricing and Customer Retention System for ECommerce Platforms

The COVID-19 pandemic brought about an acceleration in the shift of market activity to ecommerce platforms, and this has only continued to increase. In today's highly competitive ecommerce environment, dynamic pricing and customer retention are critical tools for sustaining growth and profitability. Static pricing models and generic customer engagement strategies are no longer sufficient in a market driven by personalized experiences and real-time decision-making. This project aims to develop an AI-powered dynamic pricing and customer retention system designed as a Shopify plug-service for data collection, the system utilizes advanced machine learning techniques: a reinforcement learning-based dynamic pricing engine that optimizes product pricing within admin-set ranges, a collaborative filtering-based recommender system that personalizes product suggestions for shoppers.

Source: Ashesi
Air quality monitoring and control in school classrooms

Studies show that humans spend most of their time indoors, where they are likely to get exposed to air pollutants. Students are more susceptible as most of their time is spent indoors, mainly in classrooms. Health implications due to pollutant exposure in classrooms include headaches, fatigue, and body irritations which affect students' concentration levels and hence their academic cognition. Health implications of pollutant exposure can also be aggravated by environmental conditions such as temperature and the number of class occupants. Sadly, it is hard for students to distinguish between polluted and clean air until their bodies react to exposure. The desire to set a safe, clean study area for students inspired an engineering design to monitor and apply smart means to control pollutant concentration. The system consists of indoor carbon dioxide and temperature sensors and an outdoor particulate matter (PM) sensing element. When indoor air quality is poor, the system checks outdoor PM levels. Depending on the levels, the system opens or closes windows or sends an email message to the person in charge for manual intervention. The system also controls the fan depending on the temperature levels. A small box-shaped house with a sliding window attached to an actuator was constructed for a quick demo. The system was then checked on different carbon dioxide levels, PM, temperature, and recorded responses.

Source: Ashesi
Akuafoↄ: Smart irrigation system with USSD and web-based controlling system for farms in rural Ghana

This capstone project aims to demonstrate the importance of Smart Irrigation in Ghana by developing an irrigation management system that can operate without relying on the internet or smartphones. The system will use a web-based control system and an Unstructured Supplementary Service Data (USSD) control system to enable small-scale farmers in Ghana to manage their irrigation systems efficiently and effectively. The project addresses the challenges of insufficient water resources and climate change in Ghana's agricultural sector, increasing food security and livelihood opportunities for rural communities. The target market is mainly small-scale farmers in Ghana. The project aims to develop algorithms to accurately estimate and measure soil moisture levels, reducing water wastage and increasing efficiency in crop yield. The project also aims to test and evaluate the developed Smart Irrigation System in small scale farms in Ghana and recommend its adoption and scaling in Ghana's agricultural industry.

Source: Ashesi
Alcoholism among women and its impact on the socio-economic development of Ghana: A case study of Sandema

Alcoholism or abuse of alcohol is not new in Ghana. But alcoholism among women is something most people would be shocked to learn about. However, it should not be a surprise to anyone who comes from Sandema. Sandema is the capital of the Builsa District located in the Upper East Region of Ghana. It is an economically and socially deprived community with high levels of unemployment and illiteracy rate. The situation is even worse when you factor in gender. Women have it bad in many impoverished communities. However, when you factor in illiteracy, poverty and alcohol abuse; the reality is even worse. This paper explores the causes and impact of alcoholism among women in Sandema. It reports the findings of a qualitative study using surveys and personal interviews. The snowball sampling2 technique was employed to solicit data pertinent to the research questions. This study reveals that most women in society mainly abuse alcohol to avoid coping with the realities of life. However, this avoidance coping strategy often leads to more dangerous drinking that affects the health and socio-economic wellbeing of the women. It is thus hoped that policy options and community efforts are directed towards alleviating this growing menace to promote individual and societal development.

Source: Ashesi
Algorithmic Personalization Of Ghanaian Food Recommendations In Nutrition Applications

This study explores the use of Artificial Intelligence (AI) in creating a culturally sensitive food recommendation system to aid weight management for users in Ghana. This work particularly contrasts the comparative performance of two AI models, K-Nearest Neighbors (KNN) and an Artificial Neural Network (ANN) autoencoder, for proposing personalized, feasible, and culturally acceptable meals. Data was collected from nutritional databases of Ghanaian foods, Dietician consultation and from user satisfaction interviews, thereby making sure that both technical modeling and achievable user requirements informed the design of the system. The system developed offered personalized meal planning designed to fit users' caloric requirements for weight loss, maintenance, or gain. User feedback indicated great satisfaction with the cultural relevance and real-world usefulness of the guidelines.

Source: Ashesi
An access control facial recognition system using TinyML: Hardware component

Technological advancement brought about improvements in the way IoT systems are implanted. The introduction of Tiny Machine Learning (TinyML) and edge computing has made it possible to process data at the edge and carry out machine learning processes at the edge without relying solely on cloud platforms for such purposes. Due to the advancement in technology, security can be improved and achieved at a cheaper cost. This project exploits TinyML technology to develop an access control system that uses facial recognition to control access to a room. The proposed system sends data that contains the name of the person who accessed the room and the time the room was accessed. This allows the user to monitor when and who accessed a room. The project focuses on the hardware aspect of the system. The system was able to grant access to authorized persons and can also be controlled from the web application.

Source: Ashesi
An adaptive household sampling method for rural African communities

Investigators working in rural communities and small towns in Africa face many obstacles to obtaining a random and representative sample of households for their research. The civic infrastructure used as the building blocks of survey sampling in developed countries are mostly absent in rural Africa. The purpose of the study described in this paper was to pilot an innovative and cost-effective approach to household sampling designed to generate probability samples representative of the socio-economic diversity of the small town of Berekuso, in the Eastern Region of Ghana, without relying on existing census data, household registers, or a regular layout of roads and dwellings. Utilizing Google Earth images and a Graphical Information System (GIS) map of Berekuso, sampling units were defined as 15-degree wedge-shaped sectors radiating from the center of the original township. All households within randomly selected sectors were surveyed, and based on a household classification scheme, each household type was identified. Additional sectors were randomly selected and surveyed in sequence until no new household types were identified – a notion recognized by laboratory scientists as an‘end point’. The adaptive sampling strategy was cost and time effective: freely available versions of Google Earth and QGIS software were employed along with inexpensive handheld Global Positioning System (GPS) devices; a total of 57 households were surveyed by teams of two enumerators over three consecutive Sundays. The survey method yielded a probability sample that is representative of the socioeconomic diversity of Berekuso, and produced generalizable results for median household size, median age of residents, sources of potable water and toilet types, among others. For example, based on the results of the survey, a 95% confidence interval estimate of the proportion of residents of Berekuso under the age of 20 years is between 0.49 and 0.58. These figures are consistent with results of Ghana’s 2010 census which pegged the proportion of the population of the Eastern Region under the age of the 20 years at 0.49. The authors believe that the methodology described in this paper may be applicable to household research in many rural African villages and small towns where little civic infrastructure exists to create more traditional sampling frames.

Source: Ashesi
An advanced and inclusive logistics management system for TECHAiDE Ltd: Towards and equitable colour blindness user

The proposed project aimed to develop an advanced and inclusive logistics management system for TECHAiDE. The plan was designed to cater to the needs of color-blind users mainly, ensuring equal accessibility to all. The project employed the MERN (MongoDB, Express, React, and Node.js) stack to build a web-based platform that offers real-time logistics management solutions, including customer orders and details, invoices, inventory management, and sales overview. We designed the system to provide a user-friendly interface with an efficient and secure logistics management solution. The project involved various stages: requirement gathering, system design, development, testing, and deployment. The proposed system will improve TECHAiDE's logistics management efficiency and enhance the user experience, including for those with color vision deficiencies.

Source: Ashesi
An affordable density-based traffic management system

Traffic management systems at road intersections of most African cities is based on fixed timings where equal length of green light is assigned to each lane at the intersection. This method of traffic management is inefficient because it causes unnecessarily long waiting times for vehicles at the intersections especially during periods when some lanes have few or no vehicles passing through them. In this project, an affordable density based traffic management system is designed and implemented. This is achieved by selecting components that are low cost, consume low amounts of power and do not require digging onto the road pavements. A pi camera with night vision capability is used as the vehicle detection device for every lane. Image processing is then done using an algorithm running on a raspberry pi to determine the number of vehicles on each lane. This information on the number of vehicles is sent to a central raspberry pi via a radio transceiver which then uses an algorithm to determine the next lane to receive green light, as well as the green light duration. In testing the system, the best position of the camera relative to the lane is investigated in an experiment. The furthest distance the camera can be mounted is determined to be 30m. The night vision capability of the camera is also tested. Despite the advantages that come with this low-cost system, it has some limitations. The camera quality is low hence the image processing algorithm may not be completely accurate in distinguishing between objects in the image. The camera’s field of view is also small, necessitating the use of an extra lens that will increase its field of view.

Source: Ashesi
An AI-Powered E-Participation Platform For Enhancing Student Governance At Ashesi University

This project presents an AI-powered e-participation platform aimed at enhancing student governance at Ashesi University. It addresses challenges in student engagement through a mobile-friendly interface that promotes transparent interactions between students and the student council. Key features include proposal submissions, polls, forum discussions, and event updates. The use of AI, like sentiment analysis, helps process feedback efficiently, providing valuable insights to the council. Developed with Flutter for cross-platform use and Firebase for real-time management, the platform successfully boosts participation and decision-making. Future enhancements will focus on AI capabilities, gamification, and open-source development, serving as a model for modernizing student governance in higher education across Africa and beyond.

Source: Ashesi
An alcohol detection system for vehicles

Drunk driving is a contributing factor to road accidents in several parts of the world. Road accidents caused by drunk driving are usually fatal and have an adverse on the victims and the government, in the long run. Government policies have been implemented to deter individuals from indulging in drunk driving. Positioning police officers with alcohol breath analysers at vantage points on the road has also employed to curb the menace of drunk driving. However, these strategies have barely been able to reduce the rate at which road accidents caused by drunk driving occur. In this project, alcohol breath analysers have been integrated with the ignition system of vehicles to prevent drunk drivers from moving their vehicles.

Source: Ashesi
An algorithm for multi tempo music lyric transcription

This paper documents an attempt to create an algorithm for multi-tempo music lyric transcription. This paper reviews music information retrieval as a field of study and identifies music lyric transcription as a subset of the music information retrieval field. The difficulties of music lyric transcription are highlighted and a gap in knowledge in the field is identified. There are no algorithms for music transcription that are applicable to all forms of music; they are usually specialised by instrument or by genre. The author attempts to fill this gap by creating a method for multi-tempo music lyric transcription. The methodology used to achieve this goal is a three-step process of taking audio as input, processing it using the REPET separation technique, and transcribing the separated audio file. The result of this paper was a relative success, with the music being separated successfully and the lyrics being transcribed but with accuracy lost.

Source: Ashesi
An assessment on the effectiveness of credit risk management tools utilized by financial institutions in Ghana.

Credit risk management is a very essential model for any financial institution, since most of the financial decisions revolve around the corporate cost of holding risk. This management practice is particularly important to banks since credit risk constitutes their core business processes. The banking industry in Ghana though is thriving profitably; the industry suffers the problem of credit risk. Results of the study revealed that credit risk management is a very effective and efficient management tool that helps to reduce credit default rate, non-performing loans, and increase in cash flows as well as portfolio growth amongst financial institutions. However, this paper concludes that methods and procedures used by financial institutions to manage credit risk are credit assessment, purpose of the credit, track records of applicant, monitoring of credit, controlling of credit, evaluating collateral provided by the customer, and proposing of terms and conditions in relation to the loan requested. Further, the study also revealed that both external and internal factors of environment affected the operations of credit risk management. These factors are: interest rates, currency exchange rates, efficient management of government fiscal policies and political stability of the country.

Source: Ashesi
An automated baby monitor for deaf parents

An automated baby monitor for financially challenged deaf parents in Africa. This system is operating on a 433MHz RF module to aid wireless communication. Its input is from a pulse/heartbeat sensor, which checks the beat per minute (BPM) of the baby when it is higher than 145BPM, indicating that the baby is crying. The Microcontroller used is ATMega328P. The focus of this system is to help a deaf parent recognize that their baby is crying when the parent is not in plain sight of the baby or the parent is sleeping. Hence, this system has two devices, one of which will be worn by the baby and another which will be worn by the parent, and communication is wireless. This system is made from affordable electronics components to ensure that the final product is cheaper as compared to its competitions.

Source: Ashesi
An automated bacterial colony counting device on the edge using computer vision.

Conventionally, microbial colony counting has been conducted manually by highly qualified experts, which can be error-prone, daunting and time-consuming. To drift from the manual counting approach, computer applications have been developed to count colonies in images captured of cultured agar plates. This new approach presents a challenge due to the numerous pre-processing steps that are usually required aside the manual effort and time expended in capturing the images of several plates to be investigated. As such, the development of an automated imaging, counting and classification system for microbiological lab applications on the edge is developed in this capstone project. The design takes advantage of computer vision on the edge to perform a real-time count of Escherichia coli colonies on agar plates. An external dataset comprising 9,940 images of colonies of five bacterial species, namely Escherichia coli, Candida albicans, Bacillus sutilis, Staphylococcus aureas, and Pseudomonas aeruginosa, from the internal AGAR dataset were annotated, trained, validated, and tested. The custom training was done with existing object detection models such as the MobileNet SSD, Roboflow version 3, and YOLO versions 8, 9 and 10. By comparison of each model's accuracy in detecting E-coli, the YOLO version 9 trained model was selected for deployment on the Raspberry Pi 4 edge device as its accuracy of E-coli detection pegged at 96.8%. Inferencing on images captured from the Bioengineering lab were conducted and results showed accurate detection of E-coli colonies on cultured agar plate. The device was finally tested with actual samples from the lab and the counts were displayed successfully on the LCD screen. The imagine device also solved the problem of difficulty in enumerating overlapping colonies thereby improving count accuracy.

Source: Ashesi