ghanaopendata/ghanaian-research-english huggingface dataset:
Liver Disease Prediction Using Machine Learning Algorithms

The study investigates the use of machine learning algorithms to predict liver disease, addressing a significant public health concern. The process of analysing large datasets containing patient information such as medical history, blood test results, and imaging scans, the research aims to enhance the accuracy and efficiency of liver disease prediction. Four machine learning algorithms namely; Decision Tree, Random Forest, Artificial Neural Network and Logistic Regression are compared to determine their predictive performance. The study highlights the importance of early detection in managing liver disease and explores the practical applications of integrating predictive models into healthcare systems. The results indicate machine learning, particularly with dimensionality reduction techniques such as PCA and LDA, can significantly improve the accuracy of liver disease prediction, offering valuable insights for healthcare professionals and contributing to better patient outcomes.

Source: Ashesi
Locating the nearest pharmacy with the desired medicine

This project seeks to make locating medicine easier for patients. Through the proposed application, patients can search for medicine, and the application would suggest the nearest pharmacy with the desired drug. In addition, patients can make medicine reservation and access directions to the pharmacy via google maps. The proposed application also enables pharmacists to view medicine reservation from potential clients. In addition, they can update medicine availability on their drug database. They can also access the most searched drug to make better decisions on the drugs they need to have in their pharmacies. The result of this project is that patients do not need to worry about moving from one pharmacy to the other searching for medication. Just by a click of a button, they can locate the drugs they want and make a reservation for it.

Source: Ashesi
Location determination algorithms

The public transport industry is an essential part of people’s lives. However, the problem associated with the local public transport industry is, travellers usually get frustrated when waiting for a bus to arrive at a bus stop due to uncertainty on when the bus may arrive or if it may arrive at all. This leads to anxiety on the part of the traveller as well as time wastage, especially if this was not planned for in the trip. To solve this, historically GPS receivers are used in conjunction with GSM/GPRS modules or RFID technologies to keep track of the location of the buses. The caveat here is this results in a relatively expensive solution to a fundamental problem travellers face. This is because of associated costs in this solution; namely, the cost of equipping each vehicle with a GPS module and charges incurred on bus operators as these modules send the bus data to a cloud data server before being queried by travellers’ devices. In this project, alternative technologies and location determination algorithms are explored to propose a cost-effective solution using LoRa technology and a variation of the trilateration algorithm to determine the location of a bus. The results from the project confirm the feasibility of using alternative methods to determine vehicle location.

Source: Ashesi
loT based water KP] analysis ofwaterbodics in Ghana

Internet of Things (IoT) has become a key area of exploration for smart cities 1n recent years. Based on human interaction-free communication, it supports the use of remote sensors in urban cities and communities (Akyildiz et al., 2020). In Ghana, the need for smart cities, especially for monitoring, has become more urgent. In 2024, galamsey ographical reach of testing (Asante & Bempah, 2021). Although IoT offers a promising path for continuous water monitoring, most IoT systems require strong internet connectivity, which rural communities often lack. As a result, these areas, most at risk of poor water quality, are left out of IoT-based monitoring systems. This highlights the need for real-time, efficient monitoring systems. [oT innovations align directly with Sustainable Development Goal (SDG) 6, which promotes clean water and sanitation, and SDG 11, which focuses on building sustainable, safe, and resilient cities and communities (United Nations, 2015). Integrating smart technologies into water monitoring is not just a technical improvement, but a crucial step toward achieving global sustainability.

Source: Ashesi
Low cost vehicle driving assistance system

The project intends to increase vehicle operator awareness with the integration of a Low-Cost Driving Assistance system in older car models. Situational awareness during driving significantly reduces the number of road traffic accidents, as proved by literature. A 4-wheel mobile robot is used as the plant representing a vehicle for easier and rapid prototyping. The plant is controlled by a Raspberry Pi3 to achieve the desired control choice as well as do all required computational processes. Image processing using a retrained Resnet50 neural network is adopted for road traffic signs. A 54% accuracy rate for image recognition is recorded. The wheeled mobile robot is successfully modeled and deemed unstable while the circuit is simulated and works as expected.

Source: Ashesi
Low cost, portable facial emotion recognition device for autistic children

This paper details the use of Raspberry Pi 4 to develop a facial emotion recognition device for autistic children. This device will be used to recognize seven basic emotions: Happiness, Anger, Sadness, Disgust, Fear, Surprise and Neutrality. The face detection algorithm used is the Viola-Jones face algorithm whilst the feature extraction algorithm used is a fusion of local binary patterns and histogram of oriented gradients. The machine learning algorithm used for classification is the Linear Support Vector Machine. This system produced an accuracy of 35.68% with the most accurately classified emotion being happy. This paper investigates the use of both local binary patterns and histogram of oriented gradients as feature extraction methods as well as both Support Vector Machine and Multilayer perceptron neural network as classification methods.

Source: Ashesi
Low-bandwidth low-latency HTTP adaptive bit rate controller for streaming online classes

HTTP adaptive streaming is the standard for streaming nowadays. MPEG-DASH is the current standing for streaming content on HTTP streams. MPEG-DASH is an open-source streaming platform that allows for editing adaptive bitrate strategies that adapt or enhance the quality of user experience while streaming. In the research, the aim is to solve the problem of streaming over low bandwidth connections. In the not too recent past, physical education was intermittently halted to address the COVID-19 pandemic until the transition to online learning via platforms such as Microsoft teams and Zoom Meetings. In the online period, low bandwidth connections faced many issues such as call dropping, intermittence lagging and many others. The issue of inconsistent connection in online classes was attributed to the high requirements of the online learning platforms, such as a very high and stable bandwidth connection. The identified gap is that no adaptive bitrate strategy can effectively adapt the stream to a low bandwidth connection. This research aimed to build a novel adaptive bitrate strategy that can effectively adapt to a low bandwidth connection. The novel bitrate algorithm is CC4LB which stands for Consistent Connection for Low-Bandwidth. CC4LB was implemented in the DASH player using JavaScript. Tests show that CC4LB performed outstandingly compared to the in-built ABR strategies in low bandwidth connections such as 2G and 3G connections.

Source: Ashesi
Low-cast smart traffic management system

In Ghana and other parts of Africa, most traffic management systems are implemented by the use of a timer at each phase. This method of traffic management is inefficient because equal length of green light is assigned to each lane at the intersection; resulting in long wait times for vehicles behind a traffic light with the red signal on, especially when other lanes are vacant. This project addresses problems such as the one stated in the previous sentence through designs and implementation of a low-cost smart traffic management system. The project is carried out by the use of electrical components that are affordable, easy to maintain and reliable. An inductive loop is used as the vehicle detection device for each lane. Traffic density acquisition is then done using an algorithm running on a single board computer, Raspberry Pi, to obtain the number of vehicles on each lane. After the acquisition of traffic density, the data is processed to obtain the green light length for a lane. After green light length determination, the traffic density acquired earlier is sent to a database on a server via Wi-Fi to enable another microcontroller to use the data to execute traffic coordination between a lane and its preceding lane. In testing the coordinated control system, the statistical analysis showed that the coordinated traffic control between a lane and its preceding lane can be improved to gain the optimal performance.

Source: Ashesi
Low-cost artificial intelligence powered smart house

This paper presents the design and implementation of M.I.S.C.H.I.E.F, an intelligent home automation system using Raspberry Pi 4 Model B, ESP32 microcontroller, and MQTT messaging protocol. Chapter 1 provides the background and problem definition of the study. Chapter 2 reviews related work and literature. Chapter 3 describes the design and fabrication of the hardware and software, including the selection of the Raspberry Pi 4 Model B, ESP32 microcontroller, and other sensors and actuators. The chapter also presents a cost analysis and explains the reasons for using the Raspberry Pi 4 Model B and MQTT protocol. Chapter 4 presents the results and discussions, including the functionality of the ESP32-based web server and MQTT client program, the training results of the wake word model, and the capabilities of M.I.S.C.H.I.E.F.

Source: Ashesi
Low-cost automatic number plate detection system

The ability to detect vehicle license plates has been implemented to reduce traffic violations with varying success. Some of these applications have limited coverage or reduced functionality when used in real-time, as some were only tested with still images and datasets. In this paper, I propose a technique for implementing the Automatic Number Plate Recognition (ANPR) System using Python and Open Computer Vision Library integrated with a traffic light to capture and interpret the number plate of moving vehicles in different lighting conditions and at varying times speeds. The system is 100% successful at detecting vehicle number plates for cars traveling at 5kmph.

Source: Ashesi
Low-cost wheel driven crop sprayer

Agriculture in Africa is an essential economic activity, employing about two-thirds of the continent's working population. Much of the agricultural activities are confined to subsistence farming; however, the machinery available to the majority of these subsistence farmers requires significant manual labour, causing farmers to accumulate health problems and experience low production capacity. Subsistence farmers engaged in crop spraying rely on knapsack sprayers, which leads to many of them citing back problems. High costs of alternatives, such as drone sprayers, prohibit their adoption. This study presents the design, fabrication, and testing of a low-cost crop spraying device. The device reduces the load carrying effort required. The user manually pushes a single-wheel frame carrying a tank and allows a gear-chain system to operate the pump's lever automatically. The performance of the prototype was characterized through experimental testing, revealing that the device can carry a 50-litre larger tank while requiring a manual equivalent to 32% of the load under firm mud conditions and staying below the 50% load limit for loose sand. Appropriate gear sizing was confirmed to enable the desired flow rate of 1.2 L/min while the user walks with a regular gait, e.g., an operational speed of 1.45 m/s.

Source: Ashesi
Low-Resource Pulmonary Diagnostic System: Using Neural Networks for Early Lung Diseases Detection

Chronic Obstructive Pulmonary Disease (COPD) remains a major cause of death in resource constrained regions, where there is limited access to diagnostic equipment, thereby impeding early detection and intervention. This study proposes a dual hybrid solution combining an inexpensive, portable breath analyzer and a telemedicine system to close this gap. The portable device employs a 1D convolutional neural network (CNN) model to analyze volatile organic compounds (VOCs) in exhaled breath with 96.20% accuracy to distinguish between COPD, healthy, smoker, and air samples with 100% precision for COPD cases, minimizing false positives. Leveraging edge AI, the device performs real-time, offline inference (0.02 ms latency) on a Raspberry Pi 5 microprocessor, rendering it suitable for application in connectivity-limited environments. A complimentary telemedicine system aggregates anonymized data to determine high-risk areas and connect patients with medical professionals, facilitating targeted policy initiatives. The telemedicine platform can connect to the portable device via Bluetooth Low Energy (BLE). By incorporating Internet of Things (IoT) enabled diagnostic tools with multilingual NLP-based patient support, which complies with Health Insurance Portability and Accountability Act (HIPAA) standards for data privacy, this study addresses key deficits in equal access to healthcare. It provides a scalable early detection model for COPD detection disorders in marginalized populations, reassuring the audience about its potential widespread application and the reach of its impact.

Source: Ashesi
M’AYARESA: An Improved Health Information Management System for Korle-Bu Teaching Hospital

In 2017, Ghana’s Ministry of Health adopted the Lightwave Health Information Management System (LHIMS), an outsourced project aimed at streamlining workflows in government hospitals. Despite the portability of patient data, LHIMS poses significant challenges as it does not address the specific needs of medical staff in Ghanaian government hospitals, particularly at Korle-Bu Teaching Hospital (KBTH). Additionally, the system fails to consider context-related issues such as power outages, poor network connectivity, and the No Bed Syndrome prevalent in the Ghanaian healthcare space. This project addresses the shortcomings of LHIMS by proposing a new software, M’ayaresa, which is research-driven and tailored to meet the needs of medical staff at KBTH. The end product is a progressive web application that streamlines the hospital’s unique operational demands, incorporating biometric patient facial identification, real-time data integration, and comprehensive analytics and reporting. This project contributes to Sustainable Development Goal 3 – Good Health and Well-being by addressing the bed shortage at Korle-Bu, serving as a model for contextualized hospital management systems, and contributing to sustainable healthcare solutions.

Source: Ashesi
Machine learning based electronic scarecrow for protecting agricultural yields from predator damage

Predators pose a significant threat to agricultural production in Africa, causing substantial economic losses and jeopardizing food security. Traditional predator deterrence methods, such as scarecrows, often lack adaptability and responsiveness, relying on static stimuli and failing to adjust based on real-time predator behavior. To address these limitations, this project proposes the development of an intelligent electronic scarecrow that utilizes machine learning technologies to deter predators effectively, specifically in rice farms. This project presents the design, development, and testing of an electronic scarecrow system aimed at deterring birds from rice farms using machine learning and automated mechanisms. The core of the system leverages a Raspberry Pi 4, which integrates a YOLO-based object detection model to accurately identify birds in real-time. Upon detection, the system randomly selects and activates one of several deterrent mechanisms, including a servo motor to produce movements that mimic human activity and a speaker to emit sounds designed to scare away birds. The detection model achieved an accuracy of 98%, validated through extensive testing in varied environmental conditions. Key functionalities, such as the responsiveness of the servo motor for raising and lowering the arm and the operation of the sound deterrent mechanism, were thoroughly evaluated. The system demonstrated consistent performance, though certain limitations were identified, including the occasional slow response of the servo motor. Future enhancements could include refining the mechanical components, expanding detection capabilities, and incorporating additional deterrent methods. The successful development and implementation of an intelligent electronic scarecrow has the potential to significantly reduce predator-induced crop losses, enhancing agricultural productivity and food security. This aligns with the global goal of achieving zero hunger by 2030, as outlined in the FAO's State of Food Security and Nutrition in the World 2022 report 1 . The project also contributes to the African Development Bank's initiative to accelerate progress towards zero hunger in Africa, as highlighted in their report "The road to zero hunger in Africa: Accelerating progress towards 2030" 2 . By reducing predator damage to rice crops, the electronic scarecrow will help to improve food security and nutrition across the continent. In conclusion, developing an intelligent electronic scarecrow is crucial to protecting agricultural yields, enhancing food security, and achieving the global goal of zero hunger. This electronic scarecrow offers a scalable, automated solution for protecting rice farms, contributing to improved agricultural productivity and sustainability.

Source: Ashesi
Machine learning for credit risk assessment in Ghana: A study of micro-lending institutions

The transformation of Ghana's financial system has made credit risk management an indispensable component of the financial industry. Traditional methods of assessing credit risk have proven insufficient for micro borrowers and micro-lenders in the country. Using a German dataset, this study seeks to construct and evaluate machine learning models for predicting credit risk default in the Ghanaian financial landscape. Three ensemble models, Random Forest, Extra Trees and a Blended Model, were evaluated for their efficiency. At 84.25 percent the Blended Model, which incorporates the predictions of the Random Forest and Extra Trees models, demonstrated the highest accuracy. Loan interest rate, loan income percentage, loan grade, and home ownership were determined to be significant variables for predicting loan default rates. This study provides an alternative method for micro-lenders in Ghana to evaluate creditworthiness, thereby reducing credit default rates and fostering interest rate stability. The study's reliance on a singular German dataset may limit its applicability to a broader audience. Future research should concentrate on collecting more extensive and culturally pertinent data, investigating additional ensemble techniques, and refining predictive models to improve their accuracy in predicting loan defaults in the Ghanaian context.

Source: Ashesi
Machine learning for predicting economic growth: How can Zimbabwe achieve Vision 2030?

Though Machine Learning has been around for a while, it is still considered a new tool for economists and in its application to predicting economic growth. Studies that apply machine learning to predicting economic growth have found that the Random Forest algorithm is currently the best performing machine learning algorithm for predicting economic recessions and economic growth. However, besides studies evaluating the various machine learning algorithms, there is limited literature on the application of these techniques to help economists and policymakers solve problems. Developing African countries, like Zimbabwe, with their unique economic growth challenges, can harness the predictive qualities of this technology in development planning, setting, and achieving growth targets. In this thesis, I apply the random forest algorithm to make income predictions for Zimbabwe, in the country's hopes to attain upper-middle-income status by 2030.

Source: Ashesi
Machine learning-based COVID-19 diagnosis for an IoT centered community health center (Health kiosk)

Since the outbreak of the novel coronavirus, many countries have set up testing centres across states to test and isolate people with the virus to reduce its spread. Others have taken the vaccination program seriously, with Israel vaccinating over 85% of its population to make them immune to this virus as of October 2021. Unfortunately, Ghana is behind in these two initiatives to curb the virus. The lack of testing centres in rural areas, coupled with the cost of testing, makes it hard for people to get tested. In this paper, we trained a machine learning model called the Gradient Boosting Machine to serve as an alternative testing system for diagnosing COVID-19. We used haematological Rhinovirus.Enterovirus status from 5,644 patients as dependent variables, and their COVID- 19 results as the independent variable, to train the model. The top five dependent variables with the most significant relative influence on the model are Rhinovirus.Enterovirus, Leucocytes, Influenza.B, Platelets and Inf.A.H1N1.2009. The model exhibited a higher sensitivity of 97.9%, a specificity of 78% and an AUC of 92%. This diagnostic accuracy can at least complement the RT-PCR test or be compared to it.

Source: Ashesi
Mamamonitor : An Iot-Driven System For Remote Fetal Monitoring To Enhance Prenatal And Maternal Care In Ghana

Small and Medium-sized Enterprises (SMEs) are the cornerstone of Ghanat, and environmental impact, were more likely to adopt DFS. Barriers such as high transaction costs, limited internet access, and a preference for cash were found to negatively influence DFS adoption but were not overwhelming. This study provides empirical evidence that sustainable finance can act as an enabler for digital transformation in the SME sector. It contributes to the literature on green fintech in developing countries and offers insights for policymakers, financial institutions, and sustainability advocates. Promoting SGF awareness and tailoring financial products to SME needs may drive broader DFS adoption, fostering inclusive economic growth and environmental sustainability in Ghana.

Source: Ashesi
MAMAMONITOR : AN loT-DRlVEN SYSTEM FOR REMOTE FETAI, MONITORING TO ENHANCE PRENATAL AND MATERNAL CARE IN GHANA.

Advancements in Internet of Things (oT) technologies are reshaping healthcare delivery by enabling continuous, real-time monitoring and remote patient management. In fetal healthcare, there remains a critical need for solutions that enable seamless, at-home fetal and maternal monitoring beyond traditional clinical visits. This project presents MamaMonitor, an IoT-based remote health monitoring system designed to empower pregnant women and their healthcare professionals with real-time access to vital health data. By integrating wearable microcontroller-based hardware, a cloud database, and responsive mobile and desktop applications, MamaMonitor provides a comprehensive platform for proactive maternal care. Key functionalities include real-time fetal and maternal heart rate monitoring, contraction logging, alert notifications for abnormal readings, and automated report generation through Artificial Intelligence (AI) integration. Testing with simulated fetal ECG datasets demonstrated the system successfully separated fetal and maternal signals, achieving an F1 score of 86.82% and a precision of 88.15%. In addition, user acceptance testing yielded an overall satisfaction score of 88%, highlighting the system.

Source: Ashesi