ghanaopendata/ghanaian-research-english huggingface dataset:
Classification of road surface quality using Android smartphone devices

The quality of roads in a country contributes greatly to its economic development. In Ghana and elsewhere in Africa, essential goods such as agricultural produce are transported mostly by road. Good roads not only promote economic activity, they contribute positively to the quality of life of their users. In many areas, however, the road infrastructure is not of uniform quality, comprising tarred roads, dirt roads, smooth roads, bumpy roads, and roads that are barely motorable. Roads with poor surface conditions damage vehicles, slow down traffic, lead to accidents, and are uncomfortable to drive on. A possible solution to the problem is system that automatically detects and reports the surface conditions of roads. This study explores the use of an Android-based mobile application to detect and report the surface quality of roads. We start by collecting hand-labeled training data from vehicles traversing carefully selected roads of differing quality. In addition, we use GPS sensors to reliably match road quality information to specific locations. A logistic regression machine learning algorithm is used to train a road surface classifier based on the accelerometer readings collected. This study aims to detect the surface condition of roads and present that data in a manner that can be easily be embedded into maps online. We find that we are able to distinguish between good and bad roads with a true positive rate of 92%. We are able to distinguish between good and fair roads with a true positive rate of 83%. The study is however unable to reliably distinguish between fair and bad roads. To the best of our knowledge this study is the first to attempt the automatic classification of entire sections of road as opposed to the detection of individual road anomalies such as potholes.

Source: Ashesi
Classifying road surfaces using smarphone accelerometers for informed road transportation

Road navigation applications such as Google Maps and Apple Maps provide routing information to their continuously increasing number of users, enabling them to get from one destination to another. These applications provide information such as routes and traffic conditions which influence the time taken to travel by the user of the information. However, these navigation services are lacking in providing road surface quality information. Road surface quality information of a route not only influences the time taken to travel the route, but also provide salient information on the comfort of travel for the passenger and the effect the terrain will have on the vehicle. This work builds on previous work by further developing and characterizing a Logistic Regression (LR) algorithm for classifying road surface quality using accelerometer data sourced from mobile devices in moving vehicles along four different types of roads: very good, good, bad, and very bad roads.

Source: Ashesi
Classroom allocation for Ashesi University: A semi-automated web application for allocating classrooms at Ashesi University

The use of software in universities is expanding. The constant scheduling of events such as lecture events, social events, and faculty events takes place almost every day. In the educational setting, room allocation has made it possible to organize demography and engage students in academic and non-academic events to maximize resources. There has been a conventional approach to allocation rooms with spreadsheet applications. These applications were extensively used until better systems were introduced - web-based applications. Thus, software that can perform many actions based on few commands would be preferable to manage the ever-growing student body, events, and activities sharing the same resources. The primary objective of this project is to assist the academic registry in scheduling rooms for lecture-based activities at Ashesi University. The research employs agile methodologies to implement web application software to address the manual scheduling of rooms and presentable information displays.

Source: Ashesi
Closet LTD: An Entrepreneurship Capstone Project Focusing on Providing The Fragmentation Solutions in The Fashion Industry

Closet functions as a fashion digital platform dedicated to merging tailored style solutions with social media promotional opportunities and fashion business ownership within Ghana. The main focus of Closet lies in solving two integral issues which involve the restricted digital marketing presence of local fashion businesses and the style problems that mobile-first young consumers encounter daily. Through a social media format powered by AI recommendations Closet gives users tailored outfit ideas which match their preferences, body shape characteristics and special event requirements while transforming fashion decisions into structured market data available to brands and designers. The platform performs dual functions as an advising system for how consumers should style themselves and a business expansion tool for fashion creators. The social media platform Closet creates a fashion-focused community which naturally supports local creators through its abilities to promote interesting content and manage personal profiles. The backend system records and processes user activities which enables designers to receive instantaneous information about current fashion developments and market demand indicators along with product appropriateness. Closet is focused on fixing Ghana's fragmented fashion industry by building adaptable features which include user authentication alongside design sensitivity as well as expandable content structures. The digitization of consumer-creator interactions on Closet enables SMEs and designers to achieve enhanced business growth alongside market agility alongside brand fan loyalty through its user-oriented data-based platform.

Source: Ashesi
CODET: An artificial intelligence-based multisensory system for precision harvesting and purchase decisions

Fruit ripeness detection plays a key role in precision agriculture, which enables optimal harvesting and transportation of produce to retailers. Various methods have been explored as means to detect this feature; however, in modern age, no standardised method exists to classify fruits based on their ripening state. Thus, this project aims to design and develop a smart system based on Artificial Intelligence to evaluate the ripeness state. The result is a portable handheld device capable of detecting maturity based on the physical properties of the fruit in real-time. Codet is a portable fruit ripeness prediction system with hardware and software components that make these predictions possible. It consists of an RGBC sensor, an 18-channel spectrometer (from 410nm to 940nm), an 11-channel spectrometer (from 430nm to 670nm), an ultrasonic sensor, time of flight sensors and a camera. With the help of a display, the user can determine the ripeness state of a fruit with just a button press. The device can identify the ripeness state of fruits with an accuracy of 99.9%.

Source: Ashesi
Collision Avoidance At Uncontrolled Intersections

Collisions at uncontrolled intersections pose a significant threat to road safety, particularly in regions with inadequate traffic control systems. Traditional solutions, such as expensive infrastructure involving traffic lights or cameras, are often not feasible in developing countries due to cost and complexity. This project addresses the need for an affordable, scalable collision avoidance system that can enhance driver awareness and reduce accidents at these critical points. By integrating low-cost sensors, including the Honeywell HMC1022 magnetic sensor and CDM324 Doppler radar, with a fuzzy logic-based algorithm, the system determines the right-of-way for approaching vehicles in real time. Data collected from these sensors is transmitted wirelessly to a display system, providing drivers with immediate alerts to prevent collisions. The system operates autonomously, powered by solar energy, and offers a sustainable solution for intersections in both urban and rural environments. This project demonstrates the potential of low-cost, adaptable technology to improve road safety, particularly in developing countries with limited infrastructure.

Source: Ashesi
Combining Visual Based Localization with Received Signal Indicator for an Augmented Reality Indoors Navigation

Indoor navigation has recently become an important topic due to the rampant development of complex buildings that make navigation within those structures difficult. The Global Navigational Satelite Systems(GSS), like the Global Positioning System(GPS), currently cannot provide navigation services to indoor environments since they require a direct line of sight between the satellites and the receiving devices. Hence, an indoor positioning system is needed to serve as the GPS equivalent for indoor environments. There are different indoor positioning technologies, each with advantages and shortfalls. This project explored the idea of sensor fusion where two positioning methods; the Received Signal The strength Indicator(RSSI) of WiFi and the Visual Based Localisation methods are combined to implement an accurate and more reliable indoor positioning system with an augmented reality navigation experience. The Google ARCore SDK with Simultaneous Visual Inertial Odometry algorithms integrated into it was used to implement the visual base localization, while the Root Mean Square Error algorithm was used to implement the RSSI method. Testing the system on different indoor lighting conditions showed an average deviation of 16.73 cm from the actual position. The results indicated that the lighting condition of an indoor space impacts the Localization of a navigating user in the indoor environment.

Source: Ashesi
Communicating prescription information to non-literate patients using visual aids

Health literacy has always been a challenge to 100 percent user absorption rate of medicines. The sad truth is that literacy does not always translate into health literacy. And despite the education levels of people, they do not always understand their medical prescriptions. Worst of all is non-literate patients who do not have the luxury of reading their prescription information off the package of drugs especially when they have more than 3 medications with distinct prescription doses. In this project, I seek to use computer science to communicate prescription information to literate and non-literate patients using visual aids primarily.

Source: Ashesi
Comparative Analysis of Open-Source Large Language Models for Sentiment Analysis and Prompt Engineering

Machine Learning models such as Random Forests and Naïve Bayes are trained and used for sentiment analysis. Large language models (LLMs) are currently used in many tasks because of their advanced attention architecture and the large amount of data they have been trained on. Closed source LLMs like ChatGPT, from OpenAI, and Gemini, from Google, are being explored and used for various tasks. However, they do not allow the user to fully interact and fine-tune them for performing specific tasks. Hence, some users opt for open-source LLMs such as BERT and GPT-2 because they can fine-tune them to their desired tasks. Despite their widespread use, they have not been explored fully to find out how they perform against each other in sentiment analysis and prompt engineering. This paper examines how such open-source large language models perform in analyzing sentiments and their responsiveness to prompts, specifically offering more insights and details about the sentiments aside from giving the sentiment's polarity. The study aims to contribute to the knowledge of the effectiveness of the large language models in sentiment analysis and prompt engineering. This will also inform the reader of the choice between the various LLMs for their use.

Source: Ashesi
Comparative analysis of the O2-tree with alternative main-memory DBMS index structures

In today's computing world, in-memory databases are increasingly being used to increase transactional throughput and cache query results in distributed systems. One component of in-memory databases that is crecial in determining their performance is their index structure. In 2013, Ohene-Kwofie, Otoo et al. proposed the O2-Tree, and the associated O2-Tree-KV as a index structure for main memory databases and an associated key-value store, respectively. Initial tests demonstrated that the O2-Tree was a promising structure. Still, results obtained from testing the performance of the O2-Tree-KV with leading NoSQL data stores were not as conclusive, as only a small set of alternative structures were employed during testing. In this study, we demonstrate, by way of experimentation, the viability of the O2-Tree as a comparative database index by testing its performance alongside other leading index structures. We achieve this by demonstrating that the O2-Tree is competitive in two critical domains of database index-structure comparison; the ability to build index structures quickly and thus recover from unexpected and sometimes inevitable crashes; the ability to execute query sets with varying update ratios at relatively fast speeds.

Source: Ashesi
Comprehensive Visual Monitoring System For Home Security

This project addressed key objectives for a modern smart security camera: providing low-latency video monitoring, ensuring user control over cloud-stored data, offering local recording redundancy, delivering real-time event notifications, and simplifying the initial setup process. To meet these goals, I designed and implemented a system featuring a custom ESP32S3-based camera module, a central backend server, and an interactive web client. The system achieves low-latency live viewing and playback through direct peer-to-peer video streaming between the camera and the client, established via a real-time signaling mechanism. For storage, motion-triggered event images are uploaded to the user's personal cloud storage account, facilitated by the backend server, ensuring data ownership. Simultaneously, continuous video recording occurs locally on the camera's SD card, providing data resilience. Users receive instant notifications of detected events through a dedicated real-time messaging channel. I prioritized ease of use by developing a straightforward initial configuration sequence utilizing a temporary wireless access point on the camera. Comprehensive testing confirmed that the implemented system successfully fulfills these core objectives, demonstrating a functional and user-centric security solution.

Source: Ashesi
Computer aided hydroponics with reinforcement learning

Hydroponics, a system of growing crops without soil, has been successfully used to grow crops on a commercial scale. Hydroponics has the potential to fill the gap of low agricultural production in Ghana due to its high efficiency while serving as an environmentally friendly alternative to soil culture. This method of farming has benefitted from new technologies like IoT and machine learning that make it possible to integrate intelligent agents in the management of hydroponic systems as well as collecting live data. These technologies allow for increased automation and refined control of hydroponic systems. This paper details the development and implementation of a hydroponic system equipped with intelligent agents for control and internet enabled monitoring, data collection and storage.

Source: Ashesi
Computerized hostel management system for Hosanna Hostels Limited

An increase in the student population of Ashesi University has led to a greater demand for off-campus accommodation facilities. The limited number of off-campus hostels is unable to adequately meet the current demand, resulting in an annual struggle for accommodation space. Hostel managers are still using old techniques of managing their facilities, and the inefficiency of these techniques negatively impacts the efficient management of the facilities. This project aims to develop a custom computerized Hostel Management System for Hosanna Hostels Limited to streamline processes, including registration & booking and management/administrative processes/tasks. The system will be developed using Php programming language to handle the backend processes, MySQL database for the data storage processes and HTML, JavaScript & CSS for the front-end. The computerized system will eliminate the issues of the old techniques; it will be graphic-user interface oriented, secure, efficient and reliable.

Source: Ashesi
Condition monitoring of induction motors using IoT

In today's rapidly evolving technological landscape, the Internet of Things (IoT) and Machine Learning (ML) convergence has paved the way for transformative innovations across various industries. One such critical application lies in electrical engineering, where detecting faults in electrical machines in real Time is paramount for ensuring operational efficiency and safety and minimizing downtime. In this paper, a system is designed to monitor the condition of an induction motor. This system measures voltage, current, temperature, and speed, which are then processed using Fast Fourier Transforms and ML techniques. The results were used to make predictions, facilitating real-time fault diagnosis and predictive maintenance of electrical machines. By making the results of the data analysis available on a website and through an on-site system, unlike in other research papers, health monitoring of induction machines is made more accessible and reduces the effects of reactive maintenance used in most industries.

Source: Ashesi
Connecting ERP to e-commerce: Npontu as a case study

Enterprise Resource Planning (ERP) is a business process that enables companies to manage and integrate their essential business processes. ERP applications are available for companies to use for integrating all their business functions into one single system. It allows companies to manage their daily activities, including accounting, procurement, and inventory management. E-commerce, also referred to as electronic commerce, is the buying and selling (trading) of goods and services on the internet. E-commerce platforms give sellers the needed tools to provide 24/7 services to their customers unrestricted by distance. They allow sellers to accept and track orders from their customers, which helps fulfil their value proposition in ways that were not possible with previous brick-and-mortar stores. Studies show that ERP users in Ghana hardly use E-commerce and the internet as tools to expand their market. This project is aimed at integrating e-commerce systems into ERPs such that business owners can use their ERP and e-commerce system in one software suite to give business owners access to the online market.

Source: Ashesi
Conservation Voltage Reduction in low voltage distribution networks in Ghana

Conservation Voltage Reduction (CVR) is the practice of lowering voltages to the lower limit of the acceptable voltage range while keeping it in the voltage range. CVR is considered globally as part of the energy savings program. The journey to save energy in power systems has led to the exploration of Volt/Var optimizers and voltage optimization including CVR implementation. CVR is implemented because of the problems that come with operating close to the upper limit of the acceptable voltage range such as mass of wasted energy as well as higher than necessary electrical bills and reduced lifespan of equipment. The problems that come from operating at a voltage level close to the upper limit affects both consumers and distributors. This capstone project showed that implementing conservation voltage reduction in an 11kV/400V distribution network will provide some level of energy savings to both the consumer and utility distributor. In this project, PSCAD is used for designing the schematic for both the control and CVR models of the 11kV/400V distribution network. The control schematic being the original distribution network layout available and the CVR schematic being the distribution network layout with the CVR implementation using load tap changers and capacitor banks. MATLAB/Simulink was used to model both the control and CVR models with real time parameters either calculated or referenced from the Electricity Company of Ghana. The simulation results for the CVR model managed to maintain the voltage within the range of 220-230V under conditions such as overvoltage and undervoltage. Keywords: Conservation Voltage Reduction, Capacitor Banks, OLTC transformer

Source: Ashesi
Contactless mobile money payment system

Mobile money services have significantly improved financial inclusion, particularly in developing countries. However, manual input and USSD-based transactions present challenges in user experience, security, and accessibility. The COVID-19 pandemic further emphasized the need for contactless payment methods to mitigate transmission risks. This paper introduces a novel contactless mobile money system leveraging Near Field Communication (NFC) technology to address these concerns and enhance transaction safety, efficiency and security. The system comprises an NFC-enabled mobile application, an NFC reader, and a robust backend infrastructure, enabling seamless and secure transactions while minimizing human error. Advanced encryption and authentication mechanisms safeguard sensitive user data. The comprehensive implementation is detailed, and extensive testing and user feedback validate the system's efficacy in streamlining transactions and bolstering security. The NFC-based contactless mobile money system has potential applications across various sectors and can contribute to the ongoing growth of the mobile money ecosystem, fostering greater financial inclusion globally.

Source: Ashesi
Contactless payment for mobile money in Ghana

The use of Unstructured Supplementary Service Data (USSD) applications in conducting monetary transactions is an unsafe and slow means of payment. Research has shown that USSD applications utilize outdated encryption techniques, which make it susceptible to hackers during data transmission; they are unreliable and have long processes before payment is conducted. This study aims to explore how Near Field Communication (NFC) technology can be used to conduct payments. Comparing the two payment techniques, it asks: How does NFC payment compare USSD payment and what advantages does one present over the other. In this context, these payment methods are tested with Mobile Money, a mobile banking platform. Based on a review of literature on NFC and USSD as technologies and payment techniques, a system was built to allow payments to be made via NFC technology. A survey was then conducted to collect the duration of the already existing USSD payment and the NFC payment. Analysis of the data collected showed that payment using NFC technology was substantially faster than that of the USSD. The results indicate the efficiency of NFC over USSD. On that basis, it is recommended that mobile money providers implement this form of payment. Further improvements must be made to the system to improve it in terms of security and user experience.

Source: Ashesi