ghanaopendata/ghanaian-research-english huggingface dataset:
Evaluating The Computational Overhead And Latency Of AI-Based DDOS Threat Detection Systems

The growing sophistication and frequency of Distributed Denial of Service (DDoS) attacks pose a significant challenge to modern cybersecurity. While Artificial Intelligence (AI)-based systems have emerged as powerful tools for detecting such attacks, their practical deployment is often constrained by computational overhead and latency, particularly in environments requiring real-time responsiveness. These limitations hinder the effectiveness of AI models in mitigating threats in critical sectors such as healthcare, finance, and telecommunications, where system performance is paramount. This study examines the computational challenges associated with AI-based DDoS detection systems and explores optimization strategies to enhance their real-time performance. The research evaluates LUCID, a lightweight convolutional neural network, using benchmark datasets, including CICIDS 2019 and the CAIDA DDoS dataset. Baseline performance metrics such as CPU usage, memory consumption, latency, and detection accuracy are systematically analyzed. Optimization techniques, including model pruning, quantization, and multithreading, are applied and assessed for their effectiveness in reducing computational demands. The findings demonstrate that these optimization techniques can significantly enhance the computational efficiency of AI-based systems while maintaining high detection accuracy. This research advances the academic understanding of computational efficiency in AI-driven cybersecurity and offers practical insights for deploying scalable and resource efficient DDoS detection solutions.

Source: Ashesi
Evaluation of DSR on a Bluetooth Low Energy Network by Simulation

The Bluetooth Specifications Group recently released a new version of Bluetooth, called Bluetooth Low Energy. It is ideal for battery-powered IoT devices that do not require continuous streaming of information because it is more power efficient and relays information in short bursts, rather than continuously. The asynchronous nature of communication between the Bluetooth Smart nodes introduces a new challenge in terms of routing data among the different nodes. This project developed a simulation tool that simulates how Bluetooth Low Energy creates scatternets among different nodes as well as how routing is achieved. The ability of the routing algorithm to find the shortest route was also tested for and it was shown to be affected by the size of the network.

Source: Ashesi
Evaluation of mobile network security in Ghana

Mobile technology is one of the most successful technologies on the African continent. Personal and professional communication as well as critical services like banking and remittances are widely made through mobile networks and platforms in Ghana. However, little is known about the security of the underlying infrastructure and devices consumers use to interact with the mobile network. The focus of this project is to determine if the core systems of the mobile network operators, the technology infrastructure, and the 2G/3G dongles have exploitable security vulnerabilities, demonstrate some of those exploits, and make recommendations on how to mitigate or eliminate the risk of exploitation.

Source: Ashesi
Evaluation of the Bluetooth Mesh managed flooding algorithm using simulation

Bluetooth Special Interest Group released a new version of Bluetooth at the end of 2016 which is called Bluetooth 5. The version had superior features such as increased range which is 4 times the range of Bluetooth 4.2 and improved speed. It was expected to revolutionaries the use of IoTs. However, it used a star topology which limited its range within which it could operate. Both routing and flooding solutions from both Bluetooth SIG and academia have been suggested which led to the development of Bluetooth Mesh. Bluetooth Mesh uses a flooding algorithm which uses a large number of nodes just to send a single message which indicates that more energy is used. This project develops a simulator for both routing and flooding algorithms and evaluates their ability to facilitate Bluetooth Mesh functionalities and energy efficiency in terms of relays done to deliver a single message. From this simulation, although managed flooding uses a lot of energy to transmit, it is the most efficient because it takes care of the individual power needs of the nodes in the network. Keywords: Bluetooth Mesh, Internet of Things, Routing, Managed Flooding, Simulation.

Source: Ashesi
Evaluation of the role mobile-phone agricultural solutions play in the emergence of a knowledge society in the Ghanaian agricultural sector

In today’s world, the notion that knowledge has become the most vital resource in the transformational growth of societies is one that is pervasive. The significance of knowledge in modern societies has been overemphasized by several international bodies; most notable the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the World Bank. Ghana's desire to craft an economy where knowledge and information would play a significant role is visible on paper in The Ghana ICT for Accelerated Development (ICT4AD) policy document. But, in order for Ghana to achieve its ICT4AD objective of transforming its society into a knowledge economy, it is imperative that it analyzes how effective existing ICT agricultural solutions are in the emergence of an agricultural knowledge economy. This research seeks to evaluate the role mobile-based agricultural solutions play in the emergence of a knowledge economy in the Ghanaian agricultural sector. It further aims to point out areas where this mobile based solutions fall short, and also provide ideas on how this gap could be bridged.

Source: Ashesi
Examining the INfluence of Crude price Fluctuations on Inflation in South Sudan.

South Sudan, which gained independence on July 9, 2011, is one of the most oil-dependent nations globally, with oil contributing nearly 90% of government revenue and foreign exchange earnings (Apofeed, 2024). Despite this, South Sudan remains highly vulnerable to global oil price fluctuations, which affect key macroeconomic indicators, like inflation, exchange rates, and government spending. This vulnerability is further intensified by limited economic diversification and weak institutional capacity, contributing to historically high inflation rates, such as the 87.2% recorded in 2019 (Trade Economics,2024). This study examined how changes in oil prices affect inflation in South Sudan, utilizing an Autoregressive Distributed Lag model with yearly data from 2011 to 2022. The model also incorporated key macroeconomic factors such as exchange rates, interest rates, and government spending. Findings showed that while crude oil prices had a negative and statistically insignificant relationship with inflation, exchange rates exhibited a significant positive effect, particularly in the long run. Interest rates and government spending showed weaker associations. These results suggest that inflation in South Sudan is more sensitive to currency fluctuations than to oil price changes. Policymakers should therefore focus on stabilizing exchange rates and enhancing institutional capacity, while also diversifying the economy to cushion against external shocks.

Source: Ashesi
Expert finding social network for informal and semi-formal experts

Patatte is a simple social network that helps people to find and collaborate with informal and semi-formal experts. This project’s aim is to solve the ‘expert finding+ problem’ which is the problem of finding an unknown expert (for any purpose including hiring, working with or questioning the expert) by searching through real world or online social networks with skill or interest keywords. The system is designed to be simple and intuitive to use. This is because some of the targeted users (informal and semi-formal experts) are basically educated and may have limited experience with the usage of web applications and social networks. The system is also accessible by the most basic internet user requiring a computer, internet access and a basic knowledge of how to use social networks. Users can share projects they have worked on, follow other users, view those users’ projects, like some of those projects, search for users with specific skills and collaborate with them in an integrated media sharing and chat application.

Source: Ashesi
Exploring Machine Learning in Hair Care: Analyzing Textured Hair and Recommending Products for Black Women in Accra.

The lack of suitable natural hair products available to Black African women in Accra creates both a major business opportunity and perpetuates social beauty standards and chemical relaxer use. The research establishes a machine learning system that creates recommendations for personalized hair care. Figaro-1k contained categorized and uncategorized hair images, which were used to teach three different models: Convolutional Neural Networks (CNN) with ResNet18 and HairNet. The systematic strategies of data augmentation for training data produced superior diversity along with quality, thus boosting the model's operational effectiveness. By applying fine-tuning to the CNN obtained 85.19% accuracy while HairNet stopped at 67.28%. The Random Forest model functioned as an identifier to supply proper hair recommendations based on recognized hair types. The system demonstrates successful hair identification for creating personalized product suggestions that showcase potential practical usage in fashion and beauty solutions. This research leads to promoting healthier life choices for Black African women by providing them with safe, natural hair products as an outcome.

Source: Ashesi
Exploring the Advantages of Communication between Autonomous Vehicles on Road Networks

The urge for shorter travel times has led people to find better, real-time routes to minimize travel time [9]. This has also become another motivator for implementing autonomous vehicles for their efficiency. Gathering and exchanging data could enable vehicles to expand their environmental awareness for improved journey-related decisions. How does communication among autonomous vehicles impact travel time and safety? This study investigates the effects of intervehicle communication on journey time and overall traffic flow. This study uses a simulation of vehicles with and without the ability to exchange information whilst navigating road network environments based on real-world locations. The journey time of the vehicles is recorded and analysed for assessment. From the simulation, CAVs generally showed reduced journey times and improved traffic flow, although some CAVs experienced delays when encountering incidents before information dissemination. The findings support the implementation of inter-vehicle communication systems to enhance traffic efficiency, though further work is needed to address limitations and real-world complexities. Future research should integrate predictive methods and expand implementation to include infrastructure communication and integration.

Source: Ashesi
Exploring The Advantages of Communication Between Autonomous Vehicleson Road Networks

Medication non-adherence, particularly among the elderly and individuals with chronic diseases, leads to severe health complications, increased hospitalizations, and rising healthcare costs. Existing automated pill dispensers, while effective, are often expensive and rely heavily on continuous electricity, making them impractical for use in developing regions. This project addresses these challenges by designing a feature rich Smart Pill Dispenser tailored for senior citizens. The dispenser automates medication scheduling and dispensing through a suction-based mechanism controlled by an ESP32 microcontroller, ensuring accurate pill pickup and delivery. Key features include multi-container dispensing for different pill types without pre-sorting, Web- based interface for remote scheduling and adherence monitoring and Real-time reminders via email, audio alerts, and visual cues. Testing demonstrated a 75% success rate in pill pickup, with higher accuracy for non-porous tablets. By bridging affordability and automation, this project aims to improve medication adherence among the elderly, particularly in resource-limited settings, while reducing caregiver burden.

Source: Ashesi
Exploring the Efficacy of Twi-Language Interfaced ChatGPT as a Self-Learning Support Tool for Junior High School Students in Rural Communities.

Research shows that differences between the language of instruction and the primary language of students affect learning negatively. Large language models have been found to be useful in teaching and learning. Unfortunately, most large language models are not built on low resource languages like the prevalent local language in Ghana, Twi. To allow those that are most comfortable interacting in low resource languages benefit from large language models in their educational journey, this research seeks to answer the question, "Does a Twi language interface on ChatGPT make it a good learning support tool for junior high school students in rural communities in Ghana?” The main objectives of this research were to develop a Twi-interfaced ChatGPT that facilitates interaction in Twi and assess the students’ acceptance of various interaction modalities. Students were divided into three groups that interacted with two systems each of varied modalities and system configurations. Post-interaction, a questionnaire was administered to each group to deduce interaction and language preferences as well as evaluate the developed system from a user standpoint. It was found that students preferred to type as opposed to interacting in speech. They found it more efficient and enjoyable. Students also preferred to study in Twi with ChatGPT via the human translator as opposed to the Voice mode feature built into the system. In comparing their interaction in Twi with ChatGPT via the human translator to the developed system, students found the human system more efficient. This proves that the concept could work in the near future as speech recognition systems and text-to-speech system approach human level accuracy.

Source: Ashesi
Exploring The Factors For Adopting Esg In Selected Food Retail Companies In Ghana.

ESGhas becomea globally known term thatis discussedin every institution. This frameworkhas existedfor over 17 years; however, itis still relativelynew to developing countries suchas Ghana, specificallyto companiesinthe food retail sector. Itis reasonabletoask, "Whatare the effectsof adopting ESG practicesin selected food retail companiesin Ghana?" With literature havinga lotof dataon developed nations, little attentionis givento developing countries neglecting their effortsin combating sustainability issues. This paper fills thisgap by identifying facilitating factorsand barriersto adopting ESG. This qualitative study employeda phenomenological research design sincethe perceptionsof managersof food retail firms were explored. Data collectionwas done through semi-structured interviews, in whicha purposive sampling approachwas employedto identify target populations. The target populationis representedby selected food retail companiesin Ghana, specifically stakeholders suchas managersand customers, witha sampleof fifteen managersand seven customers being interviewed. In data analysis, thematic analysisand software suchas Delvewas usedin data codingand categorization. The findingsofthe study depict that ESG adoptionisin its early stageasa result, lackof knowledge, financial constraintsand minimal support fromtop managementare the barriers whiles consumer demands, regulatory complianceand moral responsibilityare the enablersfor adopting ESG. To drawthe curtainsonthe research, the findingsof this study provide valuable insightsto policymakers, suchas the Foods and Drugs Authorityof Ghana, on sustainable initiatives that will encourage food retail companiesin Ghanato adopt ESG. Also, this study providesa broader understandingand insighttothe measures that mustbe implementedto overcome barriers firmsmay face while adopting international standards.

Source: Ashesi
Exploring the Use of Advanced Machine Learning Techniques to Detect Fraudulent Cryptocurrency Exchanges

The decentralized nature, self-regulation, and rapid transaction speeds of the cryptocurrency landscape foster both innovation and fraud. As crypto gains attention, fraudulent exchanges posing as legitimate platforms present significant financial risks. Traditional reporting methods, like those of CryptoScamDB, are becoming less effective. This study addresses the need for automated techniques to identify and classify fraudulent exchanges. We found that features related to domain information, website security, and user reviews are crucial for detection. A comparative experiment using machine learning models—random forest, support vector machine, k-nearest neighbor, light gradient boosting machine, and multilayer perceptron—showed that the light gradient boosting machine (95.9%) and random forest (95.5%) had the highest accuracy. An ensemble model using soft voting achieved a higher accuracy of 96.2%, outperforming its base models. These results demonstrate that machine learning is a viable approach for automating the classification of fraudulent exchanges.

Source: Ashesi
Exploring the use of predictive analytics in reducing customer wait time in a college cafeteria

Predictive analytics, is a practice of extracting information from existing data sets in order to predict outcome and trends in the future. Techniques of predictive analytics such as data mining, statistics, modelling and machine learning are used extensively in the hospitality industry for predicting customer frequency, wait time, crop yields among others. For the purpose of this paper, we will be exploring the influence of forecasted data in reducing wait time at a college cafeteria, using modelling as the predictive analytics technique. Cafeterias provide meals and drinks to specific target groups of customers especially in schools, hospitals, organizations and companies at a charge. They are mainly different from the other food service providers in terms of their service styles, whereby there is no waiting staff but rather the customer serves from a buffets of meals and pays for meals by themselves. Due to this serving style, there is a challenge of preventing long queues and reducing wait time for customers to pick up food. An increase in wait time for customers could lead to customer dissatisfaction and this paper seeks to address this challenge. The objective of this study is to assess the impact of a predictive model in reducing the wait time at a cafeteria. To achieve this objective, a mathematical model is formulated to accurately predict the number of customers likely to visit the cafeteria at various times during the day. The impact of the predictions is then assessed in its ability to reduce wait time.

Source: Ashesi
Exploring the Use of Sentiment Analysis for High Probability Trade Entries in the Stock Market

In just the first weekof April 2025, retail investors poured over $1.5 billion into U.S. equities despite volatile swingsand conflicting economic signals signifyinghow sentiment-driven behavior continuesto challenge traditional prediction models. In such turbulent markets, achieving accurate trade entries remains difficultdue to heightened riskand unpredictability. While conventional tools like stop-loss ordersand technical indicators offer basic risk management, they often fall shortin adaptabilityand real-time responsiveness. Hybrid machine learning models attemptto improve accuracybut frequently overfit, integrate sentiment data superficially, or dependon unreliable textual sources limiting their real-world effectiveness. This study introducesa novel, privacy-compliant framework that infers investor sentiment directly from candlestick patterns, bypassing external newsor social media, and integrates these features intoa deep learning architecture enhancedby Deep Reinforcement Learning (DRL) withina GARCH-aware environment. Using historical stock data from J.P Morgan, Walmart, and Bankof America (1970 2024), we compare four configurations: baseline, price-only deep model, sentiment-augmented model, andthe full DRL-enhanced system. Results show that candlestick-derived sentiment boosts prediction accuracyby 4 5% over price-only models. The sentiment-informed DRL strategy further achievesupto 70% profitability, a Sharpe ratio above 1.1, and significantly lower drawdowns, demonstrating robustand adaptive performance under volatility. These findings confirm that internalized behavioral cues, when fused with advanced learning strategies, offera scalable, high-accuracy solutionfor real-time stock forecastingin data-constrained, risk-sensitive environments.

Source: Ashesi
Exploring the use of technology to improve adult literacy in Ghana

Adult literacy is not given much attention in Ghana. This can be clearly seen by the low number of institutions that provide adult literacy programs in Ghana. According to the Ghana Statistical Service, the literacy rate is only about 63.7%. This indicates the gap in literacy that needs to be filled in order to increase the literacy level in Ghana. Although some literacy programs exist, I believe that there must be alternative ways of providing these literacy programs to the citizens of Ghana. One solution I propose is the use of technology and that is my motivation for the study. The aim of this project is to explore the use of technology in improving adult literacy programs in Ghana. In this study I developed an Android tablet-based tool that adopts a popular reading program called DISTAR. DISTAR is an acronym for Direct Instruction System for Teaching Arithmetic and Reading. In this study, this application was tested in comparison with two other learning methods to find out if a mobile application can compliment the learning process in adult literacy programs in Ghana. The study was conducted in using students from an adult literacy program called Adesua Ye, located in Berekuso in the Eastern Region of Ghana.

Source: Ashesi
Extracting handwritten data from physical forms

Various tools have been developed to digitally analyse, visualise, and store data however not all data exists digitally. While there are many benefits to digital systems, there are many reasons while the progress into developing such systems is delayed especially in the African context. The longer an organization collects data without digitally storing it, the more data needs to be manually recorded into the digital system once implementation is possible. In order to assist in the transition, this project proposes a system to scan physical documents, with a focus on forms, into compiled digital datasets. This is done using Optical Character recognition.

Source: Ashesi