ghanaopendata/ghanaian-research-english huggingface dataset:
Integrating Artificial Intelligence for Enhanced Energy Efficiency and Security in Smart Systems

Traditional energy management solutions have proven inefficient and vulnerable as global energy demands increase alongside the need for secure and sustainable energy systems. This project addresses the dual challenge of optimizing energy consumption and enhancing security in smart environments by integrating Artificial Intelligence (AI) techniques with Internet of Things (IoT) frameworks. A real-time sensor network was developed using ESP32 microcontrollers, transmitting environmental data such as temperature, humidity, light intensity, and occupancy over secure MQTT communication protocols with Secure Hash Algorithm(SHA)-256 checksum validation, Machine learning techniques, including Isolation Forests for anomaly detection and Proximal Policy Optimization (PPO) reinforcement learning for energy optimization, were implemented to enable adaptive system control and proactive fault detection. The system s performance was validated through environmental monitoring, anomaly detection experiments, and statistical analyses, demonstrating high accuracy in sensor fault prediction and significant energy saving potential through smart device control. Results indicated that system responsiveness remained within acceptable operational limits even under fault conditions, maintaining system robustness and reliability. Overall, this project highlights the critical role of AI and secure IoT integration in building smarter, more energy-efficient, and resilient environments, setting the stage for future advancements in sustainable smart systems.

Source: Ashesi
Intelliagric: An Intelligent, Integrated Farm Assistant

The agricultural sector faces significant pressure to increase food production and efficiency due to a growing global population and climate change. The global population increase necessitates vast food production while available arable land decreases, and climate change exacerbates crop diseases and pests. These pressures are particularly acute in Africa, where agriculture is the economic backbone. Modern technologies present promising opportunities to address these challenges. This project proposes IntelliAgric, an intelligent farm assistant system leveraging the Internet of Things (IoT) and Artificial Intelligence (AI) to support African small-scale farmers. The IoT design includes soil sensors to monitor critical parameters like nutrients, pH levels, moisture content, and temperature. The system employs Machine Learning, the ConvNeXt architecture for accurate maize disease prediction, a stacked model's architecture for robust market forecasting, and transformer architecture to facilitate data interpretation and conversational interactions, offering farmers precise, context-aware advice, recommendations, and support. The integrated platform, including the e-commerce platform, was developed using the Agile methodology, ensuring flexibility and continuous improvement. Test results indicate potential improvements in disease prediction accuracy and market forecasting, empowering farmers to optimize resource use, enhance sustainability, and increase yields. IntelliAgric contributes to global food security and resilience against climate change by bridging the knowledge gap and providing timely, actionable insights.

Source: Ashesi
Intelligent Energy Management in Smart Buildings Using AIoT

The escalating national demand for energy and the need for the proper implementation of sustainable consumption practices have prompted the investigation and development of an intelligent energy management system using AIoT. Rapid advancements have provided a wide variety of technological solutions to prominent issues facing society. However, sustainable energy distribution remains a significant concern; one made more pressing by the climate change crisis. This paper explores the implementation of a Soft Actor Critic model for energy cost reduction while maintaining occupant comfort to optimize energy consumption in a smart building. This will form part of a larger AIoT system integrating renewable energy and data collection via MQTT. The system seeks to evaluate the effectiveness of intelligent energy management in providing sustainable financial benefits to the individual customer and a sustainable power economy to the Ghanaian market at large. Additionally, the system integrates a Time of Use pricing model to suggest prime energy usage periods to the user for the encouragement of off-peak hour consumption, suggesting TOU implementation in Ghana. Experimentation results suggest a SAC agent-controlled single family house would benefit from cost savings while reducing the emissions caused as a result of the pattern of electricity demand from the house. This integration not only improves convenience and energy optimization but also provides a financial incentive. Incorporating AIoT with reinforcement learning improves system adaptability and offers a scalable solution for applications in future smart grids.

Source: Ashesi
Intelligent Ghanaian Sign Language translation: Assessing the effectiveness of deep learning models in bridging the communication gap between hearing non-signers and the deaf in Ghana

The communication gap between the hearing non-signers and the deaf and hard-of-hearing (DHH) in Ghana has been a persistent issue that hinders effective communication and interaction between these groups. This thesis explores the potential of deep learning models in bridging this gap by assessing their effectiveness in accurately translating Ghanaian Sign Language (GhSL) in real time. The research question is, "How accurately can Ghanaian Sign Language be translated in real-time?" The contributions to the field of GhSL recognition include: - Contribution 1: This research adds to the limited exploration of leveraging deep learning for GhSL recognition [27]. - Contribution 2: It prioritises dynamic GhSL signs, as well as double-hand gestures while attaining high accuracy. - Contribution 3: This research provides information on the accuracy score of the predicted signs per real-time translation. Overall, the findings demonstrate the potential of deep learning models in bridging the communication between hearing non-signers and the DHH in Ghana due to its accuracy score of 93.333% and accuracy per real-time prediction ranging from 75.2017% for the sign "Thanks" to 99.9997% for the sign "Hello". The contributions to the field provide a foundation for future research in this area and have practical implications for improving communication and accessibility for the deaf community in Ghana.

Source: Ashesi
Intelligent HealthCare for Efficient Bed Occupancy

Hospital occupancy describes the number of hospital beds engaged by inpatients as compared to the total number of beds in the hospital. Knowledge of accurate occupancy is essential to very hospital in determining whether the hospital is under-occupied and wasting resources or over occupied such as that they may not be providing proper patient care. It is also very imperative especially in emergency situations that the current bed occupancy is known such that incoming patients experience reduced wait times which lead to improved patient outcomes. This thesis explores a model which uses IoT sensors and a triage framework to actively calculate the number of occupied as against the number of beds available and efficiently schedule waiting patients unto these beds according to the severity of their cases.

Source: Ashesi
Intelligent healthcare: An IoT based contact tracing system for Ashesi University

The outbreak of contagious diseases demand isolation and quarantining of infected persons and people they have been in close proximity with. This can be easily achieved if technology based systems are designed to facilitate contact tracing. In a technological where almost everyone carries a device around, features of such devices can be leveraged on in conjunction with other technologies to design robust contact tracing systems - the aim of this project. This project is divided into two main parts: the software sub-system and the hardware sub-system. The software sub-system comprise of a cross-platform mobile application that tracks users, and an admin portal to monitor user activities. The hardware sub-system is an IoT-based system that uses a Raspberry Pi to capture indoor images with the aid of a Raspberry Pi camera module. It processes the imagines to determine whether the occupants of the room have been in close proximity with one another or not while relaying feedback to them, via its actuators and at the same time updates the admin portal. The system was safely tested under a controlled environment of three users and all the features worked as expected with less errors.

Source: Ashesi
Intelligent healthcare: IoT-based monitoring of bed occupancy at health institutions

We are in a period of our lives where technology occupies or plays a part in every field. Such fields may include Agriculture, Banking, Sports, Healthcare just to mention a few. How is tracking bed occupancy a problem? Being able to monitor the number of patients being admitted to a particular healthcare institution prevents delayed medical attention and this can lead to further complications of the patient and sometimes death. Tracking bed occupancy in healthcare institutions, in general, can be a situational problem hence projecting to be an issue. This project seeks to develop a system that identifies and monitors the general activities of patients specifically bed occupancy rates in hospitals (that is a patient occupying a bed or not) so individuals with emergencies would be referred to the right available places before it is too late. This proposed system will have the functionality of also determining the state in which the person is lying, sitting, etc. The system would work during inactive visiting times of the hospital. Doctors and Health Administrators will be able to keep their patients safer and that is the importance of the Bed Monitor.

Source: Ashesi
Intelligent Learning Systems for Inclusive Education: A Focus on Dyslexia

As children grow, they learn how to read and write. Reading involves recognizing, distinguishing, and understanding words and characters to make sense of a text. By the age of 7, a child should read and understand simple texts. For some people, it is not the case. They struggle to read and write. Reading is fundamental as it is applied everywhere; for instance, a person needs to read road signs to know their current location, read the operating manual for a new device they have bought, and many others. Some people struggle to read and write because of a learning disability called Dyslexia. It makes them unable to identify words and make sense of them. Some people can overcome Dyslexia by third grade, but others struggle even in university. Students are struggling to keep up academically because of this learning disability. This Thesis undertakes research to identify what students with Dyslexia go through and what strategies work best to help them study effectively and at a reasonable pace.

Source: Ashesi
Intelligent Sql Injection Detection In Web Applications Using Machine Learning

This research is aimed the behavioral, psychological, and systemic obstacles to the uptake of sustainable food packaging in Ghana, including stakeholder attitudes, cultural processes, and structural inequalities. Amidst growing awareness of environmental hazards of traditional plastics, the study discovered entrenched inertia in the uptake of sustainable packaging, which was underpinned by economic survivalism, fragmented policy implementation, and under-theorized sociocultural discourses. The research filled this lacuna by using a qualitative critical realist approach to unearth underlying mechanisms that influence stakeholder behaviors. Data was gathered through 20 semi-structured interviews with food producers, sellers, consumers, and policymakers who were chosen through convenience sampling to give a variety of views. Thematic analysis indicated that while environmental awareness existed, it was routinely overshadowed by contextual realities: prohibitive costs of biodegradable alternatives excluded informal vendors, uncertainty about functional performance undermined acceptability, and political-economic structures favored plastic producers. Notably, health concerns emerged as a potent stimulus, with informants linking traditional conventions like waakye leaf-wrapping to reduced chemical exposure and cultural identity. The results were consistent with the Theory of Planned Behavior (TPB), illustrating how subjective norms (e.g., social acceptability of plastics) and perceived behavioral control (e.g., affordability limitations) interfered with pro-environmental intentions. The research drew the conclusion that Ghana's shift towards sustainable packaging needs culturally responsive solutions such as the reestablishment of indigenous materials and institutional interventions to render subsidies available. By reframing sustainability as a matter of public health and Ghana's food culture, policymakers can close the gap between awareness and practice.

Source: Ashesi
Intelligent SQL Injection Detection in Web Applications Using Machines Learning

Web applications have become central to modern digital infrastructures, but they remain highly vulnerable to SQL injection (SQLi) attacks. Traditional rule-based defenses often fail to detect novel and comple SQLI patterns, especially zero-day variants. This project proposes a machine learning-based detection system leverageing transformer models to improve the idenfication of malicious SQL queries. Initial experiments using a zeto-shot BART model demonstrated poor performance on structured SQL inputs, prompting a transition on a prompt-query dataset that combined SQL queries with natural language prompts to enable sentence-pair classification. Offline evaluations of the fine-tuned CodeBERT model achieved an accuracy of 71.4%, a precision of 66.67% and perfect recall, ensuring that all malicious queries were correctly identified. In addition to offline testing, the model was integrated into a Flask web application, demonstrating real-time detection capabilities by flagging malicious login attempts and preventing unauthorized access. The final system achieved a balance between detection effectiveness and deployment efficiency, confirming that fine-tuned transformer models offer a viable solution for enhancing web application security against SQL injection threats. Future work will explore improving precision, scaling to broader input contexts, and extending detection capabilities to other attack vectors.

Source: Ashesi
Intelligent streetlight monitoring system using LoRa technology

Given recent crimes that have come up due to poor street lighting systems in Ghana, this project implements a functional Internet of Things (IoT) system to improve the existing means of maintaining streetlights. The system is designed as a retrofit to be attached to an existing streetlight system. The system can report data on recorded environmental factors, the state of the streetlight lamp, display the positions of streetlights on a map, and a graph on the energy being consumed. In Ghana, although streetlights exist, the overall distribution and maintenance across communities are poor, affecting the activities of motorists and pedestrians. Hence, there is a need for an improvement in the system. The Long Range (LoRa) communication technology which is a wireless protocol with low power consumption and secure communication, was used in this project. Some key findings in this paper were that the transmission time of LoRa is barely affected by distance, and to obtain longer ranges with LoRa, the device should be positioned at higher heights above sea level. This report describes the hardware and software design and implementation and confirmation tests of the system. Data from sensors are collected at a streetlight and sent to a gateway over LoRa.Wi-Fi at the Gateway sends the data packets to a database using HTTP requests, and a dashboard built over PHP, CSS, HTML, JS, and JSON displays a graphical representation of the data in the database. In conclusion, LoRa was able to transmit all data through serial commands. The goal of attaching smartness to the streetlight was obtained, and monitoring and analysis of data were done on a dashboard.

Source: Ashesi
Intelligent traffic control system leveraging on an efficient nature-inspired algorithm

With the increasing number of road vehicles, traffic congestion has become a significant issue in many countries. This problem is particularly acute in developing countries like Ghana, where the traffic management system is considered obsolete and unable to handle the growing number of cars. Inefficient traffic systems can lead to delays and even situations where individuals may lose their lives. To tackle this issue, our research paper proposes the development of an intelligent traffic control system that uses identified nature-occurring algorithms and a machine learning model; Yolov5. The research aims to investigate how these algorithms can be used to improve the status quo of the Ghanaian traffic control system and provide optimal solutions for traffic control systems. This research will also examine how insights from transportation analytics can enhance the commuter experience. The research questions to be addressed include: How can efficient nature-inspired algorithms improve the Ghanaian traffic control system? and Will insights from transportation analytics readily enhance the commuter experience? By answering these research questions, the study will provide valuable insights into the development of intelligent traffic management systems tailored to Ghanaian commuters' specific needs. This, in turn, could lead to more efficient traffic flow, reduced road accidents, and improved productivity, which are crucial for the country's sustainable economic growth. Upon generating optimal fitness values, this research's findings can be used to identify which direction at a traffic intersection has the lowest average waiting time.

Source: Ashesi
Intelligent wind tunnel design

A wind tunnel is a mechanical assembly used to analyze the behavior of air flow and the effects they have over objects. Using a wind tunnel, one can control the stream conditions which influence forces on the test object. Through the calculations of the forces on the model, one can anticipate the forces on the full-scale test object. In the beginning, wind tunnels were used to utilized to comprehend and improve the execution of airplanes. Over the years, cars, bridges, and building were studied using wind tunnels.

Source: Ashesi
Intelligieaux: An intelligent fire detection and management system for off-campus hostels at Ashesi University

Fire has the potential to create irreversible damage. Even though several monitoring systems exist to observe and report unexpected activities within a property, considerable damage has occurred by the time response personnel arrive. This project aims to learn about existing fire detection systems' shortcomings and create a more robust, dependable design. Intelligieaux is an emergency fire detection and management system consisting of three main sections for our users: students, Student Life Engagement and the Fire Stations located close to Ashesi University, specifically Aburi and Akropong. The system offers services and functionalities such as a fire reporting feature that allows students to make a report with a button click. The geolocation and other required details of the student are recorded and then sent to both the SLE department and fire station for approval and immediate action. The system also incorporates three sensing parameters: Smoke using the MQ2 sensor, Carbon monoxide using the MQ7 and flame detection using a flame sensor. Once the mentioned sensors are connected to an ESP8266 microcontroller, they will read environmental conditions. Suppose any form of carbon monoxide, flame or smoke particles is detected. In that case, the algorithm of the system would immediately send an alert prompting both the SLE office and Fire Department of an occurrence or possible occurrence of a fire outbreak.

Source: Ashesi
Interbank money transfer system for PC and mobile

"Money makes the world go round". The importance of money has made it a very powerful tool man cannot live without. Thus, it is very important for people to have money when they need it. The mobile phone has developed drastically from a device just for making and receiving calls to one that can be used to take pictures, send emails, connect to the internet and do many other things that at one point in time were dedicated to specific devices such as computers. Mobile phone users in Ghana have increased over the years and are still increasing with some people having more than one phone. The introduction of mobile phones that have the capability of accessing the internet has brought rise to applications developed solely for the mobile platform and also websites that are built to fit the mobile specifications such as screen size, page size, size of images, and processing power.

Source: Ashesi
Internet of BioNano Things

Internet of Bio-Nano things refers to the internet of things on a nanoscale where these things can be interconnected to communicate. Similarly, the means of communication of these 'things' can be extended to other application domains, including biological systems. Thus far, nanomaterials such as graphene have inspired the concept of the internet of things; however, some challenges have been encountered concerning trying to deploy these materials in a biological system because they pose a threat to health since they are artificial. This, therefore, inaugurates an idea to reprogramme or administer synthetic bacteria cells for intrabody operations in areas such as the guts of a human body to aid in digestive complications such as gluten intolerance. Essentially, some similarities were discovered between the internet of things and biology, which inspired the various forms of communication, sensing, and actuation of a bacterium cell. Through the use of a motility recognition program, microscopic footage of bacteria activity was processed to provide a graphical representation of bacteria attraction to concentration levels of 30,10, 5, and 0.5 m/l of gluten for a test of concept. Under optimal conditions, for a recorded number of 211 bacteria, their mean speed, mean displacement, mean distance, and mean percentage motile were 9.04, 42.23, 66.59 μ/m, and 96.92, respectively.

Source: Ashesi
Introducing Dlsim: A Configurable Blockchain Simulator for the Analysis of Blockchain Platforms

This study addresses the need for a more detailed simulation environment to analyze blockchain platforms under various conditions by proposing DLSim, a configurable blockchain simulator. DLSim simulates Bitcoin, Ethereum, Hyperledger Fabric, and Slimcoin transactions and consensus algorithms, enabling experimentation and analysis under controlled conditions. The research questions driving this study are: (1) How can a configurable blockchain simulator be designed to accurately reflect the essential properties of Bitcoin, Ethereum, Hyperledger Fabric, and Slimcoin? and (2) How do these platforms compare in terms of performance within the controlled environment of DLSim? By validating DLSim’s models against real-world counterparts and conducting an empirical comparative analysis, this research bridges the gap between conceptual analyses and practical applications, ensuring a robust and durable approach to blockchain development. The results from this study showed that DLSim is a reliable blockchain simulator as it met the benchmark for performance of the actual blockchain platforms. The comparative analysis conducted with DLSim confirmed the findings in literature, showing that Hyperledger Fabric outperforms the other three platforms, with a higher throughput, lower latency, and shorter block time. This study addresses the need for a simulator that models Bitcoin, Ethereum, Hyperledger Fabric and Slimcoin, facilitating an understanding of their intricacies.

Source: Ashesi