Professor Celestine Iwendi

Accurate stock price forecasting remains a challenging yet crucial task in the financial industry due to the non-linear relationships, noisy, and time-dependent nature of the market data. This study presents a deep learning approach known as long-short-term memory (LSTM) for predicting the closing prices of stock using historical data. The model is designed to capture the complex temporal dependencies inherent in stock market sequences, addressing the limitations of traditional statistical models such as ARIMA and linear regression. Using key key characteristics such as past closing prices, the LSTM model achieved high predictive performance with a Mean Squared Error (MSE) of 0.00036, a mean absolute error (MAE) of 0.0096, and a coefficient of determination (R²) of 0.9941, indicating strong generalization and accuracy. The results demonstrate the effectiveness of LSTM architectures in time series forecasting for financial applications. This research contributes to the development of robust and automated decision support tools for investors and sets a performance benchmark for future deep learning models in stock market prediction.

Uterine cancer prediction accuracy is important in clinical decision-making because it improves the overall chances of patient recovery. Several machine learning models, such as Decision Tree, Random Forest, XGBoost Regressor, and Support Vector Regressor, were explored to determine which is more effective in predicting uterine cancer. Attributes such as mutation counts, diagnosis age, and MSI score, were used for the analysis. The different models were tested using the standard performance metrics such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 Score. Random Forest showed the highest predictive performance with an R2 score of 0.655, followed by XGBoost regressor, which was relatively close to the R2 score of Random Forest. Support Vector Regressor performed very poorly as the R2 score was negative, implying that the model is not suitable for such prediction. Ensemble-based models, which include Random Forest and XGBoost Regressor, have proven to be more effective in handling medical prediction tasks, and this is because of their robustness and their ability when it comes to handle overfitting. Though model generalizability was affected due to small data size and the absence of hyperparameter tuning. The future work will focus on expanding the dataset, implementing hyperparameter tuning, integrating deep learning, and leveraging explainable AI (XAI). The research has provided valuable insight for clinicians who wish to use machine learning for uterine cancer prognosis.

This study presents a deep learning-based approach to predicting physiological health risks in athletes engaged in contact sports using wearable sensor data. Motivated by the need to detect early warning signs of collapse or severe fatigue, this study employs a Long Short-Term Memory (LSTM) neural network to analyse multivariate time-series data. Key physiological signals, including heart rate, body temperature, and motion, were extracted from the PAMAP2 dataset to train and validate the model. The LSTM demonstrated strong predictive performance, achieving an accuracy of 98.3% in identifying potentially dangerous physiological states. In addition to its high classification accuracy, the model effectively captured temporal dependencies in the data, underscoring its suitability for health risk prediction in dynamic, high-intensity sports environments. This study highlights the potential of wearable data and LSTM-based analysis in supporting proactive athlete health management and injury prevention.

Microwave radio links play a vital role in keeping mobile networks running, especially when it comes to backhaul-the part of the network that connects base stations to the core. Gradual failure in these links could disrupt services and cost providers a lot in both revenue and customer trust. In this study, we explore how machine learning can help predict such failures before they happen. Network performance data from a mobile network operator in Nigeria was collected, cleaned and used to achieve the purpose of the study. Four algorithms belonging to machine learning (ML) and deep learning (DL) were adopted and used for training the dataset and predicting link failures. Results show that the Long ShortTerm Memory (LSTM - a deep learning model effective for handling time-series data) performed best with prediction accuracy of 92%, distantly followed by others. These findings indicate that the LSTM is better in modelling temporal patterns in network behaviours. This study provides a practical framework for automating microwave link monitoring and maintenance, thereby reducing manual diagnostics, preventing outages, and improving service reliability. The proposed solution supports the integration of predictive intelligence into network operations, enhancing the quality of service and operational efficiency for telecom providers.

This paper examines the potential of digital games as communication tools to reach global audiences, extending beyond established cultural and geopolitical divides. It shows the empirical data gathered in our EU and UKRI-funded Games Realising Effective and Affective Transformation (GREAT) project, where we collaborated with several organizations to investigate this potential. Namely, a significant case study called Play2Act was undertaken in collaboration with the United Nations Development Programme (UNDP), which forms the focus of this paper. The aims of this study were to find out how much of the world’s population could be reached via digital games and how many citizens would be willing to communicate their climate attitudes in a simple and short survey that was inserted into popular mobile games. Currently, there are 3 billion gamers in the world and the idea of reaching citizens via games to understand their opinions on critical global issues and then passing this information to policy-makers emerged. This is the main objective of our project, as to whether games can act as an effective communication channel between citizens and policy-makers, the context being the climate emergency. Governments do not typically have the opportunity to understand their citizens’ needs fully. The aim of this project is to decrease the barrier and increase representation and democracy. The findings obtained from the Play2Act study suggest that games, moreover their ability to engage, and inherent social dynamics create a unique opportunity to support meaningful dialogue with a large proportion of citizens reached, engaged and completed the surveys. The study engaged with almost 1 million players from every UN recognised country, with only two exceptions, and ca. 181,000 surveys completed, confirming the global reach of games. The next steps are for UNDP to take this information to individual countries with recommendations of appropriate climate policies based on their citizens’ voices, this having huge potential for digital games being policy transformational tools. This research contributes to knowledge on the intersection of technology, culture, and communication and offers valuable insights for policymakers, researchers, and stakeholder groups seeking to leverage digital games for social impact.

This review paper provides a comprehensive analysis of recent advancements in leveraging reinforcement learning (RL) and deep reinforcement learning (DRL) techniques for optimizing traffic signal control systems within urban environments. Traditional traffic signal management methods often fail to address the inefficiencies resulting in congestion, delays, environmental impacts, and driver dissatisfaction. In contrast, RL methodologies empower traffic signals to dynamically learn and refine signal timing strategies by iteratively interacting with real-world traffic conditions. DRL builds upon RL by integrating deep neural networks to better capture and respond to intricate traffic patterns. The paper investigates the potential of RL and DRL approaches in addressing diverse urban traffic challenges across peak and non-peak periods, as well as in areas with irregular traffic distributions. It delves into the strengths, weaknesses, underlying principles, and advanced architectures of current RL/DRL methodologies proposed for this domain. Furthermore, it evaluates practical applications and simulation studies, showcasing performance enhancements over traditional signal control methods. In conclusion, the review consolidates key findings and outlines future research directions for implementing RL/DRL systems in real-world scenarios to effectively alleviate urban traffic congestion.

This paper presents a novel Intrusion Detection System (IDS) framework for securing Internet of Things (IoT) networks, leveraging advanced machine learning techniques. The proposed framework integrates Deep Neural Networks (DNNs) and Random Forest (RF) algorithms to enhance detection accuracy and robustness. Utilising the comprehensive CICIoT2023 dataset, the IDS model is rigorously trained and evaluated, demonstrating high efficacy in detecting and mitigating potential threats. However, the results also reveal shortcomings in detecting certain attack categories, such as command injection and SQL injection, indicating areas for further refinement. These findings contribute to the advancement of IoT security through the application of advanced machine learning techniques, while also highlighting the need for continued research to address identified shortcomings.

This paper investigates the use of ensemble learning techniques to enhance the security of Internet of Things (IoT) networks through advanced Intrusion Detection Systems (IDS). Utilizing the CICIoT2023 dataset, the study evaluates four ensemble methods: Ensemble Voting, Random Subspace, Bayesian Model Averaging, and Boosting. The results demonstrate that Random Subspace and Bayesian Model Averaging significantly improve detection accuracy and robustness against various attack types, highlighting their potential in real-world IoT security applications.

In this paper, we have discussed a unique application of Blockchain technology to reform weapons management. We provide Data Integrity, Accountability, and Transparency throughout the logistic chain with our decentralized ledger technology. We secure transactions using smart contracts and advanced cryptographic methods that massively reduce fraudulence and tampering. The distributed and immutable architecture of the blockchain offers a very strong framework for the transparent tracking and verification of the movement and preservation of defence assets and guarantees that all transactions will forever be securely stored on the blockchain. The result is greater trust and coordination among a host of actors, including suppliers, manufacturers and military units, which translates into more efficient and more reliable operations. Smart contracts, moreover, enable supply chain functions such as inventory management, purchasing, and maintenance scheduling to be automated and streamlined, limiting human error and operational costs. With this enhancement, we become more operationally-efficient and operation-ready. Georgia Tech demonstrated how its novel solution for defence logistics-known for its trustworthiness and tolerance to disruption-has the potential to make a seismic change that enables the secure and efficient management of such vital assets in the defence industry.

The concept of automation in the context of hygiene and cleanliness, particularly in waste management systems, serves as the foundation for this study. Garbage dumping on public streets and in open spaces is a prevalent practice in developing nations, mostly contributing to environmental degradation and unsanitary conditions. In order to address these issues an idea known as "smart dustbin" combines hardware and software innovations, such as attaching a Wi-Fi system to a regular trash can to give users free internet access for a set amount of time. By rewarding the user for maintaining a clean environment, the technology contributes to effective waste management in a community. The goal of this project is to transform garbage disposal and monitoring procedures by introducing a Smart garbage Management System that makes use of the Internet of Things (IoT). The system offers real-time monitoring and data analytics capabilities by integrating sensor technologies with a strong IoT infrastructure. One of the sys-tem's main features is smart bins that include ultrasonic sensors to measure garbage levels. These sensors provide a full view of garbage accumulation throughout the monitored locations by wirelessly transmitting data to a centralized IoT platform. The gathered data is processed by sophisticated analytics algorithms, which then improve garbage collection routes to guarantee prompt and effective removal. The implementation of Smart Waste Management Systems not only addresses immediate concerns regarding cleanliness and hygiene but also contributes significantly to sustainable development goals. By efficiently managing waste disposal, these systems reduce the environmental footprint associated with improper waste handling, including pollution of water bodies and soil degradation. Moreover, the optimized waste collection routes minimize fuel consumption and greenhouse gas emissions, aligning with efforts to combat climate change. Beyond its technical functionalities, the Smart Garbage Management System fosters community engagement and education.