Anthony Ojo

Bank card fraud is one of the biggest challenges in digital finance space, which needs detection models to address class imbalance, interpretability, and adaptability to changing tactics of fraud. The paper proposes a neuro-adaptive architecture established on a highly structured preprocessing pipeline with stratified splitting, feature normalisation, and representation learning via a Denoising Autoencoder. At the core of this framework lays an Artificial Neural Network optimised by the Firefly Algorithm for fast hyperparameter tuning facilitated by Elastic Weight Consolidation that promotes continual learning without sacrificing past performance. The proposed Adaptive ANN + FA outperforms baseline ANN, CNN, and LSTM models mainly in F1-Score, precision, and recall-the main metrics in fraud detection. Also, SHAP breaks out feature contribution and prediction reasonability making the results very transparent. Optimised adaptive and explainable models are positioned here as strong enablers of real-world fraud discovery and subsequent robustness in the financial systems.

This study investigates the risk of job automation in London due to artificial intelligence (AI), applying supervised machine learning techniques to identify occupations most at risk. Leveraging a dataset encompassing job-specific features such as primary tasks, industry domains, and associated AI models, the research develops two predictive models. A Random Forest Classifier is used to categorize jobs as low, medium, or high automation risk, while a Linear Regression model estimates the proportion of each occupation's workload likely to be automated. The Random Forest model achieved a high accuracy rate of 97 % in classifying job risk, indicating strong predictive capability. Meanwhile, the regression model explained 85 % of the variance in the AI workload ratio, highlighting a significant relationship between job attributes and automation potential. These results suggest that job characteristics are reliable indicators of AI impact, particularly in routine, repetitive, and low-skilled roles that are more easily codified and replicated by algorithms. The findings align with broader economic theories such as creative destruction and technological waves, suggesting that AI not only displaces certain roles but also drives structural transformation within the labor market. By focusing on London, this study provides a localized understanding of how AI is reshaping employment patterns. It underscores the growing urgency for strategic workforce re-skilling and adaptive policy frameworks to mitigate negative outcomes and maximize opportunities presented by AI. Ultimately, this research contributes valuable insights into the interaction between AI technologies and employment, helping policymakers, employers, and educators anticipate change and prepare for a more resilient, inclusive labor market.

Inclusion in recruitment remains a critical challenge in the evolving landscape of work, particularly for individuals with neurodivergent cognitive profiles. This study investigates the potential of adaptive Artificial Intelligence (AI) assessments to foster more inclusive hiring practices for neurodiverse talent. Using thematic analysis and simulation-driven data, the research explores how AI systems can be optimized to support equity, reduce cognitive bias, and enhance candidate-job alignment. Grounded in Adaptive Theory, Evidence-Based Rationale, and the Input-Environment-Output (I-E-O) framework, the study models an AI-powered recruitment pipeline using psychometric clustering and retention data. Results reveal that while AI tools can personalise assessments and improve hiring outcomes, they also risk amplifying bias if not calibrated across diverse cognitive profiles. The study emphasizes the importance of neurodiversity-aware model tuning, participatory design involving neurodivergent individuals, and the integration of fairness metrics such as demographic parity and equal opportunity. The findings contribute practical insights and a replicable framework for advancing inclusive hiring through AI-enabled systems.

The impart of climatic change apparently affect accessibility of good water for agriculture irrigation system in addition to human dependency and demand on farm products for survival, increases water unavailability challenges in the farm affecting ecosystem when there is no balance between country food production capacity and population growth. Agricultural sector is challenged with unequal distribution of water in the farm plantations reducing food production strength, this is more severe in under-developing regions whereby require smart irrigation systems as promising solution to mitigate against threat of water distribution to farm products. This research work design and develop smart irrigation system with less expensive micro-controller components, implementing water distribution system using sensor captured information of soil condition, infusing precision irrigation system that calculate and supplies exact water require based on soil dryness level. The research adopts internet of things IoT technology where sensor will be used to monitor and capture soil information and control water distribution based on available soil dataset. The outcome of the research gives absolute control on over-irrigation and under-irrigation system that increases agricultural productions with advance technological means, precision irrigation system mechanisms reduces water wastage and ensure equal distributions. Multivariate soil type test will greatly enhance general acceptability of this concept in cross-functional regions.

With the increase adoption of humanoid robots in today's world, the need to understand the ways through which these robots communicate social cues has become indispensable for effective human-robot interaction (HRI) in everyday life. The focus of this study is on the examination of the influence of non-verbal behaviour of Robosen K1 (a humanoid robots) on human perceptions and emotional responses. K1 was programmed to perform expressive full-body movements, due its lack of facial expressions, such as dancing, push-ups, and standing on its head. The research design was a mixed-method approach, which combined behavioural observations from live interactions with data from an online survey. Findings from the study revealed positive emotional reactions from participants, most of which described the robot as " impressive, " " curious, " and " amusing. " Also, results indicated that 89.8% of participants were favourably disposed to engaging with similar robots in the future. Finally, it was found that the robot's gestures, being highly expressive, contributed to perceived personality traits such as " playful " and " friendly. " The study, therefore, concluded that a well-designed non-verbal cues would play critical role in enhancing emotional connection, engagement, and trust in humanoid robots, hence, their importance for successful HRI design.

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.

The fast-growing convergence of neuroscience, behaviour computing, and adaptive artificial intelligence (AI) offers the possibility to transform human, machine interaction. This work presents Psycho-Intelligence, a new, closed-loop system that merges electroencephalography (EEG) and inertial motion unit (IMU) signals to adaptively recognise and react to users' cognitive and affective states. Levying low-cost wearable sensors (Muse EEG and MPU-6050), the system has real-time signal acquisition , sophisticated preprocessing, spectral and statistical feature extraction, as well as multimodal fusion features. Dimensionality reduction and feature selection techniques, including Principal Component Analysis and XGBoost gain metrics, enhance learning optimally. Multiple machine learning algorithms like Random Forest, SVM and XGBoost are trained to identify engagement states with high accuracy, warranted by extensive testing through cross-validation, ROC AUC, and F1-scores. The pipeline is incorporated into an adaptive feedback system that can regulate chatbot tone, learning material, or interactive graphics based on detected user states. Statistical validation with linear mixed models confirms the robustness of EEG-derived measurements in engagement prediction. The research establishes a new paradigm for emotionally intelligent AI systems and provides a technical foundation for ethical, real-time psycho-behavioural intelligence for communication networks, education systems, and cognitive health monitoring.

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.