Samuel Ozechi

Samuel Ozechi, Banjo Francis, Wisdom Yakanu et al.

This study proposes a portfolio optimization framework that integrates advanced deep learning architectures with traditional financial models to enhance risk-adjusted performance. Using historical data from 2015-2023 across equities, ETFs, and bonds, the research evaluates the predictive power of Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), Transformers, and Autoencoders. The models jointly address covariance estimation, return forecasting, dynamic asset allocation, and dimensionality reduction. Hybrid approaches such as Transformer+GNN and Autoencoder+DRL are also explored to capture both relational and temporal market structures. Performance is assessed through backtesting using metrics including volatility, cumulative return, maximum drawdown, annualized return, and Sharpe ratio across seven strategies, including Equal-Weighted, 60/40 allocation, and Mean-Variance Optimization (MVO). Results show that hybrid models provide superior stability and risk control, with Transformer+GNN achieving the lowest volatility and drawdown. MVO, when paired with well-calibrated inputs, delivers the highest cumulative return and Sharpe ratio, highlighting the continued relevance of traditional methods. Standalone DRL underperforms due to limited structural awareness, while Autoencoders exhibit behavior similar to Equal-Weight strategies, emphasizing the need for dynamic policy learning. These findings align with existing literature on relational modeling and feature compression in finance. Overall, the study demonstrates that combining deep learning with financial theory yields robust and adaptive portfolio strategies and suggests exploring latent representations within traditional optimization frameworks to improve scalability and performance.

Samuel Ozechi, Jennifer Okonkwoabutu

As the Internet of Things (IoT) continues to expand across critical infrastructure, smart environments, and consumer devices, securing them against cyber threats has become increasingly vital. Traditional intrusion detection models often treat IoT threats as binary classification problems or rely on opaque models, thereby limiting trust. This work studies multiclass threat attribution in IoT environments using the CICIoT2023 dataset, grouping over 30 attack variants into 8 semantically meaningful classes. We utilize a combination of a gradient boosting model and SHAP (SHapley Additive exPlanations) to deliver both global and class-specific explanations, enabling detailed insight into the features driving each attack classification. The results show that the model distinguishes distinct behavioral signatures of the attacks using flow timing, packet size uniformity, TCP flag dynamics, and statistical variance. Additional analysis that exposes both feature attribution and the decision trajectory per class further validates these observed patterns. Our findings contribute to the development of more accurate and explainable intrusion detection systems, bridging the gap between high-performance machine learning and the need for trust and accountability in AI-driven cybersecurity for IoT environments.

Samuel Ozechi

Human attribute identification and classification are crucial in computer vision, driving the development of innovative recognition systems. Traditional gender classification methods primarily rely on facial recognition, which, while effective, struggles under non-ideal conditions such as blurriness, side views, or partial occlusions. This study explores an alternative approach by leveraging clothing identification, specifically focusing on African traditional attire, which carries culturally significant and gender-specific features. We use the AFRIFASHION1600 dataset, a curated collection of 1,600 images of African traditional clothing labeled into two gender classes: male and female. A deep learning model, based on a modified VGG16 architecture and trained using transfer learning, was developed for classification. Data augmentation was applied to address the challenges posed by the relatively small dataset and to mitigate overfitting. The model achieved an accuracy of 87% on the test set, demonstrating strong predictive capability despite dataset imbalances favoring female samples. These findings highlight the potential of clothing-based identification as a complementary technique to facial recognition for gender classification in African contexts. Future research should focus on expanding and balancing datasets to enhance classification robustness and improve the applicability of clothing-based gender recognition systems.