E. Olaye

The convergence of Artificial Intelligence (AI) with Digital One Health systems, which integrate human, animal, and environmental health data into unified platforms, has introduced unprecedented cybersecurity challenges that conventional frameworks fail to sufficiently address. This project proposes the design, implementation, and evaluation of an adaptive AI- driven security framework tailored specifically to safeguard sensitive cross-sector health data within complex hybrid cloud and on-premise infrastructures. The framework leverages foundational cybersecurity principles, such as Zero Trust Architecture and layered defensein-depth, combined with advanced AI-powered anomaly detection models including Long Short-Term Memory (LSTM) Autoencoders and Isolation Forest algorithms to enable realtime identification and mitigation of emerging threats. Employing open-source technologies integral to the system architecture; OPNsense for perimeter firewall and VPN, WireGuard for encrypted communication, Suricata IDS/IPS for intrusion detection and prevention, Wazuh Security Information and Event Management (SIEM) for centralized log aggregation, Keycloak for Multi-Factor Authentication (MFA), and Debezium for Database Activity Monitoring (DAM), the framework demonstrates a holistic approach to securing Digital One Health ecosystems. Central to this effort is the development of a robust AI-powered security dashboard based on the ELK stack (Elasticsearch, Logstash, Kibana), which provides unified visualization, real-time alerting, and compliance monitoring. The research adopts a design science approach, systematically assessing security vulnerabilities unique to hybrid Digital One Health infrastructures and implementing layered controls to address gaps in cross-sector interoperability and AI-specific risks such as adversarial attacks and data poisoning. Validation is performed using comprehensive threat simulations that mimic real-world attack vectors including SQL injection, lateral movement, privilege escalation, and Distributed Denial-of-Service (DDoS) scenarios. Quantitative performance metrics reveal a high detection rate (approximately 97%), significant reductions in both mean time to detect (MTTD) threats by 70% and false positive alerts by 58%, thereby illustrating improved operational efficiency and accuracy compared to traditional static security measures. Despite promising results, challenges remain around optimizing AI model training data quality, managing performance overhead in resource-constrained settings, and balancing stringent security controls with healthcare delivery imperatives. The study advocates for future enhancements including federated learning models to preserve data privacy, lightweight AI implementations for broader accessibility, automated compliance frameworks
to navigate complex regulatory environments, and expanded user training programs to address human-centric security gaps. This project contributes a scalable, ethically aligned cybersecurity framework specifically engineered for AI-powered Digital One Health systems, addressing the multifaceted demands of securing sensitive, interconnected health data across diverse environments. By merging advanced AI capabilities with proven cybersecurity practices and a centralized management platform, it advances the resilience and trustworthiness of modern healthcare ecosystems, fostering innovation while safeguarding critical digital health infrastructure globally.

The growing interconnection between human, animal, and environmental health has emphasized the importance of integrated surveillance systems for early detection and coordinated response to zoonotic and environmentally linked diseases. However, in many low-resource settings such as Nigeria, disease monitoring remains fragmented across sectors, resulting in delayed reporting and weak cross-sectoral collaboration. This project presents the design and implementation of a Digital One Health Surveillance (DOHS) Dashboard, a web-based system developed to unify real-time disease surveillance across the three One Health domains—human, animal, and environmental health. The system was built using a React-based frontend and a RESTful backend API, integrating modules for authentication, case management, data analytics, and geospatial visualization. HTTP polling mechanisms were implemented to achieve real-time data synchronization without requiring WebSocket infrastructure, ensuring compatibility with constrained networks. The dashboard provides role-based access control, sector-specific reporting interfaces, Excel-based data export, and an interactive GIS map powered by React-Leaflet for spatial trend analysis. Data are securely transmitted via token-authenticated API calls, while the modular architecture allows future expansion and integration with existing national systems such as SORMAS or DHIS2. Deployment was designed for both cloud-based Virtual Private Servers (VPS) and onpremise servers within health facilities, supporting continuous operation even in areas with limited internet access. The system demonstrated efficient cross-sectoral data integration, realtime monitoring of case reports, and improved accessibility for public health officers, veterinary staff, and environmental personnel. The DOHS dashboard contributes to the operationalization of the One Health approach in Nigeria by providing a scalable, adaptable, and user-friendly digital infrastructure for integrated disease surveillance. It lays the foundation for data-driven decision-making and offers a replicable model for similar low-resource environments across Africa.

The convergence of Artificial Intelligence (AI) with Digital One Health systems, which integrate human, animal, and environmental health data into unified platforms, has introduced unprecedented cybersecurity challenges that conventional frameworks fail to sufficiently address. This project proposes the design, implementation, and evaluation of an adaptive AI- driven security framework tailored specifically to safeguard sensitive cross-sector health data within complex hybrid cloud and on-premise infrastructures. The framework leverages foundational cybersecurity principles, such as Zero Trust Architecture and layered defensein-depth, combined with advanced AI-powered anomaly detection models including Long Short-Term Memory (LSTM) Autoencoders and Isolation Forest algorithms to enable realtime identification and mitigation of emerging threats. Employing open-source technologies integral to the system architecture; OPNsense for perimeter firewall and VPN, WireGuard for encrypted communication, Suricata IDS/IPS for intrusion detection and prevention, Wazuh Security Information and Event Management (SIEM) for centralized log aggregation, Keycloak for Multi-Factor Authentication (MFA), and Debezium for Database Activity Monitoring (DAM), the framework demonstrates a holistic approach to securing Digital One Health ecosystems. Central to this effort is the development of a robust AI-powered security dashboard based on the ELK stack (Elasticsearch, Logstash, Kibana), which provides unified visualization, real-time alerting, and compliance monitoring. The research adopts a design science approach, systematically assessing security vulnerabilities unique to hybrid Digital One Health infrastructures and implementing layered controls to address gaps in cross-sector interoperability and AI-specific risks such as adversarial attacks and data poisoning. Validation is performed using comprehensive threat simulations that mimic real-world attack vectors including SQL injection, lateral movement, privilege escalation, and Distributed Denial-of-Service (DDoS) scenarios. Quantitative performance metrics reveal a high detection rate (approximately 97%), significant reductions in both mean time to detect (MTTD) threats by 70% and false positive alerts by 58%, thereby illustrating improved operational efficiency and accuracy compared to traditional static security measures. Despite promising results, challenges remain around optimizing AI model training data quality, managing performance overhead in resource-constrained settings, and balancing stringent security controls with healthcare delivery imperatives. The study advocates for future enhancements including federated learning models to preserve data privacy, lightweight AI implementations for broader accessibility, automated compliance frameworks to navigate complex regulatory environments, and expanded user training programs to address human-centric security gaps. This project contributes a scalable, ethically aligned cybersecurity framework specifically engineered for AI-powered Digital One Health systems, addressing the multifaceted demands of securing sensitive, interconnected health data across diverse environments. By merging advanced AI capabilities with proven cybersecurity practices and a centralized management platform, it advances the resilience and trustworthiness of modern healthcare ecosystems, fostering innovation while safeguarding critical digital health infrastructure globally.

The growing interconnection between human, animal, and environmental health has emphasized the importance of integrated surveillance systems for early detection and coordinated response to zoonotic and environmentally linked diseases. However, in many low-resource settings such as Nigeria, disease monitoring remains fragmented across sectors, resulting in delayed reporting and weak cross-sectoral collaboration. This project presents the design and implementation of a Digital One Health Surveillance (DOHS) Dashboard, a web-based system developed to unify real-time disease surveillance across the three One Health domains—human, animal, and environmental health. The system was built using a React-based frontend and a RESTful backend API, integrating modules for authentication, case management, data analytics, and geospatial visualization. HTTP polling mechanisms were implemented to achieve real-time data synchronization without requiring WebSocket infrastructure, ensuring compatibility with constrained networks. The dashboard provides role-based access control, sector-specific reporting interfaces, Excel-based data export, and an interactive GIS map powered by React-Leaflet for spatial trend analysis. Data are securely transmitted via token-authenticated API calls, while the modular architecture allows future expansion and integration with existing national systems such as SORMAS or DHIS2. Deployment was designed for both cloud-based Virtual Private Servers (VPS) and onpremise servers within health facilities, supporting continuous operation even in areas with limited internet access. The system demonstrated efficient cross-sectoral data integration, realtime monitoring of case reports, and improved accessibility for public health officers, veterinary staff, and environmental personnel. The DOHS dashboard contributes to the operationalization of the One Health approach in Nigeria by providing a scalable, adaptable, and user-friendly digital infrastructure for integrated disease surveillance. It lays the foundation for data-driven decision-making and offers a replicable model for similar low-resource environments across Africa.

This project focuses on the design and implementation of a controlled environment vertical farming system for tomato production in Benin City. The system integrates climate control, automated irrigation, and hydroponic nutrient delivery to optimize plant growth and resource efficiency. Key components include temperature and humidity sensors, an automated irrigation system, and a microcontroller-based control unit for real-time monitoring and adjustments. The vertical farming setup was designed to maximize space utilization while reducing water consumption and dependency on
chemical fertilizers. The implementation process involved system calibration, sensor integration, and performance evaluation to assess its impact on crop yield and sustainability. Results indicate that the controlled environment significantly enhanced tomato growth, minimized pest infestations, and improved overall yield compared to conventional soil- based farming methods. However, challenges such as high initial investment costs and power dependency were noted, necessitating the integration of renewable energy sources for long-term viability. This study demonstrates the potential of vertical farming as a sustainable and scalable solution for urban agriculture, addressing food security concerns while promoting resource-efficient farming practices. The findings suggest that further research into automation, AI-driven climate control, and localized material sourcing could enhance system performance and accessibility for wider adoption..

In the contemporary landscape of business intelligence (BI), the choice of interactive dashboard tools plays a pivotal role in enabling data-driven decision-making processes within organizations. This study conducts a comprehensive comparative analysis of three leading BI tools: Looker, Tableau, and Power BI, with a specific focus on their interactive dashboard capabilities. The research aims to provide i sights into the strengths and weaknesses of each tool, aiding organizations in making informed decisions regarding their BI tool selection. Key aspects examined include user interface and ease of use, data connectivity, visualization capabilities, data modeling and transformation features, collaboration and sharing functionalities, integration with advanced analytics and machine learning, cost considerations, customization and extensibility options, as well as support and community resources

The growing incidence of zoonotic diseases and other health risks underscores the pressing necessity for integrated surveillance systems that connect the sectors of human, animal, and environmental health. This initiative centers on the creation and deployment of a Stakeholder Portal for a Digital One Health Surveillance System, which aims to improve real-time data exchange, collaboration across sectors, and informed decision-making. The portal is built using React for the frontend and Python for its backend functionalities, and it features an AI-driven chatbot powered by the Gemini API to enable automatic responses and stakeholder interaction. Notable attributes include secure user authentication with role-based access, engaging data visualization dashboards, and management of real-time surveillance data. Health personnel have unique access to enhanced dashboard features, while general users can interact with publicly available information. The system guarantees data privacy and security by adhering to global standards, offering a scalable, user-friendly platform that can be customized to fit various healthcare settings. This effort plays a crucial role in bolstering public health resilience by promoting effective communication and proactive measures against emerging health threats within the One Health approach