Olanrewaju Oluwaseun Ajayi’s scientific contributions

The rapid advancement of autonomous vehicle (AV) technology has introduced significant security vulnerabilities that pose risks to passenger safety, system integrity, and public trust. This paper investigates the critical security challenges facing AVs, including sensor spoofing, GPS jamming, adversarial machine learning attacks, and communication network breaches. Through an extensive review of recent cyber threats, the study highlights the increasing sophistication of cyber-attacks targeting AV ecosystems and the potential consequences of security breaches. To address these vulnerabilities, the paper explores the implementation of resilient frameworks and robust cyber defense systems. Specifically, it examines the role of artificial intelligence-driven anomaly detection, blockchain-based secure communication, encryption techniques, and intrusion detection and prevention systems in mitigating security risks. Additionally, it evaluates the effectiveness of cybersecurity-by-design approaches, emphasizing proactive security measures in AV development.Key findings indicate that a multi-layered security approach integrating real-time threat detection, automated response mechanisms, and continuous system monitoring is essential for enhancing AV resilience. Furthermore, collaboration between industry stakeholders, policymakers, and cybersecurity experts is crucial in developing standardized security protocols and regulatory frameworks. The study underscores the necessity of adopting an adaptive and resilient security architecture to safeguard AVs against evolving cyber threats. By implementing robust cyber defense mechanisms and fostering a security-conscious AV ecosystem, the risks associated with autonomous vehicle operations can be significantly reduced, ensuring safer and more reliable transportation systems. Keywords: Autonomous Vehicles, Cybersecurity, Security Vulnerabilities, Resilient Frameworks, Cyber Defense Mechanisms, Machine Learning Security, AI-driven Automation, Vehicle-to-Vehicle Communication.

This paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in advancing threat detection and mitigating cybersecurity risks, while concurrently highlighting their application in public health optimization to enhance healthcare outcomes in underserved communities. The study underscores the dual capability of AI-driven frameworks to address critical challenges across cybersecurity and public health, aligning with sustainable development goals (SDGs). In cybersecurity, the research identifies AI and ML as pivotal in real-time threat detection, anomaly analysis, and predictive risk mitigation. Key findings demonstrate how advanced algorithms, such as deep learning and reinforcement learning models, can anticipate and neutralize cyber threats with unparalleled precision, minimizing vulnerabilities in digital ecosystems. Concurrently, the paper examines the adaptation of AI-driven methodologies in public health optimization. By leveraging predictive analytics and resource allocation algorithms, AI frameworks are shown to improve access to healthcare, enhance disease prevention strategies, and optimize patient outcomes in resource-limited settings. The integration of these technologies fosters equity, reduces disparities, and contributes to achieving SDGs related to health and well-being. The study concludes by emphasizing the interdisciplinary application of AI and ML as a cornerstone for innovation. Recommendations include strategic investments in AI infrastructure, cross-sectoral collaborations, and ethical guidelines to ensure the responsible and sustainable deployment of these technologies. Through this integrated approach, the research establishes a roadmap for leveraging AI and ML to address global challenges, driving progress in both cybersecurity and public health sectors.

This paper explores the transformative impact of Infrastructure as Code (IaC) on disaster recovery and business continuity in cloud environments. Infrastructure as Code is defined as the practice of managing and provisioning infrastructure through machine-readable code, facilitating automation, consistency, and scalability. The relevance of IaC in disaster recovery is highlighted, demonstrating how it enhances operational efficiency and resilience by automating key processes such as backup, failover, and restoration. Furthermore, the paper discusses the importance of business continuity, emphasizing IaC’s role in maintaining and quickly restoring critical services. The advantages of using IaC tools and practices to enforce continuity plans are examined, alongside a set of best practices for successful implementation. Ultimately, the paper concludes that adopting Infrastructure as Code is essential for organizations seeking to enhance their disaster recovery and business continuity strategies in an increasingly complex digital landscape.

This research paper explores innovative cybersecurity strategies for business intelligence (BI), emphasizing their role in transforming data protection and driving competitive superiority. It examines advanced encryption techniques, AI and machine learning, and blockchain technology, highlighting how these strategies enhance data security, operational efficiency, and cost savings. The paper also discusses emerging trends in BI cybersecurity, including AI-driven threat detection, zero-trust architecture, and blockchain advancements, and identifies areas for further research. The findings underscore the importance of robust cybersecurity measures in maintaining a competitive edge and ensuring the integrity and security of BI systems. Keywords: Business Intelligence, Cybersecurity, Data Protection, Artificial Intelligence, Blockchain Technology.

In the digital age, energy infrastructure faces unprecedented cybersecurity challenges that threaten the stability and reliability of critical systems. This paper explores the current threat landscape, detailing prevalent cyber threats such as malware, ransomware, and phishing that target energy systems. It examines the technical, organizational, and regulatory challenges in securing these infrastructures, highlighting issues like legacy systems, lack of cybersecurity awareness, and stringent compliance requirements. The paper proposes comprehensive strategies for enhancing cybersecurity, emphasizing the implementation of advanced technologies such as artificial intelligence, machine learning, and blockchain. Best practices, including regular security audits, incident response planning, and employee training, are also discussed. Furthermore, the importance of collaborative efforts, such as public-private partnerships and information sharing networks, is underscored. The paper concludes with recommendations for energy organizations to strengthen their cybersecurity posture, ensuring the protection of critical systems and the continuity of operations in the face of evolving cyber threats.

Sim-to-real transfer in robotics remains a significant challenge due to the inherent differences between simulated environments and real-world conditions, often leading to performance degradation when models are deployed in practical applications. This paper reviews the current state of sim-to-real transfer, exploring the key challenges such as sensor noise, domain shifts, and modeling inaccuracies contributing to this performance gap. The paper also examines existing techniques, including domain adaptation, reinforcement learning, and hybrid approaches, and discusses their limitations. To address these issues, we propose a novel framework that emphasizes the development of more realistic simulation environments and the integrating of adaptive learning strategies for continuous model refinement during real-world deployment. This framework aims to improve the robustness and adaptability of robotic systems, facilitating more reliable performance in diverse real-world scenarios. The paper concludes by outlining the implications for future research, highlighting open challenges, and suggesting directions for further validation and refinement of the proposed framework.

The rapid advancement of AI-driven robotic systems has introduced significant challenges related to transparency and trust, particularly in safety-critical applications. This review paper critically examines the current approaches to Explainable AI (xAI) in robotics, emphasizing the inherent trade-offs between performance and transparency. While high-performance AI models are essential for complex robotic tasks, their opacity often undermines trust and limits adoption. To address this, the paper proposes a comprehensive framework for implementing xAI in robotics, including strategies such as modular architecture, hybrid models, and human-centered design. The paper also discusses key design considerations and evaluation metrics that ensure a balance between interpretability and operational effectiveness. Finally, the paper reflects on the implications of these strategies for the future of robotics. It suggests avenues for further research to enhance the integration of xAI, aiming to create more trustworthy and reliable robotic systems.

The rapid deployment of artificial intelligence (AI) in public-facing autonomous systems has introduced a range of ethical challenges that must be addressed to ensure these technologies are developed and used responsibly. This paper critically reviews the ethical issues associated with AI-driven autonomous systems, focusing on transparency, safety, fairness, and public accountability. It highlights the challenges of opaque decision-making processes, potential biases in AI algorithms, and the need for reliable and safe autonomous operations. In response, the paper proposes a robust ethical framework centered on transparency, fairness, and accountability, providing a structured approach for integrating these values into AI development and deployment. The framework emphasizes the importance of explainability, bias mitigation, and stakeholder engagement, offering strategies for ethical AI implementation. The paper concludes by discussing the implications of this framework for future research, policy-making, and industry practices and calls for ongoing dialogue and collaboration among all stakeholders to ensure the ethical deployment of AI in autonomous systems.

This review paper explores the current state of AI-driven control systems in autonomous vehicles (AVs), focusing on key techniques such as reinforcement learning (RL), Proportional-Integral-Derivative (PID) control, and hybrid approaches that combine traditional and AI-driven methods. While these techniques have enabled significant AV technology advancements, safety, reliability, scalability, and real-time decision-making challenges persist. The paper proposes several future innovations, including advanced RL techniques, integration of machine learning with traditional control systems, Model Predictive Control (MPC) and AI fusion, enhanced sensor fusion, and human-AI collaboration. These innovations address existing limitations and enhance AV control systems' adaptability, decision-making, and overall performance. The review concludes by discussing the broader implications of these innovations for the future of autonomous vehicles. It offers recommendations for future research to advance the field further.

As the world progresses towards a more connected and technologically advanced future, the evolution of wireless communication technologies plays a pivotal role in shaping the landscape of smart cities and the Internet of Things (IoT). This paper provides a comprehensive review of the anticipated impact and potential contributions of 6G technology in supporting and enhancing the infrastructure of IoT and smart cities. The introductory section outlines the historical context of wireless communication, highlighting the transformative journey from 1G to the upcoming 6G. Emphasis is placed on the escalating significance of IoT across various sectors and the need for advanced communication technologies to meet the growing demands of a connected world. The paper explores the defining characteristics and advancements offered by 6G, distinguishing it from its predecessors, particularly 5G. Attention is given to its capabilities in terms of low-latency communication, high data transfer rates, and efficient spectrum utilization. Special focus is directed towards the integration of IoT with 6G technology, elucidating how the enhanced connectivity and massive device handling capabilities of 6G can revolutionize the functioning of IoT devices. The implications of 6G on smart cities infrastructure are discussed, highlighting its role in improving communication between smart devices, enabling real-time data exchange, and supporting resource-intensive applications through edge computing. However, challenges and considerations are not overlooked, as the paper delves into the security and privacy concerns associated with a highly connected environment. Additionally, the discussion extends to the practical challenges of upgrading existing infrastructure and the financial implications of widespread 6G implementation. The paper concludes by envisioning the future implications and opportunities that 6G technology presents. It explores potential applications across diverse sectors and emphasizes the economic and social impacts, including job creation, economic growth, and improvements in the quality of life within smart cities. This paper aims to provide a holistic overview of the future role of 6G technology, offering insights into its potential to reshape the landscape of IoT and smart cities infrastructure. The findings contribute to the ongoing discourse on the intersection of advanced communication technologies, IoT, and urban development in the ever-evolving technological landscape.