With the advancement of high-performance computing and sensor technologies, Artificial Intelligence (AI) has now has been routinely used in different healthcare applications and widely used in environmental monitoring. However, there are many challenges still to be addressed. In this talk, Dr Islam will discuss his past and ongoing research in these domains and highlight some future research directions.

The convergence of IoT and AI is redefining how we build resilient, adaptive, and sustainable systems. This talk explores about AURA – how intelligent sensing networks, edge computing, and context-aware algorithms can transform health monitoring, electronics, robotics, and biotechnology into deeply integrated platforms. Drawing from advances in ultra-low-power design, regenerative electronics, and real-time analytics, I highlight architectures that not only respond to human and environmental needs but also evolve with them. The goal is to enable scalable, affordable, and inclusive solutions that empower youth, foster innovation, and position emerging economies to lead in the global race for intelligent technologies.

The rise of artificial intelligence (AI) has brought about significant advancements, but it has also given rise to digital deceptions, including highly sophisticated techniques such as DeepFake technology. This presentation explores various examples of AI-driven deceptions, illustrating how they manipulate audio-visual content to spread misinformation, compromise privacy, and pose ethical challenges. DeepFakes, among other AI-generated forgeries, serve as a focal point, highlighting their potential to undermine trust in digital media. To address these challenges, the latter part of the presentation delves into countermeasures, ranging from fostering AI awareness and ethical use to deploying advanced AI detection tools. These measures aim to equip individuals, organizations, and policymakers with the knowledge and resources necessary to combat AI-driven digital deception effectively, ensuring a safer and more trustworthy digital ecosystem.

Real-time computationally intensive task scheduling for intelligent transportation system (ITS) applications like road safety and traffic forecasting within the deadline while ensuring user quality of experience (QoE) is a complex engineering problem. Meanwhile, adopting Digital Twin (DT) as an emerging technology in vehicular edge computing (VEC) enables efficient capture of real-time state information, thereby addressing the resource scheduling problem in an unpredictable vehicular topology setting. However, exploring strategies to enhance user QoE in timeliness and reliability domains could be a compelling and underexplored research challenge, particularly within the dynamic and trust-sensitive context of vehicular edge computing. In this talk, I introduce an optimization framework that maximizes user QoE by allocating task execution responsibility to highly reliable and reputed vehicles in a DT-enabled VEC environment. The framework leverages the demand-supply theory of economics to cluster vehicles based on computational resources and applies multi-weighted subjective logic to ensure accurate reputation updates. The NP-hard nature of the formulated optimization problem instigated development of an Asynchronous Advantage Actor-Critic (A3C)-based deep reinforcement learning algorithm, namely DARQoE, for offloading tasks in the Internet of Vehicles (IoV).