Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

Background

Intelligent systems and automation are transforming how industries design, deliver, and optimize services by combining Artificial Intelligence (AI), Machine Learning (ML) algorithms, IoT, automation engineering, intelligent systems engineering, robotic engineering, and advanced algorithms. These technologies enable systems to learn from data, adapt to changing environments, and support real-time decision-making across applications, such as smart manufacturing, healthcare, logistics, and autonomous robotics. Machine learning supports intelligent systems in analysing large datasets, improving operational efficiency, and responding to emerging patterns, while the IoT connects devices and generates valuable data for prediction, monitoring, and seamless automation. In parallel, robotic engineering and intelligent algorithms support autonomous navigation, precision control, and adaptive behaviour in dynamic settings.

The ISA 2026 Symposium brings together researchers, practitioners, and educators to discuss advances in these areas, explore challenges, and identify future directions. This is supported by leading higher education institutions, which provide an international forum for knowledge exchange, collaboration, and innovation.

Goal/Rationale

The rapid growth of intelligent systems and automation is creating a pressing need for AI, machine learning, IoT, robotic engineering, and intelligent algorithms to work together in reliable and scalable ways. Conversely, a major challenge remains that such technologies are often developed separately, making it difficult to achieve seamless communication, real-time decision-making, and autonomous adaptation across dynamic environments. In practice, AI-driven systems must operate with high reliability, security, and ethical responsibility, particularly when deployed in safety-critical areas, such as healthcare, smart infrastructure, industrial automation, and autonomous robotics. Recent advances in deep learning, edge AI, robotic engineering, and intelligent algorithms have opened new possibilities by enabling AI systems to process real-time IoT data, support predictive analytics, and improve operational responsiveness. At the same time, more advanced robotic algorithms are enhancing navigation, human-robot collaboration, and self-learning capabilities. The ISA 2026 Symposium aims to address these challenges by bringing together researchers, practitioners, and educators to explore practical solutions that improve scalability, robustness, and intelligent decision-making.

By focusing on AI, ML algorithms, IoT, automation engineering, intelligent systems engineering, robotic engineering, and advanced algorithms, the symposium will promote interdisciplinary collaboration and highlight emerging opportunities for research, innovation, and real-world impact.

Scope

The Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms Symposium, part of CONF-MLA 2026, focuses on the design and application of intelligent technologies that support automation, autonomy, and data-driven decision-making across modern industries. This hybrid workshop explores the use of AI, machine learning, IoT, robotic engineering, and intelligent algorithms to develop next-generation intelligent systems that are efficient, adaptive, secure, and scalable. Contributors are invited to address key themes, including:

AI-driven automation and autonomous systems, machine learning for intelligent systems through deep learning, reinforcement learning, and edge AI; IoT-enabled smart systems for predictive analytics and real-time decision-making; Robotic engineering and intelligent algorithms for smart robotic systems (SRS); and Security, ethics, and scalability, recognising that responsible and dependable deployment is essential for real-world impact.

This symposium provides a forum for researchers, practitioners, and both postgraduate and PhD students to share innovations, challenges, and future directions in these rapidly evolving fields.