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Intelligent Automation Systems for Next Generation Applications

Author(s) Prof. Ján Murgaš
Country Slovakia
Abstract Intelligent automation systems represent an important technological pathway for developing next-generation applications capable of autonomous decision-making, adaptive operation, process optimization, and real-time information management. The integration of artificial intelligence, machine learning, robotics, Internet of Things technologies, cloud and edge computing, computer vision, and advanced analytics has expanded automation beyond conventional rule-based processes. This study examines intelligent automation systems for next-generation applications, focusing on intelligent decision-making, autonomous process execution, adaptive control, connected sensing, predictive analytics, and human-machine collaboration.
A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that intelligent automation can improve operational efficiency, accuracy, responsiveness, scalability, and resource utilization across manufacturing, logistics, healthcare, transportation, agriculture, energy, and service applications. AI-based decision mechanisms can interpret complex data, identify patterns, predict operational conditions, and initiate appropriate actions with reduced human intervention. IoT technologies provide continuous data from connected devices, while edge and cloud computing support distributed processing and scalable automation. However, implementation remains affected by data quality, cybersecurity risks, interoperability, computational requirements, system complexity, workforce adaptation, explainability, and safety considerations.
The study concludes that next-generation intelligent automation requires integrated architectures combining artificial intelligence, robotics, connected sensing, analytics, and adaptive control. Future research should emphasize autonomous learning, explainable automation, edge intelligence, secure connected systems, human-AI collaboration, sustainable automation, and interoperable architectures capable of supporting reliable and scalable intelligent operations.
Keywords Intelligent Automation, Artificial Intelligence, Automation Systems, Machine Learning, Robotics, Internet of Things, Edge Computing, Autonomous Systems, Smart Applications, Digital Transformation.
Field Engineering
Published In Volume 4, Issue 6, November-December 2022
Published On 2022-12-31
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