Development of Intelligent Systems for Modern Engineering Challenges
DOI:
https://doi.org/10.64137/31078699/IJETET-V1I2P105Keywords:
Intelligent Systems, Artificial Intelligence, Machine Learning, Cyber-Physical Systems, Smart Engineering, Industry 4.0, Automation, Predictive Analytics, System Optimization, Real-Time Decision Making, Digital Transformation, Engineering SystemsAbstract
The rapid evolution of engineering systems, driven by increasing complexity, interconnectedness, and dynamic operational environments, has necessitated the development of intelligent systems capable of autonomous decision-making and adaptive behavior. This study explores the design and implementation of intelligent systems to address modern engineering challenges across domains such as manufacturing, infrastructure, energy, and transportation. The research integrates artificial intelligence techniques, including machine learning, deep learning, and optimization algorithms, with cyber-physical systems and real-time data processing frameworks to create adaptive and resilient engineering solutions. A conceptual architecture is proposed that combines sensing, data integration, intelligent analytics, and decision-making layers to enable continuous monitoring and optimization of engineering systems. The study further examines practical applications through case-based or simulation-based validation, demonstrating improvements in efficiency, reliability, and system performance. Key challenges such as data quality, system interoperability, explainability, and cybersecurity are also analyzed. The findings highlight the transformative potential of intelligent systems in enhancing engineering processes and outcomes. This research contributes to both theoretical and practical advancements by providing a structured framework for developing intelligent systems that can effectively address complex and evolving engineering problems in the era of digital transformation.
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