Autonomous engineering systems are a major technological advancement in modern industry and science, operating with minimal human intervention while ensuring high accuracy, reliability, adaptability, and efficiency. Before 2019, developments in control engineering, artificial intelligence, robotics, and embedded systems led to advanced autonomous control architectures. Applications such as intelligent transportation, industrial automation, aerospace systems, smart manufacturing, underwater exploration, and unmanned aerial vehicles increased the demand for advanced control strategies capable of handling nonlinearities, uncertainties, and environmental disturbances. Traditional methods like PID control were effective for linear systems but limited in dynamic environments. Therefore, advanced techniques such as adaptive control, robust control, model predictive control, fuzzy logic, neural network-based control, sliding mode control, and hybrid intelligent control became widely used. These methods improved stability, trajectory tracking, fault tolerance, and decision-making. This study reviews advanced control strategies for autonomous engineering systems up to 2019, covering theoretical concepts, mathematical models, architectures, and practical applications in robotics, aerospace, industrial automation, and intelligent transportation. It also highlights challenges including sensor uncertainty, communication delays, computational complexity, and environmental disturbances. Comparative analysis shows that advanced control methods outperform traditional techniques in accuracy, robustness, energy efficiency, and disturbance rejection. The study concludes that these strategies are the foundation of next-generation autonomous systems, with future research focusing on adaptive optimization, distributed intelligence, deep reinforcement learning, and collaborative robotics.
Andrew Collins, Emma Roberts· International Journal of Mod...· 0 citations
Intelligent Document Processing (IDP) using Artificial Intelligence (AI) enables organizations to efficiently process and analyze large volumes of unstructured and semi-structured data. Traditional rule-based and manual methods struggle with scalability and complexity, whereas AI-driven IDP leverages machine learning, natural language processing, computer vision, and deep learning to enhance accuracy and decision-making. This paper presents a comprehensive study of AI-based IDP systems before 2019, focusing on their architecture, methodologies, and applications in digital enterprises. It explains how technologies like OCR convert scanned documents into machine-readable formats and how supervised and unsupervised learning improve classification and data extraction. A modular architecture including ingestion, preprocessing, classification, extraction, validation, and storage is discussed. The study highlights improved performance in terms of accuracy, precision, recall, and processing time compared to traditional systems. Applications across banking, healthcare, insurance, and logistics are examined. Finally, key challenges such as data privacy, model interpretability, and system integration are identified, along with future research directions, emphasizing the role of AI-driven automation in digital transformation.
Andrew Collins, Emma Roberts· International Journal of Art...· 0 citations
The growing adoption of Federated Learning (FL) is reshaping the way machine learning models are trained across distributed, privacy-sensitive datasets. However, the scalable and efficient orchestration of data engineering pipelines in decentralized cloud environments remains a significant challenge. This paper presents a comprehensive architectural framework for scalable data engineering tailored for FL in heterogeneous and resource-constrained environments. By integrating modern distributed computing paradigms, such as Kubernetes-based orchestration, edge-aware data preprocessing, and secure federated communication, we propose a modular architecture that addresses data heterogeneity, scalability, and compliance. A case study in a healthcare IoT scenario validates the performance and flexibility of the proposed system. Our work serves as a blueprint for deploying robust FL systems in real-world decentralized cloud ecosystems.
Laura Conti, Andrew Collins· International Journal of Dat...· 0 citations
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