This paper explores the integration of network programming and microservices architecture to build scalable, AI-driven distributed systems for real-time data processing. As artificial intelligence becomes increasingly crucial for real-time decision-making in industries like healthcare, finance, and e-commerce, there is a growing need for systems that can process vast amounts of data efficiently while ensuring scalability and low latency. Network programming techniques are foundational to distributed systems, enabling seamless communication between services. Meanwhile, microservices provide a modular approach that supports scalability and flexibility, essential for AI applications. The paper discusses the role of these technologies in building AI-powered distributed systems, challenges related to network latency, data consistency, and fault tolerance, and real-world applications across industries. Additionally, it delves into future trends such as edge computing and automated scaling in the context of AI-driven distributed systems.
Rahul Mehta, Priya Kapoor· International Journal of Mac...· 0 citations
A reinforcement learning (RL)-based autonomous optimization framework that integrates RL agents with data orchestration platforms to continuously monitor pipeline states and optimize operations that enables adaptive, self-managing data pipelines that improve scalability, resilience, and operational efficiency across enterprise, cloud, and edge environments.
Rahul Mehta· International Journal of App...· 0 citations
Structural Health Monitoring (SHM) has become increasingly important in civil engineering due to the aging of infrastructure such as bridges, buildings, tunnels, and dams. Traditional inspection methods rely on manual visual assessments, which are time-consuming, labor-intensive, and often unable to detect early-stage damage. Recent advances in Artificial Intelligence (AI) have enabled the development of intelligent damage detection systems that improve the accuracy and efficiency of structural assessments. AI techniques, including Machine Learning, Deep Learning, Computer Vision, and Pattern Recognition, analyze sensor data, vibration signals, and images to identify defects and predict structural failures. This study presents a comprehensive survey of AI-based damage detection methods for civil infrastructure. The proposed framework integrates sensor data acquisition, feature extraction, machine learning classification, and automated damage assessment. Various AI algorithms such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), and Convolutional Neural Networks (CNN) are evaluated for detecting structural deterioration. Experimental results demonstrate that AI-based approaches, particularly deep learning models, achieve higher damage detection accuracy than conventional inspection techniques. Furthermore, AI-powered SHM systems enable real-time monitoring, reduce maintenance costs, and support proactive infrastructure management, highlighting their potential to transform modern civil infrastructure maintenance and safety.
Rahul Mehta· International Journal of Mod...· 0 citations
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