Smart manufacturing analytics (sma) is a key component of industry 4.0 that combines the industrial internet of things (iiot), artificial intelligence (ai), machine learning (ml), cloud and edge computing, and big data analytics to improve manufacturing processes. It continuously collects and analyzes real-time data from sensors, machines, robots, and production systems to support intelligent decision-making.sma enables predictive maintenance, fault detection, quality control, energy optimization, and production forecasting, leading to higher productivity, reduced downtime, improved product quality, and lower operational costs. By integrating iiot with advanced analytics, smart manufacturing analytics supports the development of intelligent, autonomous, and sustainable manufacturing systems for the next generation of smart factories.
Iyengar P.K· International Journal of Int...· 0 citations
Agentic Artificial Intelligence (Agentic AI) represents the next generation of intelligent systems capable of autonomous sensing, reasoning, planning, and action with minimal human intervention. Unlike traditional AI, Agentic AI integrates large language models, reinforcement learning, multi-agent systems, planning mechanisms, orchestration layers, and memory modules to enable adaptive and goal-oriented decision-making. This paper explores Agentic AI architectures for autonomous business applications, highlighting their role in finance, healthcare, supply chain, enterprise resource planning, customer relationship management, and industrial operations. A layered architecture comprising perception, reasoning, orchestration, and execution layers is proposed to support autonomous analysis, strategic planning, and optimized action execution. The framework also incorporates memory, monitoring, and governance modules to enhance transparency, reliability, and explainability. Experimental evaluation demonstrates that the proposed architecture improves workflow automation, decision accuracy, operational efficiency, resource utilization, and response time while reducing manual intervention. The findings indicate that Agentic AI provides a scalable and robust foundation for future autonomous enterprise systems. The study also discusses key challenges, including explainability, governance, ethics, and trust, emphasizing their importance for successful enterprise adoption. Overall, Agentic AI architectures offer significant potential to accelerate intelligent automation and drive the next generation of business transformation.
Narendra Karmarkar, Iyengar P. K.· International Journal of Mod...· 0 citations
An AI-enabled Autonomous Water Distribution System (AWDS) that integrates IoT sensors, edge-cloud computing, and predictive analytics for real-time leak detection and proactive maintenance and provides a scalable and sustainable solution for intelligent water resource management.
Iyengar P. K.· International Journal of Eme...· 0 citations
This study presents a conceptual framework that combines semantic retrieval, intelligent reasoning, automated literature analysis, and workflow orchestration, demonstrating how LLM-powered systems can transform scientific research into scalable, accurate, ethical, and collaborative knowledge discovery processes.
Narendra Karmarkar, Iyengar P. K.· International Journal of Eme...· 0 citations
The proposed framework combines data preprocessing, feature engineering, predictive modeling, optimization, and sustainability assessment to identify materials that satisfy both engineering and environmental requirements.
Iyengar P.K· International Journal of Mod...· 0 citations
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