| _version_ | 1866901509693767680 |
|---|---|
| author | Desale, Girish B. |
| author_facet | Desale, Girish B. |
| contents | <p><em><span>Artificial Intelligence (AI) has transitioned from research laboratories to real industrial ecosystems, driving automation, predictive decision support, generative knowledge synthesis, and governance-aware intelligent systems. Modern advances in deep learning, transformers, MLOps, and edge intelligence have expanded AI capabilities across perception, forecasting, anomaly detection, robotics, personalization, energy optimization, and responsible governance. This paper provides a detailed, domain-aligned, sector-wise synthesis of AI applications in manufacturing, healthcare, finance, retail, logistics, energy, agriculture, education, public administration, and emerging industrial frameworks. It also highlights AI value creation models, feature extraction patterns, workflow integration strategies, practical barriers, regulatory risks, ethical implications, and standard-driven governance frameworks including NIST AI RMF, OECD principles, EU AI Act, and human-in-loop risk controls. The paper concludes that the most durable AI impact arises when AI systems are integrated into real domain pipelines with structured data engineering, bias-aware modeling, quality management, audit trails, explainability, and governance automation rather than standalone pilots.</span></em></p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18162481 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Digital India and Emerging Technologies (AI, IoT, Blockchain) Desale, Girish B. Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Industry 4.0, Intelligent Automation, Predictive Decision Support, Edge AI, MLOps, Computer Vision, AI Governance, Responsible AI, Supply Chain Optimization, Industrial Robotics, Data-Driven Forecasting, Trustworthy AI <p><em><span>Artificial Intelligence (AI) has transitioned from research laboratories to real industrial ecosystems, driving automation, predictive decision support, generative knowledge synthesis, and governance-aware intelligent systems. Modern advances in deep learning, transformers, MLOps, and edge intelligence have expanded AI capabilities across perception, forecasting, anomaly detection, robotics, personalization, energy optimization, and responsible governance. This paper provides a detailed, domain-aligned, sector-wise synthesis of AI applications in manufacturing, healthcare, finance, retail, logistics, energy, agriculture, education, public administration, and emerging industrial frameworks. It also highlights AI value creation models, feature extraction patterns, workflow integration strategies, practical barriers, regulatory risks, ethical implications, and standard-driven governance frameworks including NIST AI RMF, OECD principles, EU AI Act, and human-in-loop risk controls. The paper concludes that the most durable AI impact arises when AI systems are integrated into real domain pipelines with structured data engineering, bias-aware modeling, quality management, audit trails, explainability, and governance automation rather than standalone pilots.</span></em></p> <p> </p> |
| title | Digital India and Emerging Technologies (AI, IoT, Blockchain) |
| topic | Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Industry 4.0, Intelligent Automation, Predictive Decision Support, Edge AI, MLOps, Computer Vision, AI Governance, Responsible AI, Supply Chain Optimization, Industrial Robotics, Data-Driven Forecasting, Trustworthy AI |
| url | https://doi.org/10.5281/zenodo.18162481 |