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openalexZenodo (CERN European Organization for Nuclear Research)2026-08-09Cited by 0

Artificial Intelligence in Indian Banking: Opportunities, Challenges and Future Prospects – A Review

KAVITA HURAKADLI, Bhagirathi Halalli, Bindu. H.A., Nandini. H.M.

Artificial Intelligence (AI) is changing the way Indian banks work and interact with their customers. From smarter chatbots and automated loan approvals to better fraud detection and easier access to banking in rural areas, AI is making banking more efficient and accessible. Over the last few years, banks across India have increasingly turned to AI tools—like chatbots, machine learning, and robotic process automation—to serve customers better, cut costs, and stay competitive. This review pulls together the latest research (2024–2026) to look at how AI is being used, what benefits it brings, and what challenges remain. While AI is helping banks work faster, reduce mistakes, and reach more people, there are still important questions about data privacy, cybersecurity, fairness, and what happens to jobs as more tasks are automated. The review suggests that for AI to keep benefiting Indian banking, banks must invest in strong digital infrastructure, upskill their employees, and ensure that ethical and regulatory safeguards are in place.

Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-08-09

Artificial Intelligence and Society: Advanced Ethics Opportunities and Challenges - Digital Archaeology and AI Applications

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Artificial Intelligence (AI) is swiftly reshaping the relationship among technology and society, raising profound moral, cultural, and epistemological questions. Beyond its monetary and business implications, AI is more and more influencing how knowledge is produced, interpreted,…

Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-08-09

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Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

# Artificial Intelligence in Metallurgical Engineering: A Comprehensive Review of Applications, Challenges, and Future Direction

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Transformation in Metallurgical Engineering: From Microstructure Analysis to Smart Manufacturing and Sustainable Production"** ### Alternative Title 2 (Process-Focused)**"Machine Learning and Deep Learning…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-08-09

Benefits and Challenges of Artificial Intelligence (AI) in English Language Learning

Basavva C. Nidagundi

Artificial intelligence has become a paradigmatic shift in education generally, while simultaneously offering individualized experiences in English language learning. A discussion is presented on how AI is being applied in personalized English language learning, while particular…

Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-08-09

The Features of Democracy in the Age of Artificial Intelligence: Its Merits and Challenges – A Study

Jayaramaiah G.M.

The rapid advancement of Artificial Intelligence (AI) is transforming political systems, governance structures, and democratic participation across the globe. Democracy, traditionally grounded in citizen participation, representation, accountability, and transparency, now faces p…

Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

# Artificial Intelligence-Enabled Quantification of Cube and Goss Textures in Polycrystalline Materials: A Comprehensive Review of Machine Learning, Deep Learning, and EBSD-Based Characterization Approaches

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Analysis of Cube {100}<001> and Goss {110}<001> Textures: Machine Learning, Deep Learning, and Generative Models for Crystallographic Texture Quantification in Metallurgical Engineering"** ### Alternative T…

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