[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-115162-en":3,"doc-seo-115162-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},115162,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","AI for Decision Intelligence in Critical Systems","AI for Decision Intelligence in Critical Systems presents a multidisciplinary reference on how domain-aware artificial intelligence models can surpass generic solutions by addressing sector-specific complexities. The volume provides comparative frameworks and reproducible case studies, linking emerging AI methods to real-world deployment goals. Across twenty-one research chapters, contributors examine AI, machine learning, and deep learning architectures to strengthen decision-making, reliability, and risk mitigation in precision- and trust-critical environments.","AI for Decision Intelligence in Critical Systems  \nThis book is a multi-disciplinary reference on how domain-aware artificial intelligence (AI) models can outperform generic approaches by addressing sectorspecific complexities. It offers comparative frameworks, reproducible case studies, and real-world applications of emerging AI methods.  \nCollectively, AIfor Decision Intelligence in Critical Systems emphasizes a unifying theme: the effective deployment of AI to strengthen decision-making, enhance system reliability, and mitigate risks in domains where precision, trust, and efficiency are critical.  \nThis edited volume brings together twenty-one chapters of original research, each exploring how AI, machine learning (ML), and deep learning (DL) are shaping innovation across critical domains. The book highlights the application of advanced architectures—including Convolutional Neural Networks (CNNs), Quaternion Neural Networks (QCNNs), Large Language Models (LLMs), and GradientBoosted Decision Trees (GBDTs)—to solve complex, domain-specific challenges.  \nConcerning computer vision and infrastructure safety, chapters discuss the use of CNNs and QCNNs for automated road crack detection, offering scalable approaches to improving transportation safety while reducing dependence on manual inspections. With regard to software engineering, contributions focus on leveraging ML, DL, and LLMs to enhance software quality assurance, minimize defects, and improve resilience in high-stakes industries. Additional chapters examine ML-driven methods, particularly GBDT, to uncover non-linear drivers of equity valuation across sectors, supporting more accurate forecasts and risksensitive decision-making.  \nAcademics and researchers in computer science, AI, and data science, industry professionals in transportation, software engineering, finance, and policymakers seeking to apply AI systems effectively will find this book useful.  \nAdvances in Applied Mathematics  \nSeries Editor: Daniel Zwillinger  \nAn Introduction to Partial Differential Equations with MATLAB• Matthew P. Coleman and Vladislav Bukshtynov  \nHandbook of Exact Solutions to Mathematical Equations  \nAndrei D. Polyanin  \nIntroducing Game Theory and its Applications, Second Edition  \nElliott Mendelson and Dan Zwillinger  \nModeling Operations Research and Business Analytics  \nWilliam P. Fox and Robert E. Burks  \nDecision Analysis through Modeling and Game Theory  \nWilliam P. Fox  \nAdvances in High-Order Predictive Modelling  \nDan Gabriel Cacuci  \nIntroduction to Financial Mathematics, Second Edition  \nKevin J. Hastings  \nGeneral Quantum Variational Calculus  \nSvetlin G. Georgiev and Khaled Zennir  \nAdvanced Engineering Mathematics with MATLAB•, Sixth Edition Dean G. Duffy  \nExact Methods for Nonlinear PDEs  \nAndrei D. Polyanin  \nMarkov Processes, Second Edition  \nJames R. Kirkwood  \nAdvances in High-Order Sensitivity Analysis  \nDan Gabriel Cacuci  \nMining Complex Networks, Second Edition  \nBogumił Kamiński, Paweł Prałat, and François Théberge  \nThe Math Thought Mindset  \nLogic, Reasoning, and Proofs James L. Burk  \nNeutrosophic Computing and Vague Data Analysis  \nEdited by Florentin Smarandache and Zahid Khan  \nAI for Decision Intelligence in Critical Systems  \nEdited by Shahab Saquib Sohail, Arpita Soni, Satish Mandavalli, Shantanu Kumar, and Gautam Siddharth Kashyap  \n[https://www.routledge.com/Advances-in-Applied-Mathematics/book-series/CRCADVAPPMTH?pd=published](https://www.routledge.com/Advances-in-Applied-Mathematics/book-series/CRCADVAPPMTH?pd=published), forthcoming&pg=1&pp=12&so=pub&view=list  \nAI for Decision Intelligence in Critical Systems  \nEdited by  \nShahab Saquib Sohail, Arpita Soni, Satish Mandavalli, Shantanu Kumar and Gautam Siddharth Kashyap  \nFirst edition published 2027 by CRC Press  \n2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431  \nand by CRC Press  \n4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN  \nCRC Press is an imprint of Taylor & Francis Gro","cbCaiiVZC47LeKRY","https://ap.wps.com/l/cbCaiiVZC47LeKRY","pdf",27429708,3,1,249,"English","en",105,"# AI for Decision Intelligence in Critical Systems\n## Domain-aware AI and decision-making in critical environments\n## Machine learning, deep learning, and advanced architectures\n## Applications: computer vision, infrastructure safety, software engineering, and finance","[{\"question\":\"What does AI for Decision Intelligence in Critical Systems focus on?\",\"answer\":\"It focuses on how domain-aware AI models can outperform generic approaches by handling sector-specific complexities and improving decision-making in critical settings.\"},{\"question\":\"How many research chapters are included in the edited volume?\",\"answer\":\"The book brings together twenty-one chapters of original research.\"},{\"question\":\"Which AI techniques and model types are highlighted in the book?\",\"answer\":\"It highlights CNNs, quaternion neural networks (QCNNs), large language models (LLMs), and gradient-boosted decision trees (GBDTs) for domain-specific challenges.\"}]","AI for Decision Intelligence in Critical Systems | 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